The molecular characterization of immune subsets is important for designing effective strategies to understand and treat diseases. A team led by researchers from the University of Liverpool characterized 29 immune cell types within the peripheral blood mononuclear cell (PBMC) fraction of healthy donors using RNA-seq (RNA sequencing) and flow cytometry. Their 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, the researchers examined differences in mRNA heterogeneity and mRNA abundance revealing cell type specificity. Last, they 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 their Shiny app. They benchmarked different deconvolution and normalization methods and validated the resources in independent cohorts. This work has research, clinical, and diagnostic value by making it possible to effectively associate observations in bulk transcriptomics data to specific immune subsets.
Absolute Deconvolution of RNA-Seq PBMC Samples
(A) Exhaustive search for cell types that are suitable for deconvolution from PBMC-derived RNA-seq data. For each cell type, we report the mean and SD of Pearson correlations obtained by deconvolution of all possible combinations of cell types (merged and non-merged) that reconstitute a PBMC sample. Cell types that have been chosen for the deconvolution analysis in (B) are outlined in blue. (B) Comparison of deconvoluted and flow cytometry proportions on 17 immune cell types with respect to PBMCs. The concordance correlation coefficient (ccc) and the Pearson correlation coefficient (r) are shown on each plot.
Availability – ABsolute Immune Signal (ABIS) deconvolution: https://github.com/giannimonaco/ABIS