Pervasive Discordance Between mRNA And Protein Expression During Embryonic Stem Cell Differentiation

During in vitro differentiation, pluripotent stem cells undergo extensive remodeling of their gene expression. While studied extensively at the transcriptome level, much less is known about protein dynamics, which can differ significantly from their mRNA counterparts. Here, we present genome-wide dynamic measurements of mRNA and protein levels during differentiation of embryonic stem cells (ESCs). We reveal pervasive discordance, which can be largely understood as a dynamic imbalance due to delayed protein synthesis and degradation. Through a combination of systematic classification and kinetic modeling, we connect modes of regulation at the protein level to the function of specific gene sets in differentiation. We further show that our kinetic model can be applied to single-cell transcriptomics data to predict protein levels in differentiated cell types. In conclusion, our comprehensive data set, easily accessible through a web application, is a valuable resource for the discovery of protein-level regulation in ESC differentiation.

[1]  R. Aebersold,et al.  On the Dependency of Cellular Protein Levels on mRNA Abundance , 2016, Cell.

[2]  Allon M. Klein,et al.  On the Relationship of Protein and mRNA Dynamics in Vertebrate Embryonic Development. , 2015, Developmental cell.

[3]  F. Edfors,et al.  Gene‐specific correlation of RNA and protein levels in human cells and tissues , 2016, Molecular systems biology.

[4]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[5]  A. Martinez-Arias,et al.  Dynamic Proteomic Profiling of Extra‐Embryonic Endoderm Differentiation in Mouse Embryonic Stem Cells , 2015, Stem cells.

[6]  Colin N. Dewey,et al.  RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.

[7]  Thomas Lengauer,et al.  Improved scoring of functional groups from gene expression data by decorrelating GO graph structure , 2006, Bioinform..

[8]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[9]  Pang Wei Koh,et al.  Mapping the Pairwise Choices Leading from Pluripotency to Human Bone, Heart, and Other Mesoderm Cell Types , 2016, Cell.

[10]  N. Rajewsky,et al.  Conservation of mRNA and protein expression during development of C. elegans. , 2014, Cell reports.

[11]  B. Doble,et al.  The ground state of embryonic stem cell self-renewal , 2008, Nature.

[12]  Maxwell R. Mumbach,et al.  Dynamic profiling of the protein life cycle in response to pathogens , 2015, Science.

[13]  Marcel Martin.  Cutadapt removes adapter sequences from high-throughput sequencing reads , 2011 .

[14]  Robert Gentleman,et al.  Software for Computing and Annotating Genomic Ranges , 2013, PLoS Comput. Biol..

[15]  Allon M. Klein,et al.  Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells , 2015, Cell.

[16]  Christopher S. Poultney,et al.  One third of dynamic protein expression profiles can be predicted by a simple rate equation. , 2014, Molecular bioSystems.

[17]  T. Mikkelsen,et al.  Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells , 2016, bioRxiv.

[18]  A. Kocabas,et al.  Widespread Differential Expression of Coding Region and 3′ UTR Sequences in Neurons and Other Tissues , 2015, Neuron.

[19]  M. Selbach,et al.  Global quantification of mammalian gene expression control , 2011, Nature.

[20]  Hiromi W L Koh,et al.  Differential dynamics of the mammalian mRNA and protein expression response to misfolding stress , 2015, bioRxiv.

[21]  E. Birney,et al.  Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt , 2009, Nature Protocols.

[22]  Frank Soldner,et al.  iPSC Disease Modeling , 2012, Science.

[23]  E. Marcotte,et al.  Insights into the regulation of protein abundance from proteomic and transcriptomic analyses , 2012, Nature Reviews Genetics.

[24]  F. Markowetz,et al.  Systems-level dynamic analyses of fate change in murine embryonic stem cells , 2009, Nature.

[25]  E. Airoldi,et al.  Differential Stoichiometry among Core Ribosomal Proteins , 2014, bioRxiv.

[26]  S. Le,et al.  Sequence signatures and mRNA concentration can explain two-thirds of protein abundance variation in a human cell line , 2010, Molecular systems biology.

[27]  T. Mikkelsen,et al.  Dynamics of lineage commitment revealed by single-cell transcriptomics of differentiating embryonic stem cells , 2016, Nature Communications.

[28]  L. Foster,et al.  Protein synthesis rate is the predominant regulator of protein expression during differentiation , 2013, Molecular systems biology.

[29]  S. Varmuza,et al.  Imprinting and extraembryonic tissues-mom takes control. , 2009, International review of cell and molecular biology.

[30]  Lil Pabon,et al.  A hierarchical network controls protein translation during murine embryonic stem cell self-renewal and differentiation. , 2008, Cell stem cell.

[31]  Austin G Smith,et al.  FGF stimulation of the Erk1/2 signalling cascade triggers transition of pluripotent embryonic stem cells from self-renewal to lineage commitment , 2007, Development.

[32]  C. Eyers.  Universal sample preparation method for proteome analysis , 2009 .

[33]  Nicholas T. Ingolia,et al.  Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes , 2011, Cell.

[34]  E. Marcotte,et al.  Global signatures of protein and mRNA expression levelsw , 2009 .

[35]  John D. Storey,et al.  Significance analysis of time course microarray experiments. , 2005, Proceedings of the National Academy of Sciences of the United States of America.