Context:
We are analyzing in our work genetic edits to stem cells that will enhance their ability to survive stressful environments. After applying machine learning to identify pathways that are enriched in cells with greater resilience to known chemical stressors. This study is specifically focused on “perturbation screens” that assess how thousands of potential edits to these cells improve their resistance to these stressors after being differentiated into neurons. This study has strong relevance to guide further efforts for engineering neuronal replacement therapies (neural grafts) in later validation studies. The top 10 pathways that are defined will be selected based on gene set enrichment analysis (GSEA) ranking by normalized enrichment score of the most highly expressed genes among cells that had the highest survival after perturbation and stress exposure.
Resolution Criteria:
YES if at least 1 of the top 10 pathways after ML-regression, clustering, and pathway enrichment among the best cluster:/ranking
• "chromatin remodeling"
• "histone modification"
• "DNA methylation"
• "chromatin organization"
• "epigenetic regulation of gene expression"
• "nucleosome assembly"
• “histone acetylation/deacetylation”
Otherwise NO.
Link to original pre-registration on ResearchHub and more detailed experimental plan:
1,000