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Single-cell transcriptomics unveils gene regulatory network plasticity

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies. RESULTS: We devise a concep...

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Autores principales: Iacono, Giovanni, Massoni-Badosa, Ramon, Heyn, Holger
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547541/
https://www.ncbi.nlm.nih.gov/pubmed/31159854
http://dx.doi.org/10.1186/s13059-019-1713-4
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author Iacono, Giovanni
Massoni-Badosa, Ramon
Heyn, Holger
author_facet Iacono, Giovanni
Massoni-Badosa, Ramon
Heyn, Holger
author_sort Iacono, Giovanni
collection PubMed
description BACKGROUND: Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies. RESULTS: We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer’s disease. Using tools from graph theory, we compute an unbiased quantification of a gene’s biological relevance and accurately pinpoint key players in organ function and drivers of diseases. CONCLUSIONS: Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1713-4) contains supplementary material, which is available to authorized users.
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spelling pubmed-65475412019-06-06 Single-cell transcriptomics unveils gene regulatory network plasticity Iacono, Giovanni Massoni-Badosa, Ramon Heyn, Holger Genome Biol Research BACKGROUND: Single-cell RNA sequencing (scRNA-seq) plays a pivotal role in our understanding of cellular heterogeneity. Current analytical workflows are driven by categorizing principles that consider cells as individual entities and classify them into complex taxonomies. RESULTS: We devise a conceptually different computational framework based on a holistic view, where single-cell datasets are used to infer global, large-scale regulatory networks. We develop correlation metrics that are specifically tailored to single-cell data, and then generate, validate, and interpret single-cell-derived regulatory networks from organs and perturbed systems, such as diabetes and Alzheimer’s disease. Using tools from graph theory, we compute an unbiased quantification of a gene’s biological relevance and accurately pinpoint key players in organ function and drivers of diseases. CONCLUSIONS: Our approach detects multiple latent regulatory changes that are invisible to single-cell workflows based on clustering or differential expression analysis, significantly broadening the biological insights that can be obtained with this leading technology. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s13059-019-1713-4) contains supplementary material, which is available to authorized users. BioMed Central 2019-06-04 /pmc/articles/PMC6547541/ /pubmed/31159854 http://dx.doi.org/10.1186/s13059-019-1713-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Iacono, Giovanni
Massoni-Badosa, Ramon
Heyn, Holger
Single-cell transcriptomics unveils gene regulatory network plasticity
title Single-cell transcriptomics unveils gene regulatory network plasticity
title_full Single-cell transcriptomics unveils gene regulatory network plasticity
title_fullStr Single-cell transcriptomics unveils gene regulatory network plasticity
title_full_unstemmed Single-cell transcriptomics unveils gene regulatory network plasticity
title_short Single-cell transcriptomics unveils gene regulatory network plasticity
title_sort single-cell transcriptomics unveils gene regulatory network plasticity
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6547541/
https://www.ncbi.nlm.nih.gov/pubmed/31159854
http://dx.doi.org/10.1186/s13059-019-1713-4
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