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

Basic information
Cell
393

Technology
Smart-Seq2
Omics
scRNA-seq
Source
PBMCs

Dataset ID
29378643
Platform
Illumina NextSeq 500
Species
Human
Disease
HIV infection
Age range
0 - 0
Update date
2018-01-29
Summary

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.

Overall design

Single-cell RNA-seq profiling of HIV-1-exposed cDCs and media controls from 3 elite controllers used to identify reproducible gene expression programs associated with cell-intrinsic HIV-1 immune recognition.

Contributors

Enrique Martin-Gayo 1, Michael B Cole 2, Kellie E Kolb 1 3 4, Zhengyu Ouyang 1, Jacqueline Cronin 1, Samuel W Kazer 1 3 4, Jose Ordovas-Montanes 1 3 4, Mathias Lichterfeld 1 5, Bruce D Walker 1 6, Nir Yosef 7 8 9, Alex K Shalek 10 11 12, Xu G Yu 13 14

Contact

To be supplemented.

snRNA-Seq
Sample nameSample titleDiseaseGenderAgeSourceTreatmentTechnologyPlatformOmicsSample IDDataset IDAction
No data available