Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification

Heather M. Robison, Patricio Escalante, Enrique Valera, Courtney L. Erskine, Loretta Auvil, Humberto C. Sasieta, Colleen Bushell, Michael Welge, Ryan C. Bailey

Research output: Contribution to journalArticle

Abstract

Latent tuberculosis infection (LTBI) is estimated in nearly one quarter of the world's population, and of those immunocompetent and infected ~10% will proceed to active tuberculosis (TB). Current diagnostics cannot definitively identify LTBI and provide no insight into reactivation risk, thereby defining an unmet diagnostic challenge of incredible global significance. We introduce a new machine-learning-driven approach to LTBI diagnostics that leverages a high throughput, multiplexed cytokine detection technology and powerful bioinformatics to reveal multi-marker signatures for LTBI diagnosis and risk stratification. This approach is enabled through an individualized normalization procedure that allows disease-relevant biomarker signatures to be revealed despite heterogeneity in basal immune response. Specifically, cytokines secreted from antigen-challenged peripheral blood mononuclear cells were detected using silicon photonic sensor arrays and multidimensional data correlation of individually-normalized immune responses revealed signatures important for LTBI status. These results demonstrate a powerful combination of multiplexed biomarker detection technologies, precision immune normalization, and feature selection algorithms that revealed positively correlated multi-biomarker signatures for LTBI status and reactivation risk stratification from a relatively simple blood-based assay.

Original languageEnglish (US)
Pages (from-to)16-25
Number of pages10
JournalIntegrative biology : quantitative biosciences from nano to macro
Volume11
Issue number1
DOIs
StatePublished - Jan 1 2019

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Latent Tuberculosis
Biomarkers
Blood
Cytokines
Sensor arrays
Silicon
Bioinformatics
Photonics
Learning systems
Feature extraction
Assays
Throughput
Optics and Photonics
Technology
Antigens
Computational Biology
Blood Cells
Tuberculosis
Population

Keywords

  • biosensor
  • Keywordsbiomarker
  • multiplex

ASJC Scopus subject areas

  • Biophysics
  • Biochemistry

Cite this

Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification. / Robison, Heather M.; Escalante, Patricio; Valera, Enrique; Erskine, Courtney L.; Auvil, Loretta; Sasieta, Humberto C.; Bushell, Colleen; Welge, Michael; Bailey, Ryan C.

In: Integrative biology : quantitative biosciences from nano to macro, Vol. 11, No. 1, 01.01.2019, p. 16-25.

Research output: Contribution to journalArticle

Robison, Heather M. ; Escalante, Patricio ; Valera, Enrique ; Erskine, Courtney L. ; Auvil, Loretta ; Sasieta, Humberto C. ; Bushell, Colleen ; Welge, Michael ; Bailey, Ryan C. / Precision immunoprofiling to reveal diagnostic signatures for latent tuberculosis infection and reactivation risk stratification. In: Integrative biology : quantitative biosciences from nano to macro. 2019 ; Vol. 11, No. 1. pp. 16-25.
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