Pathway dynamics can delineate the sources of transcriptional noise in gene expression

Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular sta...

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Autores principales:	Lucy Ham, Marcel Jackson, Michael PH Stumpf
Formato:	article
Lenguaje:	EN
Publicado:	eLife Sciences Publications Ltd 2021
Materias:	gene expression stochasticity extrinsic noise noise decomposition Medicine R Science Q Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/734cb2fb4f304d4cb7a4c4331c142621
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id	oai:doaj.org-article:734cb2fb4f304d4cb7a4c4331c142621
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spelling	oai:doaj.org-article:734cb2fb4f304d4cb7a4c4331c1426212021-11-22T15:39:20ZPathway dynamics can delineate the sources of transcriptional noise in gene expression10.7554/eLife.693242050-084Xe69324https://doaj.org/article/734cb2fb4f304d4cb7a4c4331c1426212021-10-01T00:00:00Zhttps://elifesciences.org/articles/69324https://doaj.org/toc/2050-084XSingle-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells (‘intrinsic noise’) from variability across the population (‘extrinsic noise’). Here, we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that ‘pathway-reporters’ compare favourably to the well-known, but often difficult to implement, dual-reporter method.Lucy HamMarcel JacksonMichael PH StumpfeLife Sciences Publications Ltdarticlegene expressionstochasticityextrinsic noisenoise decompositionMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gene expression stochasticity extrinsic noise noise decomposition Medicine R Science Q Biology (General) QH301-705.5
spellingShingle	gene expression stochasticity extrinsic noise noise decomposition Medicine R Science Q Biology (General) QH301-705.5 Lucy Ham Marcel Jackson Michael PH Stumpf Pathway dynamics can delineate the sources of transcriptional noise in gene expression
description	Single-cell expression profiling opens up new vistas on cellular processes. Extensive cell-to-cell variability at the transcriptomic and proteomic level has been one of the stand-out observations. Because most experimental analyses are destructive we only have access to snapshot data of cellular states. This loss of temporal information presents significant challenges for inferring dynamics, as well as causes of cell-to-cell variability. In particular, we typically cannot separate dynamic variability from within cells (‘intrinsic noise’) from variability across the population (‘extrinsic noise’). Here, we make this non-identifiability mathematically precise, allowing us to identify new experimental set-ups that can assist in resolving this non-identifiability. We show that multiple generic reporters from the same biochemical pathways (e.g. mRNA and protein) can infer magnitudes of intrinsic and extrinsic transcriptional noise, identifying sources of heterogeneity. Stochastic simulations support our theory, and demonstrate that ‘pathway-reporters’ compare favourably to the well-known, but often difficult to implement, dual-reporter method.
format	article
author	Lucy Ham Marcel Jackson Michael PH Stumpf
author_facet	Lucy Ham Marcel Jackson Michael PH Stumpf
author_sort	Lucy Ham
title	Pathway dynamics can delineate the sources of transcriptional noise in gene expression
title_short	Pathway dynamics can delineate the sources of transcriptional noise in gene expression
title_full	Pathway dynamics can delineate the sources of transcriptional noise in gene expression
title_fullStr	Pathway dynamics can delineate the sources of transcriptional noise in gene expression
title_full_unstemmed	Pathway dynamics can delineate the sources of transcriptional noise in gene expression
title_sort	pathway dynamics can delineate the sources of transcriptional noise in gene expression
publisher	eLife Sciences Publications Ltd
publishDate	2021
url	https://doaj.org/article/734cb2fb4f304d4cb7a4c4331c142621
work_keys_str_mv	AT lucyham pathwaydynamicscandelineatethesourcesoftranscriptionalnoiseingeneexpression AT marceljackson pathwaydynamicscandelineatethesourcesoftranscriptionalnoiseingeneexpression AT michaelphstumpf pathwaydynamicscandelineatethesourcesoftranscriptionalnoiseingeneexpression
_version_	1718417500610232320

Pathway dynamics can delineate the sources of transcriptional noise in gene expression

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