Cookies on this website

We use cookies to ensure that we give you the best experience on our website. If you click 'Accept all cookies' we'll assume that you are happy to receive all cookies and you won't see this message again. If you click 'Reject all non-essential cookies' only necessary cookies providing core functionality such as security, network management, and accessibility will be enabled. Click 'Find out more' for information on how to change your cookie settings.

Dynamic cellular processes such as differentiation are driven by changes in the abundances of transcription factors (TFs). However, despite years of studies, our knowledge about the protein copy number of TFs in the nucleus is limited. Here, by determining the absolute abundances of 103 TFs and co-factors during the course of human erythropoiesis, we provide a dynamic and quantitative scale for TFs in the nucleus. Furthermore, we establish the first gene regulatory network of cell fate commitment that integrates temporal protein stoichiometry data with mRNA measurements. The model revealed quantitative imbalances in TFs' cross-antagonistic relationships that underlie lineage determination. Finally, we made the surprising discovery that, in the nucleus, co-repressors are dramatically more abundant than co-activators at the protein level, but not at the RNA level, with profound implications for understanding transcriptional regulation. These analyses provide a unique quantitative framework to understand transcriptional regulation of cell differentiation in a dynamic context.

Original publication

DOI

10.1016/j.molcel.2020.03.031

Type

Journal article

Journal

Mol Cell

Publication Date

04/06/2020

Volume

78

Pages

960 - 974.e11

Keywords

absolute quantification, cell fate, erythropoiesis, gene regulatory network, hematopoiesis, protein stoichiometry, proteomics, stem cells, targeted mass spectrometry, transcription, Databases, Factual, Erythropoiesis, Gene Expression Regulation, Gene Regulatory Networks, Hematopoiesis, Humans, Proteomics, Transcription Factors