Epithelial-to-mesenchymal transition proceeds through directional destabilization of multidimensional attractor

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Abstract

How a cell changes from one stable phenotype to another one is a fundamental problem in developmental and cell biology. Mathematically a stable phenotype corresponds to a stable attractor in a generally multi-dimensional state space, which needs to be destabilized so the cell relaxes to a new attractor. Two basic mechanisms for destabilizing a stable fixed point, pitchfork and saddle-node bifurcations, have been extensively studied theoretically, however direct experimental investigation at the single cell level remains scarce. Here we performed live cell imaging studies and analyses in the framework of dynamical systems theories on epithelial-to-mesenchymal transition (EMT). While some mechanistic details remain controversial, EMT is a cell phenotypic transition (CPT) process central to development and pathology. Through time-lapse imaging we recorded single cell trajectories of human A549/Vim-RFP cells undergoing EMT induced by different concentrations of exogenous TGF-β in a multi-dimensional cell feature space. The trajectories clustered into two distinct groups, indicating that the transition dynamics proceeds through parallel paths. We then reconstructed the reaction coordinates and the corresponding quasi-potentials from the trajectories. The potentials revealed a plausible mechanism for the emergence of the two paths where the original stable epithelial attractor collides with two saddle points sequentially with increased TGF-β concentration, and relaxes to a new one. Functionally the directional saddle-node bifurcation ensures a CPT proceeds towards a specific cell type, as a mechanistic realization of the canalization idea proposed by Waddington.

Data availability

The computer code are shared on GitHub, so other researchers can run to reproduce Figure 3, 4, and 5. The processed single cell trajectory data are on Dryad

The following data sets were generated

1. Wang W
(2022) A549 VIM-RFP EMT single cell trajectory data
Dryad Digital Repository, RRID:SCR_005910.

https://dx.doi.org/10.5061/dryad.7h44j0zvp

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Weikang Wang

Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States

For correspondence
weikang@pitt.edu

Competing interests
The authors declare that no competing interests exist.
Dante Poe

Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States

Competing interests
The authors declare that no competing interests exist.
Yaxuan Yang

Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States

Competing interests
The authors declare that no competing interests exist.
Thomas Hyatt

Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, United States

Competing interests
The authors declare that no competing interests exist.
Jianhua Xing

Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, United States

For correspondence
xing1@pitt.edu

Competing interests
The authors declare that no competing interests exist.

"This ORCID iD identifies the author of this article:" 0000-0002-3700-8765

Funding

National Institute of Diabetes and Digestive and Kidney Diseases (R01DK119232)

Jianhua Xing

National Cancer Institute (R37 CA232209)

Jianhua Xing

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Copyright

This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.