SavedAdd your journal

Journals / Black Sea Journal of Engineering and Science / 2026 / Cilt: 9 Sayı: 3

In Vitro Chromosome Aberrations in Genotoxicity Assessment (OECD Test No: 473): Applications, Limitations, and Digital-Automated Approaches

Genotoksisite Değerlendirmesinde In Vitro Kromozom Anormallikleri Testi (OECD Test No: 473): Uygulamalar, Sınırlılıklar ve Dijital-Otomatik Yaklaşımlar

Nur Korkmaz(İSTANBUL KENT ÜNİVERSİTESİ)0000-0003-4969-2872
Pages
1523–1530
Publication Date
2026-05-15
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Furthermore, considering the current digital/semi-automated system used in the CA test, the potential contributions of digital and automation-supported approaches being researched in the field of cytogenetic evaluation to improving workflow, enhancing evaluation consistency, and supporting the scoring process in this test are discussed.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Genotoxicity is a term that encompasses damage occurring in DNA and/or chromosomes as a result of exposure to various agents.
📊
Key Findings & Data:The aim of this review is to examine, based on the literature, the role of the in vitro CA test (Test No.473), as defined and standardized in the OECD test guideline, in genotoxicity assessment, as well as the importance of variability in the application and scoring steps for the reliability and interpretation of results.
💡
Conclusion & Impact:Furthermore, considering the current digital/semi-automated system used in the CA test, the potential contributions of digital and automation-supported approaches being researched in the field of cytogenetic evaluation to improving workflow, enhancing evaluation consistency, and supporting the scoring process in this test are discussed.
🔬Design:Review & Systematic Survey
📍Scope:Comprehensive Sample
⚙️Variables:In vitro kromozom anormallikleri testi · OECD Test No. 473 · Genotoksisite

Abstract

Genotoxicity is a term that encompasses damage occurring in DNA and/or chromosomes as a result of exposure to various agents. The identification of such damage and the evaluation of genotoxic potential are carried out through genotoxicity tests. The chromosomal aberration (CA) test is a cytogenetic method used to assess chromosomal aberrations that arise spontaneously or are induced by genotoxic agents. This test can be applied to different cell models and provides important information regarding the evaluation of genome integrity. The aim of this review is to examine, based on the literature, the role of the in vitro CA test (Test No.473), as defined and standardized in the OECD test guideline, in genotoxicity assessment, as well as the importance of variability in the application and scoring steps for the reliability and interpretation of results. Furthermore, considering the current digital/semi-automated system used in the CA test, the potential contributions of digital and automation-supported approaches being researched in the field of cytogenetic evaluation to improving workflow, enhancing evaluation consistency, and supporting the scoring process in this test are discussed.

Özet

Genotoksisite, DNA’da ve/veya kromozomlarda çeşitli ajanlara maruziyet sonucunda oluşan hasarları kapsayan bir terimdir. Bu hasarların belirlenmesi ve genotoksik potansiyelin değerlendirilmesi genotoksisite testleri aracılığı ile gerçekleştirilmektedir. Kromozom anormallikleri (KA) testi, spontan olarak oluşan ya da genotoksik ajanlar tarafından indüklenen kromozom anormalliklerinin değerlendirilmesinde kullanılan bir sitogenetik yöntemdir. Bu test, farklı hücre modellerine uygulanabilmektedir ve genom bütünlüğünün değerlendirilmesine ilişkin önemli bilgiler sunmaktadır. Bu derlemenin amacı, OECD test kılavuzunda tanımlanan ve standardize edilmiş in vitro KA testinin (Test No. 473) genotoksisite değerlendirmesindeki yerini ve uygulama ile skorlama basamaklarındaki değişkenliğin sonuçların güvenilirliği ve yorumlanması açısından taşıdığı önemi literatür temelinde ele almaktır. Ayrıca, KA testinde kullanılan mevcut dijital/yarı otomatik sistem göz önünde bulundurularak, sitogenetik değerlendirme alanında araştırılan dijital ve otomasyon destekli yaklaşımların bu testte iş akışının iyileştirilmesi, değerlendirme tutarlılığının artırılması ve skorlama sürecinin desteklenmesine yönelik olası katkıları tartışılmaktadır.

🔖

Cite This Article

Korkmaz, N. (2026). In Vitro Chromosome Aberrations in Genotoxicity Assessment (OECD Test No: 473): Applications, Limitations, and Digital-Automated Approaches. Black Sea Journal of Engineering and Science, Cilt: 9 Sayı: 3, 1523–1530. https://doi.org/10.34248/bsengineering.1861356

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (66)

  1. Arora, T., & Dhir, R. (2016). A review of metaphase chromosome image selection techniques for automatic karyotype generation. Medical & Biological Engineering & Computing, 54(8), 1147-1157.
  2. Arunachalam, K., & Sreeja, P. S. (2025). Treatment and cell collection for chromosomal aberration test. In Advanced cell and molecular techniques: Protocols for in vitro and in vivo studies (pp. 259–264). Springer.
  3. Atalay, E. Ç., & Celep, A. G. S. (2024). Gıda katkı maddeleri ve genetik etkileri. Gıda, 49(1), 1–24. https://doi.org/10.15237/gida.GD23078
  4. Aytaç, N., & Ateş, E. (2019). Toksikoloji. Turkiye Klinikleri Public Health-Special Topic, 5(3), 15–19.
  5. Balajee, A. S., & Hande, M. P. (2018). History and evolution of cytogenetic techniques: Current and future applications in basic and clinical research. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 836, 3–12. https://doi.org/10.1016/j.mrgentox.2018.08.008
  6. Bonassi, S., Norppa, H., Ceppi, M., Strömberg, U., Vermeulen, R., Znaor, A., & Boffetta, P. (2008). Chromosomal aberration frequency in lymphocytes predicts the risk of cancer: Results from a pooled cohort study of 22 358 subjects in 11 countries. Carcinogenesis, 29(6), 1178–1183. https://doi.org/10.1093/carcin/bgn075
  7. Burssed, B., Zamariolli, M., Bellucco, F. T., & Melaragno, M. I. (2022). Mechanisms of structural chromosomal rearrangement formation. Molecular Cytogenetics, 15(1), Article 23. https://doi.org/10.1186/s13039-022-00599-4
  8. Calafat, A. M. (2016). Contemporary issues in exposure assessment using biomonitoring. Current Epidemiology Reports, 3(2), 145–153. https://doi.org/10.1007/s40471-016-0075-y
  9. Canedo, A., & Rocha, T. L. (2021). Zebrafish (Danio rerio) using as model for genotoxicity and DNA repair assessments: Historical review, current status and trends. Science of the Total Environment, 762, Article 144084. https://doi.org/10.1016/j.scitotenv.2020.144084
  10. Canoy, R. J., Shmakova, A., Karpukhina, A., Shepelev, M., Germini, D., & Vassetzky, Y. (2022). Factors that affect the formation of chromosomal translocations in cells. Cancers, 14(20), Article 5110. https://doi.org/10.3390/cancers14205110
  11. Chebly, A. (2024). Cancer cytogenetics in the era of artificial intelligence: Shaping the future of chromosome analysis. Future Oncology, 20(31), 2303–2305. https://doi.org/10.1080/14796694.2024.2385296
  12. Choudhuri, S., Kaur, T., Jain, S., Sharma, C., & Asthana, S. (2021). A review on genotoxicity in connection to infertility and cancer. Chemico-Biological Interactions, 345, Article 109531. https://doi.org/10.1016/j.cbi.2021.109531
  13. Clare, G. (2011). The in vitro mammalian chromosome aberration test. In J. M. Parry & E. M. Parry (Eds.), Genetic toxicology: Principles and methods (pp. 69–91). Springer.
  14. Czakó, M., Szabó, A., Till, Á., Zsigmond, A., & Hadzsiev, K. (2025). Uncovering rare structural chromosomal rearrangements: Insights from molecular cytogenetics. International Journal of Molecular Sciences, 26(18), Article 8886. https://doi.org/10.3390/ijms26188886
  15. Darmasaputra, G. S., van Rijnberk, L. M., & Galli, M. (2024). Functional consequences of somatic polyploidy in development. Development, 151(5), Article dev202392. https://doi.org/10.1242/dev.202392
  16. de Araújo Batistão, H. K., Oliveira-Silva, J. M., de Oliveira, V. B., de Araújo Batistão, I. I., & Gamero, A. M. C. (2025). Quantitative and qualitative methods for measuring chromosomal instability in tumor cells. Cancer Genetics. Advance online publication. https://doi.org/10.1016/j.cancergen.2025.11.008
  17. Deora, G., Sambyal, V., Guleria, K., Kaur, J., Uppal, M. S., Sudan, M., & Uppal, M. S. (2025). Assessment of chromosomal aberrations and s-phase fraction in patients with esophageal cancer. Cureus, 17(5). https://doi.org/10.7759/cureus.84204
  18. Ellwanger, J. H., Kulmann-Leal, B., Ziliotto, M., & Chies, J. A. B. (2023). HIV infection, chromosome instability, and micronucleus formation. Viruses, 15(1), Article 155. https://doi.org/10.3390/v15010155
  19. Erenpreisa, J., & Cragg, M. S. (2001). Mitotic death: A mechanism of survival? A review. Cancer Cell International, 1(1), Article 1. https://doi.org/10.1186/1475-2867-1-1
  20. Farhud, D. D., & Zokaei, S. (2021). Ethical issues of artificial intelligence in medicine and healthcare. Iranian Journal of Public Health, 50(11), i–v.
  21. Farkas, G., Kocsis, Z. S., Székely, G., Dobozi, M., Kenessey, I., Polgár, C., & Jurányi, Z. (2021). Smoking, chromosomal aberrations, and cancer incidence in healthy subjects. Mutation Research/Genetic Toxicology and Environmental Mutagenesis, 867, Article 503373. https://doi.org/10.1016/j.mrgentox.2021.503373
  22. Garcia-Sagredo, J. M. (2008). Fifty years of cytogenetics: A parallel view of the evolution of cytogenetics and genotoxicology. Biochimica et Biophysica Acta (BBA)-Gene Regulatory Mechanisms, 1779(6–7), 363–375. https://doi.org/10.1016/j.bbagrm.2008.05.003
  23. Ghelli, F., Cocchi, E., Buglisi, M., Squillacioti, G., Bellisario, V., Bono, R., & Santovito, A. (2021). The role of phase I, phase II, and DNA-repair gene polymorphisms in the damage induced by formaldehyde in pathologists. Scientific Reports, 11(1), Article 10507. https://doi.org/10.1038/s41598-021-90054-z
  24. Gu, X., Xu, Y., Liu, N., Li, K., Wang, X., Zhang, J., & Yang, C. (2025). The genetic and embryo–fetal developmental toxicity profile of the novel transgelin agonist deg-AZM: Ames, micronucleus, chromosomal aberration, and rat efd studies. Biomedicines, 13(11), Article 2600. https://doi.org/10.3390/biomedicines13112600
  25. Harshavarthini, M., Campal, E. I., Singh, S., Poojary, N., Xavier, M., Rasal, K. D., & Nagpure, N. S. (2025). An economical and rapid method of comet assay and micronucleus cytome assay for genotoxicity biomonitoring using clam, Gafrarium divaricatum (Gmelin, 1791). DNA Repair, Article 103870. https://doi.org/10.1016/j.dnarep.2025.103870
  26. Hemminki, K., Niazi, Y., Vodickova, L., Vodicka, P., & Försti, A. (2025). Genetic and environmental associations of nonspecific chromosomal aberrations. Mutagenesis, 40(1), 30–38. https://doi.org/10.1093/mutage/geae006
  27. Jacobs, M. N., Hoffmann, S., Hollnagel, H. M., Kern, P., Kolle, S. N., Natsch, A., & Landsiedel, R. (2024). Avoiding a reproducibility crisis in regulatory toxicology—on the fundamental role of ring trials. Archives of Toxicology, 98(7), 2047–2063. https://doi.org/10.1007/s00204-024-03736-z
  28. Jain, A. K., Singh, D., Dubey, K., Maurya, R., & Pandey, A. K. (2018). Chapter four - chromosomal aberrations. In A. Kumar, V. N. Dobrovolsky, A. Shawan, & R. Shanker (Eds.), Mutagenicity: Assays and applications (pp. 69–92). Academic Press. https://doi.org/10.1016/B978-0-12-809252-1.00004-3
  29. Johannes, C., & Obe, G. (2025). Chromosomal aberration test in human lymphocytes. In A. Dhawan & M. Bajpaye (Eds.), Genotoxicity assessment: Methods and protocols (pp. 157–170). Springer.
  30. Juntao, L., Wenxue, L., Guangyu, Y., Xudong, L., Runxuan, Z., Bo, Z., & Wei, Z. (2024). Lanthanum nitrate demonstrated no genotoxicity in the conducted tests. Regulatory Toxicology and Pharmacology, 151, Article 105670. https://doi.org/10.1016/j.yrtph.2024.105670
  31. Kang, S., Han, J., Lee, I., Joo, H., Chung, Y., & Yang, S. (2024). Chromosome analysis method based on deep learning: Counting chromosomes and detecting abnormal chromosomes. Biomedical Signal Processing and Control, 91, Article 105891. https://doi.org/10.1016/j.bspc.2023.105891
  32. Karim, K. J. (2025). Cytogenetic monitoring of residents living near a petroleum refinery and steel factory in Erbil, Iraq. Journal of Applied Toxicology. Advance online publication. https://doi.org/10.1002/jat.70039
  33. Kim, Y. J., Lee, J. W., Cho, Y. H., Choi, Y. J., Lee, Y., & Chung, H. W. (2022). Chromosome damage in relation to recent radiation exposure and radiation quality in nuclear power plant workers. Toxics, 10(2), Article 94. https://doi.org/10.3390/toxics10020094
  34. Kopp, B., Khawam, A., Di Perna, K., Lenart, D., Vinette, M., Silva, R., & Hamel, A. (2024). Liver-on-chip model and application in predictive genotoxicity and mutagenicity of drugs. Mutation Research-Genetic Toxicology and Environmental Mutagenesis, 896, Article 503762. https://doi.org/10.1016/j.mrgentox.2024.503762
  35. Kou, F., Wu, L., Ren, X., & Yang, L. (2020). Chromosome abnormalities: New insights into their clinical significance in cancer. Molecular Therapy-Oncolytics, 17, 562–570. https://doi.org/10.1016/j.omto.2020.05.010
  36. Li, J., Fu, F., Wei, R., Sun, Y., Lai, Z., Song, N., & Yang, Y. (2024). Chromosomal structural abnormality diagnosis by homologous similarity. In Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (pp. 5317–5328). https://doi.org/10.1145/3637528.3671642
  37. Luan, Y., & Honma, M. (2022). Genotoxicity testing and recent advances. Genome Instability & Disease, 3(1), 1–21. https://doi.org/10.1007/s42764-021-00058-7
  38. Miller, C., Thurston, J., & Cohen, N. (2025). Incorporating automation in a cytogenetics laboratory: Three practitioners’ perspectives on benefits and limitations. Molecular Cytogenetics, 18(1), Article 12. https://doi.org/10.1186/s13039-025-00716-5
  39. Morris, J. P., Baslan, T., Soltis, D. E., Soltis, P. S., & Fox, D. T. (2024). Integrating the study of polyploidy across organisms, tissues, and disease. Annual Review of Genetics, 58(1), 297–321. https://doi.org/10.1146/annurev-genet-111523-102555
  40. Mosesso, P., & Cinelli, S. (2025). In vitro cytogenetic assays: Chromosomal aberrations and micronucleus tests. In A. Dhawan & M. Bajpaye (Eds.), Genotoxicity assessment: Methods and protocols (pp. 109–137). Springer.
  41. Niazi, Y., Thomsen, H., Smolkova, B., Vodickova, L., Vodenkova, S., Kroupa, M., & Försti, A. (2021). DNA repair gene polymorphisms and chromosomal aberrations in exposed populations. Frontiers in Genetics, 12, Article 691947. https://doi.org/10.3389/fgene.2021.691947
  42. OECD. (2016). Test No. 473: In vitro mammalian chromosomal aberration test. OECD Publishing. https://doi.org/10.1787/9789264264649-en
  43. Oh, S. J., Jeong, M. H., Kang, Y. R., Lee, C. G., Kim, H., Kye, Y. U., & Jo, W. S. (2025). Automated system for establishing standard radiation dose–response curves and dose estimation for the Korean population. Scientific Reports, 15(1), Article 10639. https://doi.org/10.1038/s41598-025-94678-8
  44. Paniagua, I., & Jacobs, J. J. (2023). Quantification of chromosomal aberrations in mammalian cells. Bio-Protocol, 13(16), Article e4739. https://doi.org/10.21769/BioProtoc.4739
  45. Patel, S. N., Kajavadara, C. K., Shukla, R. M., Valani, D. T., Bhatt, L. K., Sundar, R., & Jain, M. R. (2025). Evaluating solvent safety in chromosome aberration assays for genetic toxicology. Journal of Applied Toxicology. Advance online publication. https://doi.org/10.1002/jat.4894
  46. Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine, 25(1), 37–43. https://doi.org/10.1038/s41591-018-0272-7
  47. Ramos, S., Molina, B., Navarrete-Meneses, M. D. P., Cervantes-Barragan, D. E., Lozano, V., & Frias, S. (2025). Unclassified chromosomal abnormalities as an indicator of genomic damage in survivors of Hodgkin’s lymphoma. Cancers, 17(15), Article 2437. https://doi.org/10.3390/cancers17152437
  48. Roemer, E., Zenzen, V., Conroy, L. L., Luedemann, K., Dempsey, R., Schunck, C., & Sticken, E. T. (2015). Automation of the in vitro micronucleus and chromosome aberration assay for the assessment of the genotoxicity of the particulate and gas–vapor phase of cigarette smoke. Toxicology Mechanisms and Methods, 25(4), 320–333. https://doi.org/10.3109/15376516.2015.1037413
  49. Rosenblum, L. S., Holmes, J., & Taghiyev, A. F. (2025). The emergence of artificial intelligence-guided karyotyping: A review and reflection. Genes, 16(6), Article 685. https://doi.org/10.3390/genes16060685
  50. Russell, G., Thomas, A. D., Nenov, A., Mannings, G., & Hancock, J. T. (2023). The therapeutic potential of oxyhydrogen gas in oncology: A study on Epstein–Barr virus-immortalised B-lymphoblastoid (TK6) cells. Hydrogen, 4(4), 746–759. https://doi.org/10.3390/hydrogen4040047
  51. Săbău, I. D., Bohîlţea, L. C., Varlas, V., Gheorghe, A. S., Riza, M., Suciu, N., & Ceauşu, I. (2025). The evolution of prenatal whole exome sequencing: From cytogenetics to precision medicine. Archive of Clinical Cases, 12(2), Article 80. https://doi.org/10.22551/2025.47.1202.10318
  52. Sharma, N. K. (2022). Modulation of radiation and mitomycin C induced chromosomal aberrations by apigenin in human lymphocytes in vitro. Current Overview on Disease and Health Research, 3, 60–78.
  53. Srivastava, R., Mishra, N., Singh, U. M., & Srivastava, R. (2016). Genotoxicity: Mechanisms and its impact on human diseases. Octa Journal of Biosciences, 4(1), 4–10.
  54. Stimpson, K. M., Song, I. Y., Jauch, A., Holtgreve-Grez, H., Hayden, K. E., Bridger, J. M., & Sullivan, B. A. (2010). Telomere disruption results in non-random formation of de novo dicentric chromosomes involving acrocentric human chromosomes. PLoS Genetics, 6(8), Article e1001061. https://doi.org/10.1371/journal.pgen.1001061
  55. Tan, Z., Chu, D. Z. V., Chan, Y. J. A., Lu, Y. E., & Rancati, G. (2018). Mammalian cells undergo endoreduplication in response to lactic acidosis. Scientific Reports, 8(1), Article 2890. https://doi.org/10.1038/s41598-018-20186-7
  56. Timbrell, J., & Barile, F. A. (2023). Introduction to toxicology (4. bs.). CRC Press. https://doi.org/10.1201/9781003188575
  57. Tseng, J. J., Lu, C. H., Li, J. Z., Lai, H. Y., Chen, M. H., Cheng, F. Y., & Kuo, C. E. (2023). An open dataset of annotated metaphase cell images for chromosome identification. Scientific Data, 10(1), Article 104. https://doi.org/10.1038/s41597-023-02003-7
  58. Turkmen, H. I. (2024). A novel two-staged deep learning based workflow for analyzable metaphase detection. Multimedia Tools and Applications, 83(17), 52305–52333. https://doi.org/10.1007/s11042-023-17509-w
  59. Vahidi, S., Agah, S., Mirzajani, E., Asghari Gharakhyli, E., Norollahi, S. E., Rahbar Taramsari, M., & Samadani, A. A. (2024). microRNAs, oxidative stress, and genotoxicity as the main inducers in the pathobiology of cancer development. Hormone Molecular Biology and Clinical Investigation, 45(2), 55–73. https://doi.org/10.1515/hmbci-2023-0050
  60. Wang, T. S., Ruchirawat, M., Narasumrit, P., Xia, Z. L., & Au, W. W. (2022). Lymphocyte-based challenge DNA-repair assays for personalized health risk assessment. Mutation Research/Reviews in Mutation Research, 790, Article 108427. https://doi.org/10.1016/j.mrrev.2022.108427
  61. Wang, X., Zheng, B., Li, S., Mulvihill, J. J., Wood, M. C., & Liu, H. (2009). Automated classification of metaphase chromosomes: Optimization of an adaptive computerized scheme. Journal of Biomedical Informatics, 42(1), 22–31. https://doi.org/10.1016/j.jbi.2008.08.006
  62. Xia, C., Wang, J., Qin, Y., Wen, J., Liu, Z., Song, N., & Yang, J. (2023). KaryoNet: Chromosome recognition with end-to-end combinatorial optimization network. IEEE Transactions on Medical Imaging, 42(10), 2899–2911. https://doi.org/10.1109/TMI.2023.3267566
  63. Yadav, V., Fuentes, J. L., Krishnan, A., Singh, N., & Vohora, D. (2024). Guidance for the use and interpretation of assays for monitoring anti-genotoxicity. Life Sciences, 337, Article 122341. https://doi.org/10.1016/j.lfs.2023.122341
  64. Yang, C., Li, T., Dong, Q., & Zhao, Y. (2023). Chromosome classification via deep learning and its application to patients with structural abnormalities of chromosomes. Medical Engineering & Physics, 121, Article 104064. https://doi.org/10.1016/j.medengphy.2023.104064
  65. Zhai, T., Mazzucato, P., Ricciardi, C., Christiani, D. C., Liang, L., Samson, L. D., & Nagel, Z. D. (2025). Comprehensive measurement of inter-individual variation in DNA repair capacity in healthy individuals. Advanced Science, Article e13893. https://doi.org/10.1002/advs.202513893
  66. Zompit, M. D. M., & Stucki, M. (2021). Mechanisms of genome stability maintenance during cell division. DNA Repair, 108, Article 103215. https://doi.org/10.1016/j.dnarep.2021.103215