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Sci-fate characterizes the dynamics of gene expression in single cells

Gene expression programs change over time, differentiation and development and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. Here, we present a method for combined single-cell combinatorial index...

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Detalles Bibliográficos
Autores principales: Cao, Junyue, Zhou, Wei, Steemers, Frank, Trapnell, Cole, Shendure, Jay
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416490/
https://www.ncbi.nlm.nih.gov/pubmed/32284584
http://dx.doi.org/10.1038/s41587-020-0480-9
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author Cao, Junyue
Zhou, Wei
Steemers, Frank
Trapnell, Cole
Shendure, Jay
author_facet Cao, Junyue
Zhou, Wei
Steemers, Frank
Trapnell, Cole
Shendure, Jay
author_sort Cao, Junyue
collection PubMed
description Gene expression programs change over time, differentiation and development and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. Here, we present a method for combined single-cell combinatorial indexing and mRNA labelling (sci-fate), which uses combinatorial cell indexing and 4sU labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. We used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, we quantified the dynamics of the cell cycle and of glucocorticoid receptor activation, and explored their intersection. Finally, we developed software to infer and analyze cell state transitions. We anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems.
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spelling pubmed-74164902020-10-13 Sci-fate characterizes the dynamics of gene expression in single cells Cao, Junyue Zhou, Wei Steemers, Frank Trapnell, Cole Shendure, Jay Nat Biotechnol Article Gene expression programs change over time, differentiation and development and in response to stimuli. However, nearly all techniques for profiling gene expression in single cells do not directly capture transcriptional dynamics. Here, we present a method for combined single-cell combinatorial indexing and mRNA labelling (sci-fate), which uses combinatorial cell indexing and 4sU labeling of newly synthesized mRNA to concurrently profile the whole and newly synthesized transcriptome in each of many single cells. We used sci-fate to study the cortisol response in >6,000 single cultured cells. From these data, we quantified the dynamics of the cell cycle and of glucocorticoid receptor activation, and explored their intersection. Finally, we developed software to infer and analyze cell state transitions. We anticipate that sci-fate will be broadly applicable to quantitatively characterize transcriptional dynamics in diverse systems. 2020-04-13 2020-08 /pmc/articles/PMC7416490/ /pubmed/32284584 http://dx.doi.org/10.1038/s41587-020-0480-9 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Cao, Junyue
Zhou, Wei
Steemers, Frank
Trapnell, Cole
Shendure, Jay
Sci-fate characterizes the dynamics of gene expression in single cells
title Sci-fate characterizes the dynamics of gene expression in single cells
title_full Sci-fate characterizes the dynamics of gene expression in single cells
title_fullStr Sci-fate characterizes the dynamics of gene expression in single cells
title_full_unstemmed Sci-fate characterizes the dynamics of gene expression in single cells
title_short Sci-fate characterizes the dynamics of gene expression in single cells
title_sort sci-fate characterizes the dynamics of gene expression in single cells
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416490/
https://www.ncbi.nlm.nih.gov/pubmed/32284584
http://dx.doi.org/10.1038/s41587-020-0480-9
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