Emerging Microbes & InfectionsGenomicsPeer-reviewed journal article
An Atlas of Immune Cell Exhaustion in HIV-Infected Individuals Revealed by Single-Cell Transcriptomics
Shaobo WangiD, Qiong ZhangiD, Hui Hui, Kriti AgrawaliD, Maile KarrisiD, Tariq M. RanaiD
Abstract
Chronic infection with human immunodeficiency virus (HIV) can cause progressive loss of immune cell function, or exhaustion, which impairs control of virus replication. However, little is known about the development and maintenance, as well as heterogeneity of immune cell exhaustion. Here, we investigated the effects of HIV infection on immune cell exhaustion at the transcriptomic level by analyzing single-cell RNA sequencing of peripheral blood mononuclear cells from two healthy subjects (15,121 cells) and six HIV-infected donors (28,610 cells). We identified nine immune cell clusters and eight T cell subclusters according to their unique gene expression programs; three of these (exhausted CD4+ and CD8+ T cells and interferon-responsive CD8+ T cells) were detected only in samples from HIV-infected donors. An inhibitory receptor KLRG1 was identified in the exhausted T cell populations and further characterized in HIV infected individuals. We identified a novel HIV-1 specific exhausted CD8+ T cell population expressing KLRG1, TIGIT, and T-betdimEomeshi markers. Ex-vivo antibody blockade of KLRG1 restored the function of HIV-specific exhausted CD8+ T cells demonstrating the contribution of KLRG1+ population to T cell exhaustion and providing a novel target for developing immunotherapy to treat HIV chronic infection. Analysis of gene signatures also revealed impairment of B cell and NK cell function in HIV-infected donors. These data provide a comprehensive analysis of gene signatures associated with immune cell exhaustion during HIV infection, which could be useful in understanding exhaustion mechanisms and developing new cure therapies.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC=\"FIGDIR/small/678763v1_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (22K):\norg.highwire.dtl.DTLVardef@d6291borg.highwire.dtl.DTLVardef@24db3aorg.highwire.dtl.DTLVardef@1b4f65borg.highwire.dtl.DTLVardef@199d243_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 104
- FWCI
- 6.19
- Field percentile
- top 2%
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