This research project focuses on deciphering the molecular mechanisms that determine DNA repair pathway choice between Non-Homologous End Joining (NHEJ) and Homology-Directed Repair (HDR) in response to different types of DNA breaks. Understanding these mechanisms is crucial for improving genome editing technologies and developing targeted cancer therapies.
As the lead computational analyst, I developed an innovative three-layer computational pipeline to analyze single-cell RNA sequencing data from CRISPR-edited cells. This approach allows us to understand how different DNA break configurations (staggered versus blunt-end) influence the cellular repair machinery at unprecedented resolution.
Three-layer inference method combining QC, statistical analysis, and pathway enrichment
Systematic comparison of repair responses to staggered vs blunt-end DNA breaks
High-resolution analysis of cellular heterogeneity in DNA repair responses
Comprehensive protein-protein interaction networks revealing repair factor dependencies
The analysis pipeline was designed to extract meaningful biological insights from complex single-cell data while maintaining statistical rigor and reproducibility.
Implemented stringent quality control workflow with optimized parameters (nFeature: 500-6500, nCount: 1000-45000) to ensure high-quality single-cell data. Integrated multiple experimental batches using advanced computational methods to minimize batch effects while preserving biological variation.
Performed differential expression analysis to identify genes responding to different DNA break types. This layer revealed initial candidates showing break-type-specific expression patterns, providing the foundation for deeper pathway analysis.
Conducted comprehensive pathway enrichment analysis using multiple databases (GO, KEGG, Reactome) to identify biological processes and molecular functions associated with different repair responses. This revealed key pathways differentially activated between break types.
Integrated protein-protein interaction networks with expression data to identify hub genes and regulatory modules. This final layer provided mechanistic insights into how repair factors coordinate their activities in response to different DNA lesions.
Identified 77 genes specifically upregulated in response to staggered breaks (FZ+) and 5 genes specific to blunt-end breaks (SZ+), revealing distinct molecular signatures for different DNA lesion types.
Discovered 23 genes showing FZ+/SZ- patterns and 2 genes with SZ+/FZ- patterns, indicating complex regulatory networks that fine-tune repair pathway choice based on break configuration.
Revealed time-dependent activation of repair pathways, with early response genes showing break-type-independent activation followed by specialized repair factor recruitment based on lesion characteristics.
Demonstrated how cell cycle status influences repair pathway choice, with HDR preference in S/G2 phases and NHEJ dominance in G1, modulated by break type and local chromatin context.
This research has significant implications for multiple fields, from basic molecular biology to clinical applications in cancer therapy and genome editing.
Our findings provide crucial insights for designing more efficient CRISPR strategies by understanding how different Cas enzymes and guide RNA designs influence repair outcomes. This knowledge can be applied to improve knock-in efficiency and reduce unwanted mutations.
Understanding repair pathway dependencies offers new therapeutic targets for cancer treatment. By identifying factors specific to each repair pathway, we can develop strategies to sensitize cancer cells to DNA-damaging therapies.
The repair factor signatures identified in this study serve as potential biomarkers for predicting cellular responses to DNA damage, which could guide personalized treatment strategies in precision medicine.
This work advances our understanding of how cells maintain genome integrity by revealing the complex decision-making processes that govern DNA repair pathway choice at the single-cell level.
This project represents a collaborative effort between computational and experimental biologists, combining cutting-edge single-cell technologies with advanced computational analyses.
Principal Investigator: Dr. Debojyoti Chakraborty, IGIB CSIR
Experimental Team: CRISPR genome editing and single-cell library preparation
Computational Analysis: Led by Vishal Bharti
Duration: 2023 - Present
Status: Manuscript in preparation
Manuscript in preparation (2025)
Abstract: DNA double-strand breaks (DSBs) can occur with different end configurations, yet how these structural differences influence repair pathway choice remains poorly understood. Here, we present a comprehensive single-cell RNA sequencing analysis of cellular responses to staggered versus blunt-end DNA breaks induced by different CRISPR-Cas systems. Through our three-layer computational pipeline, we identified distinct molecular signatures associated with each break type, revealing previously uncharacterized repair factor dependencies. Our findings demonstrate that break geometry significantly influences the recruitment and activity of repair factors, with implications for genome editing applications and cancer therapy development.
If you're interested in collaborating on DNA repair mechanisms, single-cell genomics, or computational biology research, I'd be happy to discuss potential opportunities.