A Reproducibility-Based Computational Framework Identifies an Inducible, Enhanced Antiviral State in Dendritic Cells from HIV-1 Elite Controllers

Computational Methods Computational Methods
Genomics Genomics
Immunology Immunology
Infectious Disease Infectious Disease
Medicine Medicine
Alex K. Shalek Alex K. Shalek
José Ordovas-Montañes José Ordovas-Montañes
Kellie Kolb Kellie Kolb

Martin-Gayo et al.▾ Martin-Gayo, E., Cole, M.B., Kolb, K.E., Ouyang, Z., Cronin, J., Kazer, S.W., Ordovas-Montanes J., Lichterfeld, M., Walker, B.D.,Yosef, N., Shalek, A.K., Yu, X.G.

Genome Biology , Volume 19

January, 2018

Abstract

Background

Human immunity relies on the coordinated responses of many cellular subsets and functional states. Inter-individual variations in cellular composition and communication could thus potentially alter host protection. Here, we explore this hypothesis by applying single-cell RNA-sequencing to examine viral responses among the dendritic cells (DCs) of three elite controllers (ECs) of HIV-1 infection.

Results

To overcome the potentially confounding effects of donor-to-donor variability, we present a generally applicable computational framework for identifying reproducible patterns in gene expression across donors who share a unifying classification. Applying it, we discover a highly functional antiviral DC state in ECs whose fractional abundance after in vitro exposure to HIV-1 correlates with higher CD4+ T cell counts and lower HIV-1 viral loads, and that effectively primes polyfunctional T cell responses in vitro. By integrating information from existing genomic databases into our reproducibility-based analysis, we identify and validate select immunomodulators that increase the fractional abundance of this state in primary peripheral blood mononuclear cells from healthy individuals in vitro.

Conclusions

Overall, our results demonstrate how single-cell approaches can reveal previously unappreciated, yet important, immune behaviors and empower rational frameworks for modulating systems-level immune responses that may prove therapeutically and prophylactically useful.

Complete information about the scRAD R package is available on the Shalek Lab Resources page.