Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset

Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individua...

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Veröffentlicht in:	Frontiers in immunology Jg. 15; S. 1350111
Hauptverfasser:	Forabosco, Paola, Pala, Mauro, Crobu, Francesca, Diana, Maria Antonietta, Marongiu, Mara, Cusano, Roberto, Angius, Andrea, Steri, Maristella, Orrù, Valeria, Schlessinger, David, Fiorillo, Edoardo, Devoto, Marcella, Cucca, Francesco
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Switzerland Frontiers Media SA 02.04.2024 Frontiers Media S.A
Schlagworte:	Dendritic cells Flow cytometry Gene expression Gene Expression Profiling Gene Regulatory Networks Granulocytes Humans Immune response Immune system Immunology Leukocytes Leukocytes (neutrophilic) lncRNA Lymphocytes Lymphocytes T MicroRNAs network analysis Phenotypes RNA-Seq Transcriptome Transcriptomes WGCNA white blood cells immune system network analysis lncRNA RNA-seq white blood cells WGCNA
ISSN:	1664-3224, 1664-3224
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Abstract	Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.
AbstractList	Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells. Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.Gene co-expression network analysis enables identification of biologically meaningful clusters of co-regulated genes (modules) in an unsupervised manner. We present here the largest study conducted thus far of co-expression networks in white blood cells (WBC) based on RNA-seq data from 624 individuals. We identify 41 modules, 13 of them related to specific immune-related functions and cell types (e.g. neutrophils, B and T cells, NK cells, and plasmacytoid dendritic cells); we highlight biologically relevant lncRNAs for each annotated module of co-expressed genes. We further characterize with unprecedented resolution the modules in T cell sub-types, through the availability of 95 immune phenotypes obtained by flow cytometry in the same individuals. This study provides novel insights into the transcriptional architecture of human leukocytes, showing how network analysis can advance our understanding of coding and non-coding gene interactions in immune system cells.
Author	Cusano, Roberto Cucca, Francesco Marongiu, Mara Angius, Andrea Schlessinger, David Diana, Maria Antonietta Pala, Mauro Orrù, Valeria Steri, Maristella Forabosco, Paola Devoto, Marcella Fiorillo, Edoardo Crobu, Francesca
AuthorAffiliation	2 CRS4-Next Generation Sequencing (NGS) Core, Parco POLARIS , Cagliari , Italy 4 Dipartimento di Medicina Traslazionale e di Precisione, Università Sapienza , Roma , Italy 1 Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR) , Cagliari , Italy 3 Laboratory of Genetics and Genomics, National Institute on Aging, National Institutes of Health (NIH) , Baltimore, MA , United States 5 Dipartimento di Scienze Biomediche, Università degli Studi di Sassari , Sassari , Italy
AuthorAffiliation_xml	– name: 1 Istituto di Ricerca Genetica e Biomedica (IRGB), Consiglio Nazionale delle Ricerche (CNR) , Cagliari , Italy – name: 2 CRS4-Next Generation Sequencing (NGS) Core, Parco POLARIS , Cagliari , Italy – name: 3 Laboratory of Genetics and Genomics, National Institute on Aging, National Institutes of Health (NIH) , Baltimore, MA , United States – name: 4 Dipartimento di Medicina Traslazionale e di Precisione, Università Sapienza , Roma , Italy – name: 5 Dipartimento di Scienze Biomediche, Università degli Studi di Sassari , Sassari , Italy
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BackLink	https://www.ncbi.nlm.nih.gov/pubmed/38629067$$D View this record in MEDLINE/PubMed
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Copyright	Copyright © 2024 Forabosco, Pala, Crobu, Diana, Marongiu, Cusano, Angius, Steri, Orrù, Schlessinger, Fiorillo, Devoto and Cucca. 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. Copyright © 2024 Forabosco, Pala, Crobu, Diana, Marongiu, Cusano, Angius, Steri, Orrù, Schlessinger, Fiorillo, Devoto and Cucca 2024 Forabosco, Pala, Crobu, Diana, Marongiu, Cusano, Angius, Steri, Orrù, Schlessinger, Fiorillo, Devoto and Cucca
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Keywords	immune system network analysis lncRNA RNA-seq white blood cells WGCNA
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Notes	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Pei Shang, Mayo Clinic, United States Reviewed by: Nitin Khandelwal, University of Texas Southwestern Medical Center, United States Edited by: Issam El Naqa, University of Michigan, United States
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SubjectTerms	Dendritic cells Flow cytometry Gene expression Gene Expression Profiling Gene Regulatory Networks Granulocytes Humans Immune response Immune system Immunology Leukocytes Leukocytes (neutrophilic) lncRNA Lymphocytes Lymphocytes T MicroRNAs network analysis Phenotypes RNA-Seq Transcriptome Transcriptomes WGCNA white blood cells
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Title	Transcriptome organization of white blood cells through gene co-expression network analysis in a large RNA-seq dataset
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