Early immunologic correlates of HIV protection can be identified from computational analysis of complex multivariate T-cell flow cytometry assays

Abstract

Motivation: Polychromatic flow cytometry (PFC), has enormous power as a tool to dissect complex immune responses (such as those observed in HIV disease) at a single cell level. However, analysis tools are severely lacking. Although high-throughput systems allow rapid data collection from large cohorts, manual data analysis can take months. Moreover, identification of cell populations can be subjective and analysts rarely examine the entirety of the multidimensional dataset (focusing instead on a limited number of subsets, the biology of which has usually already been well-described). Thus, the value of PFC as a discovery tool is largely wasted.

Results: To address this problem, we developed a computational approach that automatically reveals all possible cell subsets. From tens of thousands of subsets, those that correlate strongly with clinical outcome are selected and grouped. Within each group, markers that have minimal relevance to the biological outcome are removed, thereby distilling the complex dataset into the simplest, most clinically relevant subsets. This allows complex information from PFC studies to be translated into clinical or resource-poor settings, where multiparametric analysis is less feasible. We demonstrate the utility of this approach in a large (n=466), retrospective, 14-parameter PFC study of early HIV infection, where we identify three T-cell subsets that strongly predict progression to AIDS (only one of which was identified by an initial manual analysis).

Availability: The 'flowType: Phenotyping Multivariate PFC Assays' package is available through Bioconductor. Additional documentation and examples are available at: www.terryfoxlab.ca/flowsite/flowType/

Supplementary information: Supplementary data are available at Bioinformatics online.

Contact: rbrinkman@bccrc.ca.

Fig. 1.

The computational pipeline for discovering correlates of HIV protection using PFC. (A) 59 069 cell populations were identified for 466 patients; a CPHR model was used to select the immunophenotypes with significant predictive power; (C) the correlation between the immunophenotypes suggested 3 internally correlated groups, shown in the side-bar colors and circumscribed by the bright yellow squares on the diagonal; (D) each group was represented by a specific combination of markers. The markers that were consistently positive or negative across all immunophenotypes are colored yellow and red, respectively, the markers with a mix of positive and negative values are grey; (E) the redundant markers were removed without affecting the predictive power; (F) the resulting immunophenotypes were used to partition the patients to two groups with different survival patterns.