Background:
HIV reservoir cells actively transcribing HIV persist despite ART, but remain poorly characterized since standard single cell (sc)RNAseq approaches inefficiently identify these cells. In this study, we devised a comprehensive scRNAseq-based approach that increases identification of HIV- transcribing cells, enabling a deep characterization of these cells.
Methods:
We developed HIV-seq, an approach whereby HIV primers targeting multiple regions of the HIV genome are added during scRNAseq library preparation to increase our ability to identify HIV RNA+ cells from people with HIV (PWH) under suppressive ART. We paired HIV-seq with a new bioinformatics pipeline, named 2-Step HIV Alignment, to further increase our ability to identify HIV transcripts from scRNAseq datasets. This entailed realigning initially unmapped reads to 1) a subtype B consensus sequence under reduced stringency conditions, and 2) participant-autologous viral sequences. HIV-seq combined with 2-Step HIV Alignment were implemented in the context of multi-omic single-cell sequencing analyses where transcriptomic (scRNAseq), surface phenotypic (CITEseq), and clonal expansion (TCR analysis) data were simultaneously captured on the same cells. A total of 18 PWH under ART were recruited for this study, and 4 of them also provided viremic samples.
Results:
Using this combined approach, we were able to increase by up to 40% our ability to detect HIV RNA+ cells from PWH. HIV RNA+ cells were identified from all blood samples and 7 tissue sites (spleen, lymph nodes, ileum, colon, rectum, endometrium, and endocervix) of 15 donors on suppressive ART. The host transcriptome of these HIV RNA+ cells differed in a tissue site-dependent manner, and included Th17 and Trm cells. Unlike HIV RNA+ cells from viremic individuals, those from suppressed individuals did not preferentially exhibit a cytolytic phenotype characterized by higher expression levels of granzymes, perforin, and granulysin. Shared clonotypes of HIV RNA+ cells (expressing the same TCR) were found in multiple tissue compartments; these cells showed distinct surface phenotypes and transcriptional profiles characteristic of their tissue site of residence.
Conclusions:
Our study presents new tools that improve detection of HIV RNA+ cells at the single-cell level, enabling us to establish an atlas of HIV-transcribing cells from PWH on suppressive ART. HIV-transcribing cells clonally expand and disseminate to multiple tissue sites where they adopt tissue site-specific features.