Jianhua Xing (University of Pittsburgh)

Date: Wed. December 2nd, 2020, 4:30 pm-5:30 pm
Location: Zoom - Meeting ID: 998 4762 6656 Password: 872745 (link: https://cwru.zoom.us/j/99847626656?pwd=WGZDejhnZ0JmbXo0QnRjZmFkanBOdz09 )
Website: https://www.physicsandastronomy.pitt.edu/people/jianhua-xing

Reconstructing cell phenotypic transition dynamics from single cell data

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 imaging¹. We also introduced transition path analyses and the concept of reaction coordinate from the well-established rate theories into CPT studies². We applied the framework to study human A549 cells undergoing TGF-β induced epithelial-to-mesenchymal transition (EMT).

In another direction, we aim at reconstructing single cell dynamics and governing equations from single cell genomics data³. The work is inspired by recent work of estimating RNA velocities (instant time derivatives (dx/dt, with x is the cell expression state)⁴. We generalized the procedure for more accurate estimation of RNA velocities from scRNA-seq data with metabolic labeling (and other types of single cell data). Then formulating it as a machine learning problem, 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.

1. Wang, W. et al. Live-cell imaging and analysis reveal cell phenotypic transition dynamics inherently missing in snapshot data. Science Advances 6, eaba9319 (2020).
2. Wang, W. & Xing, J. Analyses of Multi-dimensional Single Cell Trajectories Quantify Transition Paths Between Nonequilibrium Steady States. bioRxiv, 2020.01.27.920371 (2020).
3. Qiu, X. et al. Mapping Vector Field of Single Cells. bioRxiv, 696724 (2019).
4. La Manno, G. et al. RNA velocity of single cells. Nature (2018).