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

Gianni Monaco,Bernett Lee,Weili Xu,Seri Mustafah,You Yi Hwang,Christophe Carré,Nicolas Burdin,Lucian Visan,Michele Ceccarelli,Michael Poidinger,Alfred Zippelius,João Pedro De Magalhães,Anis Larbi

doi:10.1016/j.celrep.2019.01.041

Abstract

SummaryThe 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.

Highlights

The cellular heterogeneity of the immune system is essential for generating diverse and targeted immune responses
Vast amounts of transcriptomic data have been generated from the peripheral blood mononuclear cell (PBMC) fraction (Corkum et al, 2015; van Leeuwen et al, 2005; de Mello et al, 2012; Miao et al, 2013); studying PBMCs in their entirety often contributes to results that are inconclusive or difficult to interpret, as it not always possible to accurately ascertain which specific immune cell types are responsible for any given transcriptomic signal of interest
A second approach is based on quadratic programming (QP) and was originally developed for microarray and later adapted for RNA sequencing (RNA-seq) data (Gong and Szustakowski, 2013; Gong et al, 2011)

Summary

Introduction

The cellular heterogeneity of the immune system is essential for generating diverse and targeted immune responses. Vast amounts of transcriptomic data have been generated from the PBMC fraction (Corkum et al, 2015; van Leeuwen et al, 2005; de Mello et al, 2012; Miao et al, 2013); studying PBMCs in their entirety often contributes to results that are inconclusive or difficult to interpret, as it not always possible to accurately ascertain which specific immune cell types are responsible for any given transcriptomic signal of interest. A deconvolution approach can be an effective solution to discern specific immune cell type proportions from transcriptomic data of heterogeneous samples. Deconvolution methods have been tested using mainly microarray data, which present limits in terms of signal resolution. RNA-seq data are increasingly becoming available for many immune cell types, but to our knowledge, there is no single comprehensive resource that encapsulates all the immune cell

Methods

Results

Discussion

Conclusion