Single-cell Bayesian deconvolution

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• dc.contributor.author Torregrosa-Cortés, Gabriel
• dc.contributor.author Oriola, David
• dc.contributor.author Trivedi, Vikas
• dc.contributor.author García Ojalvo, Jordi
• dc.date.accessioned 2023-12-01T06:57:23Z
• dc.date.available 2023-12-01T06:57:23Z
• dc.date.issued 2023
• dc.description.abstract 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.
• dc.description.sponsorship 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).
• dc.format.mimetype application/pdf
• dc.identifier.citation Torregrosa-Cortés G, Oriola D, Trivedi V, Garcia-Ojalvo J. Single-cell Bayesian deconvolution. iScience. 2023 Sep 19;26(10):107941. DOI: 10.1016/j.isci.2023.107941
• dc.identifier.doi http://dx.doi.org/10.1016/j.isci.2023.107941
• dc.identifier.issn 2589-0042
• dc.identifier.uri http://hdl.handle.net/10230/58420
• dc.language.iso eng
• dc.publisher Elsevier
• dc.relation.ispartof iScience. 2023 Sep 19;26(10):107941
• dc.relation.projectID info:eu-repo/grantAgreement/ES/3PE/PID2021-127311NB-I00
• dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/IJC2018-035298-I
• dc.rights © 2023 The Author(s). This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by/4.0/
• dc.subject.keyword Biocomputational method
• dc.subject.keyword Complex system biology
• dc.subject.keyword Optical Signal Processing
• dc.subject.keyword Technical aspects of cell biology
• dc.title Single-cell Bayesian deconvolution
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion