학술논문
Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses
Document Type
article
Author
Alejandra Urrutia; Darragh Duffy; Vincent Rouilly; Céline Posseme; Raouf Djebali; Gabriel Illanes; Valentina Libri; Benoit Albaud; David Gentien; Barbara Piasecka; Milena Hasan; Magnus Fontes; Lluis Quintana-Murci; Matthew L. Albert; Laurent Abel; Andres Alcover; Kalla Astrom; Philippe Bousso; Pierre Bruhns; Ana Cumano; Caroline Demangel; Ludovic Deriano; James Di Santo; Françoise Dromer; Gérard Eberl; Jost Enninga; Jacques Fellay; Antonio Freitas; Odile Gelpi; Ivo Gomperts-Boneca; Serge Hercberg; Olivier Lantz; Claude Leclerc; Hugo Mouquet; Sandra Pellegrini; Stanislas Pol; Lars Rogge; Anavaj Sakuntabhai; Olivier Schwartz; Benno Schwikowski; Spencer Shorte; Vassili Soumelis; Frédéric Tangy; Eric Tartour; Antoine Toubert; Marie-Noëlle Ungeheuer
Source
Cell Reports, Vol 16, Iss 10, Pp 2777-2791 (2016)
Subject
Language
English
ISSN
2211-1247
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.