Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process
- Angélique Richard 1, Loïs Boullu 2,3,4, Ulysse Herbach 1,2,3, Arnaud Bonnafoux 1,2,5, Valérie Morin 6, Elodie Vallin 1, Anissa Guillemin 1, Nan Papili Gao 7,8, Rudiyanto Gunawan 7,8, Jérémie Cosette 9, Ophélie Arnaud 10, Jean-Jacques Kupiec 11, Thibault Espinasse 3, Sandrine Gonin-Giraud 1, Olivier Gandrillon 1,2
- Angélique Richard 1, Loïs Boullu 2,3,4, Ulysse Herbach 1,2,3
- 1Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, F-69007, Lyon, France.
- 2Inria Team Dracula, Inria Center Grenoble Rhône-Alpes, France.
- 3Université de Lyon, Université Lyon 1, CNRS UMR 5208, Institut Camille Jordan 43 blvd du 11 novembre 1918, F-69622 Villeurbanne-Cedex, France.
- 4Département de Mathématiques et de statistiques de l'Université de Montréal, Pavillon André-Aisenstadt, 2920, chemin de la Tour, Montréal (Québec) H3T 1J4 Canada.
- 5The CoSMo company. 5 passage du Vercors - 69007 LYON - France.
- 6Univ Lyon, Univ Claude Bernard, CNRS UMR 5310 - INSERM U1217, Institut NeuroMyoGène, F-69622 Villeurbanne-Cedex, France.
- 7Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
- 8Swiss Institute of Bioinformatics, Quartier Sorge - Batiment Genopode, 1015 Lausanne Switzerland.
- 9Genethon - Institut National de la Santé et de la Recherche Médicale - INSERM, Université d'Evry-Val-d'Essone - 1 rue de l'internationale 91000 Evry, France.
- 10RIKEN - Center for Life Science Technologies (Division of Genomic Technologies)-CLST (DGT), 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
- 11INSERM, Centre Cavaillès, Ecole Normale Supérieure, F-75005 Paris, France.
- 0Univ Lyon, ENS de Lyon, Univ Claude Bernard, CNRS UMR 5239, INSERM U1210, Laboratory of Biology and Modelling of the Cell, 46 allée d'Italie Site Jacques Monod, F-69007, Lyon, France.
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View abstract on PubMed
Summary
This summary is machine-generated.Cell differentiation involves dynamic gene expression changes, with single-cell analysis revealing peak variability at fate commitment. This study uses Shannon entropy to track heterogeneity during erythroid progenitor differentiation, identifying key drivers and network dynamics.
Area Of Science
- Cellular and Molecular Biology
- Developmental Biology
- Systems Biology
Background
- Stochastic dynamics in cell differentiation suggest gene expression variability peaks at fate commitment.
- Population-based studies mask crucial cell-to-cell variations in gene expression during differentiation.
- Understanding single-cell dynamics is key to deciphering complex biological processes like cell fate decisions.
Purpose Of The Study
- To test the hypothesis that gene expression variability peaks at cell fate commitment during differentiation.
- To analyze single-cell gene expression dynamics in primary chicken erythroid progenitors.
- To identify potential molecular drivers and network behaviors governing cell differentiation.
Main Methods
- Single-cell gene expression analysis of chicken erythroid progenitors at six sequential time-points.
- Quantification of cell-to-cell variability using Shannon entropy.
- Analysis of gene correlation networks and identification of dynamical network biomarkers (DNB).
Main Results
- Single-cell analysis revealed high cell-to-cell variability masked by population averaging.
- Shannon entropy peaked between 8 and 24 hours, preceding irreversible differentiation commitment (24-48h) and cell size variability increase (48h).
- A subgroup of genes related to sterol synthesis was identified as potential initial drivers of differentiation.
Conclusions
- Cell differentiation is a dynamic process driven by molecular network behavior, not a simple, identical program for all cells.
- Single-cell analysis provides new insights and observables, like entropy, crucial for understanding differentiation heterogeneity.
- Dynamical network biomarker theory and single-cell entropy measurements offer powerful tools for studying cell fate transitions.
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