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

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. [Display omitted] •Standardized whole-blood stimulation, single-step RNA extraction, and gene expression•Response to TLR ligands and microbes can be captured by induced cytokine signatures•A 44-gene signature allows deconvolution of complex responses•Online R-Shiny application permits interactive data-mining Urrutia et al. test the hypothesis that responses to TLR ligands or microbes can be captured by induced cytokine signatures. They identified 44 genes that improved segregation of complex stimuli. They also provide reference values that reflect natural variation of immune responses among humans and launched a companion online R-Shiny application for interactive data mining.