ArticlePLoS genetics2023
Integration of a multi-omics stem cell differentiation dataset using a dynamical model.
Article in PLoS genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 14 citations in OpenAlex.
- Multimodal data integration in orthopedic regenerative medicine: bridging imaging, omics, and clinical data.Frontiers in cell and developmental biology · 2026Review
- Coupling mechanisms coordinating mRNA translation with stages of the mRNA lifecycle.RNA biology · 2025Review
- Impact of protein degradation and cell growth on mammalian proteomes.bioRxiv : the preprint server for biology · 2025Article
- Dynamics of Single-Cell Protein Covariation during Epithelial-Mesenchymal Transition.Journal of proteome research · 2025Article
- Inferring post-transcriptional regulation within and across cell types in human testis.bioRxiv : the preprint server for biology · 2024Article
- Dynamics of single-cell protein covariation during epithelial-mesenchymal transition.bioRxiv : the preprint server for biology · 2024Article
- Article
- Single-cell proteomics: quantifying post-transcriptional regulation during development with mass-spectrometry.Development (Cambridge, England) · 2023Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.