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Single-cell analysis of transcription kinetics across the cell cycle
Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers re...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801054/ https://www.ncbi.nlm.nih.gov/pubmed/26824388 http://dx.doi.org/10.7554/eLife.12175 |
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author | Skinner, Samuel O Xu, Heng Nagarkar-Jaiswal, Sonal Freire, Pablo R Zwaka, Thomas P Golding, Ido |
author_facet | Skinner, Samuel O Xu, Heng Nagarkar-Jaiswal, Sonal Freire, Pablo R Zwaka, Thomas P Golding, Ido |
author_sort | Skinner, Samuel O |
collection | PubMed |
description | Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation. DOI: http://dx.doi.org/10.7554/eLife.12175.001 |
format | Online Article Text |
id | pubmed-4801054 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-48010542016-03-22 Single-cell analysis of transcription kinetics across the cell cycle Skinner, Samuel O Xu, Heng Nagarkar-Jaiswal, Sonal Freire, Pablo R Zwaka, Thomas P Golding, Ido eLife Computational and Systems Biology Transcription is a highly stochastic process. To infer transcription kinetics for a gene-of-interest, researchers commonly compare the distribution of mRNA copy-number to the prediction of a theoretical model. However, the reliability of this procedure is limited because the measured mRNA numbers represent integration over the mRNA lifetime, contribution from multiple gene copies, and mixing of cells from different cell-cycle phases. We address these limitations by simultaneously quantifying nascent and mature mRNA in individual cells, and incorporating cell-cycle effects in the analysis of mRNA statistics. We demonstrate our approach on Oct4 and Nanog in mouse embryonic stem cells. Both genes follow similar two-state kinetics. However, Nanog exhibits slower ON/OFF switching, resulting in increased cell-to-cell variability in mRNA levels. Early in the cell cycle, the two copies of each gene exhibit independent activity. After gene replication, the probability of each gene copy to be active diminishes, resulting in dosage compensation. DOI: http://dx.doi.org/10.7554/eLife.12175.001 eLife Sciences Publications, Ltd 2016-01-29 /pmc/articles/PMC4801054/ /pubmed/26824388 http://dx.doi.org/10.7554/eLife.12175 Text en © 2016, Skinner et al http://creativecommons.org/licenses/by/4.0/ This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Skinner, Samuel O Xu, Heng Nagarkar-Jaiswal, Sonal Freire, Pablo R Zwaka, Thomas P Golding, Ido Single-cell analysis of transcription kinetics across the cell cycle |
title | Single-cell analysis of transcription kinetics across the cell
cycle |
title_full | Single-cell analysis of transcription kinetics across the cell
cycle |
title_fullStr | Single-cell analysis of transcription kinetics across the cell
cycle |
title_full_unstemmed | Single-cell analysis of transcription kinetics across the cell
cycle |
title_short | Single-cell analysis of transcription kinetics across the cell
cycle |
title_sort | single-cell analysis of transcription kinetics across the cell
cycle |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801054/ https://www.ncbi.nlm.nih.gov/pubmed/26824388 http://dx.doi.org/10.7554/eLife.12175 |
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