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

Urrutia, Alejandra; Duffy, Darragh; Rouilly, Vincent; Posseme, Céline; Djebali, Raouf; Illanes, Gabriel; Libri, Valentina; Albaud, Benoit; Gentien, David and Piasecka, Barbara, et al. (2016) In Cell Reports 16(10). p.2777-2791
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... (More)

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.

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Cell Reports
volume
16
issue
10
pages
15 pages
publisher
Cell Press
external identifiers
  • scopus:85001720558
  • wos:000383880400020
ISSN
2211-1247
DOI
10.1016/j.celrep.2016.08.011
language
English
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yes
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147e947c-81ba-41b9-9ebf-1820136c12b7
date added to LUP
2017-01-12 08:51:09
date last changed
2017-11-19 04:36:36
@article{147e947c-81ba-41b9-9ebf-1820136c12b7,
  abstract     = {<p>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.</p>},
  author       = {Urrutia, Alejandra and Duffy, Darragh and Rouilly, Vincent and Posseme, Céline and Djebali, Raouf and Illanes, Gabriel and Libri, Valentina and Albaud, Benoit and Gentien, David and Piasecka, Barbara and Hasan, Milena and Fontes, Magnus and Quintana-Murci, Lluis and Albert, Matthew L. and , },
  issn         = {2211-1247},
  language     = {eng},
  month        = {09},
  number       = {10},
  pages        = {2777--2791},
  publisher    = {Cell Press},
  series       = {Cell Reports},
  title        = {Standardized Whole-Blood Transcriptional Profiling Enables the Deconvolution of Complex Induced Immune Responses},
  url          = {http://dx.doi.org/10.1016/j.celrep.2016.08.011},
  volume       = {16},
  year         = {2016},
}