Cell Fate Decision as High-Dimensional Critical State Transition

被引:222
作者
Mojtahedi, Mitra [1 ,2 ]
Skupin, Alexander [2 ,3 ]
Zhou, Joseph [2 ]
Castano, Ivan G. [1 ,4 ]
Leong-Quong, Rebecca Y. Y. [1 ]
Chang, Hannah [5 ]
Trachana, Kalliopi [2 ]
Giuliani, Alessandro [6 ]
Huang, Sui [1 ,2 ]
机构
[1] Univ Calgary, Dept Biol Sci, Calgary, AB, Canada
[2] Inst Syst Biol, Seattle, WA 98109 USA
[3] Luxembourg Ctr Syst Biomed, Esch Sur Alzette, Luxembourg
[4] Corp Parque Explora, Dept Innovat & Design, Medellin, Colombia
[5] 5AM Ventures, Menlo Pk, CA USA
[6] Ist Super Sanita, Environm & Hlth Dept, Rome, Italy
基金
加拿大自然科学与工程研究理事会;
关键词
SINGLE-CELL; STEM-CELL; LINEAGE-COMMITMENT; PROGENITOR CELLS; RNA-SEQ; GENE-EXPRESSION; DIFFERENTIATION; HETEROGENEITY; BIFURCATIONS; LANDSCAPE;
D O I
10.1371/journal.pbio.2000640
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
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.
引用
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页数:28
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