Integration of a multi-omics stem cell differentiation dataset using a dynamical model

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. Author summaryPluripotent stem cells, which can be derived from an adult individual, can be grown indefinitely in a dish and turned into each cell type of the body. These abilities enable applications of stem cells in basic research and regenerative medicine. Differentiation, the conversion into a precisely defined cell type, typically requires complex protocols that often have low efficiency. A better understanding of the molecular mechanisms underlying differentiation could help us improve existing protocols. Here, we studied the differentiation of embryonic stem cells induced by a small molecule (retinoic acid). We measured the abundances of three important classes of biomolecules-micro RNAs, messenger RNAs and proteins-at multiple time points during a 96 h-long differentiation experiment. We observed changes in the abundances of thousands of molecules. To make sense of these measurements we developed a mathematical model that connects the different classes of biomolecules and aims to predict their dynamics. Such models might help us identify new opportuntities to control differentiation at the molecular level. The data set we created, which we provide through an easily accessible web application, will also be a useful resource for other researchers interested in stem cell biology.