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A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks
BACKGROUND: The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6019795/ https://www.ncbi.nlm.nih.gov/pubmed/29940845 http://dx.doi.org/10.1186/s12859-018-2190-6 |
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author | Jambusaria, Ankit Klomp, Jeff Hong, Zhigang Rafii, Shahin Dai, Yang Malik, Asrar B. Rehman, Jalees |
author_facet | Jambusaria, Ankit Klomp, Jeff Hong, Zhigang Rafii, Shahin Dai, Yang Malik, Asrar B. Rehman, Jalees |
author_sort | Jambusaria, Ankit |
collection | PubMed |
description | BACKGROUND: The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. RESULTS: Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. CONCLUSIONS: HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2190-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6019795 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-60197952018-07-06 A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks Jambusaria, Ankit Klomp, Jeff Hong, Zhigang Rafii, Shahin Dai, Yang Malik, Asrar B. Rehman, Jalees BMC Bioinformatics Research Article BACKGROUND: The heterogeneity of cells across tissue types represents a major challenge for studying biological mechanisms as well as for therapeutic targeting of distinct tissues. Computational prediction of tissue-specific gene regulatory networks may provide important insights into the mechanisms underlying the cellular heterogeneity of cells in distinct organs and tissues. RESULTS: Using three pathway analysis techniques, gene set enrichment analysis (GSEA), parametric analysis of gene set enrichment (PGSEA), alongside our novel model (HeteroPath), which assesses heterogeneously upregulated and downregulated genes within the context of pathways, we generated distinct tissue-specific gene regulatory networks. We analyzed gene expression data derived from freshly isolated heart, brain, and lung endothelial cells and populations of neurons in the hippocampus, cingulate cortex, and amygdala. In both datasets, we found that HeteroPath segregated the distinct cellular populations by identifying regulatory pathways that were not identified by GSEA or PGSEA. Using simulated datasets, HeteroPath demonstrated robustness that was comparable to what was seen using existing gene set enrichment methods. Furthermore, we generated tissue-specific gene regulatory networks involved in vascular heterogeneity and neuronal heterogeneity by performing motif enrichment of the heterogeneous genes identified by HeteroPath and linking the enriched motifs to regulatory transcription factors in the ENCODE database. CONCLUSIONS: HeteroPath assesses contextual bidirectional gene expression within pathways and thus allows for transcriptomic assessment of cellular heterogeneity. Unraveling tissue-specific heterogeneity of gene expression can lead to a better understanding of the molecular underpinnings of tissue-specific phenotypes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12859-018-2190-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-06-07 /pmc/articles/PMC6019795/ /pubmed/29940845 http://dx.doi.org/10.1186/s12859-018-2190-6 Text en © The Author(s). 2018 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 Article Jambusaria, Ankit Klomp, Jeff Hong, Zhigang Rafii, Shahin Dai, Yang Malik, Asrar B. Rehman, Jalees A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks |
title | A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks |
title_full | A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks |
title_fullStr | A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks |
title_full_unstemmed | A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks |
title_short | A computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks |
title_sort | computational approach to identify cellular heterogeneity and tissue-specific gene regulatory networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6019795/ https://www.ncbi.nlm.nih.gov/pubmed/29940845 http://dx.doi.org/10.1186/s12859-018-2190-6 |
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