RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types.
Adult
B-Lymphocytes
/ classification
Basophils
/ classification
Benchmarking
Cell Lineage
/ genetics
Dendritic Cells
/ classification
Female
Flow Cytometry
Healthy Volunteers
High-Throughput Nucleotide Sequencing
Humans
Immunophenotyping
Killer Cells, Natural
/ classification
Male
Monocytes
/ classification
Neutrophils
/ classification
Organ Specificity
RNA, Messenger
/ genetics
Stem Cells
/ classification
T-Lymphocytes
/ classification
Transcriptome
RNA-seq
deconvolution
flow cytometry
gene modules
housekeeping
immune system
mRNA abundance
mRNA composition
mRNA heterogeneity
transcriptome
Journal
Cell reports
ISSN: 2211-1247
Titre abrégé: Cell Rep
Pays: United States
ID NLM: 101573691
Informations de publication
Date de publication:
05 02 2019
05 02 2019
Historique:
received:
22
08
2018
revised:
03
12
2018
accepted:
10
01
2019
entrez:
7
2
2019
pubmed:
7
2
2019
medline:
28
3
2020
Statut:
ppublish
Résumé
The molecular characterization of immune subsets is important for designing effective strategies to understand and treat diseases. We characterized 29 immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA-seq (RNA sequencing) and flow cytometry. Our dataset was used, first, to identify sets of genes that are specific, are co-expressed, and have housekeeping roles across the 29 cell types. Then, we examined differences in mRNA heterogeneity and mRNA abundance revealing cell type specificity. Last, we performed absolute deconvolution on a suitable set of immune cell types using transcriptomics signatures normalized by mRNA abundance. Absolute deconvolution is ready to use for PBMC transcriptomic data using our Shiny app (https://github.com/giannimonaco/ABIS). We benchmarked different deconvolution and normalization methods and validated the resources in independent cohorts. Our work has research, clinical, and diagnostic value by making it possible to effectively associate observations in bulk transcriptomics data to specific immune subsets.
Identifiants
pubmed: 30726743
pii: S2211-1247(19)30059-2
doi: 10.1016/j.celrep.2019.01.041
pmc: PMC6367568
pii:
doi:
Substances chimiques
RNA, Messenger
0
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1627-1640.e7Subventions
Organisme : Wellcome Trust
Pays : United Kingdom
Organisme : Wellcome Trust
ID : 208375/Z/17/Z
Pays : United Kingdom
Informations de copyright
Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.
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