Decoding the Multi-facets of Cellular Identity from Single-cell Data

Advances in technologies that measure gene expression at single-cell resolution have revolutionized our understanding of the heterogeneity, structure and dynamics of tissues and whole organisms in health and disease. Yet, in most single-cell experiments tissue structure, temporal trajectories, and their underlying mechanisms are lost or not directly accessible. Despite experimental advances, major gaps remain in understanding how tissues orchestrate multicellular functions. In recent years, we and others focused on computationally recovering single facets of single-cell data, such as tissue structure or differentiation trajectories. However, each cell encodes multiple layers of information about its type, location, and various biological processes. Disentangling these signals from large-scale, high-dimensional single-cell data is a major challenge. Building on my expertise in network reconstruction, probabilistic spatial inference and spectral analysis of single-cell data, I will take a unique approach to this challenge by developing computational methodologies combining machine learning and dynamical systems approaches to: (1) tease apart multiple cellular facets encoded in single-cell data; (2) infer interactions between these facets and mechanisms shaping spatiotemporal expression across them; (3) derive generative models to sample and predict unobserved cell states and design optimal perturbations, providing an interpretable platform to study conditions leading to a physiological disruption and therapies aimed at reversing it. My research program will tackle the core challenge in the single-cell era - transforming this exponentially growing, complex data into insight and principles for the underlying biology of multicellular systems. It will advance our understanding and control of collective tissue behavior, and uncover the multiple facets of cellular identity in health and disease, and thus expected to be valuable for both basic and translational research.