Transcriptomics,Genomics

Dataset Information

150

Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states


ABSTRACT: Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using live-cell microscopy, we show that individual cells undergo abrupt transitions from a naïve to primed pluripotent state. Using the inferred discrete cell states to build a probabilistic model for the underlying gene regulatory network, we further predict and experimentally verify that these states have unique response to perturbations, thus defining them functionally. Overall design: mRNA profiles of 288 single mouse embryonic stem cells undergoing various stages of in vitro early germ layer differentiation were generated on an Illumina Hi-Seq platform

INSTRUMENT(S): Illumina HiSeq 2000 (Mus musculus)

SUBMITTER: Sumin Jang 

PROVIDER: GSE105054 | GEO | 2017-10-17

SECONDARY ACCESSION(S): PRJNA414474

REPOSITORIES: GEO

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Dynamics of embryonic stem cell differentiation inferred from single-cell transcriptomics show a series of transitions through discrete cell states.

Jang Sumin S   Choubey Sandeep S   Furchtgott Leon L   Zou Ling-Nan LN   Doyle Adele A   Menon Vilas V   Loew Ethan B EB   Krostag Anne-Rachel AR   Martinez Refugio A RA   Madisen Linda L   Levi Boaz P BP   Ramanathan Sharad S  

eLife 20170315


The complexity of gene regulatory networks that lead multipotent cells to acquire different cell fates makes a quantitative understanding of differentiation challenging. Using a statistical framework to analyze single-cell transcriptomics data, we infer the gene expression dynamics of early mouse embryonic stem (mES) cell differentiation, uncovering discrete transitions across nine cell states. We validate the predicted transitions across discrete states using flow cytometry. Moreover, using liv  ...[more]

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