Multi-scale classification of single-cell gel electrophoresis assay using deep learning algorithm

Abstract

Structural and functional integrity of deoxyribonucleic acid (DNA) is crucial for the maintenance of hereditary information. However, by-products of cellular metabolism and physical or chemical factors may cause spontaneous DNA damage. The alkaline single-cell gel electrophoresis or comet assay analysis is an easy and reliable method for the determination genotoxic effects of chemical and physical factors. Simply, it is the electrophoretic analysis of intact/damaged DNA of a single cell on in a thin layer of agarose gel. The quantitative analysis of the comet assay images is performed manually by an expert researcher. In visual scoring, DNA nuclei are scored as 0, 1, 2, 3, and 4; and the correct scoring is crucial for the determination of the DNA damage. However, visual scoring depends on the professional experience of the researcher and it is a time consuming and exhausting task. Therefore, this evaluation is inevitable to have subjective results. To avoid this subjectivity and to show the effectiveness of deep learning algorithm on cell images, a Convolution Neural Network (CNN) based deep learning method is proposed to classify comet assay images. According to the results, CNN is trained and tested with high accuracy. The results show that CNN algorithm can successfully classify five different scores of comet assay images, and these results can also reduce the subjectivity.