Single-cell Bayesian deconvolution

Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noi...

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Detalhes bibliográficos
Autores: Torregrosa-Cortés, Gabriel, Oriola, David, Trivedi, Vikas, García Ojalvo, Jordi
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Recursos:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10230/58420
Acesso em linha:http://hdl.handle.net/10230/58420
http://dx.doi.org/10.1016/j.isci.2023.107941
Access Level:acceso abierto
Palavra-chave:Biocomputational method
Complex system biology
Optical Signal Processing
Technical aspects of cell biology
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spelling Single-cell Bayesian deconvolutionTorregrosa-Cortés, GabrielOriola, DavidTrivedi, VikasGarcía Ojalvo, JordiBiocomputational methodComplex system biologyOptical Signal ProcessingTechnical aspects of cell biologyIndividual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noise that masks the natural heterogeneity of cellular populations. This limits our ability to characterize cell-fate decision processes that are key for development, immune response, tissue homeostasis, and many other biological functions. It is therefore important to separate the contributions from signal and noise in single-cell measurements. Addressing this issue rigorously requires deconvolving the noise distribution from the signal, but approaches in that direction are still limited. Here, we present a non-parametric Bayesian formalism that performs such a deconvolution efficiently on multidimensional measurements, providing unbiased estimates of the resulting confidence intervals. We use this approach to study the expression of the mesodermal transcription factor Brachyury in mouse embryonic stem cells undergoing differentiation.This work was supported by the Spanish Ministry of Science and Innovation and FEDER, under projects FIS2017-92551-EXP and PID2021-127311NB-I00, by the “Maria de Maeztu” Program for Units of Excellence in R&D (grant CEX2018-000792-M), and by the Generalitat de Catalunya (ICREA Academia program). GTC is supported by an FPU doctoral fellowship from the Spanish Ministry of Education and Universities (reference FPU18/05091). D.O. acknowledges funding from Juan de la Cierva Incorporación with Project no. IJC2018-035298-I, from the Spanish Ministry of Science, Innovation and Universities (MCIU/AEI).Elsevier202320232023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttp://hdl.handle.net/10230/58420http://dx.doi.org/10.1016/j.isci.2023.107941reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)InglésiScience. 2023 Sep 19;26(10):107941info:eu-repo/grantAgreement/ES/3PE/PID2021-127311NB-I00info:eu-repo/grantAgreement/ES/2PE/IJC2018-035298-I© 2023 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10230/584202026-05-29T05:05:01Z
dc.title.none.fl_str_mv Single-cell Bayesian deconvolution
title Single-cell Bayesian deconvolution
spellingShingle Single-cell Bayesian deconvolution
Torregrosa-Cortés, Gabriel
Biocomputational method
Complex system biology
Optical Signal Processing
Technical aspects of cell biology
title_short Single-cell Bayesian deconvolution
title_full Single-cell Bayesian deconvolution
title_fullStr Single-cell Bayesian deconvolution
title_full_unstemmed Single-cell Bayesian deconvolution
title_sort Single-cell Bayesian deconvolution
dc.creator.none.fl_str_mv Torregrosa-Cortés, Gabriel
Oriola, David
Trivedi, Vikas
García Ojalvo, Jordi
author Torregrosa-Cortés, Gabriel
author_facet Torregrosa-Cortés, Gabriel
Oriola, David
Trivedi, Vikas
García Ojalvo, Jordi
author_role author
author2 Oriola, David
Trivedi, Vikas
García Ojalvo, Jordi
author2_role author
author
author
dc.subject.none.fl_str_mv Biocomputational method
Complex system biology
Optical Signal Processing
Technical aspects of cell biology
topic Biocomputational method
Complex system biology
Optical Signal Processing
Technical aspects of cell biology
description Individual cells exhibit substantial heterogeneity in protein abundance and activity, which is frequently reflected in broad distributions of fluorescently labeled reporters. Since all cellular components are intrinsically fluorescent to some extent, the observed distributions contain background noise that masks the natural heterogeneity of cellular populations. This limits our ability to characterize cell-fate decision processes that are key for development, immune response, tissue homeostasis, and many other biological functions. It is therefore important to separate the contributions from signal and noise in single-cell measurements. Addressing this issue rigorously requires deconvolving the noise distribution from the signal, but approaches in that direction are still limited. Here, we present a non-parametric Bayesian formalism that performs such a deconvolution efficiently on multidimensional measurements, providing unbiased estimates of the resulting confidence intervals. We use this approach to study the expression of the mesodermal transcription factor Brachyury in mouse embryonic stem cells undergoing differentiation.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023
2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10230/58420
http://dx.doi.org/10.1016/j.isci.2023.107941
url http://hdl.handle.net/10230/58420
http://dx.doi.org/10.1016/j.isci.2023.107941
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv iScience. 2023 Sep 19;26(10):107941
info:eu-repo/grantAgreement/ES/3PE/PID2021-127311NB-I00
info:eu-repo/grantAgreement/ES/2PE/IJC2018-035298-I
dc.rights.none.fl_str_mv http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
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