Integration of a multi-omics stem cell differentiation dataset using a dynamical model
PLoS Genetics, vol. 19, pp. e1010744
Abstract
Stem cell differentiation is a highly dynamic process involving pervasive changes in gene expression. The large majority of existing studies has characterized differentiation at the level of individual molecular profiles, such as the transcriptome or the proteome. To obtain a more comprehensive view, we measured protein, mRNA and microRNA abundance during retinoic acid-driven differentiation of mouse embryonic stem cells. We found that mRNA and protein abundance are typically only weakly correlated across time. To understand this finding, we developed a hierarchical dynamical model that allowed us to integrate all data sets. This model was able to explain mRNA-protein discordance for most genes and identified instances of potential microRNA-mediated regulation. Overexpression or depletion of microRNAs identified by the model, followed by RNA sequencing and protein quantification, were used to follow up on the predictions of the model. Overall, our study shows how multi-omics integration by a dynamical model could be used to nominate candidate regulators.
Authors 5
-
Patrick R. van den Berg Aachen
Affiliation as printed
Leiden Institute of Physics, Leiden University, Leiden, Zuid-Holland, The Netherlands
-
Affiliation as printed
Leiden Institute of Physics, Leiden University, Leiden, Zuid-Holland, The Netherlands
-
Affiliation as printed
Mass Spectrometry and Proteomics Resource Laboratory, Harvard University, Cambridge, Massachusetts, United States of America
-
Affiliation as printed
Department of Bioengineering, Northeastern University, Boston, Massachusetts, United States of America
-
Affiliation as printed
Leiden Institute of Physics, Leiden University, Leiden, Zuid-Holland, The Netherlands
Cited by 14 stored of 15
14 results
No patents citing this paper on Lens.org (checked 2026-10-11).