Informatics-Based Discovery of Disease-Associated Immune Profiles.

Autor: Delmas A; Department of Cancer Biology, The Scripps Research Institute, Jupiter, Florida, United States of America.; Department of Immunology and Microbial Sciences, The Scripps Research Institute, Jupiter, Florida, United States of America., Oikonomopoulos A; Division of Digestive Disease, University of California Los Angeles, Los Angeles, California, United States of America., Lacey PN; Division of Digestive Disease, University of California Los Angeles, Los Angeles, California, United States of America., Fallahi M; Informatics Core, The Scripps Institute, Jupiter, Florida, United States of America., Hommes DW; Division of Digestive Disease, University of California Los Angeles, Los Angeles, California, United States of America., Sundrud MS; Department of Cancer Biology, The Scripps Research Institute, Jupiter, Florida, United States of America.; Department of Immunology and Microbial Sciences, The Scripps Research Institute, Jupiter, Florida, United States of America.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2016 Sep 26; Vol. 11 (9), pp. e0163305. Date of Electronic Publication: 2016 Sep 26 (Print Publication: 2016).
DOI: 10.1371/journal.pone.0163305
Abstrakt: Advances in flow and mass cytometry are enabling ultra-high resolution immune profiling in mice and humans on an unprecedented scale. However, the resulting high-content datasets challenge traditional views of cytometry data, which are both limited in scope and biased by pre-existing hypotheses. Computational solutions are now emerging (e.g., Citrus, AutoGate, SPADE) that automate cell gating or enable visualization of relative subset abundance within healthy versus diseased mice or humans. Yet these tools require significant computational fluency and fail to show quantitative relationships between discrete immune phenotypes and continuous disease variables. Here we describe a simple informatics platform that uses hierarchical clustering and nearest neighbor algorithms to associate manually gated immune phenotypes with clinical or pre-clinical disease endpoints of interest in a rapid and unbiased manner. Using this approach, we identify discrete immune profiles that correspond with either weight loss or histologic colitis in a T cell transfer model of inflammatory bowel disease (IBD), and show distinct nodes of immune dysregulation in the IBDs, Crohn's disease and ulcerative colitis. This streamlined informatics approach for cytometry data analysis leverages publicly available software, can be applied to manually or computationally gated cytometry data, is suitable for any clinical or pre-clinical setting, and embraces ultra-high content flow and mass cytometry as a discovery engine.
Competing Interests: The authors have declared that no competing interests exist.
Databáze: MEDLINE
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