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Cell Fate Decision as High-Dimensional Critical State Transition
Author(s) -
Mitra Mojtahedi,
Alexander Skupin,
Joseph Zhou,
Ivan G. Castaño,
Rebecca Y. Y. Leong-Quong,
Hannah H. Chang,
Kalliopi Trachana,
Alessandro Giuliani,
Sui Huang
Publication year - 2016
Publication title -
plos biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.127
H-Index - 271
eISSN - 1545-7885
pISSN - 1544-9173
DOI - 10.1371/journal.pbio.2000640
Subject(s) - progenitor cell , biology , attractor , microbiology and biotechnology , progenitor , stem cell , cell fate determination , cell type , lineage (genetic) , transition (genetics) , computational biology , cell , gene , genetics , transcription factor , mathematical analysis , mathematics
Cell fate choice and commitment of multipotent progenitor cells to a differentiated lineage requires broad changes of their gene expression profile. But how progenitor cells overcome the stability of their gene expression configuration (attractor) to exit the attractor in one direction remains elusive. Here we show that commitment of blood progenitor cells to the erythroid or myeloid lineage is preceded by the destabilization of their high-dimensional attractor state, such that differentiating cells undergo a critical state transition. Single-cell resolution analysis of gene expression in populations of differentiating cells affords a new quantitative index for predicting critical transitions in a high-dimensional state space based on decrease of correlation between cells and concomitant increase of correlation between genes as cells approach a tipping point. The detection of “rebellious cells” that enter the fate opposite to the one intended corroborates the model of preceding destabilization of a progenitor attractor. Thus, early warning signals associated with critical transitions can be detected in statistical ensembles of high-dimensional systems, offering a formal theory-based approach for analyzing single-cell molecular profiles that goes beyond current computational pattern recognition, does not require knowledge of specific pathways, and could be used to predict impending major shifts in development and disease.

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