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...

Full description

Saved in:
Bibliographic Details
Published in:Frontiers in immunology Vol. 15; p. 1350111
Main Authors: 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
Language:English
Published: Switzerland Frontiers Media SA 02.04.2024
Frontiers Media S.A
Subjects:
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
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography: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
ISSN:1664-3224
1664-3224
DOI:10.3389/fimmu.2024.1350111