Identification and Classification of Damage in DNA Imagery Using Deep Learning Algorithms

Öz

In this study, the application of deep learning, particularly Convolutional Neural Networks (CNNs), to analyze comet assay images for DNA damage assessment is explored. The comet as-say is a pivotal method for detecting DNA strand breaks at the cellular level, essential in geno-toxicity and carcinogenicity research. Traditional approaches to analyze these images often in-volve manual labor or basic computational tools, which are inefficient, especially with noisy data. This research addresses these inefficiencies by developing a custom CNN model to auto-matically classify DNA damage levels in comet assay images. The dataset consists of 5,326 im-ages, categorized into six damage levels: from undamaged (C0) to extensively damaged (C4), plus an unidentifiable category (C6). Data augmentation was employed to enhance the model's robustness by creating varied inputs for training. The CNN processes the raw images through several layers to extract features and identify patterns, facilitating the classification of DNA damage levels. The model's performance was assessed using a confusion matrix, achieving an overall classification accuracy of approximately 92%. Although the model was highly accurate in distinguishing severe damage levels, it struggled with closely related classes, such as slightly and moderately damaged DNA. This study underscores the potential of deep learning in auto-mating and improving the analysis of comet assay images. CNNs offer a more accurate and effi-cient alternative to traditional methods, which could significantly advance research in genotoxi-city and clinical diagnostics, leading to a better understanding and monitoring of DNA damage in biological systems.

Anahtar Kelimeler

• The single-cell gel electrophoresis
• Deep learning
• Comet assay

Kaynakça

1. [1] Hoeijmakers, J. H. DNA damage, aging, and cancer. N. Engl. J. Med. 361, 1475–1485 (2009).
2. [2] Kadioglu, E., Sardas, S., Aslan, S., Isik, E. & Karakaya, A. E. Detection of oxidative DNA damage in lymphocytes of patients with Alzheimer’s disease. Biomarkers 9, 203–209 (2004).
3. [3] Kopjar, N., Garaj-Vrhovac, V. & Milas, I. Assessment of chemotherapy-induced DNA damage in peripheral blood leukocytes of cancer patients using the alkaline comet assay. Teratog. Carcinog. Mutagen. 22, 13–30 (2002).
4. [4] Collins, A. R. et al. DNA damage in diabetes: Correlation with a clinical marker. Free Radical Biol. Med. 25, 373–377 (1998).
5. [5] A.R. Collins, M. Ai-Guo, S.J. Duthie, The kinetics of repair of oxidative dna dam-age (strand breaks and oxidised pyrimidines) in human cells, Mutat. Res./DNA Repair 336 (1) (1995) 69–77.
6. [6] D.W. Fairbairn, P.L. Olive, K.L. O’Neill, The comet assay: a comprehensive review, Mutat. Res./Rev. Genet. Toxicol. 339 (1) (1995) 37–59.
7. [7] M. Kuchařová, M. Hronek, K. Rybáková, Z. Zadák, R. Štětina, V. Josková, A. Patková, Comet assay and its use for evaluating oxidative dna damage in some pathological states, Physiol. Res. 68 (1) (2019) 1–15.
8. [8] Ostling, O. & Johanson, K. J. Microelectrophoretic study of radiation-induced DNA damages in individual mammalian cells. Biochem. Biophys. Res. Commun. 123, 291–298 (1984).

9. [9] Singh, N. P., McCoy, M. T., Tice, R. R. & Schneider, E. L. A simple technique for quantitation of low levels of DNA damage in individual cells. Exp. Cell Res. 175, 184–191 (1988).
10. [10] Helma, C. & Uhl, M. A public domain image-analysis program for the single-cell gel-electrophoresis (comet) assay. Mutat. Res. Genet. Toxicol. Environ. Mutagenesis 466, 9–15 (2000).
11. [11] Gyori, B. M., Venkatachalam, G., Thiagarajan, P., Hsu, D. & Clement, M.-V. OpenComet: An automated tool for comet assay image analysis. Redox Biol. 2, 457–465 (2014).
12. [12] N. Van Eck, L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping, Scientometrics 84 (2) (2010) 523–538, https://doi.org/ 10.1007/s11192-009-0146-3.
13. [13] S. Alonso, F.J. Cabrerizo, E. Herrera-Viedma, F. Herrera, h-Index: a review focused in its variants, computation and standardization for different scientific fields, J. Infor. 3 (4) (2009) 273–289, https://doi.org/10.1016/j.joi.2009.04.001.
14. [14] M. Laakso, A. Klippi, A closer look at the’hint and guess’ sequences in aphasic conversation, Aphasiology 13 (4–5) (1999) 345–363, https://doi.org/10.1080/ 026870399402136.
15. [15] D.J. Clarke, Nursing practice in stroke rehabilitation: systematic review and meta-ethnography, J. Clin. Nurs. 23 (9–10) (2014) 1201–1226, https://doi.org/ 10.3109/07434618.2014.955614.
16. [16] G. Chen, L. Xiao, Selecting publication keywords for domain analysis in bibliometrics: a comparison of three methods, J. Infor. 10 (1) (2016) 212–223, https:// doi.org/10.1016/j.joi.2016.01.006.
17. [17] Sreelatha, G., Muraleedharan, A., Sathidevi, P. S., Chand, P., and Rajkumar, R. P., “CometQ: An automated tool for the detection and quantification of DNA damage using comet assay image analysis”, Computer Programs and Methods in Biomedicine, 133: 143-154 (2016)
18. [18] Lee, T., Lee, S., Sim, W. Y., Jung, Y. M., Han, S., Chung, C., Chang, J. J., Min, H., and Yoon, S., “Robust classification of DNA damage patterns in single cell gel electrophoresis”, 35th Annual International Conference of the IEEE EMBS, Osaka, 3666-3669 (2013).
19. [19] Sreelatha, G., Rashmi, P., Sathidevi, P. S., Aparma, M., Chand, P., and Rajkumar, R. P., “Automatic detection of comets in silver stained comet assay images for dna damage analysis”, 2014 IEEE International Conference on Signal Processing,
20. [20] Sansone, M., Zeni, O., and Esposito, G., “Automated segmentation of comet assay images using Gaussian filtering and fuzzy clustering”, Med Biol Eng Comput, 50 (5): 523-532 (2012).
21. [21] Böcker, W., Rolf, W., Bauch, T., Müller, W., U., and Streffer, C., “Automated comet assay analysis”, Cytometry, 35 (2): 134-144 (1999).
22. [22] Gonzalez, J. E., Romero, I., Barquinero J. E., and Garcia, O., “Automatic analysis of silver-stained comets by CellProfiler software”, Mutation Research, 748 (1): 60-64 (2012).
23. [23] Riccardo Rosati, Luca Romeo, Sonia Silvestri, Fabio Marcheggiani, Luca Tiano, Emanuele Frontoni “Faster R-CNN approach for detection and quantification of DNA damage in comet assay images” Computers in Biology and Medicine 123 (2020) 103912
24. [24] Attila Beleon, Sara Pignatta, Chiara Arienti, Antonella Carbonaro, Peter Horvath, Giovanni Martinelli, Gastone Castellani, Anna Tesei, Filippo Piccinini “CometAnalyser: A user-friendly, open-source deep-learning microscopy tool for quantitative comet assay analysis”, Computational and Structural Biotechnology Journal 20 (2022) 4122–4130
25. [25] Srikanth Namuduria, Prateek Mehta, Lise Barbe, Stephanie Lam, Zohreh Faghihmonzavi, Steve Finkbeiner, Shekhar Bhansali “Faster Deep Ensemble Averaging for Quantification of DNA Damage from Comet Assay Images With Uncertainty Estimates”, arXiv:2112.12839v1 [q-bio.QM] 23 Dec 2021
26. [26] Afiahayati, Edgar Anarossi, Ryna Dwi Yanuaryska and Sri Mulyana, “GamaComet: A Deep Learning-Based Tool for the Detection and Classification of DNA Damage from Buccal Mucosa Comet Assay Images”, Diagnostics 2022, 12, 2002. https://doi.org/10.3390/diagnostics12082002
27. [27] Lewis, N. D. (2016). Deep Learning Made Easy with R. Auscov, ABD
28. [28] Mousavi, S. S., Schukat, M., & Howley, E. (2016). “Deep reinforcement learning: an overview”. In Proceedings of SAI Intelligent Systems Conference: 426-440.
29. [29] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press, ABD.
30. [30] Agrawal, A. “Loss functions and optimization algorithms”. https://medium.com: https://medium.com/data-science-group-iitr/loss-functions-and-optimizationalgorithms-demystified-bb92daff331c
31. [31] Sharma, A. “Understanding activation functions in deep learning”. https://www.learnopencv.com/understanding-activation-functions-in-deeplearning/
32. [32] Mehrotra, K., Mohan, C. K., & Ranka, S. (1996). Elements of Artificial Neural Networks. USA: MIT Press.
33. [33] Patterson, J., & Gibson, A. (2017). Deep Learning: A Practitioner's Approach. O'Reilly Media, Inc, ABD.
34. [34] Heaton, J. (2015). Artificial Intelligence for Humans, Volume 3: Neural Networks and Deep Learning. Heaton Research Inc, Chesterfield, ABD.
35. [35] Gulli, A., & Pal, S. (2017). Deep Learning with Keras. . Packt Publishing Ltd, Birmingham.
36. [36] Maas, A.L., Hannun, A.Y., & Ng, A.Y. (2013). “Rectifier nonlinearities improve neural network acoustic models”. In Proc. icml (Vol. 30, No. 1): 3-10.
37. [37] Almufadi, N., & Qamar, A.M. (2022). Deep Convolutional Neural Network Based Churn Prediction for Telecommunication Industry. Computer Systems Science and Engineering, 43(3), 1255-1270. https://doi.org/10.32604/csse.2022.025029
38. [38] Ganapathy, S., Muraleedharan, A., Sathidevi, P. S., Chand, P. & Rajkumar, R. P. CometQ: An automated tool for the detection and quantification of DNA damage using comet assay image analysis. Comput. Methods Programs Biomed. 133, 143–154 (2016).
39. [39] Smochina, C., Manta, V., Kropatsch, W., Crypts detection in microscopic images using hierarchical structures, Pattern Recognit. Lett. 34 (8) (2013) 934–941, Computer Analysis of Images and Patterns.
40. [40] Rojas E, Lopez MC, Valverde M. Single cell gel electrophoresis assay: methodology and applications. J. Chromatogr, 1999; 722: 225-54.
41. [41] McKelvey-Martin VJ, Green MH, Schmezer P, Pool-Zobel BL, DeMeo MP, Collins A. The single cell gel electrophoresis assay (comet assay): a European review. Mutat. Res, 1993; 288(1): 47-63.
42. [42] Agarwal A, Erenpreiss J, Sharma R. Sperm chromatin assessment. In: Gardner DK, Weissman A, Howles CM, Shoham Z. Eds. Textbook of Assisted Reproductive Technologies. 3rd edition, India: Replika Pres Pvt. Ltd, 2009:67-84.
43. [43] Olive PL, Durand RE, Banath JP, Evans HH. Etoposide sensitivity and topoisomerase II activity in Chinese hamster V79 monolayers and small spheroids. Int. J. Radiat. Biol, 1991; 60(3): 453-66.
44. [44] https://medium.com/@sebinbusra/evrisimsel-sinir-agi-convolutional-neural-network-f49f7b65c72
45. [45] Xu, X., Yang, Z., Wang, Y., A method based on rank-ordered filter to detect edges in cellular image, Pattern Recognit. Lett. 30 (6) (2009) 634–640.
46. [46] Shen, D., Wu, G., Suk, H.I., Deep learning in medical image analysis, Annu. Rev. Biomed. Eng. 19 (Jun (1)) (2017) 221–248.
47. [47] Schmidt-Richberg A, Brosch T, Schadewaldt N, Klinder T, Caballaro A, Salim I, et al., "Abdomen segmentation in 3D fetal ultrasound using CNN-powered deformable models", (2017)
48. [48] Kamnitsas K, Ledig C, Newcombe VFJ, Simpson JP, Kane AD, Menon DK, et al. Zheng Y, Barbu A, Georgescu B, Scheuering M, Comaniciu D., "Four-chamber heart modeling and automatic segmentation for 3-D cardiac CT volumes using marginal space learning and steerable features", (2008)
49. [49] Dietterich, T.G., Approximate statistical tests for comparing supervised classi-fication learning algorithms, Neural Comput. 10 (7) (1998) 1895–1923, http: //dx.doi.org/10.1162/089976698300017197.
50. [50] Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A., The pascal visual object classes (voc) challenge, Int. J. Comput. Vis. 88 (2) (2010) 303–338.
51. [51] Ren, S., He, K., Girshick, R. & Sun, J. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems 91–99 (2015).
52. [52] Atila, Ü., Baydilli, Y. Y., Sehirli, E. & Turan, M. K. Classification of DNA damages on segmented comet assay images using convo-lutional neural network. Comput. Methods Programs Biomed. 186, 105192 (2020).
53. [53] Azadvatan Y., Kurt M. Azadvatan Y., Kurt M. MelNet: A Real-Time Deep Learning Algorithm for Object Detection, Computer Vision and Pattern Recognition (2024) https://doi.org/10.48550/arXiv.2401.17972