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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204997/ https://www.ncbi.nlm.nih.gov/pubmed/37167320 http://dx.doi.org/10.1371/journal.pgen.1010744 |
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author | van den Berg, Patrick R. Bérenger-Currias, Noémie M. L. P. Budnik, Bogdan Slavov, Nikolai Semrau, Stefan |
author_facet | van den Berg, Patrick R. Bérenger-Currias, Noémie M. L. P. Budnik, Bogdan Slavov, Nikolai Semrau, Stefan |
author_sort | van den Berg, Patrick R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10204997 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-102049972023-05-24 Integration of a multi-omics stem cell differentiation dataset using a dynamical model van den Berg, Patrick R. Bérenger-Currias, Noémie M. L. P. Budnik, Bogdan Slavov, Nikolai Semrau, Stefan PLoS Genet Research Article 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. Public Library of Science 2023-05-11 /pmc/articles/PMC10204997/ /pubmed/37167320 http://dx.doi.org/10.1371/journal.pgen.1010744 Text en © 2023 van den Berg et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article van den Berg, Patrick R. Bérenger-Currias, Noémie M. L. P. Budnik, Bogdan Slavov, Nikolai Semrau, Stefan Integration of a multi-omics stem cell differentiation dataset using a dynamical model |
title | Integration of a multi-omics stem cell differentiation dataset using a dynamical model |
title_full | Integration of a multi-omics stem cell differentiation dataset using a dynamical model |
title_fullStr | Integration of a multi-omics stem cell differentiation dataset using a dynamical model |
title_full_unstemmed | Integration of a multi-omics stem cell differentiation dataset using a dynamical model |
title_short | Integration of a multi-omics stem cell differentiation dataset using a dynamical model |
title_sort | integration of a multi-omics stem cell differentiation dataset using a dynamical model |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10204997/ https://www.ncbi.nlm.nih.gov/pubmed/37167320 http://dx.doi.org/10.1371/journal.pgen.1010744 |
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