Systematically inferring directional effects within cell states from single-cell data

Abstract

A deeper understanding of the molecular wiring of cells in health—and where it goes awry in disease—can enable the development of more precise therapies that target not just consequences, but also the root causes of devastating ailments. Over the past decade, advances in human genetics and technologies to assay biology at single-cell resolution have introduced high-resolution strategies to define causal mechanisms of disease. Genome-wide association studies have highlighted genetic variants that are associated with hundreds of complex traits; in parallel, single-cell RNA (scRNA)-sequencing and its derivatives have revealed the heterogeneity present across cells within a given tissue. The gap that remains is making sense of the variation observed through functional explanations: which cell states, regulatory elements, and genes play causal roles in the initiation and progression of diseases? Answering these questions systematically has proven challenging. In this thesis, we pursue these questions by developing frameworks to identify key cell states enriched for the heritability of disease, the role that clonality might play in the development of these cell states, and the gene regulatory networks at play within individual cell states of interest. We focus on immune-mediated diseases; however, our frameworks can be expanded more broadly. We first leveraged multimodal single-nucleus RNA- and ATAC-seq data from inflamed synovial tissue to identify accessible regions of chromatin associated with distinct immune cell states. For 14 autoimmune diseases, we discovered that cell-state-dependent peaks in immune cell types assayed in inflammatory tissues disproportionately captured heritability and pointed to T peripheral helper, regulatory T, dendritic, and STAT1+CXCL10+ myeloid cell states as enriched for disease-critical genetic variation. These same populations are also expanded in inflammatory tissues. We argue that dynamic regulatory elements can help identify precise cell states enriched for disease-critical genetic variation. We next sought to better understand which cell states may be clonally driven in immune-mediated responses. Thus, we tested for associations between T cell states important in immune response to infection and clonality in a COVID-19 dataset of >110,000 cells with scRNA- and scTCR-seq information to characterize which cell states—both discrete and continuous—may be driven by clonal membership. We further asked which specific clonotypes were explaining the variance for these cell states. Finally, we presented a theoretical framework to systematically identify regulatory relationships within a cell state of interest without need for perturbation. Through modeling and simulations, we showed that this is possible by leveraging the intrinsic stochasticity in transcriptional bursting across individual cells at steady state. Importantly, changing magnitude of time-shifted correlations in RNA expression make it possible to distinguish covariation due to regulatory relationships within a cell state from covariation due to confounding from the presence of multiple cell states. Overall, this work demonstrates the power of combining single-cell functional measurements with orthogonal information—from genetics to clonality to time—to identify disease-driving cell states and their associated regulatory networks.