RNA-Seq Signatures Normalized by mRNA Abundance Allow Absolute Deconvolution of Human Immune Cell Types
Open accessAbstract
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.
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The overall number of transcripts as well as the number of transcripts of individual genes can be highly cell type specific.
“In addition, the overall number of transcripts as well as the number of transcripts of individual genes can be highly cell type specific33.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [33]
There are deviations in RNA content between different cell types.
“The gene expression distribution of each cell type was modeled separately because there are deviations in RNA content between different cell types33.”
From scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies (2021) · cites this paper as [33]
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- Authors
- Monaco, G., Lee, B., Xu, W., Mustafah, S., Hwang, Y. Y., Carré, C., Burdin, N., Visan, L., Ceccarelli, M., Poidinger, M., Zippelius, A., Pedro de Magalhães, J., & Larbi, A.
- Affiliations
- Agency for Science, Technology and Research (SG)
- Sanofi (France) (FR)
- University of Sannio (IT)
- University of Basel (CH)
- Corresponding author
- Monaco, Gianni
- Larbi, Anis
- ORCID
- Venue
- Cell Reports
- Publisher
- Elsevier BV
- Published
- 2019-02
- Volume
- 26
- Issue
- 6
- Pages
- 1627-1640.e7
- ISSN
- 2211-1247 (ISSN-L)
- PMC
- PMC6367568
- OpenAlex
- W2911922390
- Cited by
- 958
- References
- 70
- Citations by year
- 2026: 120 · 2025: 156 · 2024: 156 · 2023: 164 · 2022: 130 · 2021: 145 · 2020: 65 · 2019: 22
- FWCI
- 29.96
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cell Reports (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- National University of Singapore (submittedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Greater South Information System
- Funders
- Singapore Immunology Network
- Agency for Science, Technology and Research (1434m00115, IAF 311006, H16/99/b0/011)
- Skin Research Institute of Singapore (14018)
- Wellcome Trust (208375/Z/17/Z)
- University of Liverpool
- A∗STAR/University of Liverpool
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- immune cell deconvolution, peripheral blood mononuclear cells, immune cell types, gene expression signatures, gene co-expression, housekeeping genes, bulk transcriptomics
- MeSH terms
- Adult, B-Lymphocytes, Basophils, Dendritic Cells, Female, Flow Cytometry, Humans, Killer Cells, Natural, Male, Monocytes, Neutrophils, Organ Specificity, RNA, Messenger, Stem Cells, T-Lymphocytes, Immunophenotyping, Cell Lineage, Benchmarking, High-Throughput Nucleotide Sequencing, Transcriptome, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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