COMPASS identifies T-cell subsets correlated with clinical outcomes
Lin Lin1, Greg Finak1, Kevin Ushey1
1Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, USA.
Nature Biotechnology
|May 26, 2015
Summary
This study introduces COMPASS, a computational tool for analyzing T-cell responses. It identifies antigen-specific T-cell subsets and quantifies immune responses, aiding in vaccine development and clinical outcome correlation.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- Single-cell technologies generate high-dimensional data for cell heterogeneity analysis.
- Existing computational tools are insufficient for analyzing complex single-cell data.
- Understanding T-cell polyfunctionality is crucial for immune response assessment.
Purpose of the Study:
- To present COMPASS, a computational framework for unbiased combinatorial polyfunctionality analysis of antigen-specific T-cell subsets.
- To provide a tool for quantifying T-cell responses and identifying correlates of immunity.
- To enable better characterization of antigen-specific T cells in clinical studies.
Main Methods:
- Developed a Bayesian hierarchical framework for modeling cell subsets.
- Implemented COMPASS for unbiased analysis of combinatorial polyfunctionality.
- Utilized posterior probabilities to quantify cell-subset and subject-level responses.
Main Results:
- COMPASS successfully models observed cell subsets and identifies antigen-specific responses.
- Quantified T-cell responses using posterior probabilities and summary statistics.
- Revealed cellular correlates of protection missed by other methods in the RV144 HIV vaccine trial.
Conclusions:
- COMPASS offers an improved method for characterizing antigen-specific T cells.
- The framework aids in identifying cellular correlates of protection/immunity.
- COMPASS is available as open-source software for broader research application.
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