Data from: Subsets of tissue CD4 T cells display different susceptibilities to HIV infection and death: Analysis by CyTOF and single cell RNA-seq
Citation
Luo, Xiaoyu (2023), Data from: Subsets of tissue CD4 T cells display different susceptibilities to HIV infection and death: Analysis by CyTOF and single cell RNA-seq, Dryad, Dataset, https://doi.org/10.7272/Q6SX6BFR
Abstract
CD4 T lymphocytes belong to diverse cellular subsets whose sensitivity or resistance to HIV-associated killing remains to be defined. Working with lymphoid cells from human tonsils, we characterized the HIV-associated depletion of various CD4 T cell subsets using mass cytometry and single-cell RNA-seq. CD4 T cell subsets preferentially killed by HIV are phenotypically distinct from those resistant to HIV-associated cell death, in a manner not fully accounted for by their susceptibility to productive infection. Preferentially-killed subsets express CXCR5 and CXCR4 while preferentially-infected subsets exhibit an activated and exhausted effector memory cell phenotype. Single-cell RNA-seq analysis reveals that the subsets of preferentially-killed cells express genes favoring abortive infection and pyroptosis. These studies emphasize a complex interplay between HIV and distinct tissue-based CD4 T cell subsets, and the important contribution of abortive infection and inflammatory programmed cell death to the overall depletion of CD4 T cells that accompanies untreated HIV infection.
Methods
mass cytometry; single-cell RNA-seq
mass cytometry data has been pre-gated on live singlets and normalized by CD8 cell number
single-cell RNA-seq data are raw data
Funding
National Institutes of Health, Award: S10 OD018040
National Institutes of Health, Award: R01 DA044605
National Institutes of Health, Award: P01 10018714
National Institutes of Health, Award: P01 AI124912
National Institutes of Health, Award: P30 DK063720
National Institutes of Health, Award: R01 AI127219
National Institutes of Health, Award: R01 AI147777
National Institutes of Health, Award: P01 AI131374
National Institutes of Health, Award: UM1 AI164559