קולוקוויום בביה"ס למדעי המחשב - Representation, inference and design of multicellular systems
Mor Nitzan (Harvard)
The past decade has witnessed the emergence of single-cell technologies that measure the expression level of genes at a single-cell resolution. These developments have revolutionized our understanding of the rich heterogeneity, structure, and dynamics of cellular populations, by probing the states of millions of cells, and their change under different conditions or over time. However, in standard experiments, information about the spatial context of cells, along with additional layers of information they encode about their location along dynamic processes (e.g. cell cycle or differentiation trajectories), is either lost or not explicitly accessible. This poses a fundamental problem for elucidating collective tissue function and mechanisms of cell-to-cell communication. In this talk I will present computational approaches for addressing these challenges, by learning interpretable representations of structure, context and design principles for multicellular systems from single-cell information. I will first describe how the locations of cells in their tissue of origin and the resulting spatial gene expression can be probabilistically inferred from single-cell information by a generalized optimal-transport optimization framework, that can flexibly incorporate prior biological assumptions or knowledge derived from experiments. Inference in this case is based on an organization principle for spatial gene expression, namely a structural correspondence between distances of cells in expression and physical space, which we hypothesized and supported for different tissues. We used this framework to spatially reconstruct diverse tissues and organisms, including the fly embryo, mammalian intestinal epithelium and cerebellum, and further inferred spatially informative genes. Since cells encode multiple layers of information, in addition to their spatial context, I will also discuss several approaches for the disentanglement of single-cell gene expression into distinct biological processes, based on ideas rooted in random matrix theory and manifold learning. I will finally discuss how these results can be generalized to reveal principles underlying self-organization of cells into multicellular structures, setting the foundation for the computationally-directed design of cell-to-cell interactions optimized for specific tissue structure or function. Bio: Mor Nitzan is currently a John Harvard Fellow and James S. McDonnell Fellow at Harvard University. She focuses on developing computational frameworks for learning principles underlying cellular information processing, self-organization into multicellular structures, and decoding of structural and temporal structures from single-cell data. Nitzan completed a BSc in Physics, and obtained a PhD in Physics and Computational Biology at the Hebrew University, with Prof. Hanah Margalit and Prof. Ofer Biham, working on the interplay between structure and dynamics in complex biological networks. Nitzan was hosted as a post-doctoral fellow in the group of Prof. Nir Friedman (Hebrew University), in collaboration with Prof. Aviv Regev (Broad Institute). Nitzan is the recipient of the John Harvard Distinguished Science Fellowship in the Physical Sciences, James S. McDonnell Postdoctoral Fellowship Award in Studying Complex Systems, Eric and Wendy Schmidt Postdoctoral Award for Women in Mathematical and Computing Sciences, Vatat Scholarship Award for Postdoctoral Researchers, Carl Friedrich Gauss Research Fellowship, and Azrieli Fellowship for sciences.