Computational Modeling of Lineage Decisions using Single-cell Data

Project Summary Cellular differentiation is a fundamental biological process through which complex multi-cellular organisms develop from single-cell embryos and maintain tissue homeostasis throughout life. Cells integrate signals from the microenvironment and transmit them to downstream transcriptional regulators, which execute the expression and chromatin changes to define phenotypic state transitions in differentiation trajectories. Elucidating the principles of how cells choose their fate, and the path they take to get there, is a major challenge in the field. Single-cell (sc) RNA sequencing technologies are revolutionizing our understanding of the cellular spatio- temporal trajectories that shape differentiation. The emergence of additional high throughput, multimodal technologies such as paired RNA&ATAC-seq, scCUT&Tag and spatial technologies provide unprecedented opportunities to extract mechanistic insights into the lineage decisions that underly differentiation trajectories. This proposal aims to exploit this enormous potential by developing sophisticated new algorithms that integrate single-cell measurements to model and interpret complex biology. Through analysis of multiple single-cell RNA- seq datasets, we demonstrate that phenotypic asymmetries are a pervasive feature of lineage decisions. We will develop algorithms to unravel the mechanisms that drive lineage decisions and the underlying asymmetries in three broad research directions. We will investigate the role of: (i) enhancer priming and transcriptional regulation, (ii) open and heterochromatin dynamics, and (iii) cell communication in shaping differentiation trajectories. Our studies will lead to novel insights surrounding cell-autonomous and non-autonomous mechanisms engaged by cells as they navigate the phenotypic landscape. Successful completion of this research will provide a robust mechanistic basis to delineate normal differentiation events, decipher dysregulation of these mechanisms in disease, understand repurposing of differentiation mechanisms in wound healing and regeneration, and reconstruct differentiation processes in vivo and ex vivo to unlock the therapeutic potential of cell engineering.