Decoding cellular history in longitudinally inaccessible tissues

About This Grant

The overarching objective of this project is to develop computational methods to infer cellular history from single-cell sequencing data, enabling insights into disease progression in tissues that are not longitudinally accessible, such as the brain to study Alzheimer’s disease or the heart to study cardiomyopathy. Current single-cell approaches excel at capturing static snapshots of cellular states but fall short in reconstructing earlier unobserved cell states and dynamic histories critical for understanding disease mechanisms. To address these gaps, this project proposes a three-pronged approach: 1) leveraging lineage-tracing single-cell RNA sequencing to quantify how far back cellular history can be inferred; 2) using single-cell RNA sequencing data from large donor cohorts to decode human-specific disease dynamics while accounting for biological and environmental variability; and 3) integrating single-cell multi-omics (e.g., RNA and ATAC-seq) to model regulatory mechanisms that drive cellular transitions over time. Each aim will be validated using systems where longitudinal sequencing is possible to ensure methodological rigor and biological relevance. By building upon cutting-edge statistical and artificial intelligence ideas, this research will uncover hidden cellular histories not observable in the collected tissue and identify critical early events in disease progression. These insights will advance fundamental knowledge of complex biological processes, inform therapeutic strategies, and align with the mission of NIGMS to enhance health and reduce disease burden by supporting innovative and impactful biomedical research.