Genular: Unraveling Cellular Identity and Function Across Biological Scales 2088

Abstract Description Defining cellular identity and function with precision is crucial. Current tools struggle to integrate and analyze the vast diversity within scRNA-seq data, limiting comprehensive insights into cellular heterogeneity and subtle genetic shifts across immune states, differentiation pathways, and tissue contexts. Genular addresses these gaps as an open-source platform unifying scRNA-seq analysis with extensive genomic and proteomic data from 16 databases. Aggregating over 4,042 scRNA-seq experiments encompassing 350 million cells across tissues and conditions, Genular offers scalability and computes a Cell Significance Index (CSI) for each gene, allowing quantification of gene expression across cells to derive unique profiles specific to cell types, states, and lineages in four contexts (disease, tissue, disease/tissue combinations, overall). Genular’s utility is demonstrated by differentiating T cell memory states, mapping differentiation profiles in monocyte-to-macrophage and lymphoid progenitor-to-T cell development, and capturing tissue-specific macrophage reprogramming. By integrating scRNA-seq data with cross-domain biological information and employing advanced statistical methods, Genular defines cellular identities and functional states at scale, facilitating breakthroughs in immunology, gene regulation, cellular differentiation, and disease research to advance understanding of immune cell functions in health and disease. Topic Categories Computational and Systems Immunology (COMP)