3/16 – Jianhua Xing, University of Pittsburgh School of Medicine

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Speaker: Jianhua Xing, Professor, University of Pittsburgh, School of Medicine, Department of Computational and Systems Biology, Department of Physics, and Hillman Cancer Center

Title: Reconstructing cell phenotypic transition dynamics from single cell data

Abstract:  Recent advances in single-cell techniques catalyze an emerging field of studying how cells convert from one phenotype to another, i.e., cell phenotypic transitions (CPTs). Two grand technical challenges, however, impede further development of the field. Fixed cell-based approaches can provide snapshots of high-dimensional expression profiles but have fundamental limits on revealing temporal information, and fluorescence-based live cell imaging approaches provide temporal information but are technically challenging for multiplex long- term imaging.

My lab is tackling these grand challenges from two directions, with the ultimate goal of integrating the two directions to reconstruct the spatial-temporal dynamics of CPTs. In one direction, we developed a live-cell imaging platform that tracks cellular status change in a composite multi-dimensional cell feature space that include cell morphological and texture features readily through fluorescent and transmission light imaging. We applied the framework to study human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT) (Sci Adv 2020, eLife 2022). In another direction, we aim at reconstructing single cell dynamics and governing equations from single cell genomics data (Cell 2022). We developed a procedure of learning the analytical form of the vector field F(x) and the equation dx/dt = F(x) in the Reproducing Kernel Hilbert Space. Further differential geometry analysis on the vector field reveals rich information on gene regulations and dynamics of various CPT processes.