RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types

• Published in: Cell reports (Cambridge) Vol. 26; no. 6; pp. 1627 - 1640.e7
• Main Authors: Monaco, Gianni, Lee, Bernett, Xu, Weili, Mustafah, Seri, Hwang, You Yi, Carré, Christophe, Burdin, Nicolas, Visan, Lucian, Ceccarelli, Michele, Poidinger, Michael, Zippelius, Alfred, Pedro de Magalhães, João, Larbi, Anis
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 05.02.2019; Cell Press; Elsevier
• Subjects: Adult; B-Lymphocytes - classification; B-Lymphocytes - cytology; B-Lymphocytes - immunology; Basophils - classification; Basophils - cytology; Basophils - immunology; Benchmarking; Cell Lineage - genetics; Cell Lineage - immunology; deconvolution; Dendritic Cells - classification; Dendritic Cells - cytology; Dendritic Cells - immunology; Female; Flow Cytometry; gene modules; Healthy Volunteers; High-Throughput Nucleotide Sequencing; housekeeping; Humans; immune system; Immunophenotyping; Killer Cells, Natural - classification; Killer Cells, Natural - cytology; Killer Cells, Natural - immunology; Male; Monocytes - classification; Monocytes - cytology; Monocytes - immunology; mRNA abundance; mRNA composition; mRNA heterogeneity; Neutrophils - classification; Neutrophils - cytology; Neutrophils - immunology; Organ Specificity; RNA, Messenger - genetics; RNA, Messenger - immunology; RNA-seq; Stem Cells - classification; Stem Cells - cytology; Stem Cells - immunology; T-Lymphocytes - classification; T-Lymphocytes - cytology; T-Lymphocytes - immunology; Transcriptome; mRNA heterogeneity; deconvolution; RNA-seq; mRNA abundance; transcriptome; immune system; flow cytometry; housekeeping; gene modules; mRNA composition
• ISSN: 2211-1247; 2211-1247
• DOI: 10.1016/j.celrep.2019.01.041

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. [Display omitted] •Characterization of 29 human immune cell type by RNA-seq and flow cytometry•Modules of specific, co-expressed, and housekeeping genes are defined•The mRNA heterogeneity and abundance are cell type specific•The proposed normalization approach enables absolute deconvolution Monaco et al. generate an RNA-seq dataset on 29 immune cell types and identify modules of cell type-specific, co-expressed, and housekeeping genes. The mRNA heterogeneity and abundance of the different cell types were examined. Absolute deconvolution of PBMCs was obtained by taking into account mRNA abundance when normalizing the signature matrix.