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SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks

Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with ca...

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Autores principales: Bravo González-Blas, Carmen, De Winter, Seppe, Hulselmans, Gert, Hecker, Nikolai, Matetovici, Irina, Christiaens, Valerie, Poovathingal, Suresh, Wouters, Jasper, Aibar, Sara, Aerts, Stein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482700/
https://www.ncbi.nlm.nih.gov/pubmed/37443338
http://dx.doi.org/10.1038/s41592-023-01938-4
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author Bravo González-Blas, Carmen
De Winter, Seppe
Hulselmans, Gert
Hecker, Nikolai
Matetovici, Irina
Christiaens, Valerie
Poovathingal, Suresh
Wouters, Jasper
Aibar, Sara
Aerts, Stein
author_facet Bravo González-Blas, Carmen
De Winter, Seppe
Hulselmans, Gert
Hecker, Nikolai
Matetovici, Irina
Christiaens, Valerie
Poovathingal, Suresh
Wouters, Jasper
Aibar, Sara
Aerts, Stein
author_sort Bravo González-Blas, Carmen
collection PubMed
description Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io.
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spelling pubmed-104827002023-09-08 SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks Bravo González-Blas, Carmen De Winter, Seppe Hulselmans, Gert Hecker, Nikolai Matetovici, Irina Christiaens, Valerie Poovathingal, Suresh Wouters, Jasper Aibar, Sara Aerts, Stein Nat Methods Article Joint profiling of chromatin accessibility and gene expression in individual cells provides an opportunity to decipher enhancer-driven gene regulatory networks (GRNs). Here we present a method for the inference of enhancer-driven GRNs, called SCENIC+. SCENIC+ predicts genomic enhancers along with candidate upstream transcription factors (TFs) and links these enhancers to candidate target genes. To improve both recall and precision of TF identification, we curated and clustered a motif collection with more than 30,000 motifs. We benchmarked SCENIC+ on diverse datasets from different species, including human peripheral blood mononuclear cells, ENCODE cell lines, melanoma cell states and Drosophila retinal development. Next, we exploit SCENIC+ predictions to study conserved TFs, enhancers and GRNs between human and mouse cell types in the cerebral cortex. Finally, we use SCENIC+ to study the dynamics of gene regulation along differentiation trajectories and the effect of TF perturbations on cell state. SCENIC+ is available at scenicplus.readthedocs.io. Nature Publishing Group US 2023-07-13 2023 /pmc/articles/PMC10482700/ /pubmed/37443338 http://dx.doi.org/10.1038/s41592-023-01938-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as 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 images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Bravo González-Blas, Carmen
De Winter, Seppe
Hulselmans, Gert
Hecker, Nikolai
Matetovici, Irina
Christiaens, Valerie
Poovathingal, Suresh
Wouters, Jasper
Aibar, Sara
Aerts, Stein
SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
title SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
title_full SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
title_fullStr SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
title_full_unstemmed SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
title_short SCENIC+: single-cell multiomic inference of enhancers and gene regulatory networks
title_sort scenic+: single-cell multiomic inference of enhancers and gene regulatory networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10482700/
https://www.ncbi.nlm.nih.gov/pubmed/37443338
http://dx.doi.org/10.1038/s41592-023-01938-4
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