Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.

In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure "just-in-time" assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systema...

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Autores principales:	Dan Siegal-Gaskins, Joshua N Ash, Sean Crosson
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2009
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/d0e8606b007b46e4be290813e7e4e31e
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id	oai:doaj.org-article:d0e8606b007b46e4be290813e7e4e31e
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spelling	oai:doaj.org-article:d0e8606b007b46e4be290813e7e4e31e2021-11-25T05:42:14ZModel-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.1553-734X1553-735810.1371/journal.pcbi.1000460https://doaj.org/article/d0e8606b007b46e4be290813e7e4e31e2009-08-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/19680537/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure "just-in-time" assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract "single-cell"-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.Dan Siegal-GaskinsJoshua N AshSean CrossonPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 5, Iss 8, p e1000460 (2009)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Dan Siegal-Gaskins Joshua N Ash Sean Crosson Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
description	In both prokaryotic and eukaryotic cells, gene expression is regulated across the cell cycle to ensure "just-in-time" assembly of select cellular structures and molecular machines. However, present in all time-series gene expression measurements is variability that arises from both systematic error in the cell synchrony process and variance in the timing of cell division at the level of the single cell. Thus, gene or protein expression data collected from a population of synchronized cells is an inaccurate measure of what occurs in the average single-cell across a cell cycle. Here, we present a general computational method to extract "single-cell"-like information from population-level time-series expression data. This method removes the effects of 1) variance in growth rate and 2) variance in the physiological and developmental state of the cell. Moreover, this method represents an advance in the deconvolution of molecular expression data in its flexibility, minimal assumptions, and the use of a cross-validation analysis to determine the appropriate level of regularization. Applying our deconvolution algorithm to cell cycle gene expression data from the dimorphic bacterium Caulobacter crescentus, we recovered critical features of cell cycle regulation in essential genes, including ctrA and ftsZ, that were obscured in population-based measurements. In doing so, we highlight the problem with using population data alone to decipher cellular regulatory mechanisms and demonstrate how our deconvolution algorithm can be applied to produce a more realistic picture of temporal regulation in a cell.
format	article
author	Dan Siegal-Gaskins Joshua N Ash Sean Crosson
author_facet	Dan Siegal-Gaskins Joshua N Ash Sean Crosson
author_sort	Dan Siegal-Gaskins
title	Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
title_short	Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
title_full	Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
title_fullStr	Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
title_full_unstemmed	Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
title_sort	model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.
publisher	Public Library of Science (PLoS)
publishDate	2009
url	https://doaj.org/article/d0e8606b007b46e4be290813e7e4e31e
work_keys_str_mv	AT dansiegalgaskins modelbaseddeconvolutionofcellcycletimeseriesdatarevealsgeneexpressiondetailsathighresolution AT joshuanash modelbaseddeconvolutionofcellcycletimeseriesdatarevealsgeneexpressiondetailsathighresolution AT seancrosson modelbaseddeconvolutionofcellcycletimeseriesdatarevealsgeneexpressiondetailsathighresolution
_version_	1718414546427707392

Model-based deconvolution of cell cycle time-series data reveals gene expression details at high resolution.

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