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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...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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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. |
format | Online Article Text |
id | pubmed-6547541 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
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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