Demonstration of cellular dynamic behavior in response to DNA damage through gene regulatory network analysis of single-cell resolution DNA repair phenotype and geneexpression multi-omics dataset

A lot of genes may involve in the response of cells to DNA damage occurrence and regulation of these genesare complicated at different levels –. Also, it is known that their missing activity has important roles in cancerdiseases and treatment. Droplet microfluidics and Next Generation Sequencing (NGS) are enablingtechnologies to evaluate genomic complexity in single-cell resolution. In general, these methods extract alarge amount of data from different layers of biological information which opens computational challengesto analyze these big data toward biological interpretations. Richer et. al, ۲۰۲۰, used nano-biomimeticsubstrates containing arbitrary lesion DNA damage to quantitatively evaluate the relationship of geneexpression and repair phenotype in single-cell resolution. Among their data, we have selected time-seriesexperiments on the HAP۱ cancer cell line and designed our analysis workflows to reanalyze the availablemulti-omics data. Basically, for each cell, we have both gene expression profile and DNA repair measurementsimultaneously. We used statistical inference, mathematical modeling, and graph analysis of context-specificgene regulatory networks (GRN) to mine this data –. Also, we are connecting our data with publicly availabledatasets such as protein-protein interaction (PPI) and molecular signature database (MSigDB).

Keywords:

Single-cell , RNA expression , RNA-seq , Omics , Multi-omics , Enzyme activity , DNA Damage , DNA Repair , Gene Regulatory Network , Gene-set analysis , Network analysis