comet assay research paper

EDITORIAL article

The comet assay: past, present, and future.

• 1 Environmental Risk and Health Unit, Flemish Institute of Technological Research (VITO), Mol, Belgium
• 2 Department of Pharmacology and Toxicology, University of Navarra, Pamplona, Spain
• 3 Department of Nutrition, University of Oslo, Oslo, Norway

The alkaline comet assay (single cell gel electrophoresis) is the most widely used method for measuring DNA damage in eukaryotic cells ( Neri et al., 2015 ). It detects strand breaks (SBs) and alkali-labile sites at frequencies from a few hundred to several thousand breaks per cell—a biologically useful range, extending from low endogenous damage levels to the extent of damage that can be inflicted experimentally without killing cells. Digestion of the nucleoids, after lysis, with certain lesion-specific repair endonucleases allows measurement of damage other than SBs; notably, formamidopyrimidine DNA glycosylase (FPG) has been widely used to detect altered purines, which are converted to breaks by the enzyme. Recently, ( Cortés-Gutiérrez et al., 2014 ) developed a two-dimensional Two-Tailed comet assay (TT-comet) that can differentiate between single-stranded (SSBs) and double-stranded DNA breaks (DSBs) in the same comets in sperm.

Since the first report by Ostling and Johanson (1984) the comet assay has been widely used in genotoxicity testing of chemicals, in both in vitro and in vivo models. An advantage with the latter is that cells from various tissues can be studied, in a wide variety of eukaryotic organisms. During the last 15 years, the comet assay has been extensively used in Drosophila melanogaster to test the genotoxicity of chemicals ( Gaivão and Sierra, 2014 ). This approach is very useful since Drosophila melanogaster is a valuable model for all kinds of processes related to human health, including DNA damage responses.

The use of plants as well as a wide range of terrestrial and aquatic species in the comet assay has dramatically increased in the last decade ( Costa et al., 2014 ; de Lapuente et al., 2015 ; Santos et al., 2015 ), particularly in environmental risk assessment (ERA). A recent validation study has indicated that the in vitro comet assay combined with FPG may be an effective complementary line-of-evidence in ERA even in particularly challenging natural scenarios such as estuarine environments ( Costa et al., 2014 ).

During the past decade the production and use of nano-sized materials has significantly increased, and as a consequence so has human exposure to these types of materials. Identifying and understanding the hazards of nanomaterials (NMs) in relation to human health is not a simple matter. Not only is the chemical composition of NMs responsible for their genotoxicity, but also shape, specific surface area, size, size distribution, and zeta potential determine the effects of these materials on the genome. Although there is still a debate about the suitability of standard genotoxicity assays for studying the effects of NMs, so far the most used method in nanogenotoxicology, thanks to its robustness, versatility, and reliability, has been the comet assay ( Azqueta and Dusinska, 2015 ). In addition to investigating the genotoxicity of radiation and various chemicals, the plant comet assay has recently also been used to study the genotoxic impact of NPs ( Santos et al., 2015 ).

A further application of the comet assay is as a valuable experimental tool for human biomonitoring as well as in clinical studies. Collecting blood or tissues is not always feasible in all human subjects, and other sources of cells that can be collected non-invasively have been tested with the comet assay; for example, various types of epithelial cells ( Rojas et al., 2014 ) as well as sperm ( Cortés-Gutiérrez et al., 2014 ; Brunborg et al., 2015 ).

In parallel with the development of the comet assay for DNA damage measurement, assays for DNA repair—an essential element in the genotoxic cellular response—have been developed. The simplest approach to DNA repair measurement is to treat cells with a DNA-damaging agent and then to incubate them to allow repair to proceed, measuring the amount of damage remaining at intervals. An alternative, biochemical approach to assessing repair capacity was described in 1994 ( Collins et al., 1994 ), and since then various modified versions of the assay to measure both base excision repair (BER) and nucleotide excision repair (NER) have been published (reviewed by Azqueta et al., 2014 ). This biochemical approach has been applied to study the effects of environment, nutrition, lifestyle, and occupation on DNA repair capacity, in addition to clinical investigations ( Azqueta et al., 2014 ).

This alternative in vitro approach to DNA repair assesses the repair activity of a cell extract on a DNA substrate containing defined lesions. The comet assay is used to follow the accumulation of DNA breaks (repair intermediates) with time of incubation. Recently, Slyskova and colleagues were the first to apply the in vitro DNA repair assays for BER and NER successfully on human tissue samples; specifically, colorectal carcinoma biopsies ( Slyskova et al., 2012 , 2014 ).

A different kind of DNA repair assay, allowing cells embedded in the gel to repair before lysis, was recently adopted to study DNA repair kinetics in more detail; specifically, to study the regulation of BER proteins by post-transcriptional modifications ( Nickson and Parsons, 2014 ). Yet another way to study DNA repair, at the level of specific genes, is with the comet-FISH technique, which makes use of fluorescent-labeled DNA probes that will hybridize to the single-stranded DNA in the comet tail. McAllister et al. (2014) used this method to study preferential strand break repair in bulk DNA as well as in selected regions with actively transcribed genes.

Studying the kinetics of repair of induced damage will help in our understanding of cellular responses to genotoxic chemicals. Moreover, the significance of DNA repair as a player in the (anti)carcinogenic process can be elucidated by looking at repair at the level of specific cancer target tissues. Regulation of repair—and other aspects of the cellular response to genotoxic compounds—is likely to involve epigenetic mechanisms and the comet assay has been adopted successfully to measure changes in the global DNA methylation pattern in individual cells under various growth conditions ( Lewies et al., 2014 ).

Per cent tail DNA is recommended as the best descriptor for DNA break frequencies, as the comets referred to—and extent of damage—can easily be visualized. However, many researchers still prefer the use of tail moment ( Møller et al., 2014 ). In fact the two descriptors are similarly influenced by assay conditions ( Azqueta et al., 2011 ; Ersson and Möller, 2011 ).

Variability in the comet assay is an important issue, whether it arises from the use of different protocols, or from uncontrollable or random experimental variation. The inclusion of reference standards in all experiments is recommended, especially when a large number of samples—from a biomonitoring trial, for example—are analyzed on different occasions. Reference standards are cells with a known amount of DNA damage; either untreated cells (negative control), X-ray-exposed cells (positive control), or cells treated with photosensitizer plus light (positive control for assays including FPG-incubation), batch-prepared and frozen as aliquots. If substantial variation occurs in the standards in a run of experiments, sample results can be normalized ( Collins et al., 2014 ). If reference standards are exchanged between laboratories, results from these laboratories can more easily be compared.

Reference standard cells are normally set in gels in parallel to sample gels. Internal standards—i.e., standard cells in the same gel as sample cells—would be ideal; but it is of course essential to be able to distinguish the two types of cell. Fish cells that are either larger or smaller in genome size compared to human cells have successfully been adopted for this purpose ( Brunborg et al., 2015 ). These reference cells can be used in combination with a standard or calibration curve (established with cells given different doses of ionizing radiation), enabling a more precise quantification of DNA lesions expressed as a DNA break frequency rather than % tail DNA.

Statistics are an important tool in all applications of the comet assay, to check whether small differences occur by chance. Concise descriptions of statistical analysis and recommendations for tests have been published ( Lovell et al., 1999 ; Lovell and Omori, 2008 ). Møller and Loft (2014) remind us that to keep the comet assay statistical analysis simple, appropriate study design and statistical power should be carefully considered when planning experiments.

As with all biological assays, data integration is crucial to interpret the comet assay results within the bigger picture. Integration of information provided by the comet assay with other DNA-damage indicators and cellular responses (e.g., oxidative stress, cell division, or cell death) has been applied both in ERA ( Costa et al., 2014 ; Santos et al., 2015 ) as well as human (biomonitoring) studies (e.g., Langie et al., 2010 ; Slyskova et al., 2012 ). Also including “omics” data will aid in unraveling the mode of action of genotoxic compounds ( Slyskova et al., 2012 , 2014 ; Santos et al., 2015 )—though it is worth pointing out that several studies have shown that phenotypic measures of DNA repair do not necessarily correlate with genomic or transcriptomic data ( Collins et al., 2012 ; Slyskova et al., 2012 , 2014 ); the different approaches should be regarded as complementary.

Even after three decades of development and modification, the comet assay is still a rather simple, versatile but labor-intensive assay. Various high throughput modifications of the assay were recently reviewed ( Brunborg et al., 2014 ). Both in vivo and in vitro applications would gain great advantage from further improvements in efficiency, standardization of protocol, and throughput. Automation and miniaturization are common strategies in many areas of biology, allowing orders-of-magnitude changes in the numbers of samples analyzed per experiment, reducing subjective bias, and enhancing reproducibility.

So—what can we hope for in the next 30 years? Acceptance of the in vitro comet assay for genotoxicity testing, inexpensive automated comet scoring to save researchers from interminable microscope viewing, protocol standardization (perhaps) and reliable internal reference standards, more human biomonitoring studies of DNA repair (accepting that phenotypic assays have an important place alongside genomics and transcriptomics), environmental monitoring using a variety of animal and plant species; and many more unpredictable developments and applications.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

We would like to thank all the authors as well as reviewers and editors who have contributed to this Frontiers Research Topic. SL is the beneficiary of a post-doctoral grant from the AXA Research Fund and the Cefic-LRI Innovative Science Award 2013. AA thanks the Ministerio de Economía y Competitividad (‘Ramón y Cajal’ programme, 2013) of the Spanish Government for personal support.

Azqueta, A., and Dusinska, M. (2015). The use of the comet assay for the evaluation of the genotoxicity of nanomaterials. Front. Genet. 6:239. doi: 10.3389/fgene.2015.00239

PubMed Abstract | CrossRef Full Text | Google Scholar

Azqueta, A., Gutzkow, K. B., Brunborg, G., and Collins, A. R. (2011). Towards a more reliable comet assay: optimising agarose concentration, unwinding time and electrophoresis conditions. Mutat. Res. 724, 41–45. doi: 10.1016/j.mrgentox.2011.05.010

Azqueta, A., Slyskova, J., Langie, S. A., O'Neill Gaivão, I., and Collins, A. (2014). Comet assay to measure DNA repair: approach and applications. Front. Genet. 5:288. doi: 10.3389/fgene.2014.00288

Brunborg, G., Collins, A., Graupner, A., Gutzkow, K. B., and Olsen, A.-K. (2015). Reference cells and ploidy in the comet assay. Front. Genet. 6:61. doi: 10.3389/fgene.2015.00061

Brunborg, G., Jackson, P., Shaposhnikov, S., Dahl, H., Azqueta, A., Collins, A. R., et al. (2014). High throughput sample processing and automated scoring. Front. Genet. 5:373. doi: 10.3389/fgene.2014.00373

Collins, A. R., Azqueta, A., and Langie, S. A. S. (2012). Effects of micronutrients on DNA repair. Eur. J. Nutr . 51, 261–279. doi: 10.1007/s00394-012-0318-4

Collins, A. R., El Yamani, N., Lorenzo, Y., Shaposhnikov, S., Brunborg, G., and Azqueta, A. (2014). Controlling variation in the comet assay. Front. Genet. 5:359. doi: 10.3389/fgene.2014.00359

Collins, A. R., Fleming, I. M., and Gedik, C. M. (1994). In vitro repair of oxidative and ultraviolet-induced DNA damage in supercoiled nucleoid DNA by human cell extract. Biochim. Biophys. Acta. 1219, 724–727. doi: 10.1016/0167-4781(94)90236-4

Cortés-Gutiérrez, E. I., López-Fernández, C., Fernández, J. L., Dávila-Rodríguez, M. I., Johnston, S. D., and Gosálvez, J. (2014). Interpreting sperm DNA damage in a diverse range of mammalian sperm by means of the two-tailed comet assay. Front. Genet. 5:404. doi: 10.3389/fgene.2014.00404

Costa, P. M., Pinto, M., Vicente, A. M., Gonçalves, C., Rodrigo, A. P., Louro, H., et al. (2014). An integrative assessment to determine the genotoxic hazard of estuarine sediments: combining cell and whole-organism responses. Front. Genet. 5:437. doi: 10.3389/fgene.2014.00437

de Lapuente, J., Lourenço, J., Mendo, S. A., Borràs, M., Martins, M. G., Costa, P. M., et al. (2015). The Comet Assay and its applications in the field of ecotoxicology: a mature tool that continues to expand its perspectives. Front. Genet. 6:180. doi: 10.3389/fgene.2015.00180

Ersson, C., and Möller, L. (2011). The effects on DNA migration of altering parameters in the comet assay protocol such as agarose density, electrophoresis conditions and durations of the enzyme or the alkaline treatments. Mutagenesis 26, 689–695. doi: 10.1093/mutage/ger034

Gaivão, I., and Sierra, L. M. (2014). Drosophila comet assay: insights, uses, and future perspectives. Front. Genet. 5:304. doi: 10.3389/fgene.2014.00304

Langie, S. A., Wilms, L. C., Hämäläinen, S., Kleinjans, J. C., Godschalk, R. W., and van Schooten, F. J. (2010). Modulation of nucleotide excision repair in human lymphocytes by genetic and dietary factors. Br. J. Nutr . 103, 490–501. doi: 10.1017/S0007114509992066

Lewies, A., Van Dyk, E., Wentzel, J. F., and Pretorius, P. J. (2014). Using a medium-throughput comet assay to evaluate the global DNA methylation status of single cells. Front. Genet. 5:215. doi: 10.3389/fgene.2014.00215

Lovell, D. P., and Omori, T. (2008). Statistical issues in the use of the comet assay. Mutagenesis 23, 171–182. doi: 10.1093/mutage/gen015

Lovell, D. P., Thomas, G., and Dubow, R. (1999). Issues related to the experimental design and subsequent statistical analysis of in vivo and in vitro comet studies. Teratog. Carcinog. Mutagen. 19, 109–119.

PubMed Abstract | Google Scholar

McAllister, K. A., Yasseen, A. A., McKerr, G., Downes, C. S., and McKelvey-Martin, V. J. (2014). FISH comets show that the salvage enzyme TK1 contributes to gene-specific DNA repair. Front. Genet. 5:233. doi: 10.3389/fgene.2014.00233

Møller, P., and Loft, S. (2014). Statistical analysis of comet assay results. Front. Genet. 5:292. doi: 10.3389/fgene.2014.00292

Møller, P., Loft, S., Ersson, C., Koppen, G., Dusinska, M., and Collins, A. R. (2014). On the search for an intelligible comet assay descriptor. Front. Genet. 5:217. doi: 10.3389/fgene.2014.00217

Neri, M., Milazzo, D., Ugolini, D., Milic, M., Campolongo, A., Pasqualetti, P., et al. (2015). Worldwide interest in the comet assay: a bibliometric study. Mutagenesis 30, 155–163. doi: 10.1093/mutage/geu061

Nickson, C. M., and Parsons, J. L. (2014). Monitoring regulation of DNA repair activities of cultured cells in-gel using the comet assay. Front. Genet. 5:232. doi: 10.3389/fgene.2014.00232

Ostling, O., and Johanson, K. J. (1984). Microelectrophoretic study of radi¬ation-induced DNA damages in individual mammalian cells. Biochem. Biophys. Res. Commun. 123, 291–298. doi: 10.1016/0006-291X(84)90411-X

Rojas, E., Lorenzo, Y., Haug, K., Nicolaissen, B., and Valverde, M. (2014). Epithelial cells as alternative human biomatrices for comet assay. Front. Genet. 5:386. doi: 10.3389/fgene.2014.00386

Santos, C. L. V., Pourrut, B., and Ferreira de Oliveira, J. M. P. (2015). The use of comet assay in plant toxicology: recent advances. Front. Genet. 6:216. doi: 10.3389/fgene.2015.00216

Slyskova, J., Korenkova, V., Collins, A. R., Prochazka, P., Vodickova, L., Svec, J., et al. (2012). Functional, genetic, and epigenetic aspects of base and nucleotide excision repair in colorectal carcinomas. Clin. Cancer Res. 18, 5878–5887. doi: 10.1158/1078-0432.CCR-12-1380

Slyskova, J., Langie, S. A. S., Collins, A. R., and Vodicka, P. (2014). Functional evaluation of DNA repair in human biopsies and their relation to other cellular biomarkers. Front. Genet. 5:116. doi: 10.3389/fgene.2014.00116

Keywords: comet assay, single cell gel electrophoresis, genotoxicity, ecotoxicology, DNA repair, biomonitoring, standardization

Citation: Langie SAS, Azqueta A and Collins AR (2015) The comet assay: past, present, and future. Front. Genet . 6:266. doi: 10.3389/fgene.2015.00266

Received: 22 July 2015; Accepted: 31 July 2015; Published: 13 August 2015.

Edited and reviewed by: David William Galbraith , University of Arizona, USA

Copyright © 2015 Langie, Azqueta and Collins. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Sabine A. S. Langie, [email protected]; Amaya Azqueta, [email protected]; Andrew R. Collins, [email protected]

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Advertisement

Comet assay: an essential tool in toxicological research

• Review Article
• Published: 05 July 2016
• Volume 90 , pages 2315–2336, ( 2016 )

Cite this article

• M. Glei 1 ,
• T. Schneider 1 &
• W. Schlörmann 1  

4622 Accesses

102 Citations

Explore all metrics

The comet assay is a versatile, reliable, cost-efficient, and fast technique for detecting DNA damage and repair in any tissue. It is useable in almost any cell type and applicable to both eukaryotic and prokaryotic organisms. Instead of highlighting one of the numerous specific aspects of the comet assay, the present review aims at giving an overview about the evolution of this widely applicable method from the first description by Ostling and Johanson to the OECD Guideline 489 for the in vivo mammalian comet assay. In addition, methodical aspects and the influence of critical steps of the assay as well as the evaluation of results and improvements of the method are reviewed. Methodical aspects regarding oxidative DNA damage and repair are also addressed. An overview about the most recent works and relevant cutting-edge reviews based on the comet assay with special regard to, e.g., clinical applications, nanoparticles or environmental risk assessment concludes this review. Taken together, the presented overview raises expectations to further decades of successful applications and enhancements of this excellent method.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

According to European Food Safety Authority ( 2012 ), OECD ( 2014 )

Similar content being viewed by others

The Comet Assay: Assessment of In Vitro and In Vivo DNA Damage

The Comet Assay In Vivo in Humans

Aborgiba M, Kostic J, Kolarevic S, Kracun-Kolarevic M, Elbahi S, Knezevic-Vukcevic J, Lenhardt M, Paunovic M, Gacic Z, Vukovic-Gacic B (2016) Flooding modifies the genotoxic effects of pollution on a worm, a mussel and two fish species from the Sava River. Sci Total Environ 540:358–367

Article   CAS   PubMed   Google Scholar  

Abubakar IB, Lim KH, Kam TS, Loh HS (2016) Synergistic cytotoxic effects of combined delta-tocotrienol and jerantinine B on human brain and colon cancers. J Ethnopharmacol 184:107–118. doi: 10.1016/j.jep.2016.03.004

Ahnström G, Erixon K (1981) Measurement of strand breaks by alkaline denaturation and hydroxyapatite chromatography. In: Friedberg EC, Hanawalt PC (eds) DNA repair. A laboratory manual of research procedures. Marcel Dekker, New York, pp 403–418

Google Scholar  

Akdag MZ, Dasdag S, Canturk F, Karabulut D, Caner Y, Adalier N (2016) Does prolonged radiofrequency radiation emitted from Wi-Fi devices induce DNA damage in various tissues of rats? J Chem Neuroanat. doi: 10.1016/j.jchemneu.2016.01.003

Akhtar MF, Ashraf M, Anjum AA, Javeed A, Sharif A, Saleem A, Akhtar B (2016a) Textile industrial effluent induces mutagenicity and oxidative DNA damage and exploits oxidative stress biomarkers in rats. Environ Toxicol Pharmacol 41:180–186

Akhtar MF, Ashraf M, Javeed A, Anjum AA, Sharif A, Saleem A, Akhtar B, Khan AM, Altaf I (2016b) Toxicity appraisal of untreated dyeing industry wastewater based on chemical characterization and short term bioassays. Bull Environ Contam Toxicol 96:502–507

Alarifi S, Ali D, Al-Bishri W (2016) In vitro apoptotic and DNA damaging potential of nanobarium oxide. Int J Nanomed 12:249–257

Albert O, Reintsch WE, Chan P, Robaire B (2016) HT-COMET: a novel automated approach for high throughput assessment of human sperm chromatin quality. Hum Reprod 31(5):938–946. doi: 10.1093/humrep/dew030

Article   PubMed   Google Scholar  

Alcaraz-Contreras Y, Mendoza-Lozano RP, Martinez-Alcaraz ER, Martinez-Alfaro M, Gallegos-Corona MA, Ramirez-Morales MA, Vazquez-Guevara MA (2016) Silymarin and dimercaptosuccinic acid ameliorate lead-induced nephrotoxicity and genotoxicity in rats. Hum Exp Toxicol 35:398–403

Ali H, Dixit S, Ali D, Alkahtane AA, Alarifi S, Ali BA, Alkahtani S (2016) Isolation and evaluation of biological efficacy of quercetol in human hepatic carcinoma cells. Drug Des Devel Ther 10:155–162

Article   PubMed   PubMed Central   Google Scholar  

Alves JS, Silva FR, Silva GF, Salvador M, Kvitko K, Rohr P, Santos CE, Dias JF, Henriques JA, Silva JD (2016) Investigation of potential biomarkers for the early diagnosis of cellular stability after the exposure of agricultural workers to pesticides. An Acad Bras Cienc 88:349–360

Aquino T, Zenkner FF, Ellwanger JH, Pra D, Rieger A (2016) DNA damage and cytotoxicity in pathology laboratory technicians exposed to organic solvents. An Acad Bras Cienc 88:227–236

Araldi RP, de Melo TC, Mendes TB, de Sa Junior PL, Nozima BH, Ito ET, de Carvalho RF, de Souza EB, de Cassia SR (2015) Using the comet and micronucleus assays for genotoxicity studies: a review. Biomed Pharmacother 72:74–82

Arbo MD, Silva R, Barbosa DJ, da Silva D, Silva SP, Teixeira JP, Bastos ML, Carmo H (2016) In vitro neurotoxicity evaluation of piperazine designer drugs in differentiated human neuroblastoma SH-SY5Y cells. J Appl Toxicol 36:121–130

Ataseven N, Yuzbasioglu D, Keskin AC, Unal F (2016) Genotoxicity of monosodium glutamate. Food Chem Toxicol 91:8–18

Attia SM, Ahmad SF, Saquib Q, Harisa GI, Al-Khedhairy AA, Bakheet SA (2016) Dexrazoxane mitigates epirubicin-induced genotoxicity in mice bone marrow cells. Mutagenesis 31:137–145

Au WW, Giri AK, Ruchirawat M (2010) Challenge assay: a functional biomarker for exposure-induced DNA repair deficiency and for risk of cancer. Int J Hyg Environ Health 213:32–39

Augustyniak M, Gladysz M, Dziewiecka M (2016) The Comet assay in insects—status, prospects and benefits for science. Mutat Res Rev Mutat Res 767:67–76

Azqueta A, Collins AR (2013) The essential comet assay: a comprehensive guide to measuring DNA damage and repair. Arch Toxicol 87:949–968

Azqueta A, Gutzkow KB, Brunborg G, Collins AR (2011a) Towards a more reliable comet assay: optimising agarose concentration, unwinding time and electrophoresis conditions. Mutat Res 724:41–45

Azqueta A, Meier S, Priestley C, Gutzkow KB, Brunborg G, Sallette J, Soussaline F, Collins A (2011b) The influence of scoring method on variability in results obtained with the comet assay. Mutagenesis 26:393–399

Azqueta A, Gutzkow KB, Priestley CC, Meier S, Walker JS, Brunborg G, Collins AR (2013) A comparative performance test of standard, medium- and high-throughput comet assays. Toxicol In Vitro 27:768–773

Azqueta A, Slyskova J, Langie SA, O’Neill GI, Collins A (2014) Comet assay to measure DNA repair: approach and applications. Front Genet 5:288

Article   PubMed   PubMed Central   CAS   Google Scholar  

Bakuradze T, Lang R, Hofmann T, Schipp D, Galan J, Eisenbrand G, Richling E (2016) Coffee consumption rapidly reduces background DNA strand breaks in healthy humans: results of a short-term repeated uptake intervention study. Mol Nutr Food Res 60:682–686

Baldissera MD, Sagrillo MR, de Sa MF, Grando TH, Souza CF, de Brum GF, da Luz SC, Oliveira SS, De Mello AL, Nascimento K, Tatsch E, Moresco RN, da Silva AS, Monteiro SG (2016) Relationship between DNA damage in liver, heart, spleen and total blood cells and disease pathogenesis of infected rats by Trypanosoma evansi . Exp Parasitol 161:12–19

Banerjee P, Dey TK, Sarkar S, Swarnakar S, Mukhopadhyay A, Ghosh S (2016) Treatment of cosmetic effluent in different configurations of ceramic UF membrane based bioreactor: toxicity evaluation of the untreated and treated wastewater using catfish ( Heteropneustes fossilis ). Chemosphere 146:133–144

Baron E, Dissanayake A, Vila-Cano J, Crowther C, Readman JW, Jha AN, Eljarrat E, Barcelo D (2016) Evaluation of the genotoxic and physiological effects of decabromodiphenyl ether (BDE-209) and Dechlorane Plus (DP) flame retardants in marine mussels ( Mytilus galloprovincialis ). Environ Sci Technol 50:2700–2708

Basri DF, Alamin ZA, Chan KM (2016) Assessment of cytotoxicity and genotoxicity of stem bark extracts from Canarium odontophyllum Miq. (dabai) against HCT 116 human colorectal cancer cell line. BMC Complement Altern Med 16:36

Bausinger J, Speit G (2015) DNA repair capacity of cultured human lymphocytes exposed to mutagens measured by the comet assay and array expression analysis. Mutagenesis 30:811–820

Bausinger J, Schutz P, Piberger AL, Speit G (2016) Further characterization of benzo[a]pyrene diol-epoxide (BPDE)-induced comet assay effects. Mutagenesis 31:161–169

Bedekar PA, Saratale RG, Saratale GD, Govindwar SP (2014) Oxidative stress response in dye degrading bacterium Lysinibacillus sp. RGS exposed to Reactive Orange 16, degradation of RO16 and evaluation of toxicity. Environ Sci Pollut Res Int 21:11075–11085

Bhattacharya P, Swarnakar S, Mukhopadhyay A, Ghosh S (2016) Exposure of composite tannery effluent on snail, Pila globosa : a comparative assessment of toxic impacts of the untreated and membrane treated effluents. Ecotoxicol Environ Saf 126:45–55

Bilbao C, Ferreiro JA, Comendador MA, Sierra LM (2002) Influence of mus201 and mus308 mutations of Drosophila melanogaster on the genotoxicity of model chemicals in somatic cells in vivo measured with the comet assay. Mutat Res 503:11–19

Boyacioglu M, Kum C, Sekkin S, Yalinkilinc HS, Avci H, Epikmen ET, Karademir U (2016) The effects of lycopene on DNA damage and oxidative stress on indomethacin-induced gastric ulcer in rats. Clin Nutr 35:428–435

Brunborg G, Jackson P, Shaposhnikov S, Dahl H, Azqueta A, Collins AR, Gutzkow KB (2014) High throughput sample processing and automated scoring. Front Genet 5:373

Brusehafer K, Manshian BB, Doherty AT, Zair ZM, Johnson GE, Doak SH, Jenkins GJ (2016) The clastogenicity of 4NQO is cell-type dependent and linked to cytotoxicity, length of exposure and p53 proficiency. Mutagenesis 31:171–180

Article   PubMed   CAS   Google Scholar  

Caballero-Gallardo K, Olivero-Verbel J (2016) Mice housed on coal dust-contaminated sand: a model to evaluate the impacts of coal mining on health. Toxicol Appl Pharmacol 294:11–20

Carbajal-Lopez Y, Gomez-Arroyo S, Villalobos-Pietrini R, Calderon-Segura ME, Martinez-Arroyo A (2016) Biomonitoring of agricultural workers exposed to pesticide mixtures in Guerrero state, Mexico, with comet assay and micronucleus test. Environ Sci Pollut Res Int 23:2513–2520

Carter RJ, Parsons JL (2016) Base excision repair: a pathway regulated by post-translational modifications. Mol Cell Biol 36(10):1426–1437. doi: 10.1128/MCB.00030-16

Carvalho IC, Dutra TP, De Andrade DP, Balducci I, Pacheco-Soares C, Rocha RF (2016) High doses of alcohol during pregnancy cause DNA damages in osteoblasts of newborns rats. Birth Defects Res A Clin Mol Teratol 106:122–132

Carvalho-Silva R, Pereira AC, Dos Santos Alves RP, Guecheva TN, Henriques JA, Brendel M, Pungartnik C, Rios-Santos F (2016) DNA protection against oxidative damage using the hydroalcoholic extract of Garcinia mangostana and alpha-mangostin. Evid Based Complement Alternat Med 2016:3430405

Castilla JA, Zamora S, Gonzalvo MC, Luna Del Castillo JD, Roldan-Nofuentes JA, Clavero A, Bjorndahl L, Martinez L (2010) Sperm chromatin structure assay and classical semen parameters: systematic review. Reprod Biomed Online 20:114–124

Cetinkaya N, Ercin D, Ozvatan S, Erel Y (2016) Quantification of applied dose in irradiated citrus fruits by DNA Comet Assay together with image analysis. Food Chem 192:370–373

Chandramohan B, Murugan K, Panneerselvam C, Madhiyazhagan P, Chandirasekar R, Dinesh D, Kumar PM, Kovendan K, Suresh U, Subramaniam J, Rajaganesh R, Aziz AT, Syuhei B, Alsalhi MS, Devanesan S, Nicoletti M, Wei H, Benelli G (2016) Characterization and mosquitocidal potential of neem cake-synthesized silver nanoparticles: genotoxicity and impact on predation efficiency of mosquito natural enemies. Parasitol Res 115:1015–1025

Chang L, Huang J, Wang K, Li J, Yan R, Zhu L, Ye J, Wu X, Zhuang S, Li D, Zhang G (2016a) Targeting Rad50 sensitizes human nasopharyngeal carcinoma cells to radiotherapy. BMC Cancer 16:190

Chang YT, Huang CY, Li KT, Li RN, Liaw CC, Wu SH, Liu JR, Sheu JH, Chang HW (2016b) Sinuleptolide inhibits proliferation of oral cancer Ca9-22 cells involving apoptosis, oxidative stress, and DNA damage. Arch Oral Biol 66:147–154

Chapman PM (2007) Determining when contamination is pollution—weight of evidence determinations for sediments and effluents. Environ Int 33:492–501

Chen LM, Peng F, Li GD, Jie XM, Cai KR, Cai C, Zhong Y, Zeng H, Li W, Zhang Z, Chen JC (2016) The studies on the cytotoxicity in vitro, cellular uptake, cell cycle arrest and apoptosis-inducing properties of ruthenium methylimidazole complex [Ru(MeIm)4(p-cpip)]. J Inorg Biochem 156:64–74

Cigerci IH, Ali MM, Kaygisiz SY, Liman R (2016) Genotoxicity assessment of cobalt chloride in Eisenia hortensis earthworms coelomocytes by comet assay and micronucleus test. Chemosphere 144:754–757

Claudio SR, Gollucke AP, Yamamura H, Morais DR, Bataglion GA, Eberlin MN, Peres RC, Oshima CT, Ribeiro DA (2016) Purple carrot extract protects against cadmium intoxication in multiple organs of rats: genotoxicity, oxidative stress and tissue morphology analyses. J Trace Elem Med Biol 33:37–47

Collins AR (2009) Investigating oxidative DNA damage and its repair using the comet assay. Mutat Res 681:24–32

Collins AR (2014) Measuring oxidative damage to DNA and its repair with the comet assay. Biochim Biophys Acta 1840:794–800

Collins AR, Azqueta A (2012) DNA repair as a biomarker in human biomonitoring studies; further applications of the comet assay. Mutat Res 736:122–129

Collins AR, Horvathova E (2001) Oxidative DNA damage, antioxidants and DNA repair: applications of the comet assay. Biochem Soc Trans 29:337–341

Collins AR, Duthie SJ, Dobson VL (1993) Direct enzymic detection of endogenous oxidative base damage in human lymphocyte DNA. Carcinogenesis 14:1733–1735

Collins A, Dusinska M, Franklin M, Somorovska M, Petrovska H, Duthie S, Fillion L, Panayiotidis M, Raslova K, Vaughan N (1997a) Comet assay in human biomonitoring studies: reliability, validation, and applications. Environ Mol Mutagen 30:139–146

Collins AR, Dobson VL, Dusinska M, Kennedy G, Stetina R (1997b) The comet assay: what can it really tell us? Mutat Res 375:183–193

Collins AR, Olmedilla B, Southon S, Granado F, Duthie SJ (1998) Serum carotenoids and oxidative DNA damage in human lymphocytes. Carcinogenesis 19:2159–2162

Collins AR, Oscoz AA, Brunborg G, Gaivao I, Giovannelli L, Kruszewski M, Smith CC, Stetina R (2008) The comet assay: topical issues. Mutagenesis 23:143–151

Collins A, Koppen G, Valdiglesias V, Dusinska M, Kruszewski M, Moller P, Rojas E, Dhawan A, Benzie I, Coskun E, Moretti M, Speit G, Bonassi S (2014) The comet assay as a tool for human biomonitoring studies: the ComNet project. Mutat Res Rev Mutat Res 759:27–39

Cordelli E, Keller J, Eleuteri P, Villani P, Ma-Hock L, Schulz M, Landsiedel R, Pacchierotti F (2016) No genotoxicity in rat blood cells upon 3- or 6-month inhalation exposure to CeO2 or BaSO4 nanomaterials. Mutagenesis [Epub ahead of print]

Coronas MV, Vaz Rocha JA, Favero Salvadori DM, Vargas VF (2016) Evaluation of area contaminated by wood treatment activities: genetic markers in the environment and in the child population. Chemosphere 144:1207–1215

Corredor Z, Rodriguez-Ribera L, Coll E, Montanes R, Diaz JM, Ballarin J, Marcos R, Pastor S (2016) Unfermented grape juice reduce genomic damage on patients undergoing hemodialysis. Food Chem Toxicol 92:1–7

Cortes-Gutierrez EI, Lopez-Fernandez C, Fernandez JL, Vila-Rodriguez MI, Johnston SD, Gosalvez J (2014) Interpreting sperm DNA damage in a diverse range of mammalian sperm by means of the two-tailed comet assay. Front Genet 5:404

PubMed   PubMed Central   Google Scholar  

Costa JG, Saraiva N, Guerreiro PS, Louro H, Silva MJ, Miranda JP, Castro M, Batinic-Haberle I, Fernandes AS, Oliveira NG (2016) Ochratoxin A-induced cytotoxicity, genotoxicity and reactive oxygen species in kidney cells: an integrative approach of complementary endpoints. Food Chem Toxicol 87:65–76

Cui H, Wu S, Sun Y, Wang T, Li Z, Chen M, Wang C (2016) Polysaccharide from Pleurotus nebrodensis induces apoptosis via a mitochondrial pathway in HepG2 cells. Food Funct 7:455–463

Curnow A, Owen SJ (2016) An evaluation of root phytochemicals derived from Althea officinalis (Marshmallow) and Astragalus membranaceus as potential natural components of UV protecting dermatological formulations. Oxid Med Cell Longev 2016:7053897

Danevcic T, Boric VM, Tabor M, Zorec M, Stopar D (2016) Prodigiosin induces autolysins in actively grown Bacillus subtilis cells. Front Microbiol 7:27

Darne C, Coulais C, Terzetti F, Fontana C, Binet S, Gate L, Guichard Y (2016) In vitro comet and micronucleus assays do not predict morphological transforming effects of silica particles in Syrian Hamster Embryo cells. Mutat Res Genet Toxicol Environ Mutagen 796:23–33

de Lapuente J, Lourenco J, Mendo SA, Borras M, Martins MG, Costa PM, Pacheco M (2015) The Comet Assay and its applications in the field of ecotoxicology: a mature tool that continues to expand its perspectives. Front Genet 6:180

de Oliveira LF, Santos C, Dos Reis Martinez CB (2016) Biomarkers in the freshwater bivalve Corbicula fluminea confined downstream a domestic landfill leachate discharge. Environ Sci Pollut Res Int. doi: 10.1007/s11356-016-6567-7

DeMarini DM (2013) Genotoxicity biomarkers associated with exposure to traffic and near-road atmospheres: a review. Mutagenesis 28:485–505

Dhawan A, Bajpayee M, Parmar D (2009) Comet assay: a reliable tool for the assessment of DNA damage in different models. Cell Biol Toxicol 25:5–32

Dissanayake A, Scarlett AG, Jha AN (2016) Diamondoid naphthenic acids cause in vivo genetic damage in gills and haemocytes of marine mussels. Environ Sci Pollut Res Int 23:7060–7066

Dobrovolsky VN, Pacheco-Martinez MM, McDaniel LP, Pearce MG, Ding W (2016) In vivo genotoxicity assessment of acrylamide and glycidyl methacrylate. Food Chem Toxicol 87:120–127

Du J, Wang S, You H, Jiang R, Zhuang C, Zhang X (2016) Developmental toxicity and DNA damage to zebrafish induced by perfluorooctane sulfonate in the presence of ZnO nanoparticles. Environ Toxicol 31:360–371

Duan H, Jia X, Zhai Q, Ma L, Wang S, Huang C, Wang H, Niu Y, Li X, Dai Y, Yu S, Gao W, Chen W, Zheng Y (2016) Long-term exposure to diesel engine exhaust induces primary DNA damage: a population-based study. Occup Environ Med 73:83–90

Dusinska M, Collins AR (2008) The comet assay in human biomonitoring: gene-environment interactions. Mutagenesis 23:191–205

Duthie SJ, Ma A, Ross MA, Collins AR (1996) Antioxidant supplementation decreases oxidative DNA damage in human lymphocytes. Cancer Res 56:1291–1295

CAS   PubMed   Google Scholar  

EFSA Scientific Committee (2011) Scientific opinion on genotoxicity testing strategies applicable to food and feed safety assessment. EFSA J 9:2379–2447

Article   CAS   Google Scholar  

Ersson C, Moller L (2011) The effects on DNA migration of altering parameters in the comet assay protocol such as agarose density, electrophoresis conditions and durations of the enzyme or the alkaline treatments. Mutagenesis 26:689–695

Ersson C, Thorman R, Rodhe Y, Moller L, Hylander B (2011) DNA damage in salivary gland tissue in patients with chronic kidney disease, measured by the comet assay. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 112:209–215

European Food Safety Authority (2012) Minimum criteria for the acceptance of in vivo alkaline Comet Assay reports. EFSA J 10:2977–2988

Evison BJ, Actis ML, Fujii N (2016) A clickable psoralen to directly quantify DNA interstrand crosslinking and repair. Bioorg Med Chem 24:1071–1078

Ezzi L, Salah IB, Haouas Z, Sakly A, Grissa I, Chakroun S, Kerkeni E, Hassine M, Mehdi M, Ben CH (2016a) Histopathological and genotoxic effects of chlorpyrifos in rats. Environ Sci Pollut Res Int 23(5):4859–4867. doi: 10.1007/s11356-015-5722-x

Ezzi L, Haouas Z, Salah IB, Sakly A, Grissa I, Chakroun S, Kerkeni E, Hassine M, Mehdi M, Cheikh HB (2016b) Toxicopathic changes and genotoxic effects in liver of rat following exposure to diazinon. Environ Sci Pollut Res Int 23(5):4859–4867. doi: 10.1007/s11356-015-5722-x

Faisal M, Saquib Q, Alatar AA, Al-Khedhairy AA, Ahmed M, Ansari SM, Alwathnani HA, Dwivedi S, Musarrat J, Praveen S (2016) Cobalt oxide nanoparticles aggravate DNA damage and cell death in eggplant via mitochondrial swelling and NO signaling pathway. Biol Res 49:20

Farah MA, Ali MA, Chen SM, Li Y, Al-Hemaid FM, Bou-Tarboush FM, Al-Anazi KM, Lee J (2016) Silver nanoparticles synthesized from Adenium obesum leaf extract induced DNA damage, apoptosis and autophagy via generation of reactive oxygen species. Colloids Surf B Biointerfaces 141:158–169

Farhan M, Khan HY, Oves M, Al-Harrasi A, Rehmani N, Arif H, Hadi SM, Ahmad A (2016) Cancer therapy by catechins involves redox cycling of copper ions and generation of reactive oxygenspecies. Toxins (Basel) 8(2):37. doi: 10.3390/toxins8020037

Fernandez-Encinas A, Garcia-Peiro A, Ribas-Maynou J, Abad C, Amengual MJ, Navarro J, Benet J (2016) Characterization of nuclease activity in human seminal plasma and its relationship to semen parameters, sperm DNA fragmentation and male infertility. J Urol 195:213–219

Frenzilli G, Nigro M, Lyons BP (2009) The Comet assay for the evaluation of genotoxic impact in aquatic environments. Mutat Res 681:80–92

Gaivao I, Sierra LM (2014) Drosophila comet assay: insights, uses, and future perspectives. Front Genet 5:304

Gasulla J, Picco SJ, Carriquiriborde P, Dulout FN, Ronco AE, de Luca JC (2016) Genotoxic effects induced by Cd, Cr, Cu in the gill and liver of odontesthes bonariensis (Piscies, Atherinopsidae). Bull Environ Contam Toxicol 96(5):591–595. doi: 10.1007/s00128-016-1774-y

Gelaleti RB, Damasceno DC, Santos DP, Calderon IM, Rudge MV (2016) Increased DNA damage is related to maternal blood glucose levels in the offspring of women with diabetes and mild gestational hyperglycemia. Reprod Sci 23:318–323

Girardello F, Leite CC, Vianna VI, da Silva MM, Luiz Mendes JA, Roesch-Ely M, Neves FA, Salvador M, Ntonio Pegas HJ (2016) Titanium dioxide nanoparticles induce genotoxicity but not mutagenicity in golden mussel Limnoperna fortunei . Aquat Toxicol 170:223–228

Glei M, Pool-Zobel BL (2006) The main catechin of green tea, (-)-epigallocatechin-3-gallate (EGCG), reduces bleomycin-induced DNA damage in human leucocytes. Toxicol In Vitro 20:295–300

Glei M, Schlörmann W (2014) Analysis of DNA damage and repair by comet fluorescence in situ hybridization (Comet-FISH). Methods Mol Biol 1094:39–48

Glei M, Latunde-Dada GO, Klinder A, Becker TW, Hermann U, Voigt K, Pool-Zobel BL (2002a) Iron-overload induces oxidative DNA damage in the human colon carcinoma cell line HT29 clone 19A. Mutat Res 519:151–161

Glei M, Liegibel UM, Ebert MN, Bohm V, Pool-Zobel BL (2002b) beta-Carotene reduces bleomycin-induced genetic damage in human lymphocytes. Toxicol Appl Pharmacol 179:65–73

Glei M, Matuschek M, Steiner C, Bohm V, Persin C, Pool-Zobel BL (2003) Initial in vitro toxicity testing of functional foods rich in catechins and anthocyanins in human cells. Toxicol In Vitro 17:723–729

Glei M, Habermann N, Osswald K, Seidel C, Persin C, Jahreis G, Pool-Zobel BL (2005) Assessment of DNA damage and its modulation by dietary and genetic factors in smokers using the Comet assay: a biomarker model. Biomarkers 10:203–217

Glei M, Kirmse A, Habermann N, Persin C, Pool-Zobel BL (2006a) Bread enriched with green coffee extract has chemoprotective and antigenotoxic activities in human cells. Nutr Cancer 56:182–192

Glei M, Klenow S, Sauer J, Wegewitz U, Richter K, Pool-Zobel BL (2006b) Hemoglobin and hemin induce DNA damage in human colon tumor cells HT29 clone 19A and in primary human colonocytes. Mutat Res 594:162–171

Glei M, Schaeferhenrich A, Claussen U, Kuechler A, Liehr T, Weise A, Marian B, Sendt W, Pool-Zobel BL (2007) Comet fluorescence in situ hybridization analysis for oxidative stress-induced DNA damage in colon cancer relevant genes. Toxicol Sci 96:279–284

Glei M, Hovhannisyan G, Pool-Zobel BL (2009) Use of Comet-FISH in the study of DNA damage and repair: review. Mutat Res 681:33–43

Golbamaki N, Rasulev B, Cassano A, Marchese Robinson RL, Benfenati E, Leszczynski J, Cronin MT (2015) Genotoxicity of metal oxide nanomaterials: review of recent data and discussion of possible mechanisms. Nanoscale 7:2154–2198

Gorniak JP, Cameron KM, Waldron KJ, von Zglinicki T, Mathers JC, Langie SA (2013) Tissue differences in BER-related incision activity and non-specific nuclease activity as measured by the comet assay. Mutagenesis 28:673–681

Goyal S, Amar SK, Dwivedi A, Mujtaba SF, Kushwaha HN, Chopra D, Pal MK, Singh D, Chaturvedi RK, Ray RS (2016) Photosensitized 2-amino-3-hydroxypyridine-induced mitochondrial apoptosis via Smac/DIABLO in human skin cells. Toxicol Appl Pharmacol 297:12–21

Gu L, Gao W, Yang HM, Wang BB, Wang XN, Xu J, Zhang H (2016) Control of Trx1 redox state modulates protection against methyl methanesulfonate-induced DNA damage via stabilization of p21. J Biochem 159:101–110

Guerard M, Marchand C, Plappert-Helbig U (2014) Influence of experimental conditions on data variability in the liver comet assay. Environ Mol Mutagen 55:114–121

Gugliandolo A, Gangemi C, Calabro C, Vecchio M, Di MD, Renis M, Ientile R, Curro M, Caccamo D (2016) Assessment of glutathione peroxidase-1 polymorphisms, oxidative stress and DNA damage in sensitivity-related illnesses. Life Sci 145:27–33

Gulati S, Yadav A, Kumar N, Kanupriya Aggarwal NK, Kumar R, Gupta R (2016) Effect of GSTM1 and GSTT1 polymorphisms on genetic damage in humans populations exposed to radiation from mobile towers. Arch Environ Contam Toxicol 70:615–625

Gunasekarana V, Raj GV, Chand P (2015) A comprehensive review on clinical applications of comet assay. J Clin Diagn Res 9:GE01–GE05

Gutzkow KB, Langleite TM, Meier S, Graupner A, Collins AR, Brunborg G (2013) High-throughput comet assay using 96 minigels. Mutagenesis 28:333–340

Hagmar L, Bonassi S, Stromberg U, Brogger A, Knudsen LE, Norppa H, Reuterwall C (1998) Chromosomal aberrations in lymphocytes predict human cancer: a report from the European Study Group on Cytogenetic Biomarkers and Health (ESCH). Cancer Res 58:4117–4121

Haq I, Kumar S, Kumari V, Singh SK, Raj A (2016) Evaluation of bioremediation potentiality of ligninolytic Serratia liquefaciens for detoxification of pulp and paper mill effluent. J Hazard Mater 305:190–199

Hashimoto M, Kawai K, Kawakami H, Imazato S (2016) Matrix metalloproteases inhibition and biocompatibility of gold and platinum nanoparticles. J Biomed Mater Res A 104:209–217

Hoda M, Pajaniradje S, Shakya G, Mohankumar K, Rajagopalan R (2016) Anti-proliferative and apoptosis-triggering potential of disulfiram and disulfiram-loaded polysorbate 80-stabilized PLGA nanoparticles on hepatocellular carcinoma Hep3B cell line. Nanomedicine. doi: 10.1016/j.nano.2016.02.013

PubMed   Google Scholar  

Hoeijmakers JH (2001) Genome maintenance mechanisms for preventing cancer. Nature 411:366–374

Hoeijmakers JH (2009) DNA damage, aging, and cancer. N Engl J Med 361:1475–1485

Hofer M, Falk M, Komurkova D, Falkova I, Bacikova A, Klejdus B, Pagacova E, Stefancikova L, Weiterova L, Angelis KJ, Kozubek S, Dusek L, Galbavy S (2016) Two new faces of amifostine: protector from DNA damage in normal cells and inhibitor of DNA repair in cancer cells. J Med Chem 59(7):3003–3017. doi: 10.1021/acs.jmedchem.5b01628

Horta RN, Kahl VF, Sarmento MS, Nunes MF, Porto CR, Andrade VM, Ferraz AB, Silva JD (2016) Protective effects of acerola juice on genotoxicity induced by iron in vivo. Genet Mol Biol 39:122–128

Horvathova E, Dusinska M, Shaposhnikov S, Collins AR (2004) DNA damage and repair measured in different genomic regions using the comet assay with fluorescent in situ hybridization. Mutagenesis 19:269–276

Horvathova E, Srancikova A, Regendova-Sedlackova E, Melusova M, Melus V, Netriova J, Krajcovicova Z, Slamenova D, Pastorek M, Kozics K (2016) Enriching the drinking water of rats with extracts of Salvia officinalis and Thymus vulgaris increases their resistance to oxidative stress. Mutagenesis 31:51–59

Huan Z, Luo J, Xu Z, Xie D (2016) Acute toxicity and genotoxicity of carbendazim, main impurities and metabolite to earthworms ( Eisenia foetida ). Bull Environ Contam Toxicol 96:62–69

Huang JL, Lu HH, Lu YN, Hung PS, Lin YJ, Lin CC, Yang CC, Wong TY, Lu SY, Lin CS (2016) Enhancement of the genotoxicity of benzo[a]pyrene by arecoline through suppression of DNA repair in HEp-2 cells. Toxicol In Vitro 33:80–87

Hylland K, Skei BB, Brunborg G, Lang T, Gubbins MJ, le GJ, Burgeot T (2016) DNA damage in dab ( Limanda limanda ) and haddock ( Melanogrammus aeglefinus ) from European seas. Mar Environ Res. doi: 10.1016/j.marenvres.2016.01.001

Javed M, Ahmad I, Usmani N, Ahmad M (2016a) Bioaccumulation, oxidative stress and genotoxicity in fish ( Channa punctatus ) exposed to a thermal power plant effluent. Ecotoxicol Environ Saf 127:163–169

Javed M, Ahmad I, Usmani N, Ahmad M (2016b) Studies on biomarkers of oxidative stress and associated genotoxicity and histopathology in Channa punctatus from heavy metal polluted canal. Chemosphere 151:210–219

Jung SJ, Choi YJ, Kim NN, Choi JY, Kim BS, Choi CY (2016) Effects of melatonin injection or green-wavelength LED light on the antioxidant system in goldfish ( Carassius auratus ) during thermal stress. Fish Shellfish Immunol 52:157–166

Kadam A, Dhabbe R, Gophane A, Sathe T, Garadkar K (2016) Template free synthesis of ZnO/Ag 2 O nanocomposites as a highly efficient visible active photocatalyst for detoxification of methyl orange. J Photochem Photobiol B 154:24–33

Kalaiselvan I, Senthamarai M, Kasi PD (2016) 2,3,7,8-TCDD-mediated toxicity in peripheral blood mononuclear cells is alleviated by the antioxidants present in Gelidiella acerosa : an in vitro study. Environ Sci Pollut Res Int 23:5111–5121

Kang SH, Kwon JY, Lee JK, Seo YR (2013) Recent advances in in vivo genotoxicity testing: prediction of carcinogenic potential using comet and micronucleus assay in animal models. J Cancer Prev 18:277–288

Karlsson HL, Di BS, Collins AR, Dusinska M (2015) Can the comet assay be used reliably to detect nanoparticle-induced genotoxicity? Environ Mol Mutagen 56:82–96

Katz IS, Albuquerque LL, Suppa AP, da Silva GB, Jensen JR, Borrego A, Massa S, Starobinas N, Cabrera WH, De FM, Borelli P, Ibanez OM, Ribeiro OG (2016) 7,12-Dimethylbenz(a)anthracene-induced genotoxicity on bone marrow cells from mice phenotypically selected for low acute inflammatory response. DNA Repair 37:43–52

Kelvey-Martin VJ, Green MH, Schmezer P, Pool-Zobel BL, De Meo MP, Collins A (1993) The single cell gel electrophoresis assay (comet assay): a European review. Mutat Res 288:47–63

Article   Google Scholar  

Khoei S, Shoja M, Mostaar A, Faeghi F (2016) Effects of resveratrol and methoxyamine on the radiosensitivity of iododeoxyuridine in U87MG glioblastoma cell line. Exp Biol Med 241(11):1229–1236. doi: 10.1177/1535370215622583

Kim KC, Piao MJ, Madduma H Sr, Han X, Kang KA, Jo JO, Mok YS, Shin JH, Park Y, Yoo SJ, Hyun JW (2016) Non-thermal dielectric-barrier discharge plasma damages human keratinocytes by inducing oxidative stress. Int J Mol Med 37:29–38

King YA, Chiu YJ, Chen HP, Kuo DH, Lu CC, Yang JS (2016) Endoplasmic reticulum stress contributes to arsenic trioxide-induced intrinsic apoptosis in human umbilical and bone marrow mesenchymal stem cells. Environ Toxicol 31:314–328

Kobets T, Duan JD, Brunnemann KD, Etter S, Smith B, Williams GM (2016) Structure-activity relationships for DNA damage by alkenylbenzenes in Turkey egg fetal liver. Toxicol Sci 150:301–311

Kohn KW, Grimek-Ewig RA (1973) Alkaline elution analysis, a new approach to the study of DNA single-strand interruptions in cells. Cancer Res 33:1849–1853

Kolarevic S, Kracun-Kolarevic M, Kostic J, Slobodnik J, Liska I, Gacic Z, Paunovic M, Knezevic-Vukcevic J, Vukovic-Gacic B (2016) Assessment of the genotoxic potential along the Danube River by application of the comet assay on haemocytes of freshwater mussels: the Joint Danube Survey 3. Sci Total Environ 540:377–385

Krishna G, Gopalakrishnan G, Goel S (2016) In vitro and in vivo genotoxicity assessment of the dopamine receptor antagonist molindone hydrochloride. Environ Mol Mutagen 57(4):288–298. doi: 10.1002/em.22007

Kruger K, Ziegler V, Hartmann C, Henninger C, Thomale J, Schupp N, Fritz G (2016) Lovastatin prevents cisplatin-induced activation of pro-apoptotic DNA damage response (DDR) of renal tubular epithelial cells. Toxicol Appl Pharmacol 292:103–114

Kubasek J, Vojtech D, Jablonska E, Pospisilova I, Lipov J, Ruml T (2016) Structure, mechanical characteristics and in vitro degradation, cytotoxicity, genotoxicity and mutagenicity of novel biodegradable Zn–Mg alloys. Mater Sci Eng C Mater Biol Appl 58:24–35

Kumaravel TS, Vilhar B, Faux SP, Jha AN (2009) Comet Assay measurements: a perspective. Cell Biol Toxicol 25:53–64

Kumari M, Ghosh P, Thakur IS (2016) Landfill leachate treatment using bacto-algal co-culture: an integrated approach using chemical analyses and toxicological assessment. Ecotoxicol Environ Saf 128:44–51

Kurashige T, Shimamura M, Nagayama Y (2016) Differences in quantification of DNA double-strand breaks assessed by 53BP1/gammaH2AX focus formation assays and the comet assay in mammalian cells treated with irradiation and N-acetyl- l -cysteine. J Radiat Res 57(3):312–317. doi: 10.1093/jrr/rrw001

Kusrini E, Hashim F, Azmi WN, Amin NM, Estuningtyas A (2016) A novel antiamoebic agent against Acanthamoeba sp.—a causative agent for eye keratitis infection. Spectrochim Acta A Mol Biomol Spectrosc 153:714–721

Lai SH, Li W, Yao JH, Han BJ, Jiang GB, Zhang C, Zeng CC, Liu YJ (2016) Protein binding and anticancer activity studies of ruthenium(II) polypyridyl complexes toward BEL-7402 cells. J Photochem Photobiol B 158:39–48

Lan J, Gou N, Rahman SM, Gao C, He M, Gu AZ (2016) A quantitative toxicogenomics assay for high-throughput and mechanistic genotoxicity assessment and screening of environmental pollutants. Environ Sci Technol 50:3202–3214

Lanier C, Manier N, Cuny D, Deram A (2015) The comet assay in higher terrestrial plant model: review and evolutionary trends. Environ Pollut 207:6–20

Le Hégarat L, Takakura N, Simar S, Nesslany F, Fessard V (2014) The in vivo genotoxicity studies on nivalenol and deoxynivalenol. EFSA supporting publication EN-697:1–33. http://www.efsa.europa.eu/sites/default/files/scientific_output/files/main_documents/697e.pdf . Accessed 28 June 2016

Li Y, Feng X, Du W, Li Y, Liu BF (2013) Ultrahigh-throughput approach for analyzing single-cell genomic damage with an agarose-based microfluidic comet array. Anal Chem 85:4066–4073

Li Q, Chen L, Liu L, Wu L (2016a) Embryotoxicity and genotoxicity evaluation of sediments from Yangtze River estuary using zebrafish ( Danio rerio ) embryos. Environ Sci Pollut Res Int 23:4908–4918

Li Y, Hu G, Li P, Tang S, Zhang J, Jia G (2016b) miR-3940-5p enhances homologous recombination after DSB in Cr(VI) exposed 16HBE cell. Toxicology 344–346:1–6

Li Y, Wei L, Cao J, Qiu L, Jiang X, Li P, Song Q, Zhou H, Han Q, Diao X (2016c) Oxidative stress, DNA damage and antioxidant enzyme activities in the pacific white shrimp ( Litopenaeus vannamei ) when exposed to hypoxia and reoxygenation. Chemosphere 144:234–240

Liu X, Sun B, Wang X, Nie J, Chen Z, An Y, Tong J (2016a) Synergistic effect of radon and sodium arsenite on DNA damage in HBE cells. Environ Toxicol Pharmacol 41:127–131

Liu Z, Liu H, Gao F, Dahlstrom KR, Sturgis EM, Wei Q (2016b) Reduced DNA double-strand break repair capacity and risk of squamous cell carcinoma of the head and neck—a case-control study. DNA Repair 40:18–26

Lorenzo Y, Azqueta A, Luna L, Bonilla F, Dominguez G, Collins AR (2009) The carotenoid beta-cryptoxanthin stimulates the repair of DNA oxidation damage in addition to acting as an antioxidant in human cells. Carcinogenesis 30:308–314

Lorenzo Y, Costa S, Collins AR, Azqueta A (2013) The comet assay, DNA damage, DNA repair and cytotoxicity: hedgehogs are not always dead. Mutagenesis 28:427–432

Lovato FL, de Oliveira CR, Adedara IA, Barbisan F, Moreira KL, Dalberto M, da Rocha MI, Marroni NP, da Cruz IB, Costabeber IB (2016) Quercetin ameliorates polychlorinated biphenyls-induced testicular DNA damage in rats. Andrologia 48:51–58

Lovell DP, Omori T (2008) Statistical issues in the use of the comet assay. Mutagenesis 23:171–182

Lovreglio P, Doria D, Fracasso ME, Barbieri A, Sabatini L, Drago I, Violante FS, Soleo L (2016) DNA damage and repair capacity in workers exposed to low concentrations of benzene. Environ Mol Mutagen 57:151–158

Lundqvist J, Hellman B, Oskarsson A (2016) Fungicide prochloraz induces oxidative stress and DNA damage in vitro. Food Chem Toxicol 91:36–41

Ma T, Chen L, Wu L, Zhang H, Luo Y (2016) Oxidative stress, cytotoxicity and genotoxicity in earthworm Eisenia fetida at different di-n-butyl phthalate exposure levels. PLoS ONE 11:e0151128

Machado CS, Venancio VP, Aissa AF, Hernandes LC, Mello MB, Lama JE, Marzocchi-Machado CM, Bianchi ML, Antunes LM (2016) Vitamin D3 deficiency increases DNA damage and the oxidative burst of neutrophils in a hypertensive rat model. Mutat Res Genet Toxicol Environ Mutagen 798–799:19–26

Magaye R, Gu Y, Wang Y, Su H, Zhou Q, Mao G, Shi H, Yue X, Zou B, Xu J, Zhao J (2016) In vitro and in vivo evaluation of the toxicities induced by metallic nickel nano and fine particles. J Mol Histol 47(3):273–286. doi: 10.1007/s10735-016-9671-6

Magdolenova Z, Collins A, Kumar A, Dhawan A, Stone V, Dusinska M (2014) Mechanisms of genotoxicity. A review of in vitro and in vivo studies with engineered nanoparticles. Nanotoxicology 8:233–278

Martins M, Costa PM (2015) The comet assay in environmental risk assessment of marine pollutants: applications, assets and handicaps of surveying genotoxicity in non-model organisms. Mutagenesis 30:89–106

Mavuluri J, Beesetti S, Surabhi R, Kremerskothen J, Venkatraman G, Rayala K (2016) Phosphorylation dependent regulation of DNA damage response of adaptor protein KIBRA in cancer cells. Mol Cell Biol 36(9):1354–1365. doi: 10.1128/MCB.01004-15

Menezes AP, da Silva J, Fisher C, da Silva FR, Reyes JM, Picada JN, Ferraz AG, Correa DS, Premoli SM, Dias JF, de Souza CT, Ferraz AB (2016) Chemical and toxicological effects of medicinal Baccharis trimera extract from coal burning area. Chemosphere 146:396–404

Miloshev G, Mihaylov I, Anachkova B (2002) Application of the single cell gel electrophoresis on yeast cells. Mutat Res 513:69–74

Miranda-Spooner M, Paccola CC, Neves FM, de Oliva SU, Miraglia SM (2016) Late reproductive analysis in rat male offspring exposed to nicotine during pregnancy and lactation. Andrology 4:218–231

Mitic-Culafic D, Nikolic B, Simin N, Jasnic N, Cetojevic-Simin D, Krstic M, Knezevic-Vukcevic J (2016) Effect of Allium flavum L. and Allium melanantherum Panc. Extracts on oxidative DNA damage and antioxidative enzymes superoxide dismutase and catalase. Plant Foods Hum Nutr 71:28–34

Mladenov E, Magin S, Soni A, Iliakis G (2016) DNA double-strand-break repair in higher eukaryotes and its role in genomic instability and cancer: cell cycle and proliferation-dependent regulation. Semin Cancer Biol 37–38:51–64. doi: 10.1016/j.semcancer.2016.03.003

Mohamed HR, Hussien NA (2016) Genotoxicity studies of titanium dioxide nanoparticles (TiO 2 NPs) in the brain of mice. Scientifica 2016:6710840

Moller P, Viscovich M, Lykkesfeldt J, Loft S, Jensen A, Poulsen HE (2004) Vitamin C supplementation decreases oxidative DNA damage in mononuclear blood cells of smokers. Eur J Nutr 43:267–274

Moller P, Hemmingsen JG, Jensen DM, Danielsen PH, Karottki DG, Jantzen K, Roursgaard M, Cao Y, Kermanizadeh A, Klingberg H, Christophersen DV, Hersoug LG, Loft S (2015) Applications of the comet assay in particle toxicology: air pollution and engineered nanomaterials exposure. Mutagenesis 30:67–83

Moretti EG, Yujra VQ, Claudio SR, Silva MJ, Vilegas W, Pereira CD, de OF, Ribeiro DA (2016) Acute crack cocaine exposure induces genetic damage in multiple organs of rats. Environ Sci Pollut Res Int 23(8):8104–8112. doi: 10.1007/s11356-016-6141-3

Morsy GM, El-Ala KS, Ali AA (2016) Studies on fate and toxicity of nanoalumina in male albino rats: lethality, bioaccumulation and genotoxicity. Toxicol Ind Health 32:344–359

Motohashi HH, Ishibashi H (2016) Cryopreservation of ovaries from neonatal marmoset monkeys. Exp Anim. http://doi.org/10.1538/expanim.15-0097

Munjal U, Scharlau D, Glei M (2012) Gut fermentation products of inulin-type fructans modulate the expression of xenobiotic-metabolising enzymes in human colonic tumour cells. Anticancer Res 32:5379–5386

Mustapha N, Zouiten A, Dridi D, Tahrani L, Zouiten D, Mosrati R, Cherif A, Chekir-Ghedira L, Mansour HB (2016) Comet assay with gill cells of Mytilus galloprovincialis end point tools for biomonitoring of water antibiotic contamination: biological treatment is a reliable process for detoxification. Toxicol Ind Health 32:686–693

Na TY, Ka NL, Rhee H, Kyeong D, Kim MH, Seong JK, Park YN, Lee MO (2016) Interaction of hepatitis B virus X protein with PARP1 results in inhibition of DNA repair in hepatocellular carcinoma. Oncogene. doi: 10.1038/onc.2016.82

Naidoo RN, Makwela MH, Chuturgoon A, Tiloke C, Ramkaran P, Phulukdaree A (2016) Petrol exposure and DNA integrity of peripheral lymphocytes. Int Arch Occup Environ Health 89(5):785–792. doi: 10.1007/s00420-016-1116-8

Nemmar A, Yuvaraju P, Beegam S, Yasin J, Kazzam EE, Ali BH (2016) Oxidative stress, inflammation, and DNA damage in multiple organs of mice acutely exposed to amorphous silica nanoparticles. Int J Nanomed 11:919–928

Niwa AM, D Epiro GF, Marques LA, Semprebon SC, Sartori D, Ribeiro LR, Mantovani MS (2016) Salinomycin efficiency assessment in non-tumor (HB4a) and tumor (MCF-7) human breast cells. Naunyn Schmiedebergs Arch Pharmacol 389(6):557–571. doi: 10.1007/s00210-016-1225-7

Novotnik B, Scancar J, Milacic R, Filipic M, Zegura B (2016) Cytotoxic and genotoxic potential of Cr(VI), Cr(III)-nitrate and Cr(III)-EDTA complex in human hepatoma (HepG2) cells. Chemosphere 154:124–131

Nowak A, Czyzowska A, Huben K, Sojka M, Kuberski S, Otlewska A, Slizewska K (2016) Prebiotics and age, but not probiotics affect the transformation of 2-amino-3-methyl-3H-imidazo[4,5-f]quinoline (IQ) by fecal microbiota—an in vitro study. Anaerobe 39:124–135. doi: 10.1016/j.anaerobe.2016.03.009

OECD (2014) OECD guideline for the testing of chemicals—in vivo mammalian alkaline Comet Assay. TG 489

Olive PL (1999) DNA damage and repair in individual cells: applications of the comet assay in radiobiology. Int J Radiat Biol 75:395–405

Olive PL, Banath JP (2006) The comet assay: a method to measure DNA damage in individual cells. Nat Protoc 1:23–29

Olive PL, Banath JP, Durand RE (1990) Heterogeneity in radiation-induced DNA damage and repair in tumor and normal cells measured using the “comet” assay. Radiat Res 122:86–94

Ostling O, Johanson KJ (1984) Microelectrophoretic study of radiation-induced DNA damages in individual mammalian cells. Biochem Biophys Res Commun 123:291–298

Ozcagli E, Alpertunga B, Fenga C, Berktas M, Tsitsimpikou C, Wilks MF, Tsatsakis A (2016) Effects of 3-monochloropropane-1,2-diol (3-MCPD) and its metabolites on DNA damage and repair under in vitro conditions. Food Chem Toxicol 89:1–7

Pahlke G, Tiessen C, Domnanich K, Kahle N, Groh IA, Schreck I, Weiss C, Marko D (2016) Impact of Alternaria toxins on CYP1A1 expression in different human tumor cells and relevance for genotoxicity. Toxicol Lett 240:93–104

Panzarino O, Hyrsl P, Dobes P, Vojtek L, Vernile P, Bari G, Terzano R, Spagnuolo M, de Lillo E (2016) Rank-based biomarker index to assess cadmium ecotoxicity on the earthworm Eisenia andrei . Chemosphere 145:480–486

Papa M, Ceretti E, Viola GCV, Feretti D, Zerbini I, Mazzoleni G, Steimberg N, Pedrazzani R, Bertanza G (2016) The assessment of WWTP performance: towards a jigsaw puzzle evaluation? Chemosphere 145:291–300

Park S, Choi S, Ahn B (2016) DNA strand breaks in mitotic germ cells of caenorhabditis elegans evaluated by Comet Assay. Mol Cells 39:204–210

Article   CAS   PubMed   PubMed Central   Google Scholar  

Patar A, Giri A, Boro F, Bhuyan K, Singha U, Giri S (2016) Cadmium pollution and amphibians—studies in tadpoles of Rana limnocharis . Chemosphere 144:1043–1049

Patnaik R, Padhy RN (2016) Evaluation of geno-toxicity of methyl parathion and chlorpyrifos to human liver carcinoma cell line (HepG2). Environ Sci Pollut Res Int 23(9):8492–8499. doi: 10.1007/s11356-015-5963-8

Pedro DF, Ramos AA, Lima CF, Baltazar F, Pereira-Wilson C (2016) Colon cancer chemoprevention by sage tea drinking: decreased DNA damage and cell proliferation. Phytother Res 30:298–305

Peteffi GP, Antunes MV, Carrer C, Valandro ET, Santos S, Glaeser J, Mattos L, da Silva LB, Linden R (2016) Environmental and biological monitoring of occupational formaldehyde exposure resulting from the use of products for hair straightening. Environ Sci Pollut Res Int 23:908–917

Petrovic J, Stanic D, Dmitrasinovic G, Plecas-Solarovic B, Ignjatovic S, Batinic B, Popovic D, Pesic V (2016) Magnesium supplementation diminishes peripheral blood lymphocyte DNA oxidative damage in athletes and sedentary young man. Oxid Med Cell Longev 2016:2019643

Peycheva E, Alexandrova R, Miloshev G (2014) Application of the yeast comet assay in testing of food additives for genotoxicity. LWT Food Sci Technol 59:510–517

Phark S, Park SY, Chang YS, Choi S, Lim JY, Kim Y, Seo JB, Jung WW, Sul D (2016) Evaluation of toxicological biomarkers in secreted proteins of HepG2 cells exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin and their expressions in the plasma of rats and incineration workers. Biochim Biophys Acta 1864:584–593

Poletta GL, Simoniello MF, Mudry MD (2016) Biomarkers of oxidative damage and antioxidant defense capacity in Caiman latirostris blood. Comp Biochem Physiol C Toxicol Pharmacol 179:29–36

Pourrut B, Pinelli E, Mendiola VC, Silvestre J, Douay F (2015) Recommendations for increasing alkaline comet assay reliability in plants. Mutagenesis 30:37–43

Ramos AA, Castro-Carvalho B, Prata-Sena M, Dethoup T, Buttachon S, Kijjoa A, Rocha E (2016) Crude extracts of marine-derived and soil fungi of the genus Neosartorya exhibit selective anticancer activity by inducing cell death in colon, breast and skin cancer cell lines. Pharmacogn Res 8:8–15

Raphael S, Aude J, Olivier P, Melissa PL, Aurelien B, Christophe B, Jean MP, Sylvie B, Alain D, Wilfried S (2016) Characterization of a genotoxicity biomarker in three-spined stickleback ( Gasterosteus aculeatus L.): biotic variability and integration in a battery of biomarkers for environmental monitoring. Environ Toxicol 31:415–426

Ristow M (2014) Unraveling the truth about antioxidants: mitohormesis explains ROS-induced health benefits. Nat Med 20:709–711

Ristow M, Schmeisser K (2014) Mitohormesis: promoting health and lifespan by increased levels of reactive oxygen species (ROS). Dose Response 12:288–341

Roberto MM, Matsumoto ST, Jamal CM, Malaspina O, Marin-Morales MA (2016) Evaluation of the genotoxicity/mutagenicity and antigenotoxicity/antimutagenicity induced by propolis and Baccharis dracunculifolia , by in vitro study with HTC cells. Toxicol In Vitro 33:9–15

Roberts DJ, McKeon M, Xu Y, Stankowski LF Jr (2016) Comparison of integrated genotoxicity endpoints in rats after acute and subchronic oral doses of 4-nitroquinoline-1-oxide. Environ Mol Mutagen 57:17–27

Rodrigues S, Antunes SC, Correia AT, Nunes B (2016) Acute and chronic effects of erythromycin exposure on oxidative stress and genotoxicity parameters of Oncorhynchus mykiss . Sci Total Environ 545–546:591–600

Rojas E, Lorenzo Y, Haug K, Nicolaissen B, Valverde M (2014) Epithelial cells as alternative human biomatrices for comet assay. Front Genet 5:386

Roos WP, Thomas AD, Kaina B (2016) DNA damage and the balance between survival and death in cancer biology. Nat Rev Cancer 16:20–33

Rosignoli P, Fuccelli R, Sepporta MV, Fabiani R (2016) In vitro chemo-preventive activities of hydroxytyrosol: the main phenolic compound present in extra-virgin olive oil. Food Funct 7:301–307

Roy P, Mukherjee A, Giri S (2016) Evaluation of genetic damage in tobacco and arsenic exposed population of Southern Assam, India using buccal Cytome Assay and Comet Assay. Ecotoxicol Environ Saf 124:169–176

Rubio L, Annangi B, Vila L, Hernandez A, Marcos R (2016) Antioxidant and anti-genotoxic properties of cerium oxide nanoparticles in a pulmonary-like cell system. Arch Toxicol 90:269–278

Santos CL, Pourrut B, Ferreira de Oliveira JM (2015) The use of comet assay in plant toxicology: recent advances. Front Genet 6:216

Saquib Q, Siddiqui MA, Ahmed J, Al-Salim A, Ansari SM, Faisal M, Al-Khedhairy AA, Musarrat J, Alwathnani HA, Alatar AA, Al-Arifi SA (2016) Hazards of low dose flame-retardants (BDE-47 and BDE-32): influence on transcriptome regulation and cell death in human liver cells. J Hazard Mater 308:37–49

Sasidharan A, Swaroop S, Chandran P, Nair S, Koyakutty M (2016) Cellular and molecular mechanistic insight into the DNA-damaging potential of few-layer graphene in human primary endothelial cells. Nanomedicine 12(5):1347–1355. doi: 10.1016/j.nano.2016.01.014

Sassi A, Bouhlel I, Mustapha N, Mokdad-Bzeouich I, Chaabane F, Ghedira K, Chekir-Ghedira L (2016) Assessment in vitro of the genotoxicity, antigenotoxicity and antioxidant of Ceratonia siliqua L. extracts in murine leukaemia cells L1210 by Comet Assay. Regul Toxicol Pharmacol 77:117–124

Savina NV, Nikitchenko NV, Kuzhir TD, Rolevich AI, Krasny SA, Goncharova RI (2016) The cellular response to oxidatively induced DNA damage and polymorphism of some DNA repair genes associated with clinicopathological features of bladder cancer. Oxid Med Cell Longev 2016:5710403

Schiavo S, Oliviero M, Miglietta M, Rametta G, Manzo S (2016) Genotoxic and cytotoxic effects of ZnO nanoparticles for Dunaliella tertiolecta and comparison with SiO2 and TiO2 effects at population growth inhibition levels. Sci Total Environ 550:619–627

Shaposhnikov S, Azqueta A, Henriksson S, Meier S, Gaivao I, Huskisson NH, Smart A, Brunborg G, Nilsson M, Collins AR (2010) Twelve-gel slide format optimised for comet assay and fluorescent in situ hybridisation. Toxicol Lett 195:31–34

Shaposhnikov S, Thomsen PD, Collins AR (2011) Combining fluorescent in situ hybridization with the comet assay for targeted examination of DNA damage and repair. Methods Mol Biol 682:115–132

Sharif A, Ashraf M, Anjum AA, Javeed A, Altaf I, Akhtar MF, Abbas M, Akhtar B, Saleem A (2016) Pharmaceutical wastewater being composite mixture of environmental pollutants may be associated with mutagenicity and genotoxicity. Environ Sci Pollut Res Int 23:2813–2820

Sharma K, Kumar A, Chandna S (2016) Constitutive hyperactivity of histone deacetylases enhances radioresistance in Lepidopteran Sf9 insect cells. Biochim Biophys Acta 1860:1237–1246

Shen M, Bin P, Li H, Zhang X, Sun X, Duan H, Niu Y, Meng T, Dai Y, Gao W, Yu S, Gu G, Zheng Y (2016) Increased levels of etheno-DNA adducts and genotoxicity biomarkers of long-term exposure to pure diesel engine exhaust. Sci Total Environ 543:267–273

Simonato JD, Mela M, Doria HB, Guiloski IC, Randi MA, Carvalho PS, Meletti PC, Silva de Assis HC, Bianchini A, Martinez CB (2016) Biomarkers of waterborne copper exposure in the Neotropical fish Prochilodus lineatus . Aquat Toxicol 170:31–41

Simonyan A, Gabrielyan B, Minasyan S, Hovhannisyan G, Aroutiounian R (2016) Genotoxicity of water contaminants from the Basin of Lake Sevan, Armenia evaluated by the Comet Assay in Gibel Carp ( Carassius auratus gibelio) and tradescantia bioassays. Bull Environ Contam Toxicol 96:309–313

Singh NP (2016) The comet assay: reflections on its development, evolution and applications. Mutat Res Rev Mutat Res 767:23–30

Singh NP, McCoy MT, Tice RR, Schneider EL (1988) A simple technique for quantitation of low levels of DNA damage in individual cells. Exp Cell Res 175:184–191

Skipper A, Sims JN, Yedjou CG, Tchounwou PB (2016) Cadmium chloride induces DNA damage and apoptosis of human liver carcinoma cells via oxidative stress. Int J Environ Res Public Health 13(1):88. doi: 10.3390/ijerph13010088

Article   PubMed Central   Google Scholar  

Slizewska K, Nowak A, Smulikowska S (2016) Probiotic preparation reduces faecal water genotoxicity and cytotoxicity in chickens fed ochratoxin A contaminated feed (in vivo study). Acta Biochim Pol 63(2):281–286. doi: 10.18388/abp.2015_1094

Slyskova J, Korenkova V, Collins AR, Prochazka P, Vodickova L, Svec J, Lipska L, Levy M, Schneiderova M, Liska V, Holubec L, Kumar R, Soucek P, Naccarati A, Vodicka P (2012) Functional, genetic, and epigenetic aspects of base and nucleotide excision repair in colorectal carcinomas. Clin Cancer Res 18:5878–5887

Smith AJ, Ball SS, Bowater RP, Wormstone IM (2016) PARP-1 inhibition influences the oxidative stress response of the human lens. Redox Biol 8:354–362

Soloneski S, Nikoloff N, Larramendy ML (2016) Analysis of possible genotoxicity of the herbicide flurochloridone and its commercial formulations: endo III and Fpg alkaline comet assays in Chinese hamster ovary (CHO-K1) cells. Mutat Res, Genet Toxicol Environ Mutagen 797:46–52

Souza TA, Franchi LP, Rosa LR, da Veiga MA, Takahashi CS (2016) Cytotoxicity and genotoxicity of silver nanoparticles of different sizes in CHO-K1 and CHO-XRS5 cell lines. Mutat Res Genet Toxicol Environ Mutagen 795:70–83

Speit G, Hartmann A (2005) The comet assay: a sensitive genotoxicity test for the detection of DNA damage. Methods Mol Biol 291:85–95

Sunjog K, Kolarevic S, Kracun-Kolarevic M, Visnjic-Jeftic Z, Skoric S, Gacic Z, Lenhardt M, Vasic N, Vukovic-Gacic B (2016) Assessment of status of three water bodies in Serbia based on tissue metal and metalloid concentration (ICP-OES) and genotoxicity (comet assay). Environ Pollut 213:600–607

Sutris JM, How V, Sumeri SA, Muhammad M, Sardi D, Mohd Mokhtar MT, Muhammad H, Ghazi HF, Isa ZM (2016) Genotoxicity following organophosphate pesticides exposure among orang asli children living in an agricultural island in Kuala Langat, Selangor, Malaysia. Int J Occup Environ Med 7:42–51

Tabei Y, Sonoda A, Nakajima Y, Biju V, Makita Y, Yoshida Y, Horie M (2016) Intracellular accumulation of indium ions released from nanoparticles induces oxidative stress, proinflammatory response and DNA damage. J Biochem 159:225–237

Tarnow P, Hutzler C, Grabiger S, Schon K, Tralau T, Luch A (2016) Estrogenic activity of mineral oil aromatic hydrocarbons used in printing inks. PLoS One 11:e0147239

Tekiner IH, Mutlu H, Algingil S, Dincerler E (2015) Considerations for nanosciences in food science and nutrition: enhanced food properties. Recent Pat Food Nutr Agric 7:3–8

Tice RR, Agurell E, Anderson D, Burlinson B, Hartmann A, Kobayashi H, Miyamae Y, Rojas E, Ryu JC, Sasaki YF (2000) Single cell gel/comet assay: guidelines for in vitro and in vivo genetic toxicology testing. Environ Mol Mutagen 35:206–221

Toufexi E, Dailianis S, Vlastos D, Manariotis ID (2016) Mediated effect of ultrasound treated Diclofenac on mussel hemocytes: first evidence for the involvement of respiratory burst enzymes in the induction of DCF-mediated unspecific mode of action. Aquat Toxicol 175:144–153

Tugrul S, Kocyigit A, Dogan R, Eren SB, Senturk E, Ozturan O, Ozar OF (2016) Total antioxidant status and oxidative stress in recurrent aphthous stomatitis. Int J Dermatol 55:e130–e135

Uboldi C, Orsiere T, Darolles C, Aloin V, Tassistro V, George I, Malard V (2016) Poorly soluble cobalt oxide particles trigger genotoxicity via multiple pathways. Part Fibre Toxicol 13:5

Uthaman S, Lee SJ, Cherukula K, Cho CS, Park IK (2015) Polysaccharide-coated magnetic nanoparticles for imaging and gene therapy. Biomed Res Int 2015:959175

Valencak TG, Raith J, Staniek K, Gille L, Strasser A (2016) Lactation affects isolated mitochondria and its fatty acid composition but has no effect on tissue protein oxidation, lipid peroxidation or DNA-damage in laboratory mice. Antioxidants 5(1):2. doi: 10.3390/antiox5010002

Article   PubMed Central   CAS   Google Scholar  

Valverde M, Rojas E (2009) Environmental and occupational biomonitoring using the Comet assay. Mutat Res 681:93–109

Vande Loock K, Decordier I, Ciardelli R, Haumont D, Kirsch-Volders M (2010) An aphidicolin-block nucleotide excision repair assay measuring DNA incision and repair capacity. Mutagenesis 25:25–32

Velali E, Papachristou E, Pantazaki A, Choli-Papadopoulou T, Planou S, Kouras A, Manoli E, Besis A, Voutsa D, Samara C (2016) Redox activity and in vitro bioactivity of the water-soluble fraction of urban particulate matter in relation to particle size and chemical composition. Environ Pollut 208:774–786

Venkatesh P, Panyutin IV, Remeeva E, Neumann RD, Panyutin IG (2016) Effect of chromatin structure on the extent and distribution of DNA double strand breaks produced by ionizing radiation; comparative study of hESC and differentiated cells lines. Int J Mol Sci 17(1):58. doi: 10.3390/ijms17010058

Ventura L, Giovannini A, Savio M, Dona M, Macovei A, Buttafava A, Carbonera D, Balestrazzi A (2013) Single cell gel electrophoresis (Comet) assay with plants: research on DNA repair and ecogenotoxicity testing. Chemosphere 92:1–9

Veronez AC, Salla RV, Baroni VD, Barcarolli IF, Bianchini A, Dos Reis Martinez CB, Chippari-Gomes AR (2016) Genetic and biochemical effects induced by iron ore, Fe and Mn exposure in tadpoles of the bullfrog Lithobates catesbeianus . Aquat Toxicol 174:101–108

Vilfan ID, Conwell CC, Hud NV (2004) Formation of native-like mammalian sperm cell chromatin with folded bull protamine. J Biol Chem 279:20088–20095

Viswanathan S, Manisankar P (2015) Nanomaterials for electrochemical sensing and decontamination of pesticides. J Nanosci Nanotechnol 15:6914–6923

Wang G, Lu G, Yin P, Zhao L, Jimmy YQ (2016a) Genotoxicity assessment of membrane concentrates of landfill leachate treated with Fenton reagent and UV-Fenton reagent using human hepatoma cell line. J Hazard Mater 307:154–162

Wang J, Wang J, Wang G, Zhu L, Wang J (2016b) DNA damage and oxidative stress induced by imidacloprid exposure in the earthworm Eisenia fetida . Chemosphere 144:510–517

Wang P, Lombi E, Zhao FJ, Kopittke PM (2016c) Nanotechnology: a new opportunity in plant sciences. Trends Plant Sci. doi: 10.1016/j.tplants.2016.04.005

Watson C, Ge J, Cohen J, Pyrgiotakis G, Engelward BP, Demokritou P (2014) High-throughput screening platform for engineered nanoparticle-mediated genotoxicity using CometChip technology. ACS Nano 8:2118–2133

Wilczek G, Medrzak M, Augustyniak M, Wilczek P, Stalmach M (2016) Genotoxic effects of starvation and dimethoate in haemocytes and midgut gland cells of wolf spider Xerolycosa nemoralis (Lycosidae). Environ Pollut 213:370–378

Will O, Gocke E, Eckert I, Schulz I, Pflaum M, Mahler HC, Epe B (1999) Oxidative DNA damage and mutations induced by a polar photosensitizer, Ro19-8022. Mutat Res 435:89–101

Wu X, Zhang L, Yang C, Zong M, Huang Q, Tao L (2016) Detection on emamectin benzoate-induced apoptosis and DNA damage in Spodoptera frugiperda Sf-9 cell line. Pestic Biochem Physiol 126:6–12

Xu Z, Shao J, Li L, Peng X, Chen M, Li G, Yan H, Yang B, Luo P, He Q (2016) All-trans retinoic acid synergizes with topotecan to suppress AML cells via promoting RARalpha-mediated DNA damage. BMC Cancer 16:2

Yahia D, Haruka I, Kagashi Y, Tsuda S (2016) 8-Hydroxy-2′-deoxyguanosine as a biomarker of oxidative DNA damage induced by perfluorinated compounds in TK6 cells. Environ Toxicol 31:192–200

Yang L, Yuan Y, Fu C, Xu X, Zhou J, Wang S, Kong L, Li Z, Guo Q, Wei L (2016a) LZ-106, a novel analog of enoxacin, inducing apoptosis via activation of ROS-dependent DNA damage response in NSCLCs. Free Radic Biol Med 95:155–168

Yang SM, Tsai KD, Wong HY, Liu YH, Chen TW, Cherng J, Hsu KC, Ang YU, Cherng JM (2016b) Molecular mechanism of Cinnamomum verum component cuminaldehyde inhibits cell growth and induces cell death in human lung squamous cell carcinoma NCI-H520 cells in vitro and in vivo. J Cancer 7:251–261

Yang Y, Ji F, Cui Y, Li M (2016c) Ecotoxicological effects of earthworm following long-term Dechlorane Plus exposure. Chemosphere 144:2476–2481

Yilmaz S, Ustundag A, Cemiloglu UO, Duydu Y (2016) Protective effect of boric acid on oxidative DNA damage in chinese hamster lung fibroblast V79 cell lines. Cell J 17:748–754

Yu V, Rahimy M, Korrapati A, Xuan Y, Zou AE, Krishnan AR, Tsui T, Aguilera JA, Advani S, Crotty Alexander LE, Brumund KT, Wang-Rodriguez J, Ongkeko WM (2016) Electronic cigarettes induce DNA strand breaks and cell death independently of nicotine in cell lines. Oral Oncol 52:58–65

Zapata LM, Bock BC, Orozco LY, Palacio JA (2016) Application of the micronucleus test and comet assay in Trachemys callirostris erythrocytes as a model for in situ genotoxic monitoring. Ecotoxicol Environ Saf 127:108–116

Zeeshan M, Murugadas A, Ghaskadbi S, Rajendran RB, Akbarsha MA (2016) ROS dependent copper toxicity in Hydra-biochemical and molecular study. Comp Biochem Physiol C Toxicol Pharmacol 185–186:1–12

Zeng F, Sherry JP, Bols NC (2016) Use of the rainbow trout cell lines, RTgill-W1 and RTL-W1 to evaluate the toxic potential of benzotriazoles. Ecotoxicol Environ Saf 124:315–323

Zhang C, Han BJ, Zeng CC, Lai SH, Li W, Tang B, Wan D, Jiang GB, Liu YJ (2016a) Synthesis, characterization, in vitro cytotoxicity and anticancer effects of ruthenium(II) complexes on BEL-7402 cells. J Inorg Biochem 157:62–72

Zhang L, Cheng X, Gao Y, Bao J, Guan H, Lu R, Yu H, Xu Q, Sun Y (2016b) Induction of ROS-independent DNA damage by curcumin leads to G2/M cell cycle arrest and apoptosis in human papillary thyroid carcinoma BCPAP cells. Food Funct 7:315–325

Zhao D, Shah NP (2016) Synergistic application of black tea extracts and lactic acid bacteria in protecting human colonocytes against oxidative damage. J Agric Food Chem 64:2238–2246

Zhen N, Yang Q, Wu Q, Zhu X, Wang Y, Sun F, Mei W, Yu Y (2016) A novelly synthesized phenanthroline derivative is a promising DNA-damaging anticancer agent inhibiting G1/S checkpoint transition and inducing cell apoptosis in cancer cells. Cancer Chemother Pharmacol 77:169–180

Download references

Author information

Authors and affiliations.

Department of Nutritional Toxicology, Institute of Nutrition, Friedrich Schiller University Jena, Dornburger Straße 24, 07743, Jena, Germany

M. Glei, T. Schneider & W. Schlörmann

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to M. Glei .

Ethics declarations

Conflict of interest.

The authors declare that they have no conflict of interest.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOCX 192 kb)

Rights and permissions.

Reprints and permissions

About this article

Glei, M., Schneider, T. & Schlörmann, W. Comet assay: an essential tool in toxicological research. Arch Toxicol 90 , 2315–2336 (2016). https://doi.org/10.1007/s00204-016-1767-y

Download citation

Received : 27 May 2016

Accepted : 14 June 2016

Published : 05 July 2016

Issue Date : October 2016

DOI : https://doi.org/10.1007/s00204-016-1767-y

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Comet assay
• Genotoxicity
• Single-cell gel electrophoresis
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings
• My Bibliography
• Collections
• Citation manager

Save citation to file

Email citation, add to collections.

• Create a new collection
• Add to an existing collection

Add to My Bibliography

Your saved search, create a file for external citation management software, your rss feed.

• Search in PubMed
• Search in NLM Catalog
• Add to Search

Comet assay: a reliable tool for the assessment of DNA damage in different models

Affiliation.

• 1 Developmental Toxicology Division, Indian Institute of Toxicology Research (formerly Industrial Toxicology Research Centre), PO Box 80, M.G. Marg, Lucknow, 226 001, India. [email protected]
• PMID: 18427939
• DOI: 10.1007/s10565-008-9072-z

New chemicals are being added each year to the existing burden of toxic substances in the environment. This has led to increased pollution of ecosystems as well as deterioration of the air, water, and soil quality. Excessive agricultural and industrial activities adversely affect biodiversity, threatening the survival of species in a particular habitat as well as posing disease risks to humans. Some of the chemicals, e.g., pesticides and heavy metals, may be genotoxic to the sentinel species and/or to non-target species, causing deleterious effects in somatic or germ cells. Test systems which help in hazard prediction and risk assessment are important to assess the genotoxic potential of chemicals before their release into the environment or commercial use as well as DNA damage in flora and fauna affected by contaminated/polluted habitats. The Comet assay has been widely accepted as a simple, sensitive, and rapid tool for assessing DNA damage and repair in individual eukaryotic as well as some prokaryotic cells, and has increasingly found application in diverse fields ranging from genetic toxicology to human epidemiology. This review is an attempt to comprehensively encase the use of Comet assay in different models from bacteria to man, employing diverse cell types to assess the DNA-damaging potential of chemicals and/or environmental conditions. Sentinel species are the first to be affected by adverse changes in their environment. Determination of DNA damage using the Comet assay in these indicator organisms would thus provide information about the genotoxic potential of their habitat at an early stage. This would allow for intervention strategies to be implemented for prevention or reduction of deleterious health effects in the sentinel species as well as in humans.

PubMed Disclaimer

Similar articles

• The comet assay as an indicator test for germ cell genotoxicity. Speit G, Vasquez M, Hartmann A. Speit G, et al. Mutat Res. 2009 Jan-Feb;681(1):3-12. doi: 10.1016/j.mrrev.2008.03.005. Epub 2008 Mar 30. Mutat Res. 2009. PMID: 18462987 Review.
• The comet assay in human biomonitoring: gene-environment interactions. Dusinska M, Collins AR. Dusinska M, et al. Mutagenesis. 2008 May;23(3):191-205. doi: 10.1093/mutage/gen007. Epub 2008 Mar 7. Mutagenesis. 2008. PMID: 18326867 Review.
• The comet assay in male reproductive toxicology. Baumgartner A, Cemeli E, Anderson D. Baumgartner A, et al. Cell Biol Toxicol. 2009 Feb;25(1):81-98. doi: 10.1007/s10565-007-9041-y. Epub 2007 Oct 31. Cell Biol Toxicol. 2009. PMID: 17972149 Review.
• In vivo Comet assay on isolated kidney cells to distinguish genotoxic carcinogens from epigenetic carcinogens or cytotoxic compounds. Nesslany F, Zennouche N, Simar-Meintières S, Talahari I, Nkili-Mboui EN, Marzin D. Nesslany F, et al. Mutat Res. 2007 Jun 15;630(1-2):28-41. doi: 10.1016/j.mrgentox.2007.02.010. Epub 2007 Apr 19. Mutat Res. 2007. PMID: 17507283
• The comet assay for the detection of genotoxic damage in the earthworms: a promising tool for assessing the biological hazards of polluted sites. Salagovic J, Gilles J, Verschaeve L, Kalina I. Salagovic J, et al. Folia Biol (Praha). 1996;42(1-2):17-21. Folia Biol (Praha). 1996. PMID: 8831022
• The In Vitro Toxicity Profile of ZnS and CdS Quantum Dots in Polysaccharide Carriers (Starch/Chitosan). Grzesiakowska A, Dzióbek M, Kuchta-Gładysz M, Wojciechowska-Puchałka J, Khachatryan K, Khachatryan G, Krystyjan M. Grzesiakowska A, et al. Int J Mol Sci. 2023 Dec 27;25(1):361. doi: 10.3390/ijms25010361. Int J Mol Sci. 2023. PMID: 38203532 Free PMC article.
• Investigation of protective effect of resveratrol and coenzyme Q 10 against cyclophosphamide-induced lipid peroxidation, oxidative stress and DNA damage in rats. Akbel E, Kucukkurt I, Ince S, Demirel HH, Acaroz DA, Zemheri-Navruz F, Kan F. Akbel E, et al. Toxicol Res (Camb). 2023 Dec 30;13(1):tfad123. doi: 10.1093/toxres/tfad123. eCollection 2024 Feb. Toxicol Res (Camb). 2023. PMID: 38173543
• Effect of Melissa officinalis L. leaf extract on manganese-induced cyto-genotoxicity on Allium cepa L. Üstündağ Ü, Macar O, Kalefetoğlu Macar T, Yalçın E, Çavuşoğlu K. Üstündağ Ü, et al. Sci Rep. 2023 Dec 13;13(1):22110. doi: 10.1038/s41598-023-49699-6. Sci Rep. 2023. PMID: 38092949 Free PMC article.
• The Comet Assay in Drosophila: A Tool to Study Interactions between DNA Repair Systems in DNA Damage Responses In Vivo and Ex Vivo. Rodríguez R, Gaivão I, Aguado L, Espina M, García J, Martínez-Camblor P, Sierra LM. Rodríguez R, et al. Cells. 2023 Jul 31;12(15):1979. doi: 10.3390/cells12151979. Cells. 2023. PMID: 37566058 Free PMC article.
• Daidzein attenuated paclitaxel-induced neuropathic pain via the down-regulation of TRPV1/P2Y and up-regulation of Nrf2/HO-1 signaling. Zafar S, Luo Y, Zhang L, Li CH, Khan A, Khan MI, Shah K, Seo EK, Wang F, Khan S. Zafar S, et al. Inflammopharmacology. 2023 Aug;31(4):1977-1992. doi: 10.1007/s10787-023-01225-w. Epub 2023 May 5. Inflammopharmacology. 2023. PMID: 37145202

Publication types

• Search in MeSH

LinkOut - more resources

Full text sources.

• Ovid Technologies, Inc.
• Citation Manager

NCBI Literature Resources

MeSH PMC Bookshelf Disclaimer

The PubMed wordmark and PubMed logo are registered trademarks of the U.S. Department of Health and Human Services (HHS). Unauthorized use of these marks is strictly prohibited.

• Search Menu
• Sign in through your institution
• Original Articles
• Communications
• High-Impact Collection
• Author Guidelines
• Submission Site
• Open Access Policy
• Self-Archiving Policy
• Why Publish with Us?
• About Toxicology Research
• Editorial Board
• Authorship Guidelines
• Advertising and Corporate Services
• Journals on Oxford Academic
• Books on Oxford Academic

Article Contents

Introduction, dna lesions detected by the comet assay, endogenous vs exogenous sources of dna damage, efforts to improve the comparability and interpretation of comet assay results, the standardization of in vivo comet assay and its application within regulatory test batteries, quantitative approaches for the assessment of a genotoxic risk, case studies: interpretation of results, concluding remarks, acknowledgements, conflict of interest statement.

• < Previous

Comet assay: a versatile but complex tool in genotoxicity testing

• Article contents
• Figures & tables
• Supplementary Data

Eugenia Cordelli, Margherita Bignami, Francesca Pacchierotti, Comet assay: a versatile but complex tool in genotoxicity testing, Toxicology Research , Volume 10, Issue 1, January 2021, Pages 68–78, https://doi.org/10.1093/toxres/tfaa093

• Permissions Icon Permissions

The comet assay is a versatile method for measuring DNA strand breaks in individual cells. It can also be applied to cells isolated from treated animals. In this review, we highlight advantages and limitations of this in vivo comet assay in a regulatory context. Modified versions of the standard protocol detect oxidized DNA bases and may be used to reveal sites of DNA base loss, DNA interstrand crosslinks, and the extent of DNA damage induced indirectly by reactive oxygen species elicited by chemical-induced oxidative stress. The assay is, however, at best semi-quantitative, and we discuss possible approaches to improving DNA damage quantitation and highlight the necessity of optimizing protocol standardization to enhance the comparability of results between laboratories. As a genotoxicity test in vivo , the in vivo comet assay has the advantage over the better established micronucleus erythrocyte test that it can be applied to any organ, including those that are specific targets of chemical carcinogens or those that are the first sites of contact of ingested or inhaled mutagens. We illustrate this by examples of its use in risk assessment for the food contaminants ochratoxin and furan. We suggest that improved quantitation is required to reveal the full potential of the comet assay and enhance its role in the battery of in vivo approaches to characterize the mechanisms of toxicity and carcinogenicity of chemicals and to aid the determination of safe human exposure limits.

The comet assay is a method for measuring DNA strand breaks in individual cells. It is based upon the principle that fragmented DNA migrates more rapidly than intact DNA under electrophoresis through an agarose matrix. The detailed description of the technique is outside the scope of this review and is extensively illustrated in other publications [ 1 , 2 ]. Briefly, single-cell suspensions in agarose are layered on microscopic slides, lysed with detergent and high molarity NaCl to disrupt membranes and remove histones, and then electrophoresed. In the presence of strand breaks, DNA migrates toward the anode forming an image resembling the tail of a comet when stained with a fluorescent dye and viewed under fluorescence microscopy ( Fig. 1A ). Its versatility and sensitivity have led to its application to assess DNA damage induced by chemical or physical agents in cells from numerous different organisms under a wide variety of experimental conditions [ 3 , 4 ]. The comet assay is used in human monitoring studies as a biomarker of exposure to agents causing damage to DNA [ 5 , 6 ] and in ecotoxicological studies in a variety of sentinel organisms [ 7–9 ]. Its application in hazard characterization under controlled laboratory conditions contributes to the understanding of the mode of action of chemicals and informs risk assessment [ 10–12 ].

Alkaline comet assay and its variants. (A) Main steps of the standard protocol; (B) protocol modifications introduced to detect modified bases (generally oxidized bases). These modifications consist in treating slides after lysis (see arrow) with BER enzymes that produce DNA breaks at the site of modified base. The slides then undergo the other steps of the standard protocol. The increase of %TI of BER enzyme treated slides over the non-treated slides gives the amount of modified bases; (C) protocol modification introduced to detect bulky adducts. This modification consists in treating cells (in colture) (see arrow) with inhibitors of the NER enzymatic pathway in order to accumulate incomplete DNA repair sites detected as strand breaks by the comet assay. The slides then undergo the other steps of the standard protocol. The increase of %TI of NER inhibitor treated cells over the untreated cells gives the amount of bulky adducts; (D) protocol modification introduced to detect DNA crosslinks. This modification consists in increasing DNA migration by treating slides (see arrow) with a potent clastogen (generally ionizing radiations) or by potentiating electrophoresis conditions. The slides then undergo the other steps of the standard protocol. The decrease of %TI indicates the presence of DNA crosslinks.

The test can be conducted under neutral or alkaline electrophoresis conditions each one enhancing the detection of different types of DNA lesions. Initially, the prevalent opinion was that under neutral conditions mostly double-strand breaks (DSBs) were revealed, whereas under alkaline conditions, a broader spectrum of lesions was detected including, in addition to DSBs, single-strand breaks (SSBs) and apurinic/apyrimidinic sites (AP sites). However, more recently, a better understanding of DNA migration processes and comet formation suggests that the spectrum of lesions detected under the different electrophoresis conditions largely overlaps [ 13 ]. DNA breaks or AP sites are continuously produced under physiological conditions by processes such as hydrolysis or damage by •OH and other free radicals. Exposure to physical and chemical agents produces numerous types of DNA lesion including damaged bases, AP sites, inter- and intrastrand crosslinks and direct strand breaks with a variety of termini [ 14 ]. In its current form the comet assay employs several modifications each designed to reveal a particular type of DNA lesion thereby extending its range of utility ( Fig. 1B–D ). In particular, the use of lesion-specific DNA glycosylases/endonucleases, which remove altered bases and introduce DNA AP sites or strand breaks, is widespread ( Fig. 1B ). The most widely used enzymes include the Escherichia coli formamidopyrimidine DNA glycosylase (Fpg) and endonuclease III (Endo III) and the mammalian counterpart of Fpg, 8-oxoguanine DNA glycosylase (OGG1) [ 15 ]. Fpg excises oxidized purines from DNA. Its substrates include the ring-opened purines 2,6-diamino-4-hydroxy-5-formamidopyrimidine and 4-6-diamino-5 formamidopyrimidine as well as 8-oxo-7,8-dihydroguanine (8-oxoGua). Endo III acts similarly to remove oxidized pyrimidines, including DNA thymine glycol and uracil glycol. The specificity of these enzymes for a particular base alteration is not absolute. Thus, Fpg and EndoIII also recognize some types of alkylation damage [ 16 , 17 ], These Fpg-mediated SSBs might derive from AP sites following spontaneous hydrolysis or enzymatic excision of N7-methylguanine (N7-meGua) and N3-methyladenine (N3-meAde). In addition, alkali treatment of N7-meGua can lead to the formation of ring-opened derivatives (FaPy-Gua) that are also recognized and incised by Fpg. In contrast, the eukaryotic OGG1 is a more specific enzyme that is unable to recognize methylated bases [ 18 ]. As a consequence, no significant increase in SSBs was observed following treatment with the alkylating agent methylmethanesulphonate or ethylnitrosourea when assessed by the hOGG1 modified comet assay, whereas an increase was observed with the Fpg modified comet assay [ 17 , 19 ].

In a similar approach, the uracil DNA glycosylase has been used to detect uracil (U) misincorporated in DNA [ 20 ] and the 3-meAde DNA glycosylase and AlkA to increase the sensitivity and selectivity of the comet assays against alkylated bases [ 21 ].

Many environmental carcinogens react with DNA bases to produce bulky adducts. These important DNA lesions block replication and transcription and contribute to cell-cycle arrest and cell death; they also induce mutations possibly leading to carcinogenesis [ 22–24 ]. Most of these lesions are repaired by nucleotide excision repair (NER), a repair pathway in which the incision and excision steps of lesion removal are highly coordinated to minimize the generation of persistent DNA strand break intermediates [ 25 ]. This repair strategy compromises the ability of the comet assay to detect bulky DNA adducts. To counteract this limitation, inhibitors of NER are included in the assay to permit the accumulation of incomplete DNA repair events that are detected as SSBs. DNA synthesis inhibitors aphidicolin, hydroxyurea (HU) and 1-β-D-arabinofuranosyl cytosine (AraC) have all been successfully applied to allow the detection of DNA strand breaks formed during NER of bulky DNA adducts induced by UVC-radiation and the carcinogens benzo[a]pyrene and aflatoxin B1 [ 26 , 27 ] ( Fig. 1C ). Among nine known in vivo genotoxic agents, only one generated SSBs measurable by the standard comet assay in the absence of HU/AraC, whereas seven were positive in the presence of the inhibitors [ 27 ]. In addition, the DNA T4 endonuclease V, which repairs UV-damaged DNA, has also been used to detect cyclobutane pyrimidime dimers in the comet assay [ 28 ].

DNA interstrand crosslinks (ICLs) are among the most cytotoxic DNA lesions. They block both DNA replication and transcription and inhibit recombination by preventing the separation of DNA strands. Several known or suspected carcinogens induce ICLs. ICLs repair is complex and entails different repair pathways, such as NER, structure-specific endonucleases, translesion DNA polymerases (TLS) and homologous recombination (HR). NER plays an important role in ICLs removal in quiescent cells, whereas the ICLs repair in S phase requires a more complex orchestration of multiple factors, including structure-specific endonucleases, TLS and HR [ 29 ].

The presence of ICLs retards DNA migration in the comet assay. Their presence can be inferred from the decreased migration of DNA from cells treated with a suspected ICL-inducing agent followed by a known SSBs inducing agent (generally ionizing radiation) immediately prior to electrophoresis. Alternatively, ICLs can be revealed by a reduction of the background level of DNA migration under extreme electrophoretic conditions ( Fig. 1D ). This approach was used to quantify ICLs induced by the photoactivated 4-hydroxymethyl-4,5,8-trimethylpsoralen (HMT) in vitro [ 30 ]. Under electrophoresis conditions that maximized DNA migration, a dose-dependent decrease of DNA migration was observed shortly after HMT treatment. However, at later times, the incisions formed during ICLs repair caused the formation of DNA strand breaks, which antagonize the migration-inhibiting effect of ICLs in the comet assay. This causes a progressive increase of DNA migration in HMT-treated samples that became equal to the untreated ones, and, at even longer times, results in a further increase of DNA migration over the untreated control. These results indicate that antagonistic phenomena acting on DNA migration should be considered when assessing DNA crosslinking by the comet assay. On one hand, DNA ICLs reduce DNA migration; on the other hand, incisions occurring during ICL repair increase DNA migration. Therefore, the extent of migration depends on the kinetics of ICL production and recognition, DNA incision and processing by repair proteins and sampling time becomes a critical variable in the experimental design. These steps in turn depend on the characteristics of the chemical and the cell physiology. These factors may underlie the relative low sensitivity of the assay in revealing DNA crosslinks and for disparate results obtained in studies assessing the genotoxicity of chemicals suspected of crosslinking. This may be particularly true in in vivo studies when the timing between exposure and cell analysis cannot be completely controlled. Indeed, the detection of crosslinking agents is not included among the purposes of the ‘ In Vivo Mammalian Alkaline Comet Assay’ (OECD TG guideline 489) [ 31 ] for which ‘further work would be needed to adequately characterize the necessary protocol modifications’.

It has been estimated that human genome sustains ~40,000–70,000 lesions per day [ 32–36 ]. The vast majority of these are SSBs arising from oxidation (by metabolic by-products) or base loss via glycosyl bond hydrolysis ( Fig. 2 ). SSBs may be occasionally converted into the more dangerous DSBs (25 events per cell per day). Spontaneous DNA base loss is largely due to spontaneous depurination events, with a minor contribution from depyrimidination (10,000 and 500 lesions per cell per day, respectively). Spontaneous deamination of cytosine to U occurs around 200 times per cell per day. If uncorrected, the resulting U:G mismatches will give rise to G > A transition mutations.

Endogenous and exogenous DNA lesions and their repair. (a) The estimated frequencies refer to apyrimidinic and apurinic sites, respectively; (b) the estimated frequency refers only to mismatches derived from deamination of cytosine to uracil (mismatches due to DNA replication errors by mammalian DNA polymerases are excluded); (c) the estimated frequencies refer only to 8-oxoGua (G*); (d) the estimated frequency refers only to N3-meAde (A°). DNA adduct frequencies are taken from references [ 32–36 ].

Oxidation is an important source of endogenous genomic damage. Reactive oxygen species (ROS) are generated during normal aerobic cellular metabolism. Depending on the analytic method used, the rate of production of DNA 8-oxoGua, the major mutagenic oxidized base, can vary from 450 to 6300 lesions per cell per day. 8-OxoGua pairs preferentially with adenine rather than cytosine and GC > TA transversions are considered to be the signature mutation of oxidative stress.

S-adenosylmethionine (SAM), a methyl group donor and a cofactor in several transmethylation reactions is also an endogenous genotoxic agent. SAM non-enzymatically methylates the ring nitrogen of DNA purines to generate around 600 N3-meAde and 4000 N7-meGua lesions per day in the human genome.

The effects of endogenous DNA damage are mitigated by DNA repair. The main repair pathways are NER, base excision repair (BER), HR, non-homologous end joining and mismatch repair (MMR) [ 33–37 ]. Most of them also protect the genome against DNA damage induced by noxious foreign chemicals (some examples are shown in Fig. 2 ). Exposure to some exogenous chemicals can also enhance ‘spontaneous’ DNA decay. One example of this enhancement is the accelerated depurination that accompanies modifications of DNA bases induced by alkylating agents.

One of the properties of the comet assay is the capacity of this test to detect DNA lesions derived either from direct damage to DNA or identify DNA intermediates occurring during the repair process of an initial damage. Thus, we can consider an untreated control in the comet assay as the reflection of the steady-state level of endogenous DNA lesions, including the intermediates of repair processes. In contrast, exposure to a genotoxic agent introduces a burst of additional DNA lesions that may overwhelm the DNA repair machinery possibly leading to irreversible gene or chromosome mutations. Thus, measuring an increase of DNA migration in treated samples vs. matched controls provides a marker of the agent genotoxic mode of action useful for the classification and regulation of chemical exposures.

Since its first appearance >30 years ago [ 38 ], the comet assay has been widely used. Its low cost and versatility have led to its adoption as an assay of choice worldwide. The sheer number of laboratories using the comet assay has meant that numerous small variations to the protocol have occurred. This multitude of assay protocols presents a problem of comparability of results among laboratories. In addition, problems of intra-laboratory variability among experiments are frequent. A significant effort has been expended to identify crucial parameters that affect the performance of the comet assay and to clarify how different factors, from sample preparation to cell scoring and analysis, can influence the results [ 2 , 13 , 39–41 ]. Some progress has been made, and the variation among laboratories and experiments has been reduced. Despite this advance, there are currently no consensus guidelines for the application of the comet assay in in vitro or in biomonitoring studies.

The problem of intra- and inter-laboratory variability limits the direct comparison of DNA damage levels among different studies or multicentre trials. It is particularly challenging in human biomonitoring studies. To address these issues, the European Standards Committee on Oxidative DNA Damage and the European Comet Assay Validation Group (ECVAG) launched international trials focused especially on the measurement of oxidatively damaged DNA in human cells. These led to some improvements both in the standard and in the Fpg modified comet assay [ 42–45 ].

To reduce the inter-laboratory variability in assay data, it has been proposed that comet assay DNA damage parameters (e.g. % DNA in tail) be transformed to the number of lesions per basepair (bp) based on calibration curves generated from cells exposed to ionizing radiation. The relationship between ionizing radiation dose and DNA SSBs was established by alkaline sucrose gradient sedimentation studies. About 0.3 strand breaks/10 9 dalton DNA are introduced per Gy of radiation. This approximates to 1000 breaks per diploid mammalian cell/Gy [ 46 ]. Based on this dose equivalence, cell-type-specific calibration curves were generated by different laboratories and used to calculate the number of lesions/10 6  bp DNA corresponding to a particular percentage of DNA in the tail. This conversion significantly reduced inter-laboratory variation in an ECVAG trial conducted among 10 laboratories, indicating that it could facilitate the comparisons between data from different research groups [ 45 ].

In a recent paper, Moller and collaborators [ 47 ] searched PubMed database for human biomonitoring studies that had used comet assays to measure oxidatively damaged DNA in nucleated blood cells. The study applied the ECVAG calibration curve [ 48 ] in which 1% DNA in tail corresponds to 0.0273 lesions/10 6  bp to data from the published assays. Results showed a large variation in the reported level of Fpg-sensitive sites in human white blood cells (ranges: 0.05–1.31 lesions/10 6  bp), whereas less variation was found for hOGG1 (0.04–0.18 lesions/10 6  bp). However, these data suggest that still problems exist regarding the quantitative comparison of results from different publications.

One approach to counteracting the variability among measurements is the inclusion of reference standards. These may be slides prepared from batches of cells untreated or treated with a DNA-damaging agent undergoing the same comet procedures as the analyzed samples, or even of cells, morphologically recognizable, placed on the same slide. They can be used to monitor the performance of the assay or even to normalize sample data to reference values [ 49 , 50 ].

In 2007, Ueno and coworkers [ 51 ] proposed also for in vivo studies with comet assay the application of radiation-based calibration curves to transform comet parameters into number of breaks. Linear dose–effect relationships for % tail DNA in nine different organs (liver, kidney, lung, spleen, colon, urinary bladder, thymus, brain and bone marrow) of mice irradiated with 3, 6, 12 or 24 Gy of X-rays were reported. Of note, organs were collected only 3 minutes after a high dose-rate irradiation to avoid as much as possible the bias introduced by fast repair mechanisms occurring during and immediately after irradiation. The slopes of the dose–response curves varied among the organs tested: e.g., 1Gy X-rays increased the % tail DNA above the respective background level by 1.8% in the liver and 0.6% in bone marrow. This finding suggested differences in the radiosensitivity of nuclear DNA and DNA repair capacity among organs and pointed to the need of obtaining organ-specific calibration curves for comet assay in vivo .

Although scientifically sound, these calibration approaches have not been extensively applied probably because their most rigorous applications would require for each laboratory producing its own cell-type-specific curves, and not all laboratories have the necessary expertise or instrumentation.

A confounding factor for the interpretation of comet assay results might be the induction of aspecific cell toxicity irrespectively of DNA damage. In fact, cell toxicity may result in the appearance of highly damaged comets (the so-called hedgehogs) that could lead to false-positive results. For this reason, it is strongly advised to record this category of comets separately from the other ones and not include them in the calculation of the assay parameters. However, because their mechanism of formation is unclear, they generally are not used as marker of toxicity [ 31 ]. To assist the investigators in the recognition and classification of this type of cells, an atlas of comet assay images has been published by the Japanese Environmental Mutagen Society [ 52 ].

Although initially developed as an in vitro test, the versatility of the comet assay makes it extremely attractive for in vivo studies. In particular, a genotoxicity assay that could be employed to assess DNA damage in tissues of toxicological relevance or in site-of-contact organs was needed for studies of human risk assessment.

Between 2006 and 2013, the Japanese Center for the Validation of Alternative Methods led a project aimed at the validation of the liver and stomach comet assays [ 53 ], which, eventually, culminated in the release in late 2014 of the OECD TG 489 ‘ In Vivo Mammalian Alkaline Comet Assay’ [ 31 ]. The project methodology and results were extensively reported in ref. [ 53 ]. The assays were conducted on 40 coded chemicals with known genotoxic and carcinogenic activity. Necrosis and apoptosis were evaluated in parallel in the target tissues by histopathological analysis as markers of acute cytotoxicity. The comet assay proved to be reasonably sensitive with 6 of 19 genotoxic carcinogens yielding negative results. Some of the apparent inconsistencies could be rationalized when the modes of action for genotoxicity and/or carcinogenicity were taken into consideration. For example, considering that the genotoxicity of busulfan is predominantly via DNA crosslinking formation, it is not surprising that this chemical was not detected by the standard comet assay that has a limited sensitivity for this type of DNA lesions. A good specificity of the comet assay was shown by the finding that only 2 of 21 chemicals that were either genotoxic non-carcinogens or non-genotoxic carcinogens yielded positive results.

Even before its formal validation [ 31 ], the test had been introduced by several national and international regulatory agencies as an integral part of the genotoxicity testing strategies, which generally consists of multi-tier approaches comprising in vitro and in vivo tests.

Since 2011, the ‘Guidance on Genotoxicity Testing and Data Interpretation for Pharmaceuticals Intended for Human Use’ included the comet assay in liver as an additional in vivo assay, further to micronucleus (MN) assay in rodent hematopoietic cells, for substances that induce gene mutations in vitro . This guideline to the selection of genotoxicity tests for new pharmaceuticals has since been adopted worldwide [ 54 ].

In Europe, the ‘European Community Regulation on chemicals and their safe use’ introduced in 2007, requires companies manufacturing or importing chemical substances into the European Union in quantities ≥1 ton/year to register these substances with the European Chemicals Agency (ECHA) [ 55 ]. ECHA requirements for registration depend on the tonnage and the potential mutagenicity of a substance is among the information required for its registration. The strategies to provide this information are based on the use of different pieces of information, including non-testing data (Structure activity relationship, quantitative structure activity relationship and read-across approaches) and results from in vitro and in vivo testing. Information from a bacterial gene mutation test is required for all the tonnage groups. A negative result with this test is sufficient for the registration of substances in the low-level tonnage group. For compounds in higher tonnage groups, a second in vitro MN or chromosome aberration test is needed. In case of positive results, an in vivo follow-up is required. Depending on the type of damage elicited in vitro (gene mutation or chromosomal damage) Transgenic Rodent (TGR) Somatic and Germ Cell Gene Mutation Assays [ 56 ], comet assay [ 31 ], MN test [ 57 ] or chromosome aberration test [ 58 ] would be considered as appropriate follow-up tests.

Genotoxicity testing strategies applicable to food and animal feed safety assessment have been defined by the European Food Safety Authority (EFSA) [ 59 , 60 ]. A step-wise approach is recommended for the generation and evaluation of data on genotoxic potential. This begins with a battery of in vitro tests, comprising a bacterial reverse mutation assay and an in vitro MN assay. In case of positive in vitro results, review of the available relevant literature data on the test substance and, where necessary, appropriate in vivo studies are recommended. Suitable in vivo tests are considered the mammalian erythrocyte MN test, the TGR assay and the comet assay. A single positive test is sufficient to classify the compound as an in vivo genotoxin. Both a negative systemic genotoxicity test (i.e. MN test) and a negative test on site-of-contact or metabolically competent organ (i.e. TGR or comet assays in gastrointestinal (GI) tract and liver) are deemed necessary to classify a compound as non-genotoxic in vivo .

The in vivo genotoxicity tests can detect different kind of damage and each has its own advantages and limitations. The MN test detects clastogenic and aneuploidogenic events in bone marrow erythrocyte precursors. Its main limitation is that the compound or its metabolite(s) have to reach the bone marrow in a sufficient concentration to induce DNA damage. On the other hand, both TGR and comet assays can be performed on different organs, including the target organs for toxicity and carcinogenicity. TGR assay mainly detects gene mutations, whereas the comet assay detects DNA lesions, including those produced during DNA repair. Although both MN and TGR assays reveal irreversibly fixed DNA alterations, the comet assay reveals transient lesions potentially leading to irreversible damage. In a comparative review of group 1 carcinogens selected from the International Agency for Research on Cancer, a 90% sensitivity of comet assay (any organ) was reported, higher than the 70% sensitivity achieved by MN test [ 61 ]. A working group on in vivo genotoxicity testing strategies convened on the occasion of the seventh International Workshop on Genotoxicity Testing [ 62 ] compared the ability of comet and TGR assays (any organ) to detect chemical carcinogens. The study revealed that more carcinogens tested positive in the comet assay than in the TGR assay. The same study noted that some non-carcinogens also tested positive in the comet assay. This lower specificity reflected indirect mechanisms of DNA breakage driven by toxicity, oxidative stress or extreme pharmacology. Eventually, the working group concluded that the comet assay in the liver or GI tract was as suitable as the TGR assay as a follow-up approach after evidence of in vitro genotoxicity.

Table 1 reports some examples of the performance of in vivo standard alkaline comet assays in identifying non-genotoxic and genotoxic chemicals characterized by different mechanisms of action.

Results of standard alkaline comet assays following in vivo exposure to chemicals in relation to their mechanisms of genotoxicity a

a The positive/negative classification according to the quoted reference(s) does not take into account the organ(s) tested and does not exclude that different results may be obtained in other published experiments.

b International agency for research on cancer classification.

Another genotoxicity assay that detects the initial events of the DNA damage response is based on the phosphorylation of histone H2AX (γ-H2AX). This is assessed by the formation of microscopically visible foci analyzed by flow cytometry or by western blotting. The assay was originally proposed in the field of radiation biology as a sensitive assay for detecting DSBs [ 63 ]. More recently, it has been validated with some promising results against the in vitro comet assay for the detection of chemical mutagens [ 64 , 65 ]. More limited data have been collected so far in vivo . Thus, additional validation studies are recommended in view of the potential application of the assay in multiple organs and to verify the possible superior performance for the detection of some classes of compounds such as the crosslinkers [ 64 ].

Based on their complementarity, a combination of multiple tests is suggested to provide an added value [ 61 ]. Also, the combination of in vivo tests assessing different endpoints in different tissues in the same animal is encouraged by regulatory authorities in light of 3R principle. The International workshops on genotoxicity testing working group [ 62 ] concluded that a combination of the MN test in bone marrow and the liver comet assay was adequate to detect in vivo mutagens or genotoxic carcinogens. For orally administered compounds, a comet assay in a single GI site was recommended.

In the last 10 years, improvements in the determination and interpretation of dose–effect relationships [ 66 ] have led to a more quantitative approach to the characterization of in vivo genotoxicity. At present, regulatory authorities determine health-based guidance values (HBGV) and margin of exposure (MoE) largely on experimental cancer and toxicity data. More recently, however, gene and chromosome mutations have been advocated as a bona fide , quantifiable toxicological endpoint which can inform risk assessment [ 67 ].

DNA damage, as measured by the comet assay, could be regarded as a too transient and heterogeneous endpoint to lend itself to such analyses, but it has not totally escaped attention. A quantitative analysis of toxicological data is generally based on mathematical modeling of the dose–response relationships that allow establishing the so-called ‘point of departure’ (POD), which is a point on the dose–response curve roughly corresponding to an estimated low effect level or no effect level. The best method to establish PODs is still a matter of debate. A recent paper that analyzed data sets obtained by the comet, the MN, the TGR and the Pig-a gene mutation assays came to the conclusion that PODs must be assay specific and based on historical control data [ 68 ]. Interestingly, in the case of a model monofunctional alkylating compound (temozolomide), a close concordance of PODs across all these assays was shown [ 69 ], suggesting that, for this chemical and most likely other monofunctional alkylating agents, the comet assay was as suitable to determine POD values as were the assays for irreversible gene or chromosome mutations. In 2018, the UK Committee on Mutagenicity regretted that an evaluation of the use of comet assay data in quantitative analyses had not yet been undertaken [ 70 ]. Surely, for DNA tail intensity to be included among the mutagenicity endpoints deserving a more quantitative analysis, further progress needs to be made toward test standardization and data interpretation.

Currently, EFSA guidelines recommend the use of an MoE approach for substances that are both genotoxic and carcinogenic [ 71 ]. The MoE provides a comparison between the observed experimental data and the environmental level of interest. The aim is to help decide on acceptable or tolerable level of exposure taking into account the risk management options available. An MoE of 10,000 or higher is considered to be of low concern from a public health point of view.

However, the Scientific Committee Opinion on genotoxicity testing strategies [ 59 , 60 ] describes some circumstances under which genotoxicity might occur only at doses resulting in saturation of detoxification pathways. Examples include mutagens that might act through a threshold mechanism. Thus, the mutagenic potential of oxidants depends on their capability to overcome the physiological cellular defense against ROS. Other examples are substances that interact with molecular targets other than DNA (e.g. alterations of DNA polymerases, topoisomerases, DNA repair and spindle proteins). In such cases, establishing an HBGV (Tolerable Daily Intake, Tolerable Weekly Intake, etc.) might be possible.

Here, we review two examples of how the comet assay provides an important tool in evaluating the genotoxicity associated with exposures to food contaminants [ochratoxin A (OTA) and furan] [ 72 , 73 ]. In particular, we show how comet assays can help to clarify the mechanisms underlying the genotoxic properties of chemical contaminants, e.g. whether these are the consequence of a direct, DNA-reactive mode of action (formation of chemical-specific covalent adducts) or are due to an indirect production of DNA damage (induction of oxidative stress, damage to DNA repair proteins, etc.). We highlight the difficulties associated with the interpretation of the results of the comet assays and stress the importance of considering all the available data, including mechanistic studies, for a proper risk assessment of food contaminants.

Ochratoxin A

OTA is a mycotoxin produced by Aspergillus and Penicillium fungi and is found as a contaminant in various foods. It causes kidney tumors in rodents. However, the mechanisms of genotoxicity are unclear and both direct (DNA reactive) and indirect genotoxic and non-genotoxic modes of action have been proposed.

In vitro exposure of mammalian cells to OTA induced both gene mutations [ 74 ] and chromosomal damage [ 75 ]. Several observations suggested that at least some of these in vitro genotoxic effects are secondary to oxidative stress induced by OTA. These included high levels of ROS and of 8-oxoGua in DNA [ 76 , 77 ] as well as comet assays showing an increase in SSBs following incubation with the Fpg/EndoII enzymes [ 78 ].

In rodents, OTA induces a narrow spectrum of chromosomal damage concentrated in the portion of the kidney that is also target for OTA carcinogenesis (the kidney outer medulla). DNA damage includes chromosome hypercondensation, abnormally separated chromatids, multipolar mitotic spindles, endoreduplications, polyploidy and aneuploidy [ 79 ]. OTA is also a weak inducer of gene mutations in rats and mice [ 80–83 ]. The mutations are detected in rats after a short exposure to a carcinogenic OTA dose and are specifically restricted to the cancer target site. The mutational spectrum identified large deletions as the main mutagenic events, with no apparent increase in the GC > TA transversions typically associated with oxidative DNA damage. In addition, no changes in the levels of DNA 8-oxoGua were observed in the outer medulla [ 80–83 ]. Thus, OTA-induced mutations in rat and mouse kidneys are clearly not a simple consequence of oxidative DNA damage. The molecular mechanisms underlying in vivo OTA genotoxicity remain unclear since the formation of OTA induced DNA adducts is controversial [ 84 , 85 ]; their chemical nature remains undefined and the reported levels are extremely low (20-70 × 10 −9 nucleotides) [ 85 ].

The results obtained by in vivo comet assays indicate that OTA exposure increases SSBs levels in the kidney [ 79 , 86 ] but also in non-target tissues such as liver and blood [ 79 ]. In some studies OTA-induced SSBs were increased by DNA digestion with Fpg/EndoIII [ 86 ].

In summary, the detection of mutations in the target tissue pointed to a genotoxic mechanism of OTA-induced carcinogenesis, and, despite some evidence of oxidative stress, the mutational spectrum suggested that DNA oxidation was not involved. The comet assay provided supportive evidence in vitro and in vivo regarding the induction of both oxidation- and non-oxidation- mediated DNA damage. Unfortunately, the analysis of DNA adducts yielded inconclusive results hampering the definitive classification of OTA as a genotoxic carcinogen. This example shows how complex it can be in some cases to resolve the complexity of a chemical mode of action even when a full set of data with reliable and sensitive methods is available.

Furan is a volatile compound formed in food during thermal processing. It induces cholangiofibrosis in rats and hepatocellular adenomas/carcinomas in mice [ 73 ]. Furan is metabolized by cytochrome P450 2E1 (CYP2E1) to the reactive metabolite cis-but-2-ene-1,4-dialdehyde (BDA). In vitro studies indicate that BDA-induced ethano DNA adducts are unstable and can be transformed into substituted etheno-acetaldehyde adducts. These secondary lesions retain an aldehyde group and therefore have the potential to form ICLs [ 87 ]. The in vitro genotoxic properties of BDA are clearly manifested by its ability to induce gene mutations both in bacteria [ 88 , 89 ] and in mammalian cells [ 90 ].

In vitro BDA was reported to be positive in the alkaline elution assay with Chinese hamster ovary cells for both DNA strand breaks and crosslinks induction at millimolar concentrations [ 91 ], whereas a comet assay on BDA-treated L5178Y tk +/− cells showed a dose-dependent increase only in SSBs, mostly evident at toxic doses [ 90 ].

Pros and cons of in vivo comet assay within a regulatory context

The greatest degree of uncertainty concerns the carcinogenic mode of action of furan. Is furan directly or indirectly genotoxic? What is the contribution of oxidative DNA damage to its tumorigenicity? A very low level of furan-induced DNA adducts was observed in vivo , and these were not identical to BDA-induced DNA adducts [ 92 ]. It is also unclear whether furan induces gene mutations in vivo [ 93 , 94 ]. Nevertheless, there is convincing evidence that chronic exposure to furan induces micronuclei, chromosome aberrations and DSBs in proliferating, but not in quiescent, splenocytes from mice and rats. DNA lesions responsible for these effects remain undefined, and it is unclear whether the observed chromosomal instability is due to direct damage to DNA (formation of ICLs or DNA adducts) or is the consequence of secondary events associated with oxidative stress-induced DNA damage.

A similarly complex scenario can also be derived from the results of the in vivo comet assays which reveal that furan-induced DNA damage is dependent on treatment protocol and dose. Thus, no induction of SSBs and/or ICLs was observed in the liver of mice chronically exposed per os to furan (28-days, 2-15 mg/kg bw), whereas both types of DNA lesion were increased after a single acute oral dose (250 mg/kg bw) [ 95 , 96 ]. These data suggest that high levels of DNA lesions must be introduced into DNA to be revealed by the ‘modified’ comet assay, most likely because of the limited sensitivity of this assay in identifying DNA ICLs.

The kinetics of comet assays on the liver of mice chronically exposed to furan revealed that SSBs appeared rapidly after exposure and disappeared within a few hours post-treatment [ 97 ]. This observation might explain some of the negative results observed in comet assays in which liver cells were analyzed at late times post-treatment [ 98 ]. In addition, they also suggest that repair of oxidized DNA bases might underlie this set of rapidly formed and resealed SSBs. Indeed, the increased number of SSBs following digestion with Fpg and EndoIII of liver DNA from furan treated animals confirms the presence of oxidatively damaged DNA. Finally, increased levels of DNA 8-oxoGua (and ROS) have been observed in liver and blood of furan-treated rats and mice [ 99–100 ].

In the absence of a clear identification and detection of DNA adducts, comet assay results were useful to evidence, at least at high dose level, the formation of ICLs, a lesion with the highest potential of causing chromosomal aberrations. At the same time also oxidative DNA damage was demonstrated. It was out of comet capability the assessment of the relative weight of each mechanism in the formation of chromosomal aberrations. Moreover, in vivo comet assays proved to be useful for searching DNA damage in tumor target and non-target organs, demonstrating specific induction of DNA lesions in the liver, the target organ for furan-induced carcinogenesis.

Comet assay is a versatile genotoxicity test that measures DNA breaks or AP sites induced directly by physical and chemical agents or produced during repair processes of primary lesions. The assay has several assets but also some limitations as summarized in Table 2 . One of its great assets is the applicability to any type of cells, cycling and not cycling, which translates into the possibility of using it to test the effects in a variety of tumor targets and site-of-contact organs of experimental rodents. Indeed, comet assay data may contribute to characterize the mode of action of suspect chemical carcinogens, showing the possible induction of DNA damage at tumor target sites. Furthermore, one comet assay variant has been developed to detect an increase of oxidized DNA bases. This assay may contribute to assess the relative weights of DNA damage induced directly by covalent binding of chemicals to DNA vs DNA damage induced indirectly by an increase of ROS elicited by chemical-induced oxidative stress.

Despite these capacities, comet assay still suffers of important limitations that limit its usefulness. The assay conditions have not yet been stringently established and this has made sometimes difficult to compare results obtained by different laboratories. This might be an issue especially for a test that measures not an irreversible genotoxic effect, like micronuclei or gene mutations, but a transient, dynamic, still repairable DNA damage. Improved test standardization would also enhance the applicability of the assay in the field of human biomonitoring, helping to dissect the main variables influencing the results (e.g. sex, age, life style and genetics of DNA repair) and to better exploit studies of human exposed cohorts for risk assessment.

In addition, comet assay data do not return a number of genetic alterations, but a variation of parameters with respect to untreated controls. This is why proposals have been made to calibrate these parameters vs. number of DNA breaks, using ionizing radiation dose–effect relationships, but this promising approach needs to be further developed. When these issues are improved, comet assay will display its full potential within a complementary battery of in vivo t ests, from organ-specific DNA adduct measurement to erythrocyte MN frequency, to characterize the mechanisms of toxicity and carcinogenicity of chemicals, which are increasingly considered to set human exposure limits.

The authors thank Peter Karran for manuscript revision. MB is currently a member of the CONTAM panel of EFSA. E.C. is currently a member of the Working Group on flavorings of EFSA.

None declared.

Azqueta   A , Collins   AR . The essential comet assay: a comprehensive guide to measuring DNA damage and repair . Arch Toxicol   2013 ; 87 : 949 – 68 .

Google Scholar

Azqueta   A , Muruzabal   D , Boutet-Robinet   E  et al.    Technical recommendations to perform the alkaline standard and enzyme-modified comet assay in human biomonitoring studies . Mutat Res   2019 ; 843 : 24 – 32 .

Møller   P . Genotoxicity of environmental agents assessed by the alkaline comet assay . Basic Clin Pharmacol Toxicol   2005 ; 96 : 1 – 42 .

Speit   G , Vasquez   M , Hartmann   A . The comet assay as an indicator test for germ cell genotoxicity . Mutat Res   2009 ; 681 : 3 – 12 .

Costa-Amaral   IC , Carvalho   LVB , Santos   MVC  et al.    Environmental assessment and evaluation of oxidative stress and genotoxicity biomarkers related to chronic occupational exposure to benzene . Int J Environ Res Public Health   2019 ; 16 : 2240 .

Ladeira   C , Smajdova   L . The use of genotoxicity biomarkers in molecular epidemiology: applications in environmental, occupational and dietary studies . AIMS Genet   2017 ; 4 : 166 – 91 .

de   Lapuente   J , Lourenço   J , Mendo   SA  et al.    The comet assay and its applications in the field of ecotoxicology: a mature tool that continues to expand its perspectives . Front Genet   2015 ; 6 : 80 .

Santos   CL , Pourrut   B , Ferreira de Oliveira   JM . The use of comet assay in plant toxicology: recent advances . Front Genet   2015 ; 6 : 16 .

Gajski   G , Žegura   B , Ladeira   C  et al.    The comet assay in animal models: from bugs to whales - (part 1 invertebrates) . Mutat Res   2019 ; 779 : 82 – 113 .

Crebelli   R , Caiola   S , Conti   L  et al.    Can sustained exposure to PFAS trigger a genotoxic response? A comprehensive genotoxicity assessment in mice after subacute oral administration of PFOA and PFBA . Regul Toxicol Pharmacol   2019 ; 106 : 169 – 77 .

Cordelli   E , Keller   J , Eleuteri   P  et al.    No genotoxicity in rat blood cells upon 3- or 6-month inhalation exposure to CeO2 or BaSO4 nanomaterials . Mutagenesis   2017 ; 32 : 13 – 22 .

Booth   ED , Rawlinson   PJ , Maria Fagundes   P  et al.    Regulatory requirements for genotoxicity assessment of plant protection product active ingredients, impurities, and metabolites . Environ Mol Mutagen   2017 ; 58 : 325 – 44 .

Collins   AR , Oscoz   AA , Brunborg   G  et al.    The comet assay: topical issues . Mutagenesis   2008 ; 23 : 143 – 51 .

Abbotts   R , Wilson   DM  3rd . Coordination of DNA single strand break repair . Free Radic Biol Med   2017 ; 107 : 228 – 44 .

Collins   AR . Measuring oxidative damage to DNA and its repair with the comet assay . Biochim Biophys Acta   1840 ; 2014 : 794 – 800 .

David   SS , Williams   SD . Chemistry of glycosylases and endonucleases involved in base-excision repair . Chem Rev   1998 ; 98 : 1221 – 61 .

Speit   G , Schütz   P , Bonzheim   I  et al.    Sensitivity of the FPG protein towards alkylation damage in the comet assay . Toxicol Lett   2004 ; 146 : 151 – 8 .

Rosenquist   TA , Zharkov   DO , Grollman   AP . Cloning and characterization of mammalian 8-oxoguanine DNA glycosylase . Proc Natl Acad Sci U S A   1997 ; 94 : 7429 – 34 .

Smith   CC , O’Donovan   MR , Martin   EA . hOGG1 recognizes oxidative damage using the comet assay with greater specificity than FPG or ENDOIII . Mutagenesis   2006 ; 21 : 185 – 90 .

Duthie   SJ , McMillan   P . Uracil misincorporation in human DNA detected using single cell gel electrophoresis . Carcinogenesis   1997 ; 18 : 1709 – 14 .

Collins   A , Dusinska   M , Horska   A . Detection of alkylation damage in human lymphocyte DNA with the comet assay . Acta Biochim Pol   2001 ; 48 : 611 – 4 .

Henkler   F , Stolpmann   K , Luch   A . Exposure to polycyclic aromatic hydrocarbons: bulky DNA adducts and cellular responses . Experientia Suppl   2012 ; 101 : 107 – 31 .

Baird   WM , Hooven   LA , Mahadevan   B . Carcinogenic polycyclic aromatic hydrocarbon-DNA adducts and mechanism of action . Environ Mol Mutagen   2005 ; 45 : 106 – 14 .

Veglia   F , Matullo   G , Vineis   P . Bulky DNA adducts and risk of cancer: a meta-analysis . Cancer Epidemiol Biomarkers Prev   2003 ; 12 : 157 – 60 .

Staresincic   L , Fagbemi   AF , Enzlin   JH  et al.    Coordination of dual incision and repair synthesis in human nucleotide excision repair . EMBO J   2009 ; 28 : 1111 – 20 .

Gedik   CM , Ewen   SW , Collins   AR . Single-cell gel electrophoresis applied to the analysis of UV-C damage and its repair in human cells . Int J Radiat Biol   1992 ; 62 : 313 – 20 .

Ngo   LP , Owiti   NA , Swartz   C  et al.    Sensitive CometChip assay for screening potentially carcinogenic DNA adducts by trapping DNA repair intermediates . Nucleic Acids Res   2020 ; 48 : e13 .

Collins   AR , Mitchell   DL , Zunino   A  et al.    UV-sensitive rodent mutant cell lines of complementation groups 6 and 8 differ phenotypically from their human counterparts . Environ Mol Mutagen   1997 ; 29 : 152 – 60 .

Hashimoto   S , Anai   H , Hanada   K . Mechanisms of interstrand DNA crosslink repair and human disorders . Genes and Environment   2016 ; 38 : 9 .

Rothfuss   A , Grompe   M . Repair kinetics of genomic interstrand DNA cross-links: evidence for DNA double-strand break-dependent activation of the Fanconi anemia/BRCA pathway . Mol Cell Biol   2004 ; 24 : 123 – 34 .

OECD ( 2016 ), Test No. 489: In Vivo Mammalian Alkaline Comet Assay, OECDGuidelines for the Testing of Chemicals, Section 4 , OECD Publishing , Paris . doi: 10.1787/9789264264885-en .

Google Preview

Lindahl   T . Instability and decay of the primary structure of DNA . Nature   1993 ; 362 : 709 – 16 .

Lindahl   T , Barnes   D . Repair of endogenous DNA damage . Cold Spring Harb Symp Quant Biol   2000 ; 65 : 127 – 33 .

Nakamura   J , Mutiu   E , Sharma   V  et al.    The endogenous exposome . DNA Repair   2014 ; 19 : 3 – 13 .

Tubbs   A , Nussenzweig   A . Endogenous DNA damage as a source of genomic instability in cancer . Cell   2017 ; 168 : 644 – 56 .

Collins   AR , Cadet   J , Möller   L  et al.    Are we sure we know how to measure 8-oxo-7,8-dihydroguanine in DNA from human cells?   Arch Biochem Biophys   2004 ; 423 : 57 – 65 .

Dogliotti   E , Bignami   M . DNA repair and genetic instability. Endogenous and exogenous sources of damage and hereditary syndromes. In: Wild   CP , Weiderpass   E , Stewart   BW (eds). World Cancer Report 2020 . International Agency for Research on Cancer , 170 – 80 .

Singh   NP , McCoy   MT , Tice   RR  et al.    A simple technique for quantitation of low levels of DNA damage in individual cells . Exp Cell Res   1988 ; 175 : 184 – 91 .

Azqueta   A , Gutzkow   KB , Brunborg   G  et al.    Towards a more reliable comet assay: optimising agarose concentration, unwinding time and electrophoresis conditions . Mutat Res   2011 ; 724 : 41 – 5 .

Enciso   JM , Gutzkow   KB , Brunborg   G . et al.   Standardisation of the in vitro comet assay: influence of lysis time and lysis solution composition on the detection of DNA damage induced by X-rays . Mutagenesis   2018 ; 33 : 25 - 30 .

Glei   M , Schneider   T , Schlörmann   W . Comet assay: an essential tool in toxicological research . Arch Toxicol   2016 ; 90 : 2315 – 36 .

Ersson   C , Møller   P , Forchhammer   L  et al.    An ECVAG inter-laboratory validation study of the comet assay: inter-laboratory and intra-laboratory variations of DNA strand breaks and FPG-sensitive sites in human mononuclear cells . Mutagenesis   2013 ; 28 : 279 – 86 .

Forchhammer   L , Johansson   C , Loft   S  et al.    Variation in the measurement of DNA damage by comet assay measured by the ECVAG inter-laboratory validation trial . Mutagenesis   2010 ; 25 : 113 – 23 .

Forchhammer   L , Ersson   C , Loft   S  et al.    Inter-laboratory variation in DNA damage using a standard comet assay protocol . Mutagenesis   2012 ; 27 : 665 – 72 .

Johansson   J , Møller   P , Forchhammer   L  et al.    An ECVAG trial on assessment of oxidative damage to DNA measured by the comet assay . Mutagenesis   2010 ; 25 : 25 – 132 .

Ahnstrom   G , Erixon   K . Measurement of strand breaks by alkaline denaturation and hydroxyapatite chromatography. In: Friedberg   EC , Hanawalt   PC (eds). DNA Repair. A Laboratory Manual of Research Procedures . New York : Marcel Dekker , 1981 , 403 – 18 .

Møller   P , Jantzen   K , Løhr   M  et al.    Searching for assay controls for the Fpg- and hOGG1-modified comet assay . Mutagenesis   2018 ; 33 : 9 – 19 .

Møller   P , Cooke   MS , Collins   A  et al.    Harmonising measurements of 8-oxo-7,8-dihydro-2′-deoxyguanosine in cellular DNA and urine . Free Radic Res   2012 ; 46 : 541 – 53 .

Collins   AR , El Yamani   N , Lorenzo   Y  et al.    Controlling variation in the comet assay . Front Genet   2014 ; 5 : 359 .

Zainol   M , Stout   J , Bowman   KJ  et al.    ECVAG introducing a true internal standard for the comet assay to minimize intra- and inter-experiment variability in measures of DNA damage and repair . Nucleic Acids Res   2009 . doi: 10.1093/nar/gkp826 .

Ueno   S , Kashimoto   T , Susa   N  et al.    Assessment of DNA damage in multiple organs of mice after whole body X-irradiation using the comet assay . Mutat Res   2007 ; 634 : 135 – 45 .

Mammalian Mutagenesis Study Group of the Environmental Mutagen Society of Japan. 2014. Atlas of Comet Assay Images . Tokyo, Japan : Scientist Press Co., Ltd.

Uno   Y , Kojima   H , Omori   T  et al.    JaCVAM-organized international validation study of the in vivo rodent alkaline comet assay for detection of genotoxic carcinogens: II. Summary of definitive validation study results . Mutat Res Genet Toxicol Environ Mutagen   2015 ; 786-788 : 45 – 76 .

ICH . 2011 . International Conference on Harmonisation of technical requirements for registration of pharmaceuticals for human use. ICH harmonised tripartite guideline. Guidance on genotoxicity testing and data interpretation for pharmaceuticals intended for human use. S2(R1). Current Step 4 version dated 9 November 2011 .

ECHA (European Chemical Agency) . Guidance on Information Requirements and Chemical Safety Assessment, Chapter R.7a: Endpoint Specific Guidance , 2017 , Version 6.0 . https://echa.europa.eu/documents/10162/13632/information_requirements_r7a_en.pdf/e4a2a18f-a2bd-4a04-ac6d-0ea425b2567f (8 December 2020, date last accessed).

OECD , 2020 . OECD Guideline for the Testing of Chemicals: Transgenic Rodent Somatic and Germ Cell Gene Mutation Assays (TG 488) . https://doi.org/10.1787/9789264203907-en (8 December 2020, date last accessed).

OECD ( 2016 ), Test No. 474: Mammalian Erythrocyte Micronucleus Test, OECD Guidelines for the Testing of Chemicals, Section 4 , OECD Publishing , Paris . doi: 10.1787/9789264264762-en .

OECD ( 2016 ), Test No. 475: Mammalian Bone Marrow Chromosomal Aberration Test, OECD Guidelines for the Testing of Chemicals, Section 4 , OECD Publishing , Paris . doi: 10.1787/9789264264786-en .

EFSA Scientific Committee, 2011 . Scientific opinion on genotoxicity testing strategies applicable to food and feed safety assessment . EFSA J   2011 ; 9 : 2379 .

EFSA Scientific Committee . Clarification of some aspects related to genotoxicity assessment . EFSA J   2017 ; 15 : 5113 .

Bhagat   J . Combinations of genotoxic tests for the evaluation of group 1 IARC carcinogens . J Appl Toxicol   2018 ; 38 : 81 – 99 .

Kirkland   D , Uno   Y , Luijten   M  et al.    In vivo genotoxicity testing strategies: report from the 7th international workshop on genotoxicity testing (IWGT) . Mutat Res   2019 ; 847 : 403035 .

Golfier   S , Jost   G , Pietsch   H  et al.    Dicentric chromosomes and gamma-H2AX foci formation in lymphocytes of human blood samples exposed to a CT scanner: a direct comparison of dose response relationships . Radiat Prot Dosimetry   2009 ; 134 : 55 – 61 .

Nikolova   T , Marini   F , Kaina   B . Genotoxicity testing: comparison of the γH2AX focus assay with the alkaline and neutral comet assays . Mutat Res   2017 ; 822 : 10 – 8 .

Kopp   B , Khoury   L , Audebert   M . Validation of the γH2AX biomarker for genotoxicity assessment: a review . Arch Toxicol   2019 ; 93 : 2103 – 14 .

Gollapudi   BB , Johnson   GE , Hernandez   LG  et al.    Quantitative approaches for assessing dose-response relationships in genetic toxicology studies . Environ Mol Mutagen   2013 ; 54 : 8 – 18 .

Heflich   RH , Johnson   GE , Zeller   A  et al.    Mutation as a toxicological endpoint for regulatory decision-making . Environ Mol Mutagen   2020 ; 61 : 34 – 41 .

Zeller   A , Duran-Pacheco   G , Guérard   M . An appraisal of critical effect sizes for the benchmark dose approach to assess dose-response relationships in genetic toxicology . Arch Toxicol   2017 ; 91 : 3799 – 807 .

Guérard   M , Johnson   G , Dertinger   S  et al.    Dose-response relationship of temozolomide, determined by the Pig-a , comet, and micronucleus assay . Arch Toxicol   2017 ; 91 : 2443 – 53 .

COM (Committee on Mutagenicity of Chemicals in Food, Consumer Products and the Environment) . 2018 . Statement 2018/S1: Statement on the Quantitative Approaches to the Assessment of Genotoxicity Data . https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/704462/COM_statement_on_quantitative_approaches.pdf (8 December 2020, date last accessed).

EFSA . Scientific committee; scientific opinion on the applicability of the margin of exposure approach for the safety assessment of impurities which are both genotoxic and carcinogenic in substances added to food/feed . EFSA J   2012 ; 10 : 2578 .

EFSA CONTAM Panel (EFSA Panel on Contaminants in the Food Chain) , Schrenk   D , Bodin   L  et al.    Scientific opinion on the risk assessment of ochratoxin a in food . EFSA J 2020   2020 ; 18 : 6113 .

EFSA CONTAM Panel (EFSA Panel on Contaminants in the Food Chain) , Knutsen   HK , Alexander   J  et al.    Scientific opinion on the risks for public health related to the presence of furan and methylfurans in food . EFSA J 2017   2017 ; 15 : 5005 .

Palma   N , Cinelli   S , Sapora   O  et al.    Ochratoxin A-induced mutagenesis in mammalian cells is consistent with the production of oxidative stress . Chem ResToxicol   2007 ; 20 : 1031 – 7 .

Mosesso   P , Cinelli   S , Pinero   J  et al.    In vitro cytogenetic results supporting a DNA nonreactive mechanism for ochratoxin A, potentially relevant for its carcinogenicity . Chem Res Toxicol   2008 ; 21 : 1235 – 43 .

Ali   R , Mittelstaedt   RA , Shaddock   JG  et al.    Comparative analysis of micronuclei and DNA damage induced by Ochratoxin A in two mammalian cell lines . Mutat Res   2011 ; 723 : 58 – 64 .

Liu   J , Wang   Y , Cui   J  et al.    Ochratoxin A induces oxidative DNA damage and G1 phase arrest in human peripheral blood mononuclear cells in vitro . Toxicol Lett   2012 ; 211 : 164 – 71 .

Ali   R , Guo   X , Lin   H  et al.    Mutant frequency in comparison to oxidative DNA damage induced by ochratoxin A in L5178Y tk+/− (3.7.2C) mouse lymphoma cells . Drug Chem Toxicol   2014 ; 37 : 227 – 32 .

Mally   A , Pepe   G , Ravoori   S  et al.    Ochratoxin A causes DNA damage and cytogenetic effects but no DNA adducts in rats . Chem Res Toxicol   2005 ; 18 : 1253 – 61 .

Hibi   D , Suzuki   Y , Ishii   Y  et al.    Site-specific in vivo mutagenicity in the kidney of gpt delta rats given a carcinogenic dose of ochratoxin A . Toxicol Sci   2011 ; 122 : 406 – 14 .

Kuroda   K , Hibi   D , Ishii   Y  et al.    Ochratoxin A induces DNA double-strand breaks and large deletion mutations in the carcinogenic target site of gpt delta rats . Mutagenesis   2014 ; 29 : 27 – 36 .

Kuroda   K , Hibi   D , Ishii   Y  et al.    Role of p53 in the progression from ochratoxin A-induced DNA damage to gene mutations in the kidneys of mice . Toxicol Sci   2015 ; 144 : 65 – 76 .

Hibi   D , Kijima   A , Suzuki   Y  et al.    Effects of p53 knockout on ochratoxin A-induced genotoxicity in p53-deficient gpt delta mice . Toxicology   2013 ; 304 : 92 – 9 .

Mally   A , Volkel   W , Amberg   A  et al.    Functional, biochemical, and pathological effects of repeated oral administration of ochratoxin A to rats . Chem Res Toxicol   2005 ; 18 : 1242 – 52 .

Mantle   PG , Faucet-Marquis   V , Manderville   RA  et al.    Structures of covalent adducts between DNA and ochratoxin A: a new factor in debate about genotoxicity and human risk assessment . Chem Res Toxicol   2010 ; 23 : 89 – 98 .

Domijan   AM , Zeljezic   D , Kopjar   N  et al.    Standard and Fpg-modified comet assay in kidney cells of ochratoxin A- and fumonisinB (1)-treated rats . Toxicology   2006 ; 222 : 53 – 9 .

Halila   S , Velasco   T , De Clercq   P  et al.    Fine-tuning furan toxicity: fast and quantitative DNA interchain cross-link formation upon selective oxidation of a furan containing oligonucleotide . Chem Commun   2005 ; 21 : 936 – 8 .

Peterson   LA , Naruko   KC , Predecki   DP . A reactive metabolite of furan, cis-2-butene-1,4-dial, is mutagenic in the Ames assay . Chem Res Toxicol   2000 ; 13 : 531 – 4 .

Byrns   MC , Vu   CC , Neidigh   JW  et al.    Detection of DNA adducts derived from the reactive metabolite of furan, cis-2-butene-1,4-dial . Chem Res Toxicol   2006 ; 19 : 414 – 20 .

Kellert   M , Brink   A , Richter   I  et al.    Tests for genotoxicity and mutagenicity of furan and its metabolite cis-2-butene-1,4-dial in L5178Y Tk +/− mouse lymphoma cells . Mutat Res   2008 ; 657 : 127 – 32 .

Marinari   UM , Ferro   M , Bassi   AM  et al.    DNA-damaging activity of biotic and 2247 xenobiotic aldehydes in chinese hamster ovary cells . Cell Biochem Funct   1984 ; 2 : 243 – 8 .

Neuwirth   C , Mosesso   P , Pepe   G  et al.    Furan carcinogenicity: DNA binding and genotoxicity of furan in rats in vivo . Mol Nutr Food Res   2012 ; 56 : 1363 – 74 .

Terrell   AN , Huynh   M , Grill   AE  et al.    Mutagenicity of furan in female big blue B6C3F1 mice . Mutat Res Genet Toxicol and Environ Mutagen   2014 ; 770 : 46 – 54 .

Hibi   D , Yokoo   Y , Suzuki   Y  et al.    Lack of genotoxic mechanisms in early-stage furan-induced hepatocellular tumorigenesis in gpt delta rats . J Appl Toxicol   2017 ; 37 : 142 – 9 .

Leopardi   P , Cordelli   E , Villani   P  et al.    Assessment of in vivo genotoxicity of the rodent carcinogen furan: evaluation of DNA damage and induction of micronuclei in mouse splenocytes . Mutagenesis   2010 ; 25 : 57 – 62 .

Cordelli   E , Leopardi   P , Villani   P  et al.    Toxic and genotoxic effects of oral administration of furan in mouse liver . Mutagenesis   2010 ; 25 : 305 – 14 .

Ding   W , Petibone   DM , Latendresse   JR  et al.    In vivo genotoxicity of furan in F344 rats at cancer bioassay doses . Toxicol Appl Pharmacol   2012 ; 261 : 164 – 71 .

McDaniel   LP , Ding   W , Dobrovolsky   VN  et al.    Genotoxicity of furan in big blue rats . Mutat Res   2012 ; 742 : 72 – 8 .

Hickling   KC , Hitchcock   JM , Oreffo   V  et al.    Evidence of oxidative stress and associated DNA damage, increased proliferative drive, and altered gene expression in rat liver produced by the cholangiocarcinogenic agent furan . Toxicol Pathol   2010 ; 38 : 230 – 43 .

Wang   E , Chen   F , Hu   X  et al.    Protective effects of apigenin against furan-induced toxicity in mice . Food Funct   2014 ; 5 : 1804 – 12 .

Email alerts

Citing articles via.

• Recommend to Your Librarian
• Journals Career Network

Affiliations

• Online ISSN 2045-4538
• Copyright © 2024 Oxford University Press
• About Oxford Academic
• Publish journals with us
• University press partners
• What we publish
• New features  
• Open access
• Institutional account management
• Rights and permissions
• Get help with access
• Accessibility
• Advertising
• Media enquiries
• Oxford University Press
• Oxford Languages
• University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers the University's objective of excellence in research, scholarship, and education by publishing worldwide

• Copyright © 2024 Oxford University Press
• Cookie settings
• Cookie policy
• Privacy policy
• Legal notice

This Feature Is Available To Subscribers Only

Sign In or Create an Account

This PDF is available to Subscribers Only

For full access to this pdf, sign in to an existing account, or purchase an annual subscription.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• v.1(2); 2011

Comet Assay: A Method to Evaluate Genotoxicity of Nano-Drug Delivery System

Somayeh vandghanooni.

Research Center for Pharmaceutical Nanotechnology, Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran

Morteza Eskandani

Introduction.

Drug delivery systems could induce cellular toxicity as side effect of nanomaterials. The mechanism of toxicity usually involves DNA damage. The comet assay or single cell gel electrophoresis (SCGE) is a sensitive method for detecting strand damages in the DNA of a cell with applications in genotoxicity testing and molecular epidemiology as well as fundamental research in DNA damage and repair.

In the current study, we reviewed recent drug delivery researches related to SCGE.

We found that one preference for choosing the assay is that comet images may result from apoptosis-mediated nuclear fragmentation. This method has been widely used over the last decade in several different areas. Overall cells, such as cultured cells are embedded in agarose on a microscope slide, lysed with detergent, and treated with high salt. Nucleoids are supercoiled DNA form. When the slide is faced to alkaline electrophoresis any breakages present in the DNA cause the supercoiling to relax locally and loops of DNA extend toward the anode as a ‘‘comet tail’’.

This article provides a relatively comprehensive review upon potentiality of the comet assay for assessment of DNA damage and accordingly it can be used as an informative platform in genotoxicity studies of drug delivery systems.

The human genome is stably exposed to agents that damage DNA. Mechanisms that damage DNA and lead to a perplexing array of DNA lesions are harmful to the human genome and also cancer development. However, acute effects arise from disturbed DNA, halt cell-cycle progression and causes cell death. So induction of DNA damage in cancer cells can be recognized as a therapeutic strategy for killing cancer (Bohr 2002; Fenech 2010) . Three main mechanisms, which induce DNA damage are (1) environmental agents such as ultraviolet light (UV) (2) normal cellular metabolism products which vocalize a continuous source of damage to DNA accuracy ; and (3) chemical agents which bond to DNA and tend to cause spontaneous disintegration of DNA (Hoeijmakers, 2001). Recently drug delivery technologies are used for in vitro cancer therapy studies and nanoparticles are used vastly for this purpose (Ahmad et al. 2010; Alexis et al. 2010; Yu et al. 2010; Rahimi et al. 2010). So, the risk of human, in particular DNA exposure to these materials is rapidly increased and reliable toxicity test systems are urgently needed. Currently, nanoparticle genotoxicity testing is based on in vitro methods established for hazard characterization of chemicals (Donner et al., 2010, Fubini et al., 2010, Landsiedel et al., 2010, Warheit and Donner, 2010). Comet assay is one of the important and well applied in vitro methods in genotoxicology and DNA damage studies. It is an in situ method in which embedded cell on agarose base is lysed and electrophoresed on neutral or alkaline conditions. Acridine orange/ethidium bromide is used for staining of its DNA. Comet metaphor which is risen from astronomy and visually appropriate image obtained with this technique looks like a ‘‘comet” with a distinct head consisting of intact DNA, and a tail including damaged or broken pieces of DNA (Collins et al. 2008; Fucic 1997; Collins et al. 1997; Liao et al. 2009). The comet assay developed as microgel electrophoresis technique for the first time (Ostling and Johanson, 1984). In this technique, embedded cells in agarose gel were placed on a microscope slide. They are lysed by detergents and high salt treatment and the released chromatin is electrophoresed under neutral conditions (pH of 9.5). DNA then is stained with a fluorescent dye (ethidium bromide), resulting a comet with head and tail. Two versions of Comet assay are currently in use; one introduced by Singh et al known as the ‘‘single cell gel electrophoresis (SCGE)’’ technique (Singh et al., 1988), in which alkaline electrophoresis is used (pH.13) for analysis of DNA damage and, is capable of detecting alkali labile sites and DNA single-strand breaks in individual cells. However many investigators refer to this method as the ‘‘Comet assay’’. Subsequently, Olive et al developed versions of the neutral technique of Ostling and Johanson, with minor changes including lysis in alkali treatment followed by electrophoresis at either neutral or mild alkaline (pH 12.3) conditions (Olive et al., 1990a, Olive et al., 1990b). In comparison with other genotoxicity tests, most important advantages of the comet assay are: ability of the assay for DNA damage identifying at the single cell level, its sensitivity for detecting low levels of DNA damage, requirement of small numbers of cells per sample, its ease of application, low cost, flexibility of the assay as it can be used to evaluate various types of DNA damage, modifiability for adaptation to a variety of experimental requirements (Olive et al., 1990b) and need of the short time period for performing the assay as compared. However the fact that it is successful at demonstrating DNA damage is enough to justify its use in many studies to investigate DNA damage and repair in a wide range of tumor cells with a variety of DNA-damaging agents and it is no wonder that the comet assay has been used in a wide variety of the toxicity studies (McKenna et al., 2008, Karlsson, 2010, Eskandani et al., 2010). However here, we present an overview of comet assay as one of the toxicity test methods for nanoparticle’s risk assessment.

The SCGE methodology

The basic procedure of comet assay was described in detail previously (Olive et al., 1990a, Olive et al., 1990b, Phillips and Arlt, 2009, Singh et al., 1988). As schema views, cells are mixed with 0.5% low-melting point agarose and then placed on a microscope slide pre-coated with 1% normal agarose. When the agarose has solidified, an additional layer of agarose is added. The last layer eliminates in some described SCGE method. Then, the cells are lysed in a detergent solution for 1-14 h. To allow the DNA unwinding, the slides are put into an alkaline or neutral buffer in an electrophoresis chamber for 20 min and then the electrophoresis is carried out. Then electrophoresis slides are rinsed with neutralization buffer or PBS and cells are stained with a fluorochrome dye ( Fig.1 .).

Schematic images to represent the procedure of the alkaline comet assay. A shows slide preparation. B panel shows cell lysation, Unwinding and micro electrophoresis. C shows comet visualization and scoring procedures.

Cell suspension preparation

Since the comet assay is designed to evaluate DNA damage in individual cells, clearly the cells or tissues for their evaluation will need to be assayed in a way that allows distinction between the cells. Virtually any eukaryotic cell can be processed for the analysis of DNA damage using this assay. The existence of various methods for generating single cell suspensions is documented in papers covering a wide range of biological fields. The obvious concern for measuring DNA damage and rejoining strand break in tissues from animal or clinical samples is that the samples should be isolated and processed without allowing additional repair or creating additional strand breaks (Rojas et al., 1999).

a) Whole Blood: 75 µL of LMPA (0.5%; 51ºC) mixed with 5-15 µL heparinized whole blood is added to the pre-coated slide. It is possible that as described in hematology procedure addition of equal amount of 0.5% LMPA, blood be diluted with PBS. (DMSO is added in the lysing solution to scavenge radicals generated by the iron released from hemoglobin when whole blood or animal tissues are used. Therefore, it is not needed for other situations)

b) Monolayer and suspend Cultures: In the monolayer types of cultured cells, the media must be removed and 0.005% Trypsin is added to detach the cells from the flask surface at 37° C for 5 minutes. Trypsination is omitted in the suspend method. (Very low concentration of Trypsin (0.005%) is used because higher concentrations increase DNA damage.) Then equal amount of medium (with FBS) is added to quench Trypsin. The suspend cells is centrifuged (900g, 6 min) and supernatant is removed. Sequentially, very small amount of PBS is added to sediment cells. So ~10,000 cells in 10 µl or less volume per 75 µl LMPA is dissolved and the process is continued accordingly.

Slide preparation

In the slide preparation the aim is to obtain uniform gels sufficiently to ensure easily visualized comets with minimal background noise. For this purpose, a layer of agarose is prepared by dipping a fully frosted and chilled microscope slide in to high melting point agarose (formation of flat layer of agarose on the slide surface is vital for imaging and for avoiding to miss-focusing because of multi-surface gel features ). However after cleaning the other side of the slide, it is vital to solidify quietly agarose-surfaced slide. Then, the cells are suspended in low-melting point (LMP) agarose at 37º C are dropped on first solidified agarose layer. An appropriate sized coverslip is used to flatten out each molten agarose layer, and the slides are often chilled during the process to enhance gelling of the agarose. The important parameters for ensuring a successful analysis are the cells concentrations in the agarose for avoiding significant proportion of overlapping comets, especially at high rates of DNA migration, and the concentration of the agarose. Higher agarose concentrations can affect the extent of DNA migration.

Viability Assay

Before cells preparation to comet assay, the minimum viability required of the cells must be approved. For this purpose some easy and comfortable methods such as viability assay using Trypan blue dye and MTT assay were described in previous papers (Fitzgerald and Hosking, 1982, Plumb, 2004, Stoddart, 2011, Supino, 1995). In the simple viability assay, 10 µL of at least 10 6 cells/ml is placed in a microcentrifuge tube and consequentially 10 µL of Trypan blue dye is added to the tube. After about two minutes a drop of the prepared cells is placed on microscopic slides and a coverslip is put on the cells. 100 cells per each slide are scored in the final step and the number of viable cells (shiny) versus dead cells (blue) is recorded. (If the rates of viable cells become lower than 80%, cell preparation must be repeated AGAIN).

Cell lysis and Alkali (pH > 13) unwinding

After the gel containing cells has solidified, the slides are dipped in a lysis solution consisting of high salts and detergents generally for at least 4 h. There are not many significant differences among the various alkaline lysis methods, all of which employ high salt/detergent lysis for a variable period (1–24 h). The most known reagent of the lysis solution that has been used includes 1% Triton X-100, 2.5 mM NaCl, 0.1 mM Na2EDTA, 10 mMTris, pH 10. However, in rare some cell types may require the second detergent for complete lysis, and this is based on case by case. To maintain the stability of the agarose gel, lysis solution is chilled prior to use. The liberated DNA can be incubated with proteinase K (PK) between lysis and alkali unwinding to remove residual proteins or probed with DNA repair enzymes/antibodies to identify specific classes of DNA damage (e.g. oxidative damage). In order to avoid more DNA damages, all steps must be carried out in dark room. However, after lysis of the cells the slide must be washed three times in 0.4 M Tris-HCl, pH 7.5 (Naturalization buffer) at 4°C. Prior to electrophoresis to formation of a comet picture and also production of single-stranded DNA, the slides are incubated in alkaline (pH > 13) electrophoresis buffer, knowing alkali unwinding step. The alkaline solution developed by Singh et al. consists of 1 mM EDTA and 300 mM sodium hydroxide, pH > 13.0 (Singh et al., 1988).

Electrophoresis, Comet staining and scoring

After alkali unwinding, all chromatin, especially the single-stranded DNA is subjected in the thin layer gel to electrophoresis under alkaline conditions to form comets. During electrophoresis, the alkaline buffer pH, which is used during alkali unwinding has same pH > 13 buffer which is used during alkali unwinding. Since DNA migration of only a minor distance is required in the comet formation, thus only very short electrophoresis running time (10–20 min) and low voltages (0.5–5.0 V/cm) are needed. However comet electrophoresis differs from conventional DNA electrophoresis. The electrophoretic conditions are 25 V and 300 mA.

However, the optimal voltage/amperage depends on the extent of DNA migration seen in the control cells, and range of migration under evaluation among the treated cells. After electrophoresis, the slides should be stained with 25μl of 0.6 μM Ethidium Bromide for visualization of the comet and followed by slipping a coverslip on the vision window. Then comets are detectable with the fluorescent microscope. ( Fig.2 .) However, Nadin et al. carried out for first time a procedure to stain the comet with silver to avoid toxicological effects of Ethidium bromide as a carcinogen material. Surprisingly, they contended that their silver staining method significantly increases the sensitivity/reproducibility of the comet assay in comparison with the fluorescent staining that is very questionable (Nadin et al., 2001).

Comet images ×40 taken by fluorescent microscope with Nikon camera; Acquisition with Photoshop CS4.Comet images evaluated by CASP software and categorized from A - E to show grade ‘0’ to ‘4’ with red and green curve for head and tail DNA, respectively.

However, 100 cells is always selected randomly for the analysis of comet quantity and analyzed under fluorescent microscope by quantifying the DNA damage (%tail DNA/head DNA) through using softwares analyzing comet image such as CASP & Comet IV, or manually with consideration and classification of the comet with damage range from 0 to 4 using following formula. In this formula DD is the rate of the DNA damage, whereas n 0 - n 4 are the type of the comet including 0-4 types. Finally, Σ is the sum of the scored comet including types 1-4 also '0' type comets.

DD = (0n 0 + 1n 1 + 2n 2 + 3n 3 + 4n 4 )/ (Σ/100)

Nanoparticle-based drug delivery

Nanomaterial is a material which has the minimum length<100 nm in size. They have many different forms such as tubes, rods, wires or spheres, with more complicated structures such as nano-onions and nanopeapods (Cheng, 2004, Kokubo et al., 2003, Pramod et al., 2009). Even though, they have novel physico-chemical properties and medical applications, such as faming as drug delivery machine directed specific drugs to the site of tumors, they also may be responsible for unfavorable biological side effects (Dhawan and Sharma, 2010, Singh et al., 2009, Jang et al., 2010, Sohaebuddin et al., 2010). Biodegradable polymers have been studied over the past few decades for the construction of drug delivery systems, in view of their applications and limitation in controlling the release of drugs, stabilizing adjective molecules (e.g., peptides, proteins, or DNA) from degradation, and site-specific drug targeting (Dolatabadi et al., 2011). Additionally, a vesicular system in which the drug is encapsulated by a polymer membrane is nanocapsules, whereas nanospheres are matrix systems in which the drug is physically dispersed. Typically, in the drug delivery systems the drug can be dissolved, entrapped, absorbed, attached and/or encapsulated into/onto a nano-matrix (Dolatabadi et al., 2011). Depending on the method of preparation nanoparticles, nanospheres, or nanocapsules can be constructed in order to possess different properties and release characteristics for the best delivery or encapsulation of the therapeutic agent (Beaux et al., 2008, Yang and Webster, 2009, Hammond, 2011). Some characteristics having importance for drug delivery using nanoparticles are the properties of the nanoparticle surface, drug loading ability, drug releasing preference and finally the properties of the nanoparticle in target therapy (Suri et al., 2007). Also, the size can influence drug loading, drug release and stability of nanoparticles. The size of the nanoparticles determine the in vivo biological fate, toxicity, and targeting. Indeed, nanoparticles can be recognized by the host immune system as antigen and cleared by phagocytes from the blood circulation, when they used intravenously (Li and Liu, 2004, Petros and DeSimone, 2010).

Nanoparticle hydrophobicity (e.g. opsonins) determines the level of blood components that bind to the surface of nanomaterial, yet the size of the nanoparticle has vital role in drug delivery. To increase the success rate of drug targeting, it is necessary to minimize the opsonization in contrast, prolonging the in vivo circulation of nanoparticles (Owens and Peppas, 2006). However, by coating nanoparticles with hydrophilic polymers/surfactants or formulating nanoparticles with biodegradable copolymers owning hydrophilic characteristics, such as polyethylene glycol (PEG), polyethylene oxide, polyoxamer, poloxamine, and polysorbate 80 (Tween 80) the time of intravenously circulating increases , hence the impressments of the nano-drug delivery system become higher (Arayne et al., 2007). On the other hand, a successful nano delivery system should have a high drug-loading capacity. Also, it is revealed that when the macromolecules, drugs or protein are used in nanoparticles, they show the greatest loading efficiency in the isoelectric point of the nanoparticles near to the pI of consignments (Calvo et al., 1997). Indeed, the other factor that defines the efficiency of the drug delivery system is the potentiality of nano-carrier in drug releasing ratio. It is demonstrated that the drug release rate depends on some physico-chemical features of both drugs and nanoparticle including drug solubility, desorption of the surface-bound or absorbed drug, nanoparticle matrix erosion or degradation, drug diffusion through the nanoparticle matrix and finally the combination of erosion and diffusion processes. Hence, solubility, diffusion, and biodegradation of the particle matrix control the release process (Zhang et al., 2003, Frank et al., 2005, Efentakis et al., 2007). Aside from these features, the most important aspect of drug delivery systems is targeted properties of the nanoparticles. Targeted delivery can be actively or passively achieved. In active targeting, therapeutic agent or carrier system conjugates to a tissue or cell-specific ligand (Lamprecht et al. 2001). But in passive, targeting is achieved by incorporating the therapeutic agent into a macromolecule or nanoparticle that reaches the target organ. However, the most important view in drug delivery systems is the non-toxicity of the nanoparticles having used in targeted delivery to raise the efficiency of nonmaterial therapy and also to decrease the biological side effect of such intelligent and targeted nano-structure materials.

Toxicity of advanced materials

There are some limited information indicating that nanoparticles induce cytotoxicity, oxidative stress and inflammatory responses (Park and Park, 2009, Reddy et al., 2010, Neubauer et al., 2008). However, some of these investigations failed to see minor cellular changes that may arise at lower concentrations, which may not result in cell demise but could relate to human health risks. Indeed, due to drug delivery purposes nanoparticles joins also to physico-chemical features such as metal contaminants and charged surfaces, and may well have unpredictable genotoxic properties. Due to the fact that genotoxins can cause genetic alterations and many cancers in the absence of cell death, DNA is the most important macromolecule that mostly faces this overlooking. DNA damage could not only trigger cancer development, but also can have an impact on fertility and the health. Hence, a main area presiding over health risk assessment of new pharmaceuticals and chemicals agents such as nanoparticle is genotoxicology. As a result, genotoxicity testing and evaluation of the carcinogenic or mutagenic potential of new substances is a main part of preclinical safety testing of novel pharmaceutical nanoparticles. Nanoparticles can affect mechanisms leading to penetration of the nanomaterials to the cells and subsequently the nucleus, inducing DNA damage. Nanomaterials may be able to penetrate directly into the nucleus through diffusion across the nuclear membrane (if they are small enough), transport through the nuclear pore complexes, and finally may become surrounded in the nucleus following mitosis when the nuclear membrane is dissolved during cell division and then reorganized in each next generation cells (Feldherr 1998; Macara 2001; Moroianu 1999). If the nanomaterials locate within the nucleus, they will then interact directly with DNA molecule or DNA-related proteins and may cause physical damage to the intelligent and inheritance material.

Due to the anticipated development in the field of nanomaterials, trustworthy toxicity of these nanoscale agents, based on drug delivery systems, and the increasing risk of the exposure to nanomaterials must have been investigated. Therefore nowadays a meticulous defy is to declare safety and usefulness for nanoscale biomedical systems to characterize their toxicological properties requiring unique measurement protocols and criteria for healthful versus harmful exposure results.

Comet assay and genotoxicity of nanoparticles used in drug delivery system

Revealing of the genotoxicity and cytotoxicity properties of nanoparticles based drug delivery systems on the different parts of cells may enhance their usefulness in targeting therapeutic approaches in many aspects. Ever since many techniques have been developed to investigate toxicological effect of nanoparticles, genomically. Additionally, cellular in vitro poisonousness assays such as ROS production assays, cell viability assays and cell stress assays (studying the protein/gene expression, inflammatory markers, Cell visualization, internalization, and organelle interaction) have been used to evaluate toxicity of nanomaterial (Jones and Grainger, 2009). Genotoxicity tests detect DNA damage with some special techniques such as comet assay, Chromosome aberration test, HPRT forward mutation assay, g-H2AX staining, 8-hydroxydeoxyguanosine DNA adducts, micronucleus test and Ames test (Singh et al., 2009). In order to evaluate genotoxicity of nanomaterials, the most often used technique is the single-cell gel electrophoresis assay (Comet test). As best our knowledge with 20 studies, 14 have positive result, and 6 the negative outcome. Comet assay has been used for evaluation of the genotoxicity of some nanoparticles included in drug delivery systems such as single-walled carbon nanotubes (SWCNT), C 60 fullerenes, titanium dioxide (TiO2), Carbon black particles, and other nanoparticles that are described in table 1 . Briefly, some nanoparticles that their genotoxicity evaluated with comet assay describe flowing.

Cationic polymers and lipids

Cationic polymers (at physiological pH; polycations or polycation-containing block copolymers), are a series of polymers which can be made of a variety of polymers including polyethyleneimine (PEI), chitosan, Poly ethylene glycol-based polymers. Cationic polymer can be combined with polynucleic acids (e.g., DNA or RNA) to form a particulate complex, such as interpolyelectrolyte complexes (IPECs) or block ionomer complexes (BICs) and so, is able to transfer the genes into the targeted cells (El-Aneed, 2004). Polyethylenimines (PEIs) are series of synthetic cationic polymers, well-known as an efficient non-viral nucleic acid vector bearing a high cationic charge density which provides to condense and compact the carried DNA into complexes (Boussif et al., 1995). Different types of polyethylenimine (PEI), viz., branched (25 and 800kDa) and linear (25kDa), have been successfully used as transfection agents (Demeneix et al., 1998). The most other important series of polymeric based nanoparticles that have application in drug carrier and gene therapy systems are cationic lipids. It is composed of three basic domains: a positive charged head group, a hydrophobic chain, and a linker which joins the polar region to the non-polar (Gao and Hui, 2001). The polar and hydrophobic domains of cationic lipids may have remarkable effects on both transfection and toxicity levels. The most obvious difference between cationic polymers and cationic lipids is in that cationic polymers do not contain a hydrophobic moiety and are completely soluble in water (Elouahabi and Ruysschaert, 2005). The straight facing of the cationic polymers and lipids are sufficient to the host genome and vast usage of the mentioned polymers in gene/drug delivery to the researchers that consider their side cell/geno-toxcicity effect. Recently Omidi et al evaluated genotoxic impacts of linear and branched polyethylenimine nanostructures in A431 cells (Kafil and Omidi, 2011). Using comet assay, they reported that these types of the nanostructure induce DNA damage to some degree. In addition, they evaluated the genotoxicity of Oligofectamine (OF) nanosystems – a type of the cationic lipid – in human alveolar epithelial A549 cells. Surprisingly, they found no genomic damage detected by the comet assay (Omidi et al., 2008).

A fullerene is in the form of a hollow sphere, ellipsoid, or tube that is composed completely of carbon. Fullerenes structurally are similar to graphite, which is composed of stacked graphene sheets of linked hexagonal rings, yet they may also contain pentagonal or sometimes heptagonal rings. Spherical fullerenes also called buckyballs and cylindrical ones are called carbon nanotubes or buckytubes. Fullerenes attract attention due to their radical scavenging and anti-oxidant properties. Currently they are used in targeted drug delivery, polymer modifications, energy application and cosmetic products (Wang et al., 2004).

C 60 fullerene

A Buckminsterfullerene or C 60 fullerene (C 60 ) is a spherical molecule and the smallest one with the formula C 60 (Wang et al., 2004). It is used in some studies as targeted drug delivery carrier (Aschberger et al., 2010). To evaluate the genotoxic effects of the C 60 fullerenes, Dhawan et al (2006) assessed C 60 fullerenes free of toxic organic solvents prepared by either ethanol to water solvent exchange (‘‘EtOH/nC 60 suspensions’’) or by mixing in water (‘‘aqu/nC 60 suspensions’’) using the comet assay on human lymphocytes. Results showed a strong correlation between the genotoxic response and nC 60 concentration, and also genotoxicity observed at concentrations as low as 2.2 mg/L for aqu/nC 60 and 4.2 mg/L for EtOH/nC 60 (Dhawan et al., 2006).

Single-walled carbon nanotubes (SWCNT)

Carbon nanotubes are nanomaterials made up of thin graphitic sheets formed tubular structure. Two main types of carbon nanotubes including single-walled carbon nanotubes (SWCNTs) with a single tube of carbon (0.4–2 nm) and multiple-walled carbon nanotubes (MWCNTs) composed of several concentric tubes (2–100 nm) (Ji et al., 2010). CNTs have some applications in nanomedicine: they provide appropriate substrate for growth of cells in tissue renewal; they can also be used as nanocarriers for a diversity of therapeutic or diagnostic agents, and finally as vectors for gene transfection (Chen et al., 2006). Consequently, many studies have been performed concerning the toxicity of these nanoparticles used with different techniques. Kisin et al. examined the genotoxicity of SWCNT with diameters between 0.4 and 1.2 nm in V79 cells with comet assay technique. Results showed the significant DNA damage after only 3h of incubation with 96 mg/cm2 of SWCNT (Kisin et al., 2007).

Titanium dioxide (TiO2)

Titanium dioxide (TiO2) is one of the promising materials being considered for various applications. Usually, it is used as a material in the memristor, a new electronic circuit element and other applications related to solar cells (Sohaebuddin et al., 2010, West and Halas, 2003). Additionally the use of TiO2 as drug carrier in nanomedicine has been considered in different aspects. Recently, the effect of textural specifications of nanoporous TiO2 matrices was assessed on the drug delivery activities by M. Signoretto et al. They demonstrated that nanoporous can be used as matrix for the continued release of a drug; they showed also a close association between the pores dimension of TiO2 and the drug release (Signoretto et al., 2011). In addition, several investigations have been performed on the genotoxicity effect of TiO2 on various cell lines. Wang, Jing J et al evaluated the genotoxicity of TiO2 nanoparticles on cultured human B-cell lymphoblastoid WIL2-NS cells with comet assay procedure. At a concentration of 65 mg/mL, the nanoparticles induced significant genotoxicity after an exposure of 24 h in cultured human B-cell lymphoblastoid WIL2-NS cells (Wang et al., 2007). Also in another study Nakagawa et al. reported comet positive results in L5178Y mouse lymphoma cells exposed to Titanium dioxide P25 (Nakagawa et al., 1997).

Carbon Black

Carbon Black (CB) is a family of small particles consisting carbon fractal aggregates. Carbon black is chemically and physically distinct from soot and black carbon. The basic building units of CB are nano-sized particles formed by stacked graphene layers exhibiting random orientations about the staking axis and also parallel to the layers in translation (turbostratic structure) (Sanjinés et al.). Due to their electrical properties, carbon blacks are widely used as conducting fillers in polymers. CB/polymer composites have wide range of applications including graded semiconductors for optoelectronic applications, conducting electrodes, solid electrolytes for batteries, anti-reflection coatings, room temperature gas sensors, electrical switching devices, etc (Chung, 2004). The potential use of carbon black for delivery of molecules was reported by Prerona Chakravarty in to the cell, in 2010. Their initial results suggest that interaction between the laser energy and carbon black nanoparticles may generate photoacoustic forces by chemical reaction in order to create transient holes in the membrane for intracellular delivery (Chakravarty et al., 2010). Also in the study about the genotoxic effect of carbon black with comet assay, N.R. Jacobsen reported 75 mg/mL particles induced in FE1 MutaTM mouse lung epithelial cells within 3 h, a significant increase in DNA strand breaks(p = 0.02) detected in the alkaline Comet assay (Jacobsen et al. 2007). In another study, Mroz et al. reported positive comet assay result of Carbon black in A549 human adenocarcinoma cells (Mroz et al., 2008).

Silica nanoparticles

The non-metal oxide of silicon (silica) exists in two main forms; amorphous, which has no long range order, and crystalline, where oxygen and silicon atoms are in a fixed, ordered periodic arrangement (Greenberg et al., 2007). Mesoporous silica is a form of silica which is a recent development in nanotechnology. The most common types of mesoporous nanoparticles are MCM-41 and SBA-15, their main component is amorphous silica. Research continues on the particles, which have applications in catalysis, drug delivery and imaging. Many studies have been performed about use of silica nanoparticles as drug delivery vehicles (Vallet-Regi et al. 2007; Vivero-Escoto et al. 2010; Trewyn et al. 2008). For example, Yufang Zhu et al reported the potential of PEGylated hollow mesoporous silica (HMS-PEG) nanoparticles as drug vehicles for drug delivery (Zhu et al., 2011). Recently, three surfactant-templated mesoporous silica nanoparticles (Surf@MSNs) were developed as anticancer drug delivery systems. The Surf@MSNs exhibit the high drug (surfactant) loading capacities, the sustained drug (surfactant) release profiles and the high and long-term anticancer efficacy (He et al., 2010).Crystalline silica has been shown to be both cytotoxic and genotoxic on in vitro testing by some studies (Fanizza et al. 2007; Wang et al. 2007b). Other groups have found that high doses of crystalline silica are necessary to produce detectable genotoxicity using the single cell gel electrophoresis (Cakmak et al., 2004). Also in the other study performed by Barnes Clifford A. et al the comet assay results indicated no significant genotoxicity at either 4 or 40 μg/ml doses for any of the tested amorphous silica samples in 3T3-L1 fibroblasts (Barnes et al., 2008). Alternatively, this study is in contrast to the results of Pacheco et al. They used MCF-7 as model systems for genotoxicity testing with comet assay (Pacheco et al., 2007).

Based on the best of our knowledge, this article is the first review on the application of comet assay in genotoxicity of nanoparticles used in the drug delivery systems. Application of the advanced nanomaterials in wide range of the biomedical researches and applications such as drug delivery system compel the researcher to work within these materials discreetly because of their probable toxicity effects on the inherent material. However risk appraisal of nanomaterials, challenges some of the present DNA damage assessment methods due to the unique nature of material at nano-scale. Considering large number of studies on the genotoxic effect of the nanomaterials and high potential of comet assay in precise DNA damage detection, using alkaline single cell gel electrophoresis is a very useful tool for diagnosis of the nanoparticle genotoxicity that is used in drug delivery system and also are highly recommended to reduce side effect of these types of therapies.

Ethical Issues

None to be declared.

Conflict of interests

The authors declare no conflict of interests.

Acknowledgments

We thank Miss Ilghami for her kind editing of this work. Also, we acknowledge Research Center for Pharmaceutical Nanotechnology (RCPN) of Tabriz University of Medical Sciences for financial support.

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

• View all journals
• Explore content
• About the journal
• Publish with us
• Sign up for alerts
• Open access
• Published: 18 June 2024

Plasma proteomics identify biomarkers predicting Parkinson’s disease up to 7 years before symptom onset

• Jenny Hällqvist   ORCID: orcid.org/0000-0001-6709-3211 1 , 2   na1 ,
• Michael Bartl   ORCID: orcid.org/0000-0002-7752-2443 3 , 4   na1 ,
• Mohammed Dakna 3 ,
• Sebastian Schade   ORCID: orcid.org/0000-0002-6316-6804 5 ,
• Paolo Garagnani   ORCID: orcid.org/0000-0002-4161-3626 6 ,
• Maria-Giulia Bacalini 7 ,
• Chiara Pirazzini 6 ,
• Kailash Bhatia   ORCID: orcid.org/0000-0001-8185-286X 8 ,
• Sebastian Schreglmann   ORCID: orcid.org/0000-0002-4129-5808 8 ,
• Mary Xylaki   ORCID: orcid.org/0000-0002-7892-8621 3 ,
• Sandrina Weber 3 ,
• Marielle Ernst 9 ,
• Maria-Lucia Muntean 5 ,
• Friederike Sixel-Döring 5 , 10 ,
• Claudio Franceschi   ORCID: orcid.org/0000-0001-9841-6386 6 ,
• Ivan Doykov 1 ,
• Justyna Śpiewak 1 ,
• Héloїse Vinette   ORCID: orcid.org/0009-0000-4360-1293 1 , 11 ,
• Claudia Trenkwalder 5 , 12 ,
• Wendy E. Heywood   ORCID: orcid.org/0000-0003-2106-8760 1 ,
• Kevin Mills 2   na2 &
• Brit Mollenhauer   ORCID: orcid.org/0000-0001-8437-3645 3 , 5   na2  

Nature Communications volume  15 , Article number:  4759 ( 2024 ) Cite this article

20k Accesses

2101 Altmetric

Metrics details

• Parkinson's disease

Parkinson’s disease is increasingly prevalent. It progresses from the pre-motor stage (characterised by non-motor symptoms like REM sleep behaviour disorder), to the disabling motor stage. We need objective biomarkers for early/pre-motor disease stages to be able to intervene and slow the underlying neurodegenerative process. Here, we validate a targeted multiplexed mass spectrometry assay for blood samples from recently diagnosed motor Parkinson’s patients ( n  = 99), pre-motor individuals with isolated REM sleep behaviour disorder (two cohorts: n  = 18 and n  = 54 longitudinally), and healthy controls ( n  = 36). Our machine-learning model accurately identifies all Parkinson patients and classifies 79% of the pre-motor individuals up to 7 years before motor onset by analysing the expression of eight proteins—Granulin precursor, Mannan-binding-lectin-serine-peptidase-2, Endoplasmatic-reticulum-chaperone-BiP, Prostaglaindin-H2-D-isomaerase, Interceullular-adhesion-molecule-1, Complement C3, Dickkopf-WNT-signalling pathway-inhibitor-3, and Plasma-protease-C1-inhibitor. Many of these biomarkers correlate with symptom severity. This specific blood panel indicates molecular events in early stages and could help identify at-risk participants for clinical trials aimed at slowing/preventing motor Parkinson’s disease.

Similar content being viewed by others

Plasma GFAP as a prognostic biomarker of motor subtype in early Parkinson’s disease

Parkinson’s progression prediction using machine learning and serum cytokines

A six-metabolite panel as potential blood-based biomarkers for Parkinson’s disease

Introduction.

Parkinson’s disease (PD) is a complex and increasingly prevalent neurodegenerative disease of the central nervous system (CNS). It is clinically characterised by progressive motor and non-motor symptoms that are caused by α-synuclein aggregation predominantly in dopaminergic cells, which leads to Lewy body (LB) formation 1 . The failure of neuroprotective strategies in preventing disease progression is due, in part, to the clinical heterogeneity of the disease—it has several phenotypes—and to the lack of objective biomarker readouts 2 . To facilitate the approval of neuroprotective strategies, governing agencies and pharmaceutical companies need regulatory pathways that use objectively measurable markers—potential therapeutical targets as well as state and rate biomarkers—directly associated with PD pathophysiology and clinical phenotypes 3 .

The recently emerged α-synuclein seed amplification assays (SAA) can identify α-synuclein pathology in vivo and support stratification purposes but still rely on cerebrospinal fluid (CSF) obtained through relatively invasive lumbar punctures 4 . Therefore, this test remains specialised and not readily suitable for large-scale clinical use. As peripheral fluid biomarkers are less invasive and easier to obtain, they could be used in repeated and long-term monitoring, which is necessary for population-based screenings for upcoming neuroprotective trials. While the only emerged serum biomarker in the last years, axonal marker neurofilament light chain (NfL), increases longitudinally and correlates with motor and cognitive PD progression 5 , it is non-specific to the disease process.

Growing data support evidence of PD pathology in the peripheral system, which increases the likelihood of finding a source of matrices for less invasive biomarkers. We know α-synuclein aggregation induces neurodegeneration, which is propagated throughout the CNS. Evidence indicates that additional inflammatory events are an early and potentially initial step in a pathophysiological cascade leading to downstream α-synuclein aggregation that activates the immune system 6 . Inflammatory risk factors in circulating blood (i.e. C-reactive-protein and Interleukin-6 and α-synuclein-specific T-cells) are associated with motor deterioration and cognitive decline in PD 7 , 8 . These inflammatory blood markers can even be identified in plasma/serum samples of individuals with isolated REM sleep behaviour disorder (iRBD), the early stage of a neuronal synuclein disease (NSD), and the most specific predictor for PD and dementia with Lewy bodies (DLB) 6 . NSD was recently proposed as a biologically defined term, for a spectrum of clinical syndromes, including iRBD, PD and DLB, that follow an integrated clinical staging system of progressing neuronal α-synuclein pathology (NSD-ISS) 9 .

In this study, we used mass spectrometry-based proteomic phenotyping to identify a panel of blood biomarkers in early PD. In the initial discovery stage, we analysed samples from a well-characterised cohort of de novo PD patients and healthy controls (HC) who had been subjected to rigorous collection protocols 10 . Using unbiased state-of-the-art mass spectrometry, we identified putatively involved proteins, suggesting an early inflammatory profile in plasma. We thereafter moved on to the validation phase by creating a high-throughput and targeted proteomic assay that was applied to samples from an independent replication cohort, consisting of de novo PD, HC and iRBD patients. Finally, after refining the targeted proteomic panel to include a multiplex of only the biomarkers which were reliably measured, an independent analysis was performed on a larger and independent cohort of longitudinal, high-risk subjects who had been confirmed as iRBD by state-of-the-art video-recorded polysomnography (vPSG), including follow-up sampling of up to 7 years.

In summary, using a panel of eight blood biomarkers identified in a machine-learning approach, we were able to differentiate between PD and HC with a specificity of 100%, and to identify 79% of the iRBD subjects, up to 7 years before the development of either DLB or motor PD (NSD stage 3). Our identified panel of biomarkers significantly advances NSD research by providing potential screening and detection markers for use in the earliest stages of NSD for subject identification/stratification for the upcoming prevention trials.

Proteomic discovery phase 0

We performed a bottom-up proteomics analysis of plasma, which had been depleted of the major blood proteins, using two-dimensional in-line liquid chromatography fractionation into ten fractions and label-free mass spectrometric analysis by QTOF MS E . The discovery cohort consisted of ten randomly selected drug-naïve patients with PD and ten matched HC from the de novo Parkinson’s disease (DeNoPa 10 ) cohort (details can be found in Supplementary Table  1 ). This analysis identified 1238 proteins when restricting identification to originate from at least one peptide per protein and at least two fragments per peptide. After excluding proteins with less than two unique peptides or with an identification score below a set threshold (see method section below), 895 distinct proteins remained. Of these proteins, 47 were differentially expressed between the de novo PD and control groups on a nominal significance level of 95%. Pathway analysis suggested enrichment in several inflammatory pathways. Workflow and Results are shown in Fig.  1 , and 2 Supplementary Figs.  1 , 2.

The study included three phases. Phase 0 consisted of discovery proteomics by untargeted mass spectrometry to identify putative biomarkers, followed by phase I in which targets from the discovery phase were transferred to a targeted, mass spectrometric MRM method and applied to a new and larger cohort of samples, and finally phase II in which the targeted MRM method was refined and a larger number of samples were analysed to evaluate the clinical feasibility of the targeted protein panel.

The circle radii in the Volcano plot represent the identification certainty, where large radii represent proteins identified by at least two unique peptides and an identification score >15, smaller radii are given for proteins identified by two or more unique peptides or a confidence score >15. The horizontal axis shows log 2 of the average fold-change and the vertical axis shows −log 10 of the p values. The significantly different proteins are annotated by gene name and coloured in pink, while the non-significant proteins are coloured in grey. GO annotations for the significant proteins are shown, the dashed line represents p  = 0.05. Disease and function annotations from IPA are shown, divided into annotations with a positive or negative activation score. Source data are provided as a Source Data file.

Selection of proteins for the targeted proteomic assay

We next developed a validatory, high-throughput and multiplexed, mass spectrometric targeted proteomic assay based on the potential biomarkers identified in the discovery phase. Additional proteins were also included in the assay, several of which had been identified in previous discovery studies of PD, Alzheimer’s disease (AD), and ageing 11 . In addition, we also included several known pro- and anti-inflammatory proteins identified in the literature 12 , 13 , 14 , 15 , which had been previously developed into an in-house targeted proteomic neuroinflammatory panel. Using this approach, we created a targeted proteomic panel, including biomarkers from current scientific developments and preliminary findings from our own work 16 , 17 . This targeted proteomic and multiplexed assay included 121 proteins and aimed to validate biomarkers and probe the pathways identified as being perturbed in the discovery phase. Details can be found in Supplementary Table  2 and Fig.  3 .

Workflow and overview of the results of the targeted proteomic analysis of de novo Parkinson’s disease (PD) subjects, healthy controls (HC), and the validation cohorts of other neurological disorders (OND) and isolated REM sleep behaviour disorder (iRBD). A A targeted mass spectrometric proteomic assay was developed and optimised. The assay was then applied to plasma samples from cohorts comprising de novo PD ( n  = 99) and HC ( n  = 36), and validated in patients with OND ( n  = 41) and prodromal subjects with iRBD ( n  = 18). The protein expression difference between the groups was compared using Mann–Whitney’s two-sided U -test with Benjamini–Hochberg FDR adjustment at 5%. The lollipop charts show the log 10 p values, signed according to fold-changes. Pink icons represent a protein upregulated in an affected group and grey represents a protein upregulated in controls. B Significantly differentially expressed proteins in the comparison between de novo PD and healthy controls. C Significantly differentially expressed proteins between iRBD, OND and HC. Source data are provided as a Source Data file.

Demographics-targeted proteomic validation phase (phase I)

For the targeted proteomics analysis, we used plasma samples, independent from the proteomic discovery step, from 99 individuals recently diagnosed with de novo PD (48 men, 50%, mean age 67 years) and 36 healthy controls (HC; 20 men, 57%, mean age 64 years). This was the main cohort, to which we added further samples for validation that consisted of a heterogeneous group of 41 patients with other neurological diseases (OND) (29 men, 71%, mean age 70 years) and 18 patients with vPSG-confirmed iRBD (10 men, 56%, mean age 67 years). Further details can be found in Table  1 and Fig.  3 .

The identification of biomarkers that were significantly and differentially expressed biomarkers between patients with de novo Parkinson’s disease and healthy controls- Targeted proteomic validation phase (phase I)

Our targeted proteomic assay was developed for 121 proteins, 32 of which we consistently and reliably detected in plasma. Of these 32 markers, 23 were confirmed as being significantly and differentially expressed between PD and HC. We identified six differentially expressed proteins in the comparison between iRBD patients and HC and between OND and HC (Fig.  3 ). Both the de novo PD and iRBD groups demonstrated an upregulated expression of the serine protease inhibitors SERPINA3, SERPINF2 and SERPING1, and of the central complement protein C3. Granulin precursor protein was shown to be downregulated in all three patient groups (PD, iRBD and OND) compared to HC. The OND and PD groups had a shared and upregulated expression of the proteins PTGDS, CST3, VCAM1 and PLD3. Detailed information about the diagnoses of the OND group can be found in Table  1 , and detailed information about the proteins can be found in Supplementary Table  2 . Figure  4 shows the significantly different proteins as Box-scatter plots.

The data are displayed as Box and Whisker plots overlaid with scatter plots of the individual measurements. The whiskers show the minimum and maximum, and the boxes show the 25th percentile, the median and the 75th percentile. The protein expression difference between the groups was compared using Mann–Whitney’s two-sided U -test with Benjamini–Hochberg multiple testing correction (FDR adjustment at 5%). ns not significant, * p  < 0.05, ** p  < 0.01, *** p  < 0.001 and **** p  < 0.0001. The proteins are represented by gene names. Source data are provided as a Source Data file.

The biological significance of the differentially expressed proteins- Targeted proteomic validation phase (phase I)

The involvement of the differentially expressed proteins and their impact on biological processes were evaluated using pathway analysis (Ingenuity Pathway Analysis [IPA], Qiagen). The significantly differentially expressed proteins between PD and HC were used as input, with a fold-change set as the expression observation. We considered pathways as significant if they had an enrichment p value <0.05. At least two of the input proteins were included. Three major pathway clusters were identified and consisted of (i) the expression of serine protease inhibitors or serpins and complement and coagulation components, (ii) endoplasmic reticulum (ER) stress/heat shock-related proteins and (iii) the expression of VCAM1, SELE and PPP3CB. The highest enrichment scores were identified in the pathways acute phase response signalling ( p  = 7.8 E −10 ), coagulation system ( p  = 7.4 E −6 ), complement system ( p  = 8.1 E −6 ), LXR/RXR activation ( p  = 9.1 E −6 ), FXR/RXR activation ( p  = 9.8 E −6 ) and glucocorticoid receptor signalling ( p  = 2.0 E −5 ). These are all pathways involved in inflammatory responses. We also identified pathways related to the unfolded protein response ( p  = 0.004) and neuroinflammation ( p  = 0.04), although with lower enrichment scores. For details, see Supplementary Fig.  1 .

Inflammation-related pathways (including both the complement system and the acute phase response) demonstrated the highest significance levels, followed by pathways regulating protein folding, ER stress, and heat shock proteins. A network representation of proteins and pathways showed clusters consisting of inflammation/coagulation/lipid metabolism (FXR/RXR and LXR/RXR), heat shock proteins/protein misfolding, and more heterogenous pathway clusters related to Wnt-signalling and extracellular matrix proteins. Figure  5 illustrates the potential detrimental and protective mechanisms suggested to be taking place based on the protein expressions observed in this study, leading to oligomerisation and accumulation of α-synuclein in neuronal Lewy body inclusions and, finally, dopaminergic neuronal cell loss.

Oligomerisation and accumulation of α-synuclein in Lewy body inclusions is a key process in the pathophysiology of neuronal synuclein disease, i.e. Parkinson’s disease and dementia with Lewy bodies from aggregation and accumulation, the pathological pathway includes different steps finally leading to the loss of dopaminergic neurons. Protective and detrimental mechanisms influence these processes, based on the differently expressed protein profiles, assessed by targeted mass spectrometry in our study. Detailed information about the proteins can be found in Supplementary Table  2 .

Multivariate analysis shows differences between the proteomes of Parkinson’s disease and controls- Targeted proteomic validation phase (phase I)

Principal component analysis (PCA) demonstrated that the HC and PD groups formed two clusters separate from each other over the first and second principal components (PC), attributed with 23.5% and 13.9% of the model’s total variance, respectively. The iRBD group was situated in the middle of HC and PD, and the OND group varied considerably with no evident clustering, as expected due to the heterogeneity of diseases. The corresponding loadings of PC1 and PC2 demonstrated that those with PD correlated with lower levels of PPP3CB, DKK3, SELE and GRN, and higher levels of most of the other proteins. The loadings plot had a high level of covariation in the expression of the PPP3CB, DKK3 and SELE proteins, which were all downregulated in PD. These proteins correlated negatively with the expression of SERPINs, complement C3 and HPX, which all showed a high degree of covariation, and were upregulated in the PD group. Data are displayed in Supplementary Fig.  2 .

The use of multiplexed protein panels of protein biomarkers for the prediction of de novo Parkinson’s disease- Targeted proteomic validation phase (phase I)

We next applied machine learning to construct a discriminant OPLS-DA model using the PD and HC samples from the validation phase. The samples clustered into two distinct and well-separated classes, and evaluation of the model showed that it was highly significant ( p  = 2.3E −27 permutations p  = <0.001). The proteins with the greatest influence on the class separations were GRN, DKK3, C3, SERPINA3, HPX, SERPINF2, CAPN2, SERPING1 and SELE. We predicted the iRBD samples in the model, which resulted in 13 subjects classified as PD (72%) and five not belonging to either group. None of the iRBD samples were classified as controls. We additionally predicted the OND samples, out of which nine were classified as HC, 12 as PD and 19 were not classified as belonging to either group. The 12 samples predicted as PD did not demonstrate enrichment according to the OND groups. The random distribution of the OND samples between PD and HC indicates that the heterogenous group of OND individuals does not share a distinct protein expression with either the HC or PD groups. The iRBD samples that were classified as PD, and not as HC, strongly suggest a shared proteomic profile between iRBD and the protein expression observed in the newly diagnosed PD patients.

We subsequently explored if the observed protein expressions could be used to build a regression model capable of predicting whether individuals belonged to the PD or HC groups. We identified a panel of proteins that discriminated between PD and HC with 100% accuracy and then constructed a linear support vector classification model and applied recursive feature elimination to pinpoint the most discriminating variables. The data were divided into two parts: one consisting of 70% for model training and one containing 30% for testing. The proportion of PD and control samples was maintained in each part. The number of features included in the model was determined by feature ranking with cross-validated recursive feature elimination in the training dataset. The feature selection resulted in a model with eight predictors: GRN, MASP2, HSPA5, PTGDS, ICAM1, C3, DKK3 and SERPING1. The training data were predicted in the model and resulted in all samples being classified in the correct class. We further constructed receiver operating characteristic (ROC) and precision-recall (PR) curves to illustrate the ability of each protein to distinguish between PD and HC and compared this with the ability of the combined multiplexed protein panel. The combined panel achieved an AUC of 1.0 on both ROC and PR curves. The AUC of the individual predictors ranged from 0.53 to 0.92 in the ROC curve, and from 0.79 to 0.96 in the PR curve (Fig.  6 ). We further evaluated the whole dataset by performing repeated cross-validation with six splits of the data and 40 repetitions. The resulting classification metrics (Supplementary Fig.  3 ) demonstrated average and standard deviation for precision, recall, F1 score, and balanced accuracy score of 0.87 ± 0.09, 0.87 ± 0.08, 0.86 ± 0.09 and 0.82 ± 0.12, respectively, thereby indicating a highly robust classification model. Testing the model’s specificity for PD, we predicted the heterogenous group of OND, resulting in 26 of the 42 samples being classified as PD-like. Prediction of the prodromal iRBD group resulted in 17 of 18 samples being classified as PD-like. We compared the prediction of the OND and iRBD samples between the OPLS-DA and SVM models, finding that most of the samples were classified in the same group in both models (out of the samples with a classification in the OPLS-DA model: 82% in OND and 100% in iRBD). The proportion of iRBD samples classified as PD in our models (72% in the OPLS-DA model and 94% in the SVM model) is in line with clinical evidence based on longitudinal cohort studies, reporting that over 80% of iRBD subjects will develop an advanced NSD with motor impairment and/or cognitive decline 18 . We evaluated the influence of age and sex on the proteins included in the support vector model and found that neither influenced the model’s classification ability (see Supplementary Methods  2 for details).

The model was trained on 70% of the samples to establish the most discriminating features. Applying cross-validated recursive feature elimination, the top predictors were determined as a granulin precursor, mannan-binding lectin-serine peptidase 2, endoplasmic reticulum chaperone-BiP, prostaglandin-H2 d -isomerase, intercellular adhesion molecule-1, complement C3, dickkopf-3 and plasma protease C1 inhibitor. The remaining 30% of samples were predicted in the model and resulted in 100% prediction accuracy. Receiver operating characteristics (ROC) and precision-recall (PR) curves of the individual and combined proteins in the test set demonstrated that the individual proteins achieved ROC area under the curve (AUC) values 0.53–0.92 and PR values 0.79–0.96, while the combined predictors reached an area under the curve = 1.0. Source data are provided as a Source Data file.

Development of a rapid and refined LC-MS/MS method and evaluation of an independent and longitudinal iRBD cohort (Independent replication cohort-phase II)

To evaluate the results from the initial prediction models focusing on at-risk subjects, we developed and refined our targeted and multiplexed proteomic test to quantitate only those proteins that were readily and reliably detectable from the initial targeted proteomic assay ( n  = 32). Next, we analysed an additional set of 146 longitudinal samples from an independent cohort of 54 individuals with iRBD. This cohort was available from continuing recruitment at the same centre and consisted of longitudinally followed iRBD subjects. Deep phenotyping revealed 100% (54/54) had RBD on PSG, 88.9% (48/54) had hyposmia as identified with the Sniffin’ Stick Identification Test, and 91.7 % (22/24) had neuronal α-synuclein positivity as shown by α-synuclein Seed Amplification Assay (SAA) in cerebrospinal fluid (CSF) 19 . Longitudinal follow-up was available for up to 10 years, during which 16 subjects (20%) phenoconverted to either PD ( n  = 11) or dementia with Lewy bodies (DLB; n  = 5). Since only serum samples were available from the independent replication cohort (further details can be found in Supplementary Table  3 ), we investigated how the proteins in our assay correlated between plasma, serum, and CSF and found good correlations between plasma and serum, but poor correlations between these blood matrices and CSF. The limited correlations between blood and CSF proteins correspond to those of other studies comparing the protein expression between plasma/serum and CSF 20 , 21 and underscore that our test does not necessarily reflect a prodromal and PD-specific proteomic signature of the protein expression in the CSF in proximity to the brain, but rather shows an earlier change in the blood protein expression between healthy status and very early PD patients (Details from this comparison can be found in Supplementary Methods  1 and Supplementary Fig.  4 ).

We applied all available longitudinal iRBD samples ( n  = 146) from phase II to the two machine-learning models (OPLS-DA and support vector machine) constructed in phase I (PD vs. HC). The OPLS-DA model, based on all 32 detected proteins, identified 70% of the iRBD samples as PD, while the SVM model, which was based on a panel of eight proteins, identified 79% of the samples as PD. As mentioned above, at the time of analysis, 16 of the 54 subjects in our longitudinal iRBD validation cohort had developed PD/DLB. The earliest correct classification was 7.3 years prior to phenoconversion and the latest was 0.9 years prior to diagnosis (average 3.5 ± 2.4 years). Detailed information can be found in Fig.  7 and Supplementary Methods  3 .

146 new serum samples from individuals diagnosed with iRBD, several with longitudinal follow-up samples, were predicted in the OPLS-DA model. 70% of the samples were predicted as Parkinson’s disease (PD), and 23 of 40 individuals had all their longitudinal samples predicted as PD. In the more refined support vector machine (SVM) model, 79% of the 146 new samples were predicted as PD and 27 of 40 individuals consistently had all their longitudinal samples predicted as PD. Source data are provided as a Source Data file.

The correlation between differentially expressed protein biomarkers and patients’ clinical data in the targeted proteomic validation phase (phase I)

We next evaluated the relationship between proteins and clinical data by correlating the protein expression in PD and HC (from phase I) with clinical scores (Mini-Mental State Examination [MMSE], Hoehn & Yahr stage [H&Y] and UPDRS [Unified Parkinson’s Disease Rating Scale; I–III, and total score]). We found negative correlations for GRN, DKK3, PPP3CB, and SELE with H&Y and UPDRS parts II, III, and total score, possibly indicating a connection between a more severe clinical (especially motor) impairment and lower expression of markers in the Wnt-signalling pathways (DKK3 and PPP3CB). Higher Cystatin C plasma levels correlated with higher numbers in UPDRS part III (motor performance) and UPDRS total score. The same was found for PTGDS plasma levels, which were also negatively correlated with MMSE. The central complement cascade protein, C3, negatively correlated with MMSE, and positively correlated with H&Y, UPDRS part III, and total score. The UPR-regulating protein BiP (HSPA5) correlated negatively with MMSE, and positively with H&Y and UPDRS parts II, III, and total score. The ERAD-associated proteins, HSPAIL and adiponectin, were positively correlated with H&Y, and UPDRS parts II, III, and total score. SERPINs (SERPINA3, SERPINF2 and SERPING1) and hemopexin (HPX) correlated negatively with MMSE and positively with H&Y and UPDRS parts II, III, and total score. In general, the MMSE score was inversely correlated with H&Y stage and UPDRS scores. For detailed information, see Fig.  8 and Table  2 .

The correlation was performed using Spearman’s procedure, and the clustering method was set to average. The clustering metric was Euclidean. The heatmap is coloured by correlation coefficient where red represents positive and blue negative correlations. The proteins are represented by gene names. Detailed information about the protein correlations can be found in Supplementary Table  3 . De novo Parkinson’s disease ( n  = 99) and healthy controls ( n  = 36). MMSE mini-mental state examination, UPDRS unified Parkinson’s disease rating Scale. Source data are provided as a Source Data file.

Comparison of clinical outcomes and measurements in the longitudinal iRBD cohort-Independent replication cohort-phase II

The longitudinal expression in the iRBD samples was evaluated using linear mixed-effects models. Conditional growth models with random slopes and random intercepts between the individuals were constructed. After adjusting the p values for multiple testing by applying the Benjamini–Hochberg (BH) procedure with alpha = 0.05, we found that Butyrylcholinesterase (BCHE) was significantly decreased over the timepoints in the iRBD individuals ( p  = 0.01). We next focused only on the iRBD samples with at least two timepoints and for which PD had consistently been predicted in the SVM model ( n  = 90). This produced comparable results to the initial model with BCHE significantly related with time since baseline ( p  = 0.01), but also TUBA4A was nominally significantly increased ( p  = 0.04) although not passing the BH FDR threshold. The modelling also demonstrated that the clinical measurements H&Y ( p  = 0.02), UPDRS I–III ( p  = 0.02), and UPDRS I and III ( p  = 0.03 and 0.03, respectively), were significantly related to the time since baseline in the iRBD group post multiple testing correction. PD non-motor symptoms, as measured on the PD NMS sum score, were strongly correlated with longitudinal motor progression ( p  = 5E −8 ). Similarly, the questionnaire for quality of life PDQ-39’s mean values also correlated with longitudinal motor progression ( p  = 0.005). From available routine blood values, cholesterol was associated with longitudinal timepoints ( p  = 0.02). Details can be found in Supplementary Table  4 . Correlating the clinical measurements with the targeted proteomic data, we applied Spearman’s correlation and found that cholesterol was positively correlated with six of the identified proteins (Supplementary Table  5 ), including HSPA8, APOE and MASP2 ( p  = 5E −9 , 0.0003 and 0.003, respectively). Also significantly correlated, but to a lesser degree and not passing the BH FDR threshold, were the PD NMS sum which correlated negatively with TUBA4A (p unadjusted = 0.01) and the PDQ-39 mean values, which correlated negatively with CST3 and PTGDS ( p unadjusted = 0.03 and 0.05, respectively).

PD has emerged as the world’s fastest-growing neurodegenerative disorder and currently affects close to 10 million people worldwide. Consequently, there is an urgent need for disease-modifying and prevention strategies 22 , 23 . The development of such strategies is hampered by two limitations: there are major gaps in our understanding of the earliest events in the molecular pathophysiology of PD, and we lack reliable and objective biomarkers and tests in easily accessible bio-fluids. We, therefore, need biomarkers that can identify PD earlier, preferably a significant time before an individual develops significant neuronal loss and disabling motor and/or cognitive disease. Such biomarkers would advance population-based screenings to identify individuals at risk and who could be included in upcoming prevention trials.

In the last years, CSF SAA emerged as the most specific indicator for NSD, in prodromal stages like iRBD, with an impressively high sensitivity and specificity of up to 74 and 93%, respectively, across various cohorts 9 , 24 . Despite the many questions surrounding SAA that need to be answered, including the ultimate understanding of its functionality, it is a true milestone for advancing prevention trials. It is, however, hampered by having only been shown to be robust in CSF and by the slow development and high variability of SAA in peripheral blood 25 , as well as by the lack of quantification capabilities. An easier and more accessible biofluid test would enable screening large population-based cohorts for at-risk status to develop an NSD. Therefore, the identification of additional biomarkers is needed, as is further knowledge of the biomarkers and pathways of the underlying pathophysiology (e.g. inflammation) during the earliest stage of NSD.

Other emerging multiplex technologies are increasingly used to identify individual proteomic biomarkers. However, these techniques are not true proteomic or ‘eyes open’ methods, as they rely on selected large panels of specific antibodies/and other (e.g. aptamer)-based assay technologies. These techniques, although useful, have not provided consistent results 3 , 26 . Proteomics using mass spectrometry measures all expressed proteins in an unbiased fashion as opposed to those selectively included in a panel that also includes variability due to cross-reactivity. Therefore, proteomic screening using mass spectrometry-based techniques is much more likely to identify pathways or biomarkers and provides more meaningful insights into the disease mechanisms involved in PD. We found a discrepancy between the detected markers during the discovery and the targeted phases. This is a known phenomenon in biomarker translation 27 that is also reflected in the low number of biomarkers having received FDA approval 28 . We addressed this by using previously reported successful improvement strategies in proteomic approaches, namely by refining our panel, reducing the number of markers, and increasing the sample size 29 . Furthermore, the validation of potential biomarkers was performed on a second and different type of mass spectrometer (triple quadrupole), which has the advantage of being available in all large hospitals.

Targeted MS has been previously applied in PD, including by the current authors, but the biological fluid used in the majority of studies is CSF 30 and not peripheral fluids such as blood. Here we demonstrate that even with a very low required volume of plasma/serum (10 µl) targeted proteomic is feasible.

The targeted proteomic assay presented here was developed from proteins identified in an unbiased discovery study, from our previous research, and from the literature. It included several inflammatory markers, Wnt-signalling members, and proteins indicative of protein misfolding. When analysing PD, OND, iRBD and HC in the targeted proteomic validation phase, we identified and confirmed 23 distinct and differentially expressed proteins between PD and HC. Our analysis moreover demonstrated that iRBD possesses a significantly different protein profile compared to HC, consisting of decreased levels of GRN and MASP2 and increased levels of the complement factor C3 and SERPINs (SERPINA3, SERPINF2 and SERPING1), thus indicating early involvement of inflammatory pathways in the initial pathophysiological steps of PD. Comparing these results to previous findings by our and other groups 8 , 31 highlights the link between these proteins and the pathways of complement activation, coagulation cascades, and Wnt-signalling.

By applying machine-learning models, we classified and separated de novo PD or control samples with 100% accuracy based on the expression of eight proteins (GRN, MASP2, HSPA5, PTGDS, ICAM1, C3, DKK3 and SERPING1).

With an independent validation, we added (a) a larger sample set and (b) longitudinal samples from the most interesting subgroup with 54 iRBD subjects and a total of 146 serum samples. We were able to validate our previous panel with a high prediction rate (79%) of these individuals as seen in PD in the targeted approach. Interestingly, the biomarker panel itself did not correlate with longitudinal expression but remained robust after the initial classification of iRBD. So far, 16 of the 54 iRBD subjects converted to PD/DLB (stage 3 NSD). Out of these samples, the SVM model predicted ten individuals with all their timepoints classified as PD, and of the 11 iRBD subjects who converted to PD/DLB, eight were identified as PD by the proteome analysis. Our panel, therefore, identified a PD-specific change in blood up to 7 years before the development of the stage 3 NSD.

The main shortcoming with many previously explored PD biomarkers is weak or no correlation with clinical progression data. So far, outcome measures in clinical trials are primarily based on motor progression, often by a clinical rating scale such as the UPDRS and/or wearable technologies. More objective biomarkers correlating with or reflecting the progression of the pathophysiology and clinical symptoms would be of the utmost importance. We, therefore, calculated correlations with clinical parameters and identified an association with multiple markers, including DKK3, PPP3CB and C3, indicating downregulation of Wnt-signalling pathways. Increased activity of the complement cascade correlated with higher scores in symptom severity (UPDRS part III and total score) and lower scores in cognitive performance (MMSE).

Protein (i.e. α-synuclein) misfolding is a well-known component of PD pathology and is believed to be the key factor behind Lewy body formation 32 . The transport of excessive amounts of misfolded proteins or increased folding cycles can induce ER stress. A cellular defence mechanism to alleviate ER stress is the unfolded protein response (UPR) reducing ER protein influx and increasing protein folding capacity 33 . The UPR is mainly activated by BiP-bound misfolded proteins 34 . The higher expressed markers HSPA5 (UPR-regulating protein BiP) and HSPA1L in our plasma samples of early PD indicate ER stress as a significant factor in the disease process and has been previously linked to PD in both mouse models and brain tissue studies 35 , 36 .

As mentioned by other groups and confirmed in our results, increasing evidence suggests inflammation is a specific feature in early PD. Complement activation has been associated with the formation of α-synuclein and Lewy bodies in PD and deposits of the complement factors iC3b and C9 have been found in Lewy bodies 37 . C3 is a central molecule in the complement cascade and was highly upregulated in blood in both PD and both independent iRBD sample sets analysed in this study. This upregulation in the earliest phase of motor PD (stage 3 NSD), and even in the prodromal phase (stage 2 NSD), clearly indicates inflammation as an early, if not the initial, event in PD neurodegeneration. Complement C3 levels correlated positively with indicators of motor dysfunction (H&Y stage and UPDRS)—indicating a direct connection between high plasma levels of inflammatory proteins and motor symptoms—and negatively with cognitive decline, here with the MMSE.

The protein Mannan-binding serine peptidase 2 (MASP2), an initiator of the lectin part of the complement cascade, was significantly downregulated in PD and iRBD. MASP1 and MASP2 proteins are inhibited by plasma protease C1 inhibitor SERPING1 in the lectin pathway, with SERPING1 modulating the complement cascade as it belongs to the SERPIN family of acute phase proteins 38 . In experimental PD mice models, increased SERPING1 levels are associated with dopaminergic cell death 39 . Acting as a serine/cysteine proteinase inhibitor, SERPING1 can increase serine levels, which could also affect αSyn phosphorylation. This can play a crucial role in PD pathology, as almost 90% of αSyn in Lewy bodies is phosphorylated on Serine129 40 , 41 . We identified increased SERPING1 plasma levels in both PD and iRBD in our analysis (compared to HC), thus contributing to conditions with increased αSyn phosphorylation, consecutive aggregation, Lewy body formation, and finally degeneration of dopaminergic neurons. Furthermore, we observed a strong correlation of SERPING1 plasma levels with UPDRS II, III and total score, as a direct measure of dopaminergic cell loss 39 .

Alpha-2-antiplasmin (SERPINF2) was also significantly upregulated in PD and iRBD. SERPINF2 is a major regulator of the clotting pathway, acting as an inhibitor of plasmin, a serine protease formed upon the proteolytic cleavage of its precursor, plasminogen, by tissue-type plasminogen activator (t-PA) or by the urokinase-type plasminogen activator (u-PA). Plasmin has been reported to cleave and degrade extracellular and aggregated αSyn 42 . Recently, we showed that activation of the plasminogen/plasmin system is decreased in PD, indicated by decreased plasma levels of uPA and its corresponding receptor uPAR, while t-PA was associated with faster disease progression 8 . The upregulation of SERPINF2 observed here is another indicator of decreased plasmin activity. Alpha-1-antichymotrypsin (SERPINA3), a third member of the SERPIN family, was also upregulated in the PD subjects. In the CNS, the primary source of SERPINA3 is astrocytes, where its expression is upregulated by various inflammatory receptor complexes 38 .

Overall, independent upregulation of these three members of the SERPIN (SERPING1, SERPINF2, SERPINA3) family is also indicative of increased inflammatory activity, combined with less activation of the plasmin system, and correlation with motor and non-motor symptom severity. In addition, a strong downregulation of progranulin ( GRN ) was detected, indicating a potential loss of neuroprotection and increased susceptibility to neuroinflammation. GRN may act as a neurotrophic factor, promoting neuronal survival and modulating lysosomal function. Loss-of-function mutations in the GRN gene are a cause of frontotemporal dementia and familial DLB. GRN gene variants are also known to increase the risk of developing Alzheimer’s disease (AD) and PD 43 . The main characteristics of neurodegeneration related to GRN are TDP43(-Transactive response DNA binding protein 43) inclusions, but Lewy body pathology is also very common. Loss of progranulin has further been linked to increased production of pro-inflammatory species such as tumour necrosis factor (TNF) and IL-6 in microglia 15 . A study in mice showed that Grn -/- mice had elevated levels of complement proteins, including C3, even before the onset of neurodegeneration 44 . Additionally, previous studies have found GRN downregulated in serum samples of advanced PD compared to AD and healthy individuals 45 .

As a possible compensatory reaction to the described increased inflammatory markers, the levels of Prostaglandin-H 2 d -isomerase (PTGDS)/Prostaglandin-D 2 synthase (PGDS2), better known as β-trace protein, were upregulated. PDGDS is an important brain enzyme producing prostaglandin D2 (PGD2), which has a neuroprotective and anti-inflammatory function. The upregulation reported here could be a reaction to the amount of neuronal cell loss, which is also seen in the significant correlation with the clinical motor and cognitive scales (see below). Furthermore, β-trace protein is a marker for CSF and is used to identify the fluid in clinical routine diagnostics, thus helping detect CSF leakage 46 . Increased plasma levels could be indicative of a disrupted blood–brain barrier (BBB), often discussed in PD pathology 47 and demonstrated in our cohorts.

Our study shows that the Wnt-related proteins DKK3 and PPP3CB are strongly downregulated in de novo PD. DKK3 is an activator of the canonical Wnt/β-catenin branch and PPP3CB is a component of the non-canonical Wnt/Ca 2+ signalling pathway. Wnts are secreted, cysteine-rich glycoproteins that act as ligands to locally stimulate receptor-mediated signal transduction of the Wnt-pathway 48 . Wnt-signalling is crucial for the development and maintenance of dopaminergic neurons 49 , shows protective effects on midbrain dopaminergic neurons 50 , and seems to be involved in the maintenance of the BBB 48 , 51 . Wnt-ligands and agonists trigger a “Wnt-On” stage, characterised by neuronal plasticity and protection, while the opposite “Wnt-Off” stage, potentially leading to neurodegeneration, triggered by the phosphorylation activity of glycogen synthetase kinase-3β (GSK-3beta) 50 , 52 . Wnt-inhibitors are separated into secreted Frizzled-related proteins (sFRP) and Dickkopf proteins (DKK). DKK1, DKK2 and DKK4 act as antagonists, while DKK3 is an agonist and activator 53 . Adult neurogenesis is primarily governed by canonical Wnt/β-catenin signaling 54 and downregulation of Wnt-signalling promotes dysfunction and/or death of dopaminergic neurons. Restoration of dopaminergic neurons was shown in mice where β-catenin was activated in situ 52 and neural stem cells transplanted to the substantia nigra of medically PD-induced mice induced re-expression of Wnt1 and repair dopaminergic neurons 55 . DKK3 and PPP3CB were strongly downregulated in de novo PD, removing an important line of defence against the detrimental loss of dopaminergic neurons. The downregulation of the Wnt-signalling pathways was further correlated with higher motor scores (UDPRS and H&Y stages).

Wnt-signalling in PD is not only promising as a potential biomarker. In oncology, drugs can modify Wnt-pathways, which is of interest to the PD field 56 . Some substances show no BBB-permeability. As a disrupted BBB seems to be apparent in PD, these drugs may be effective. Furthermore, these substances are also relevant for PD treatment: research points towards a peripheral starting point of PD and future therapies should be administered as early as possible 57 . These promising substances include DKK- as well as GSK inhibitors, but to date, no drugs targeting the Wnt-signalling pathways have been effectively tested in clinical trials, including in those with neurodegenerative diseases. Progress and clinical trials are urgently needed here.

The transfer of multi-omics analysis to clinically meaningful results that directly impact future drug trial planning and biomarker validation, depends fundamentally on correlating these results and altered pathway regulations with established clinical scores. The markers we analysed in our targeted mass spectrometry panel did not only show different expression patterns between HC, PD, and in both of our independent iRBD sample sets, but most of the markers also robustly correlated with important clinical scores (UPDRS and MMSE, see Table  1 ). Cognitive decline correlated negatively with the SERPINs and complement factor C3. The burden of motor and non-motor symptoms and overall symptom severity rated by UPDRS and its subscores correlated positively with the SERPINs, Complement C3, and negatively with DKK3, GRN, and SELE. So, increased inflammatory activity and downregulation of Wnt-signalling seem to strongly affect the clinical picture of PD subjects.

The iRBD subjects showed decreased levels of BCHE over time compared to controls. BCHE has been reported as decreased in serum samples of PD with cognitive impairment 58 . Validation of this easily assessable marker in serum is needed to evaluate its predictive potential.

While we did not find significant differences when we compared paired serum and plasma samples; the analysis of paired samples of plasma/serum and CSF only correlated weakly with the marker concentrations in these peripheral and central compartments. This discrepancy has been reported by several groups 20 , 21 . One reason is that mass spectrometry-based proteome analysis is always biased towards quantification and detection of the most abundant proteins in each sample matrix, and the total protein concentrations in human plasma/serum are more than two orders of magnitude higher than that in CSF. Further, the regulatory function of the blood–brain barrier seems to play a different role for different proteins, as some, like c-reactive protein, show a strong correlation between CSF and plasma, but most of the proteins do not. CSF and blood proteome show complex dynamics influenced by multiple and still mostly unknown factors. The protein shift in samples with a known BBB dysfunction (determined by the CSF/serum albumin index or the CSF/plasma ratio) can not be determined for individual proteins nor the dysfunction be localised by mass spectrometry 20 .

Our model could not correctly predict phenoconversion in all cases. The reasons for this can be varied: The proteome pattern changes over time and the period between sampling and phenconversion may play a role. The three PD phenoconverters that were not predicted as PD neither differ clinically or demographically from the phenoconverters, nor from the non-phenoconverters. iRBD diagnosis in our study was confirmed by vPSG, supported by a high percentage of additional measurements including hyposmia and CSF SAA positivity. Therefore, even those iRBD cases that do not show the PD-proteome pattern still have a high-risk constellation of converting to PD/DLB on three different levels (PSG, olfaction, and SAA). Continuing further longitudinal follow-up of these subjects will elucidate our understanding of when and potentially why conversion occurs/does not occur. It is known that around 80% of iRBD subjects develop NSD, i.e. PD/DLB, with a rate of 6% per year, as shown in a multicenter cohort including ours 59 . To a lesser extent, iRBD subjects develop the intracytoplasmic glial α-synuclein aggregation disorder Multiple Systems Atrophy (MSA) 59 , 60 . Although RBD is common in MSA (summary prevalence of 73% 61 ), none of our iRBD subjects have, as yet converted to MSA. Recruiting and following large longitudinal at-risk cohorts is, therefore, very important and future studies will not only identify biomarkers for phenoconversion from stage 1 or 2 to eventually stage 3 NSD or MSA, but also identify the many possible factors of resilience (including genetics, etc.) of NON-conversion which will be as, if not more important than identifying indicators for phenoconversion. Both direction progression biomarkers from stage 1 and 2 cohorts will have tremendous implications for future neuroprevention trials as phenoconversion itself is (due to the low annual rate) unlikely to be an outcome measure.

A significant strength of our biomarker discovery to translation pipeline is that it allows for the developed test to be easily validated and translated to any clinical laboratory equipped with a tandem LC-MS instrument. One advantage of using triple quadrupole platforms is that additional and better biomarkers can easily be augmented into the test described in this manuscript. Thus, any test could be refined and optimised over time with very little modification to the assay as additional biomarkers are discovered. Clinical testing for neurological disorders is limited to the use of a selected few well-characterised individual markers and translating biomarkers to eventual clinical application is notoriously challenging. The power of using multiplexed biomarker technologies with machine learning enables biomarkers to be evaluated in context with other markers of pathological events, thereby creating a ‘disease profile’ as opposed to individual markers. This approach opens the biomarker discovery field for many disorders and increases the specificity and sensitivity of testing, as demonstrated in this study. The combination of multiplexed analysis of biomarker panels analysed on triple quadrupole platforms can advance biomarker translation to clinical application; this mass spectral technology is already embedded in many clinical diagnostics labs for routine small molecule analyses.

Our peripheral blood protein pattern for PD helps not only to classify but also to predict the earliest stage of the disease. We find differently expressed proteins in pre-motor iRBD and early motor stages of the disease compared to HC. Multiple markers also correlated with the progression of motor and non-motors symptoms. Thus, our blood panel can also identify subjects at risk (stage 2) to develop PD up to 7 years before advancing to motor stage 3. Next steps will be the independent validation in other (and even earlier) non-motor cohorts, e.g. in subjects with hyposmia also at-risk for PD 62 and in our population-based Healthy Brain Ageing cohort in Kassel 63 . It would further be interesting to evaluate the predictive potential of these identified markers with continuing clinical follow-up and together with other established PD progression markers like serum neurofilament light chain 5 and dopamine transporter imaging in a longitudinal analysis.

Our work was predominantly focused on the similarities between PD and iRBD. The authors are unaware of any study that has analysed longitudinally collected samples and prodromal cohorts, including iRBD and phenoconverters. Future work would include (i) validation of our findings in independent cohorts consisting of iRBD and other at-risk subjects for the synuclein aggregation disorders in neurons (PD, DLB) and oligodendrocytes (MSA), (ii) refinement of the panels of biomarkers developed in this study including sensitivity and technical performance, (iii) and using the pipeline described in this manuscript, the identification and validation of additional biomarkers that could distinguish between the different clinical syndromes with the ultimate goal of identifying progression biomarkers as outcome measures for prevention trials.

In summary, instead of single biomarkers, in a univariate approach, we have created a pipeline using a targeted proteomic test of a multiplexed panel of proteins, together with machine learning. This powerful combination of multiple well-selected biomarkers with state-of-the-art machine-learning bioinformatics, allowed us to use a panel of eight biomarkers that could distinguish early PD from HC. This biomarker panel provided a distinct signature of protective and detrimental mechanisms, finally triggering oxidative stress and neuroinflammation, leading to α-synuclein aggregation and LB formation. Moreover, this signature was already present in the prodromal non-motor (stage 2 NSD), up to 7 years before the development of motor/cognitive symptoms (stage 3), supporting the high specificity of iRBD and its high conversion rate to PD/DLB 18 . Most importantly, this blood panel can, in the future, upon further validation help identify subjects at risk of developing PD/DLB and stratify them for upcoming prevention trials.

Patient cohorts and sample collection and processing

Our research complies with all relevant ethical regulations. Institutional review board statements were obtained from the University Medical Centre in Goettingen, Germany, Approval No. 9/7/04 and 36/7/02. The study was conducted according to the Declaration of Helsinki, and all participants gave written informed consent. All plasma, serum and CSF samples from subjects were selected from known cohorts using identical sample processing protocols designed by the Movement Disorder Center Paracelsus-Elena-Clinic.

Patients with de novo PD were diagnosed according to the UK Brain Bank Criteria, without PD-specific medication. Diagnosis in all subjects was supported by (1) a positive (i.e. >30% improvement of UPDRS III after 250 mg of levodopa) acute levodopa challenge testing 64 in all PD subjects, (2) hyposmia by smell identification test (Sniffin Sticks 65 ) in all PD subjects and (3) 1.5-tesla Magnetic Resonance Imaging (MRI) without significant abnormalities or evidence for other diseases in all but three subjects who were excluded (due to significant vascular lesions or evidence for hydrocephalus) from the analysis. Participants not fulfilling the above criteria and meeting criteria for other neurological disorders were named as other neurological disorders (OND). OND consists of subjects with vascular parkinsonism ( n  = 10), essential tremor ( n  = 7), progressive supranuclear palsy; PSP ( n  = 7), multiple system atrophy; MSA ( n  = 3), corticobasal syndrome; CBS ( n  = 2), DLB ( n  = 2), drug-induced tremor ( n  = 2), dystonic tremor ( n  = 2), restless legs syndrome ( n  = 1), hemifacial spasm ( n  = 1), motoneuron disease ( n  = 1), amyotrophic shoulder neuralgia ( n  = 1), and Alzheimer’s disease ( n  = 1). The initial exploratory cohort consisted of ten PD subjects (8 men, mean age 67.1 ± 10.6) and ten healthy controls (5 men, mean age 65,7, SD ± 8,6.). For details, see Supplementary Table  3 ). The validation cohort included 99 PD subjects (49 men, mean age 66,1, SD ± 10,8), 36 healthy controls (20 men, mean age 63.7, SD ± 6,5.) and the described (see above) 41 OND subjects (29 men, mean age 70, SD ± 8.9. For details, see Supplementary Table  1 . The prodromal validation cohort consisted of 54 patients with iRBD (27 men, mean age 67.5, SD ± 8.1, for details, see Supplementary Table  4 ). RBD was diagnosed with two nights of state-of-the-art vPSG. Samples from HC were selected from the DeNoPa cohort 10 and matched for age and sex with the PD patients, had to be between 40 and 85 years old, without any active known/treated CNS condition, and with a negative family history of idiopathic PD. Antipsychotic drugs were an exclusion criterion. The provided data for sex are based on self-report.

The paired sample analysis of CSF, plasma and serum was applied in samples from subjects with OND 7 men, mean age 74 years, SD ± 7; diagnosis: four Alzheimer’s disease, three vascular Parkinsonism, one essential tremor, one multiple system atrophy one progressive supranuclear palsy).

Clinical assessments included the UPDRS subscores (parts I–III), the sum (UPDRS total score), and cognitive screening using the MMSE 10 .

Plasma and serum samples for both cohorts were collected in the morning under fasting conditions using Monovette tubes (Sarstedt, Nümbrecht, Germany) for EDTA plasma and serum collection by venipuncture. Tubes were centrifuged at 2500× g at room temperature (20 °C) for 10  min and aliquoted and frozen within 30 min of collection at −80 °C until analysis 10 , 66 . Single- use aliquots were used for all analyses presented here. For further details, we refer to the following publication 67 .

CSF was collected in polypropylene tubes (Sarstedt, Nümbrecht, Germany) directly after the plasma collection by lumbar puncture in the sitting position. Tubes were centrifuged at 2500× g at room temperature (20 °C) for 10 min and aliquoted and frozen within 30 min after collection at −80 °C until analysis. Before centrifugation, white and red blood cell counts in CSF were determined manually 10 , 66 . CSF β-amyloid 1–42, total tau protein (t-tau), phosphorylated tau protein (p-tau181) and neurofilament light chains (NFL) concentrations were measured by board-certified laboratory technicians, who were blinded to clinical data, using commercially available INNOTEST ELISA kits for the tau and Aβ markers (Fujirebio Europe, Ghent, Belgium) and the UmanDiagnostics NF-light® assay (UmanDiagnostics, Umeå, Sweden) for NFL. Total protein and albumin levels were measured by nephelometry (Dade Behring/Siemens Healthcare Diagnostics) 66 .

For the α-synuclein seeding aggregation assay (αSyn-SAA) the CSF samples were blindly analyzed in triplicate (40 μL/well) in a reaction mixture (0.3 mg/mL recombinant α-Syn (Amprion [California, USA]; catalogue number S2020), 100 mM piperazine- N , N ′-bis(2-ethanesulfonic acid) (PIPES) pH 6.50, 500 mM sodium chloride, 10 μM thioflavin T, and one bovine serum albumin (BSA)–blocked 2.4-mm silicon nitride G3 bead (Tsubaki-Nakashima [Georgia, USA]). Beads were blocked in 1% BSA 100 mM PIPES pH 6.50 and washed with 100 mM PIPES pH 6.50. The assay was performed in 96-well plates (Costar [New York, USA], catalogue number 3916) using a FLUOstar Omega fluorometer (BMG [Ortenberg, Germany]). Plates were orbitally shaken (800 rpm for 1 min every 29 min at 37 °C). Results from the triplicates were considered input for a three-output probabilistic algorithm with sample labelling as “positive,” “negative,” or “inconclusive”, based on the parameters: Maximum fluorescence (Fmax), time to reach 50% Fmax (T50), slope, and the coefficient of determination for the fitting were calculated for each replicate using a sigmoidal equation available in Mars data analysis software (BMG). The time to reach the 5000 relative fluorescence units (RFU) threshold (TTT) was calculated with a user-defined equation in Mars 19 .

Discovery plasma proteomics (phase 0)

In the mass spectrometry-based proteomic discovery analysis of plasma, we depleted the control and de novo PD samples from the twelve most abundant plasma proteins using Pierce Top12 columns (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacturer’s instructions. The depleted samples were freeze-dried before the addition of 20 µL of lysis buffer (100 mM Tris pH 7.8, 6 M urea, 2 M thiourea, and 2% ASB-14). The samples were shaken on an orbital shaker for 60 min at 1500 rpm. To break disulphide bonds, 45 µg DTE was added, and the samples were incubated for 60 min. To prevent disulphide bonds from reforming, 108 µg IAA was added, and the samples were incubated for 45 min covered in light. About 165 µL MilliQ water was added to dilute the concentration of urea and 1 µg trypsin gold (Promega, Mannheim, Germany) was added before 16 h of incubation at +37 °C to digest the proteins into peptides. To purify the peptides, solid phase extraction was performed using 100 mg C18 cartridges (Biotage, Uppsala, Sweden). The cartridges were washed with two 1 mL aliquots of 60% ACN, and 0.1% TFA before equilibration by two 1 mL aliquots of 0.1% TFA. The concentration of TFA in the samples was adjusted to 0.1%. The samples were loaded, and the flow-through was captured and re-applied. Salts were washed away from the bound peptides by two 1 mL aliquots of 0.1% TFA. The peptides were eluted by two 250 µL aliquots of 60% ACN, and 0.1% TFA. Solvents were evaporated using a vacuum concentrator. The samples were re-suspended in 50 µL 3% ACN, 0.1% FA prior to analysis. About 4 µL was injected into a 2D-NanoAquity liquid chromatography system (Waters, Manchester, UK). All samples were fractionated online into ten fractions over 12 h. The mobile phase in the first chromatographic system consisted of A1: 10 mM ammonium hydroxide titrated to pH 9 and B1: acetonitrile. The second chromatographic system’s mobile phase was A2: 5% dimethylsulfoxide (DMSO) + 0.1% formic acid, B2: acetonitrile with 5% DMSO + 0.1% formic acid. 2D-liquid chromatography fractionation was performed by loading the sample onto a 300 µm × 50 mm, 5 µm Peptide BEH C18 column (Waters). The peptides were eluted from the first column at a flow rate of 2 µL/min. The initial condition of the gradient elution was 3% B, held over 0.5 minutes before linearly increasing the proportion of organic solvent B, fraction per fraction over 0.5 min. The conditions thereafter remained static for 4 min before returning to the initial conditions over 0.5 min and equilibration prior to the next elution for 10 min. The eluted peptides from the first-dimensional column were loaded into a 180 µm × 20 mm, 5 µm Symmetry C18 trap column (Waters) before entering the analytical column, a 75 µm × 150 mm, 1.7 µm Peptide BEH C18 (Waters). The column temperature was +45 °C. The gradient elution applied to the analytical column started at 3% B and was linearly increased to 40% B over 40 min after which it was increased to 85% B over 2 min and washed for 2 min before returning to initial conditions over 2 min followed by equilibration for 15 min before the subsequent injection. The eluted peptides were detected using a Synapt-G2-S i (Waters) equipped with a nano-electrospray ion source. Data were acquired in positive MS E mode from 0 to 60 min within the m/z range 50−2000. The capillary voltage was set to 3 kV and the source temperature to +100 °C. The desolvation gas consisted of nitrogen with a flow rate of 50 L/h, and the desolvation temperature was set to +200 °C. The purge and desolvation gas consisted of nitrogen, operated at a flow rate of 600 mL/h and 600 L/h, respectively. The gas in the IMS cell was helium, with a flow rate of 90 mL/h. The low energy acquisition was performed by applying a constant collision energy of 4 V with a 1-s scan time. High energy acquisition was performed by applying a collision energy ramp, from 15 to 40 V, and the scan time was 1 s. The lock mass consisted of 500 fmol/µL [glu1]-fibrinopeptide B, continuously infused at a flow rate of 0.3 µL/min and acquired every 30 s. The doubly charged precursor ion, m/z 785.8426, was utilised for mass correction. After acquisition, data were imported to Progenesis QI for proteomics (Waters), and the individual fractions were processed before all results were merged into one experiment. The Ion Accounting workflow was utilised, with UniProt Canonical Human Proteome as a database (build 2016). The digestion enzyme was set as trypsin. Carbamidomethyl on cysteines was set as a fixed modification; deamidation of glutamine and asparagine, and oxidation of tryptophan and pyrrolidone carboxylic acid on the N-terminus were set as variable modifications. The identification tolerance was restricted to at least two fragments per peptide, three fragments per protein, and one peptide per protein. A FDR of 4% or less was accepted. The resulting identifications and intensities were exported and variables with a confidence score less than 15 and only one unique peptide were filtered out.

Targeted plasma proteomics (phase I)

The peptides included in the targeted assay were selected from several proteomic screening studies in which we analysed plasma, serum, urine, and CSF in ageing, PD and AD. The analytical method is described by ref. 17 . Furthermore, due to the suggested involvement of inflammation in neurodegenerative diseases, several known pro- and anti-inflammatory proteins identified from the literature were included in the multiplexed assay. The final panel consisted of 121 proteins (Supplementary Table  2 ), out of which a number were measured with two peptides, leading to a total of 167 unique peptides. When possible, the peptides were chosen to have an amino acid sequence length between 7 and 20. The amino acid sequences were confirmed to be unique to the proteins by using the Basic Local Alignment Search Tool (BLAST) provided by UniProt 68 . Synthetic peptide standards were purchased from GenScript (Amsterdam, Netherlands). To establish the most optimal transitions, repeated injections of 1 pmol peptide standard onto a Waters Acquity ultra-performance liquid chromatography (UPLC) system coupled to a Waters Xevo-TQ-S triple quadrupole MS were performed. The most high-abundant precursor-to-product ion transitions and their optimal collision energies were determined manually or using Skyline 69 . Detection was performed in positive ESI mode. The capillary voltage was set to 2.8 kV, the source temperature to 150 °C, the desolvation temperature to 600 °C, and the cone gas and desolvation gas flows to 150 and 1000 L/h, respectively. The collision gas consisted of nitrogen and was set to 0.15 mL/min. The nebuliser operated at 7 bar. Two transitions were chosen, one quantifier for relative concentration determination and one qualifier for identification, totally rendering 334 analyte transitions. Cone and collision energies varied depending on the optimal settings for each peptide. Each peptide was measured with a minimum of 12 points per peak and a dwell time of 10 ms or more to ensure adequate data acquisition. The optimised transitions were distributed over two multiple reaction monitoring (MRM) methods, always keeping the quantifier and qualifier for each peptide in the same MRM segment. Plasma, serum, and CSF samples were depleted from albumin and IgG using Pierce Top2 cartridges (Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer’s instructions. About 150 µg whole protein yeast enolase (ENO1) was added to the cartridges as an internal standard to account for digestion efficiency. Digestion was performed as described above. Solid phase extraction was carried out on BondElute 100 mg C18 96-well plates (Agilent, Santa Clara, USA) using the same methodology as in the preparation of untargeted proteomic analyses. Quality control samples were prepared from acetone-precipitated plasma, digested and solid phase extracted. Calibration curves ranging from 0 to 1 pmol/μL were constructed in blank and matrix by spiking increasing amounts of peptides into blank and QC samples. Before analysis, the samples were reconstituted in 30 µL 3% ACN, 0.1% FA containing 0.1 μM heavy isotope labelled peptides from the following proteins (annotated by gene name): ALDOA, C3, GSTO1, RSU1 and TSP1. About 5 µL were injected. The peptides were separated and detected on an Acquity UPLC system coupled to a Xevo-TQ-S triple quadrupole mass spectrometer (Waters, Manchester, UK). Chromatographic separation of the peptides was performed using a 1 × 100 mm, 1.7 μm ACQUITY UPLC Peptide CSH C18 column (Waters).

The mobile phase consisted of A: 0.1% formic acid and B: 0.1% formic acid in acetonitrile pumped at a flow rate of 0.2 mL/min. The column temperature was set to +55 °C. The initial mobile phase composition was 3% B, which was kept static for 0.8 min before initialising the linear gradient, running for 7.6 min to 25% B, eluting most of the peptides. B was thereafter linearly increased to 80% over 0.5 min and held for 1.9 min, eluting the most apolar peptides and washing the column before returning to the initial conditions over 0.1 minutes followed by equilibration for 6 min prior to the subsequent injection. Two subsequent injections of each sample were performed, each paired with one of the two MRM acquisition methods.

After acquisition, peak-picking and integration were performed using TargetLynx (version 4.1, Waters) or an in-house application ('mrmIntegrate') written in Python (version 3.8). mrmIntegrate is publicly available to download via the GitHub repository https://github.com/jchallqvist/mrmIntegrate . The application takes text files as input (.raw files are transformed into text files through the application 'MSConvert' from ProteoWizard 70 and applies a LOWESS filter over five points of the chromatogram. The integration method to produce areas under the curve is trapezoidal integration. The application enables retention time alignment and simultaneous integration of the same transition for all samples. Peptide peaks were identified by the blank and matrix calibration curves. The integrated peak areas were exported to Microsoft Excel, where first, the ratio between quantifier and qualifier peak areas were evaluated to ensure that the correct peaks had been integrated. The digestion efficiency was evaluated by monitoring the presence of baker’s yeast ENO1 in the samples, all samples without a signal were excluded from further analysis. After the initial quality assessment, the quantifier area was divided by the area of one of the internal standards, ALDOA or GSTO1 to yield a ratio used for the determination of relative concentrations. Any compound that also showed an intensity signal in the blank samples had the blank signal subtracted from the analyte peak intensity. Pooled plasma quality control samples were additionally evaluated to assess the robustness of the run.

Refined LC-MS/MS method (phase II)

The rapid and refined targeted proteomics LC-MS/MS method contained only peptides from the 31 proteins observed in the original targeted proteomics method (121 proteins). We utilised a Waters Acquity (UPLC) system coupled to a Waters Xevo-TQ-XS triple quadrupole operating in positive ESI mode. The column was an ACQUITY Premier Peptide BEH C18, 300 Å, 1.7 µm, maintained at 40 °C. The mobile phase was A: 0.1% formic acid in water, and B: 0.1% formic acid in acetonitrile. The gradient elution profile was initiated with 5% B and held for 0.25 min before linearly increasing to 40% B over 9.75 min to elute and separate the peptides. The column was washed for 1.6 min with 85% B before returning to the initial conditions and equilibrating for 0.4 min. The flow rate was 0.6 mL/min. The settings of the mass spectrometer and the peak-picking method were the same as described in the prior section. Baker’s yeast ENO1 was utilised to monitor digestion efficiency and as an internal standard.

Statistical methods

Most of the statistical analyses were performed in Python (version 3.8.5). The untargeted and targeted datasets were inspected for outliers and instrumental drift using principal component analysis (PCA) and orthogonal projection to latent variables (OPLS) in SIMCA, version 17 (Umetrics Sartorius Stedim, Umeå, Sweden). Outliers exceeding ten median deviations from each variable’s median were excluded. Instrumental drift was corrected by applying a non-parametric LOWESS filter from statsmodels (version 0.14.0) using 0.5 fractions of the data to estimate the LOWESS curve 71 . The data were evaluated for normal distribution using D’Agostino and Pearson’s method from SciPy (version 1.9.3) 72 . The non-normally distributed variables in the untargeted data were transformed to normality by the Box-Cox procedure using the SciPy function 'boxcox'. Significance testing between the independent groups of HC and PD/OND/iRBD individuals was performed by Student’s two-tailed t -test for the untargeted proteomic data and by Mann–Whitney’s non-parametric U -test (SciPy) for the targeted data. Due to the limited sample numbers, no multiple testing correction was performed in the untargeted data. In the targeted data, the Benjamini–Hochberg multiple testing correction procedure (statsmodels) was applied with an accepted false discovery rate of 5%. Fold-changes were calculated by dividing the means of the affected groups by the control group. Correlation analyses in the targeted data were performed by Spearman’s correlation (SciPy) and the correlation p values were adjusted variable-wise by the Benjamini–Hochberg procedure (FDR = 5%).

We implemented a support vector classifier model to discriminate between PD and HC and to predict new samples. The data were first z-scored protein-wise and any 'not a number'-values were replaced by the median. We used the 'LinearSVC' method from SciKit Learn and applied cross-validated recursive feature elimination to determine the number of variables to use in the model. The most discriminating variables for distinguishing between controls and PD were thereafter chosen by recursive feature elimination 73 . Feature selection and model training were performed on 70% of the data, partitioned using the SciKit Learn function “train_test_split”, and cross-validation was performed using a stratified k-fold with five splits. The remaining 30% of the data were predicted in the model. PR and ROC curves were constructed from the test data and consisted of each predictor and from the combined predictors, the packages precision_recall_curve and roc_curve from SciKit Learn were implemented. Linear mixed models were performed using the R-to-Python bridge software pymer4 (version 0.8.0), where individual was set as a random effect and the correlations between the MS measured proteins and clinical variables were evaluated for significance post Benjamini–Hochberg’s procedure for multiple testing correction. Plots of the data were constructed using the Seaborn and Matplotlib packages (versions 0.12.2 and 3.6.0, respectively) 74 .

All multivariate analyses were performed in SIMCA, version 17. OPLS and OPLS-discriminant analysis (OPLS-DA) models were evaluated for significance by ANOVA p values and by permutation tests applying 1000 permutations, where p  < 0.05 and p  < 0.001 were deemed significant, respectively.

Data were analysed for pathway enrichment using IPA (QIAGEN Inc. Data were analysed for pathway enrichment using IPA (QIAGEN Inc., https://digitalinsights.qiagen.com/products-overview/discovery-insights-portfolio/analysis-and-visualization/qiagen-ipa/ .). Input variables were set to proteins demonstrating a significant difference between PD individuals and HC, with fold-change as expression observation. The accepted output pathways were restricted to p  < 0.05 and at least two proteins were included in the pathways. Gene Ontology (GO) annotations were extracted using DAVID Bioinformatics Resources (2021 build) 75 , 76 . Networks were built in Cytoscape 77 (version 3.8.0) by applying the “Organic layout” from yFiles 77 .

Obtaining biological materials

Patient samples can be provided to other researchers for certain projects after contact with the corresponding authors and upon availability approval of the team in Kassel, Germany.

Reporting summary

Further information on research design is available in the  Nature Portfolio Reporting Summary linked to this article.

Data availability

The chromatograms from the targeted mass spectrometric data generated in this study have been deposited in the ProteomeXchange database under accession code PXD041419 and in the Panorama repository ( https://panoramaweb.org/DNP_Pub.url , https://doi.org/10.6069/p9cy-h335 ). The integrated targeted mass spectrometric data generated in this study are provided in the Supplementary Information. Source data for all data presented in graphs within the figures are provided in a source data file.  Source data are provided with this paper.

Code availability

Peak-picking and integrations were performed in TargetLynx (part of the MassLynx suite, version 4.1), or using an in-house application written in Python which can be found on GitHub ( https://github.com/jchallqvist/mrmIntegrate ). The data visualisation and statistical analyses were performed in Python (version 3.8.5) using the packages SciPy (version 1.9.3), statsmodels (version 0.14.0), SciKit Learn (version 1.1.2), Seaborn (version 13.0) and Matplotlib (version 3.6.0). The code used can be found on GitHub ( https://github.com/jchallqvist/DNP_Pub/blob/main/DNP_Code , https://doi.org/10.5281/zenodo.11130369 ).

Simuni, T. et al. Baseline prevalence and longitudinal evolution of non-motor symptoms in early Parkinson’s disease: the PPMI cohort. J. Neurol. Neurosurg. Psychiatry 89 , 78–88 (2018).

Article   PubMed   Google Scholar  

Michell, A. W., Lewis, S. J., Foltynie, T. & Barker, R. A. Biomarkers and Parkinson’s disease. Brain 127 , 1693–1705 (2004).

Article   CAS   PubMed   Google Scholar  

Kieburtz, K., Katz, R., McGarry, A. & Olanow, C. W. A new approach to the development of disease-modifying therapies for PD; fighting another pandemic. Mov. Disord. 36 , 59–63 (2021).

Shahnawaz, M. et al. Development of a biochemical diagnosis of Parkinson disease by detection of α-synuclein misfolded aggregates in cerebrospinal fluid. JAMA Neurol. 74 , 163–172, (2017).

Mollenhauer, B. et al. Validation of serum neurofilament light chain as a biomarker of Parkinson’s disease progression. Mov. Disord . https://doi.org/10.1002/mds.28206 (2020).

Lindestam Arlehamn, C. S. et al. α-Synuclein-specific T cell reactivity is associated with preclinical and early Parkinson’s disease. Nat. Commun. 11 , 1875 (2020).

Article   ADS   CAS   PubMed   PubMed Central   Google Scholar  

Mollenhauer, B. et al. Baseline predictors for progression 4 years after Parkinson’s disease diagnosis in the De Novo Parkinson Cohort (DeNoPa). Mov. Disord. 34 , 67–77 (2019).

Bartl, M. et al. Blood markers of inflammation, neurodegeneration, and cardiovascular risk in early Parkinson’s disease. Mov. Disord . https://doi.org/10.1002/mds.29257 (2022).

Simuni, T. et al. A biological definition of neuronal α-synuclein disease: towards an integrated staging system for research. Lancet Neurol. 23 , 178–190 (2024).

Mollenhauer, B. et al. Nonmotor and diagnostic findings in subjects with de novo Parkinson disease of the DeNoPa cohort. Neurology 81 , 1226–1234 (2013).

Hällqvist, J. et al. A multiplexed urinary biomarker panel has potential for Alzheimer’s disease diagnosis using targeted proteomics and machine learning. Int. J. Mol. Sci . https://doi.org/10.3390/ijms241813758 (2023).

Hu, W., Ralay Ranaivo, H., Craft, J. M., Van Eldik, L. J. & Watterson, D. M. Validation of the neuroinflammation cycle as a drug discovery target using integrative chemical biology and lead compound development with an Alzheimer’s disease-related mouse model. Curr. Alzheimer Res. 2 , 197–205 (2005).

Notter, T. et al. Translational evaluation of translocator protein as a marker of neuroinflammation in schizophrenia. Mol. Psychiatry 23 , 323–334 (2018).

Jonsson, M., Gerdle, B., Ghafouri, B. & Backryd, E. The inflammatory profile of cerebrospinal fluid, plasma, and saliva from patients with severe neuropathic pain and healthy controls-a pilot study. BMC Neurosci. 22 , 6 (2021).

Article   PubMed   PubMed Central   Google Scholar  

Chen, X. et al. Progranulin does not bind tumor necrosis factor (TNF) receptors and is not a direct regulator of TNF-dependent signaling or bioactivity in immune or neuronal cells. J. Neurosci. 33 , 9202–9213 (2013).

Article   CAS   PubMed   PubMed Central   Google Scholar  

Captur, G. et al. Plasma proteomic signature predicts who will get persistent symptoms following SARS-CoV-2 infection. EBioMedicine 85 , 104293 (2022).

Doykov, I. et al. The long tail of Covid-19’ - The detection of a prolonged inflammatory response after a SARS-CoV-2 infection in asymptomatic and mildly affected patients. F1000Res 9 , 1349 (2020).

Hu, M. T. REM sleep behavior disorder (RBD). Neurobiol. Dis. 143 , 104996 (2020).

Concha-Marambio, L. et al. Accurate detection of α-synuclein seeds in cerebrospinal fluid from isolated rapid eye movement sleep behavior disorder and patients with Parkinson’s disease in the de novo Parkinson (DeNoPa) cohort. Mov. Disord. 38 , 567–578 (2023).

Dayon, L. et al. Proteomes of paired human cerebrospinal fluid and plasma: relation to blood-brain barrier permeability in older adults. J. Proteome Res. 18 , 1162–1174 (2019).

Whelan, C. D. et al. Multiplex proteomics identifies novel CSF and plasma biomarkers of early Alzheimer’s disease. Acta Neuropathol. Commun. 7 , 169 (2019).

Bloem, B. R., Okun, M. S. & Klein, C. Parkinson’s disease. Lancet 397 , 2284–2303 (2021).

Dorsey, E. R., Sherer, T., Okun, M. S. & Bloem, B. R. The emerging evidence of the Parkinson pandemic. J. Parkinsons Dis. 8 , S3–s8 (2018).

Grossauer, A. et al. α-Synuclein seed amplification assays in the diagnosis of synucleinopathies using cerebrospinal fluid-A systematic review and meta-analysis. Mov. Disord. Clin. Pr. 10 , 737–747 (2023).

Article   Google Scholar  

Okuzumi, A. et al. Propagative α-synuclein seeds as serum biomarkers for synucleinopathies. Nat. Med. 29 , 1448–1455 (2023).

Raffield, L. M. et al. Comparison of proteomic assessment methods in multiple cohort studies. Proteomics 20 , e1900278 (2020).

Hernández, B., Parnell, A. & Pennington, S. R. Why have so few proteomic biomarkers “survived” validation? (Sample size and independent validation considerations). Proteomics 14 , 1587–1592 (2014).

Füzéry, A. K., Levin, J., Chan, M. M. & Chan, D. W. Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin. Proteom. 10 , 13 (2013).

Bader, J. M., Albrecht, V. & Mann, M. MS-based proteomics of body fluids: the end of the beginning. Mol. Cell Proteom. 22 , 100577 (2023).

Article   CAS   Google Scholar  

Pan, C. et al. Targeted discovery and validation of plasma biomarkers of Parkinson’s disease. J. Proteome Res. 13 , 4535–4545 (2014).

Qin, X. Y., Zhang, S. P., Cao, C., Loh, Y. P. & Cheng, Y. Aberrations in peripheral inflammatory cytokine levels in Parkinson disease: a systematic review and meta-analysis. JAMA Neurol. 73 , 1316–1324, (2016).

Choi, M. L. & Gandhi, S. Crucial role of protein oligomerization in the pathogenesis of Alzheimer’s and Parkinson’s diseases. FEBS J. 285 , 3631–3644 (2018).

Walter, P. & Ron, D. The unfolded protein response: from stress pathway to homeostatic regulation. Science 334 , 1081–1086 (2011).

Article   ADS   CAS   PubMed   Google Scholar  

Bertolotti, A., Zhang, Y. H., Hendershot, L. M., Harding, H. P. & Ron, D. Dynamic interaction of BiP and ER stress transducers in the unfolded-protein response. Nat. Cell Biol. 2 , 326–332 (2000).

Colla, E. Linking the endoplasmic reticulum to Parkinson’s disease and alpha-synucleinopathy. Front. Neurosci. 13 , 560 (2019).

Mercado, G., Castillo, V., Soto, P. & Sidhu, A. ER stress and Parkinson’s disease: pathological inputs that converge into the secretory pathway. Brain Res. 1648 , 626–632 (2016).

Loeffler, D. A., Camp, D. M. & Conant, S. B. Complement activation in the Parkinson’s disease substantia nigra: an immunocytochemical study. J. Neuroinflamm 3 , 29 (2006).

Zattoni, M. et al. Serpin signatures in Prion and Alzheimer’s diseases. Mol. Neurobiol. 59 , 3778–3799 (2022).

Seo, M. H. & Yeo, S. Association of increase in Serping1 level with dopaminergic cell reduction in an MPTP-induced Parkinson’s disease mouse model. Brain Res. Bull. 162 , 67–72 (2020).

Anderson, J. P. et al. Phosphorylation of Ser-129 is the dominant pathological modification of alpha-synuclein in familial and sporadic Lewy body disease. J. Biol. Chem. 281 , 29739–29752 (2006).

Fujiwara, H. et al. alpha-Synuclein is phosphorylated in synucleinopathy lesions. Nat. Cell Biol. 4 , 160–164 (2002).

Kim, K. S. et al. Proteolytic cleavage of extracellular alpha-synuclein by plasmin implications for Parkinson disease. J. Biol. Chem. 287 , 24862–24872 (2012).

Reho, P. et al. GRN mutations are associated with Lewy body dementia. Mov. Disord. 37 , 1943–1948 (2022).

Kao, A. W., Mckay, A., Singh, P. P., Brunet, A. & Huang, E. J. Progranulin, lysosomal regulation and neurodegenerative disease. Nat. Rev. Neurosci. 18 , 325–333 (2017).

Mateo, I. et al. Reduced serum progranulin level might be associated with Parkinson’s disease risk. Eur. J. Neurol. 20 , 1571–1573 (2013).

Bachmann-Harildstad, G. Diagnostic values of beta-2 transferrin and beta-trace protein as markers for cerebrospinal fluid fistula. Rhinology 46 , 82–85 (2008).

PubMed   Google Scholar  

Pediaditakis, I. et al. Modeling alpha-synuclein pathology in a human brain-chip to assess blood-brain barrier disruption. Nat. Commun. 12 , 5907 (2021).

Serafino, A., Giovannini, D., Rossi, S. & Cozzolino, M. Targeting the Wnt/β-catenin pathway in neurodegenerative diseases: recent approaches and current challenges. Expert Opin. Drug Discov. 15 , 803–822 (2020).

Arenas, E. Wnt signaling in midbrain dopaminergic neuron development and regenerative medicine for Parkinson’s disease. J. Mol. Cell Biol. 6 , 42–53 (2014).

L’Episcopo, F. et al. A Wnt1 regulated Frizzled-1/β-Catenin signaling pathway as a candidate regulatory circuit controlling mesencephalic dopaminergic neuron-astrocyte crosstalk: therapeutical relevance for neuron survival and neuroprotection. Mol. Neurodegener. 6 , 49 (2011).

Sweeney, M. D., Sagare, A. P. & Zlokovic, B. V. Blood-brain barrier breakdown in Alzheimer disease and other neurodegenerative disorders. Nat. Rev. Neurol. 14 , 133–150 (2018).

L’Episcopo, F. et al. Wnt/beta-catenin signaling is required to rescue midbrain dopaminergic progenitors and promote neurorepair in ageing mouse model of Parkinson’s disease. Stem Cells 32 , 2147–2163 (2014).

Marchetti, B. Wnt/beta-catenin signaling pathway governs a full program for dopaminergic neuron survival, neurorescue and regeneration in the MPTP mouse model of Parkinson’s disease. Int. J. Mol. Sci. 19 , 3743 (2018).

Marchetti, B. et al. Parkinson’s disease, aging and adult neurogenesis: Wnt/beta-catenin signalling as the key to unlock the mystery of endogenous brain repair. Aging Cell 19 , e1310110 (2020).

L’Episcopo, F. et al. Neural stem cell grafts promote astroglia-driven neurorestoration in the aged Parkinsonian brain via Wnt/beta-catenin signaling. Stem Cells 36 , 1179–1197 (2018).

Serafino, A. et al. Developing drugs that target the Wnt pathway: recent approaches in cancer and neurodegenerative diseases. Expert Opin. Drug Discov. 12 , 169–186 (2017).

Harms, A. S., Ferreira, S. A. & Romero-Ramos, M. Periphery and brain, innate and adaptive immunity in Parkinson’s disease. Acta Neuropathol. 141 , 527–545 (2021).

Dong, M. X. et al. Serum butyrylcholinesterase activity: a biomarker for Parkinson’s disease and related dementia. Biomed. Res. Int. 2017 , 1524107 (2017).

Postuma, R. B. et al. Risk and predictors of dementia and parkinsonism in idiopathic REM sleep behaviour disorder: a multicentre study. Brain 142 , 744–759 (2019).

Zhang, H. et al. Risk factors for phenoconversion in rapid eye movement sleep behavior disorder. Ann. Neurol. 91 , 404–416 (2022).

Palma, J. A. et al. Prevalence of REM sleep behavior disorder in multiple system atrophy: a multicenter study and meta-analysis. Clin. Auton. Res. 25 , 69–75 (2015).

Jennings, D. et al. Imaging prodromal Parkinson disease: the Parkinson Associated Risk Syndrome Study. Neurology 83 , 1739–1746 (2014).

Schade, S. et al. Identifying prodromal NMS in a population-based recruitment strategy: Kassel data of Healthy Brain Ageing. Zenodo (2023).

Schade, S. et al. Acute levodopa challenge test in patients with de novo Parkinson’s disease: data from the DeNoPa cohort. Mov. Disord. Clin. Pr. 4 , 755–762 (2017).

Hummel, T., Sekinger, B., Wolf, S. R., Pauli, E. & Kobal, G. ‘Sniffin’ sticks’: olfactory performance assessed by the combined testing of odor identification, odor discrimination and olfactory threshold. Chem. Senses 22 , 39–52 (1997).

Mollenhauer, B. et al. Monitoring of 30 marker candidates in early Parkinson disease as progression markers. Neurology 87 , 168–177 (2016).

Mollenhauer, B. et al. α-Synuclein and tau concentrations in cerebrospinal fluid of patients presenting with parkinsonism: a cohort study. Lancet Neurol. 10 , 230–240 (2011).

UniProt. BLAST https://www.uniprot.org/blast/ . Accessed January 2024.

MacLean, B. et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics 26 , 966–968 (2010).

Chambers, M. C. et al. A cross-platform toolkit for mass spectrometry and proteomics. Nat. Biotechnol. 30 , 918–920 (2012).

Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python in Science Conference (SciPy, 2010).

Virtanen, P. et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat. Methods 17 , 261–272 (2020).

Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12 , 2825–2830 (2011).

MathSciNet   Google Scholar  

Hunter, J. D. Matplotlib: A 2D graphics environment. Comput. Sci. Eng. 9 , 90–95 (2007).

Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50 , W216–221, (2022).

Huang Da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4 , 44–57 (2009).

Shannon, P. et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13 , 2498–2504 (2003).

Download references

Acknowledgements

This work was supported by the Michael J Fox Foundation, PDUK, The Peto Foundation, The TMSRG (UCL), The BRC at Great Ormond Street Hospital, and the Horizon 2020 Framework Programme (Grant number 634821, PROPAG-AGING). We thank the PROPAG-AGING consortium, a full list of the members can be found in the supplementary material.

Open Access funding enabled and organized by Projekt DEAL.

Author information

These authors contributed equally: Jenny Hällqvist, Michael Bartl.

These authors jointly supervised this work: Kevin Mills, Brit Mollenhauer.

Authors and Affiliations

UCL Institute of Child Health and Great Ormond Street Hospital, London, UK

Jenny Hällqvist, Ivan Doykov, Justyna Śpiewak, Héloїse Vinette & Wendy E. Heywood

UCL Queen Square Institute of Neurology, Clinical and Movement Neurosciences, London, UK

Jenny Hällqvist & Kevin Mills

Department of Neurology, University Medical Center Goettingen, Goettingen, Germany

Michael Bartl, Mohammed Dakna, Mary Xylaki, Sandrina Weber & Brit Mollenhauer

Institute for Neuroimmunology and Multiple Sclerosis Research, University Medical Center Goettingen, Goettingen, Germany

Michael Bartl

Paracelsus-Elena-Klinik, Kassel, Germany

Sebastian Schade, Maria-Lucia Muntean, Friederike Sixel-Döring, Claudia Trenkwalder & Brit Mollenhauer

Department of Experimental, Diagnostic, and Specialty Medicine (DIMES), University of Bologna, Bologna, Italy

Paolo Garagnani, Chiara Pirazzini & Claudio Franceschi

IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy

Maria-Giulia Bacalini

National Hospital for Neurology & Neurosurgery, Queen Square, WC1N3BG, London, UK

Kailash Bhatia & Sebastian Schreglmann

Institute of Diagnostic and Interventional Neuroradiology, University Medical Center Goettingen, Goettingen, Germany

Marielle Ernst

Department of Neurology, Philipps-University, Marburg, Germany

Friederike Sixel-Döring

UCL: Food, Microbiomes and Health Institute Strategic Programme, Quadram Institute Bioscience, Norwich Research Park, Norwich, UK

Héloїse Vinette

Department of Neurosurgery, University Medical Center Goettingen, Goettingen, Germany

Claudia Trenkwalder

You can also search for this author in PubMed   Google Scholar

Contributions

J.H., M.B., K.M., and B.M. conceptualised, planned and oversaw all aspects of the study. J.H., K.M., J.S., H.V., M.B. and S. Schreglmann performed and analyzed most of the experiments. S. Schade, S.W. and M.B. consented to the subjects and collected the samples. M.-L.M., F.S.-D. and S. Schade analyzed the sleep lab data and diagnosed the iRBD subjects. J.H. and M.D. performed the statistical data analysis. J.H. applied the machine learning methods and designed the figures. W.H., I.D., C.F., M.-G.B., P.G., C.P., K.B. and M.X. provided substantial contributions to the conception of the work, acquisition and interpretation of the data, particularly for the mass spectrometry setup and the refinement of the targeted panel. S. Schade, S.W., C.T., M.B., B.M., M.-L.M. and F.S.D. conceptualised the clinical study, analyzed the clinical data and reevaluated the diagnosis. M.E. provided substantial contributions to the clinical data analyzes, particularly the imaging patient data in regard to differential diagnosis. J.H., M.B., K.M. and B.M. wrote the manuscript with input and substantial revisions from all authors.

Corresponding authors

Correspondence to Jenny Hällqvist or Michael Bartl .

Ethics declarations

Competing interests.

JH, MD, MX, SW, KB, ME, PG, MGB, CP, KM, ID, WH, JS, HV and CF and have no competing interests to report. MB has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 413,501,650. CT has received honoraria for consultancy from Roche, and honoraria for educational lectures from UCB, and has received research funding for the PPMI study from the Michael J. Fox Foundation and funding from the EU (Horizon 2020) and stipends from the (International Parkinson’s and Movement Disorder Society) IPMDS. BM has received honoraria for consultancy from Roche, Biogen, AbbVie, UCB, and Sun Pharma Advanced Research Company. BM is a member of the executive steering committee of the Parkinson Progression Marker Initiative and PI of the Systemic Synuclein Sampling Study of the Michael J. Fox Foundation for Parkinson’s Research and has received research funding from the Deutsche Forschungsgemeinschaft (DFG), EU (Horizon 2020), Parkinson Fonds Deutschland, Deutsche Parkinson Vereinigung, Parkinson’s Foundation and the Michael J. Fox Foundation for Parkinson’s Research. MLM has received honoraria for speaking engagements from Deutsche Parkinson Gesellschaft e.V., and royalties from Gesellschaft fur Medien + Kommunikation mbH + Co. FSD has received honoraria for speaking engagements from AbbVie, Bial, Ever Pharma, Medtronic and royalties from Elsevier and Springer. She served on an advisory board for Zambon and Stada Pharma. FSD participated in Ad Boards for consultation: Abbvie, UCB, Bial, Ono, Roche and got honorary for lecturing: Stada Pharm, AbbVie, Alexion, Bial. S. Schade received institutional salaries supported by the EU Horizon 2020 research and innovation programme under grant agreement No. 863664 and by the Michael J. Fox Foundation for Parkinson’s Research under grant agreement No. MJFF-021923. He is supported by a PPMI Early Stage Investigators Funding Programme fellowship of the Michael J. Fox Foundation for Parkinson’s Research under grant agreement No. MJFF-022656. S. Schreglmann received institutional salaries supported by the EU Horizon 2020 research and innovation programme under grant agreement No. 863664, support from the Advanced Clinician Scientist programme by the Interdisciplinary Centre for Clinical Research, Wuerzburg, Germany, and from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project-ID 424778381-TRR 295. He is a fellow of the Thiemann Foundation. He serves as a scientific adviser to Elemind Inc.

Peer review

Peer review information.

Nature Communications thanks Kanta Horie and the other, anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary information, peer review file, reporting summary, source data, source data, rights and permissions.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ .

Reprints and permissions

About this article

Cite this article.

Hällqvist, J., Bartl, M., Dakna, M. et al. Plasma proteomics identify biomarkers predicting Parkinson’s disease up to 7 years before symptom onset. Nat Commun 15 , 4759 (2024). https://doi.org/10.1038/s41467-024-48961-3

Download citation

Received : 06 April 2023

Accepted : 20 May 2024

Published : 18 June 2024

DOI : https://doi.org/10.1038/s41467-024-48961-3

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

By submitting a comment you agree to abide by our Terms and Community Guidelines . If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Quick links

• Explore articles by subject
• Guide to authors
• Editorial policies

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.