30 Oct 2020
Jianhua Xing
Department of Computational and Systems Biology
University of Pittsburgh
Mammalian cells assume different phenotypes that can have drastically different morphology and gene expression patterns, and can change between distinct phenotypes when subject to specific stimulation and microenvironment. Recent advances in snapshot single cell techniques further catalyze an emerging field of studying cell phenotypic transition (CPT) regulation and dynamics as one of the most exciting frontiers of cell and developmental biology.
In many aspects CPTs are analogous to chemical reactions at a molecular level, but typically take place at much larger and broader spatial-temporal scales and are far away from thermodynamic equilibrium. The parallel of molecular and cellular systems (1) inspired us to introduce the concepts and tools of chemical physics, such as reaction rate theories (2), to CPT studies. In this talk I will present our continuous efforts of reconstructing cellular dynamics, with an ultimate goal of controlling cell fates.
In one direction, we aim at reconstructing dynamics from high throughput single cell snapshot data. In collaboration with the Weissman lab, we developed a procedure of reconstructing the vector field governing gene regulation dynamics from date sets of scRNA-seq with metabolic labeling (3). Next we developed a procedure to coarse-grain the continuous dynamics into Markov chain state network, which allows further analyses using theories and approaches from chemical dynamics and network sciences.In another direction, we developed a quantitative framework that integrates standard imaging facilities and state-of-the-art computational analysis approaches to extract high-dimensional dynamical features of single live cell trajectories (4). The framework allows one to use the same mathematical language to quantitatively describe cell phenotypic transition dynamics as one describes particle motions in physics and chemistry, and apply modern chemical reaction rate theories to CPT processes (5).
An ongoing effort is to combine these two directions, and provide a more complete spatialtemporal description of how cells proceed step-by-step in a high-dimensional state space.