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

TLDR

Molecular characterization of immune subsets is essential for developing strategies to understand and treat diseases. The authors profiled 29 PBMC immune cell types with RNA‑seq and flow cytometry, identified cell‑type–specific and housekeeping gene sets, analyzed mRNA heterogeneity and abundance, and performed absolute deconvolution of immune subsets using mRNA‑normalized signatures, benchmarking and validating the approach in independent cohorts. Absolute deconvolution of PBMC transcriptomes is available via a Shiny app, enabling researchers to link bulk transcriptomic observations to specific immune subsets for research, clinical, and diagnostic applications.

Abstract

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.