Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses

TLDR

Systems approaches to immune signaling have traditionally used purified cells or cultured lines, yet in vivo responses arise from coordinated interactions among multiple cell types that shape inflammatory microenvironments. The study tests whether whole‑blood stimulation can define responses to Toll‑like receptor ligands or whole microbes by their cytokine transcriptional signatures. The authors employed standardized whole‑blood stimulation, analyzed donor variability to map inter‑cellular pathways and cytokine loops, and developed an interactive R‑Shiny application with healthy donor reference values for induced inflammatory genes. Support Vector Machine analysis identified 44 genes that distinguish distinct innate immune stimuli, providing a dimensionality‑reduction strategy for deconvolving complex responses and characterizing immunomodulatory molecules.

Abstract

Systems approaches for the study of immune signaling pathways have been traditionally based on purified cells or cultured lines. However, in vivo responses involve the coordinated action of multiple cell types, which interact to establish an inflammatory microenvironment. We employed standardized whole-blood stimulation systems to test the hypothesis that responses to Toll-like receptor ligands or whole microbes can be defined by the transcriptional signatures of key cytokines. We found 44 genes, identified using Support Vector Machine learning, that captured the diversity of complex innate immune responses with improved segregation between distinct stimuli. Furthermore, we used donor variability to identify shared inter-cellular pathways and trace cytokine loops involved in gene expression. This provides strategies for dimension reduction of large datasets and deconvolution of innate immune responses applicable for characterizing immunomodulatory molecules. Moreover, we provide an interactive R-Shiny application with healthy donor reference values for induced inflammatory genes.