There is no cure for the human immunodeficiency virus infection (HIV), but anti-retroviral drugs allow infected people to keep the virus at bay and lead a normal life. These drugs suppress the growth of HIV, but they do not eliminate the virus. If the treatment is interrupted, the virus bounces back within weeks in most individuals. HIV can start growing again because it hides within particular immune cells, called T cells. These infected cells stay in the infected person’s body for their whole life in a dormant or “latent” state, and represent the main barrier to an HIV cure. If these cells could be eliminated or prevented from producing more virus without daily treatment, then HIV could be cured. The fact that HIV hides inside T cells has been known for a long time, but it has remained unclear exactly what kinds of T cells the virus prefers.

One challenge to characterizing latently-infected cells is that there is no single protein made by them that is not also made by uninfected T cells. The latently-infected T cells are also very rare: HIV mainly attaches to a protein called CD4, but only one in about a million T cells with CD4 contain the virus. To figure out which CD4-carrying T cells in a patient sample are latently infected, the cells are extracted from the patient’s body and ‘reactivated’ so the virus will start growing again. Unfortunately, the mixture of drugs used to reactivate the T cells changes the cells and the proteins they are producing, which obscures the features the latently-infected T cells had before reactivation.

Neidleman, Luo et al. developed a new approach to trace the infected, reactivated T cells back to their state before reactivation. Using computational methods and a laboratory technique called mass cytometry, the levels of approximately 40 different proteins were measured in millions of T cells from people living with HIV. These experiments provided an ‘atlas’ of overall T cell features onto which each reactivated cell could be mapped. The population of latently-infected T cells exhibited common features among all the participants. Selecting a few of the most abundant proteins on the surface of the latently-infected cells allowed these cells to be physically separated from all other immune cells.

In the future, this relatively pure population of infected T cells could be used to study how HIV can persist for many decades. The ‘map’ of these cells’ features will provide a valuable resource for HIV researchers and might enable the discovery of new drugs to eliminate the latent T cells.

Combination antiretroviral therapy (ART) suppresses HIV replication but does not eliminate the latent reservoir, which persists for decades in CD4+ T cells and forms a major barrier to HIV cure (Chun et al., 1997; Finzi et al., 1997; Wong et al., 1997). Our understanding of the phenotypic features of latent cells in vivo is limited, in large part because the lack of a universal surface biomarker makes it impossible to directly identify these cells. As a result, fundamental questions remain, such as whether latent cells distribute equally among all CD4+ T cell subsets, and to what extent the reservoir is similar between individuals and between blood and tissues.

In the absence of direct ways to phenotype latent cells, characterizing the composition of the HIV reservoir has largely entailed sorting CD4+ T cells based on predefined sets of cell-surface proteins, followed by quantitating HIV through a variety of methods. A vast majority of infected cells in virally suppressed individuals harbor defective HIV genomes (Hiener et al., 2017; Ho et al., 2013; Imamichi et al., 2016; Lee et al., 2017). Older quantitation methods based on measuring total HIV DNA/RNA levels, regardless of provirus integrity, are being replaced with methods quantitating levels of replication-competent or genome-intact provirus. These methods afford a more accurate picture of the cells leading to viral rebound during treatment interruption, which are the most relevant targets for HIV cure. The ‘gold standard’ in virus quantitation has been the quantitative viral outgrowth assay (qVOA; Finzi et al., 1997). qVOA has not identified significant differences in the amount of replication-competent HIV in the most commonly studied subsets of memory CD4+ T cells – central memory (Tcm), effector memory (Tem), and transitional memory (Ttm) (Buzon et al., 2014; Kwon et al., 2020). Such observations have led many researchers to consider the replication-competent reservoir as equally residing in all cell subsets, which if true makes targeting it more difficult.

The analysis of proviral sequences, on the other hand, has revealed differences in the frequencies of genome-intact provirus in different sorted subsets. For instance, sequencing of near full-length HIV provirus has identified Th1 and Tem, particularly those that are HLADR+, as subsets preferentially harboring intact viral genomes (Hiener et al., 2017; Horsburgh et al., 2020; Lee et al., 2017). Viral sequencing has also revealed many intact proviruses within clonally expanded populations, particularly within the Tem and Th1 subsets, and those expressing Ox40 or CD161 (De Scheerder et al., 2019; Hiener et al., 2017; Kuo et al., 2018; Lee et al., 2017; Li et al., 2019). These and many other studies (Cohn et al., 2020; Liu et al., 2020) have solidified the view that clonal expansion plays a major role in maintaining the long-lived HIV reservoir.

Although analyses of sorted populations have provided meaningful insights into HIV persistence, they have been limited to comparing cell populations that differ in one or two markers. Thus, they do not provide a comprehensive view of the phenotypic features of latent cells. Several methods have recently enabled the direct phenotyping of reservoir cells at single-cell resolution (Baxter et al., 2016; Cohn et al., 2018; Grau-Expósito et al., 2017; Pardons et al., 2019), providing deeper analyses of the reservoir. These methods entail stimulating cells from ART-suppressed patients with a mitogen to induce the expression of HIV RNA and/or proteins, which serve as markers for identifying or isolating cells using FACS. However, one important limitation with these methods is that they require ex vivo stimulation before phenotyping as viral proteins are not typically expressed at detectable levels in unstimulated latent cells (Baxter et al., 2016; Cohn et al., 2018; Pardons et al., 2019). This stimulation will induce phenotypic changes associated with T cell activation, and expression of HIV accessory genes from reactivation will result in host cell remodeling (e.g. downregulation of cell-surface CD4), which will further alter the phenotypes of the cells (Cavrois et al., 2017; Ma et al., 2020). Therefore, although current methods enable phenotyping of reactivated cells, there is at present no methodology to directly assess the phenotypes of individual latent cells in their original dormant state.

To overcome this limitation, we applied the principles behind Predicted Precursor as determined by SLIDE (PP-SLIDE), an analytic method we recently established that combines high-dimensional single-cell phenotyping and computational approaches. We have used PP-SLIDE to predict the state of CD4+ T cells before phenotypic changes induced by productive HIV infection (Cavrois et al., 2017; Ma et al., 2020). Here, we applied it to determine the features of in vivo latent CD4+ T cells before their reactivation by ex vivo stimulation, and validated these findings with patient specimens. Through PP-SLIDE analysis, we were able to address questions fundamental to our understanding of HIV latency and to isolate highly enriched populations of replication-competent latent cells for mechanistic studies of reservoir maintenance.

Although the latent reservoir has long been recognized as a major barrier to HIV cure, the phenotype of these latently infected cells in vivo has remained somewhat obscure. A common viewpoint, based on the limited number of studies that have conducted viral outgrowth assays on sorted cell populations, is that latent HIV persists in all types of CD4+ T cells with no preference for one subset over another. If so, targeting latently infected cells as a curative strategy would be all the more difficult. Taking advantage of recent developments in high-parameter single-cell phenotyping and our PP-SLIDE analysis pipeline, we now show that latently infected cells are not randomly distributed among CD4+ T cells, and provide an in-depth view of the phenotypic features of the in vivo blood and tissue reservoir.

Multiple observations make us confident that PP-SLIDE identifies the physiologically relevant reservoir. First, our in vitro assay with J-Lat cells showed the method could predict the correct precursor cell 99.2% of the time. Second, cells sorted based on PP-SLIDE were enriched for genome-intact and replication-competent reservoir cells in the expected populations. Of note, our PP-SLIDE characterization had charted the translation-competent reservoir, defined as latent cells able to produce viral proteins upon stimulation (Baxter et al., 2018), which may include some replication-incompetent HIV provirus (Baxter et al., 2016; Imamichi et al., 2020). Nevertheless, our viral outgrowth results closely aligned with the enrichment of kNN latent cells, confirming that PP-SLIDE identifies markers of the replication-competent HIV reservoir most relevant for a cure. Third, using our ‘universal panel,’ we validated the preferential presence of the replication-competent reservoir in expected populations in four untested donors, and PP-SLIDE’s prediction that the latent reservoir is preferentially maintained in TIGIT-expressing cells in some but not all donors.

The sorting strategies used in our validation experiments were designed to enable comparison of different populations of memory CD4+ T cells expressing high levels of PD1, a marker preferentially expressed on reservoir cells (Banga et al., 2016; Chomont et al., 2009; Fromentin et al., 2016; Pardons et al., 2019) and which we found expressed at high levels on kNN latent cells. This was implemented for two reasons: First, to demonstrate that we can enrich for latent cells beyond the selection afforded by PD1 alone. Indeed both our viral outgrowth and proviral sequencing results confirmed PP-SLIDE’s prediction that some but not all PD1-expressing memory CD4+ T cells harbor high frequencies of latent HIV. Second, to enable mechanistic comparisons of highly similar populations of sorted cells harboring vastly different levels of replication-competent HIV. We found that in 7/8 cases, the enriched populations initiated more HIV transcription than their disenriched counterparts, suggesting that the transcriptionally active reservoir associates most closely with the replication-competent reservoir and therefore should be a target of HIV curative strategies. We also found that relative to disenriched populations, the enriched ones harbored higher proportions of EIS with intact p24, consistent with the importance of clonal expansion in driving the maintenance of the replication-competent HIV reservoir (Bui et al., 2017; Hosmane et al., 2017; Lorenzi et al., 2016), although it should be noted that cells with different integration sites can harbor the same proviral sequence (Patro et al., 2019).

Of particular interest was our in-depth comparison of two PD1-expressing memory CD4+ T cell populations from PID5003. A striking 65.2% of proviruses in the enriched population from this donor were fully intact, versus 0.9% in the disenriched, despite both being PD1 expressors. To put these numbers in perspective, on average only 2–3% of sequences from infected individuals treated during chronic infection are intact (Bruner et al., 2016; Hiener et al., 2017). The overall frequency of intact provirus in the enriched PID5003 population was also extremely high: at 214 per million cells, it was more than an order of magnitude higher than what is observed in Tem (range = 1–26, median, 13 per million), the subset with the highest frequencies of intact provirus as assessed by FLIPS (Hiener et al., 2017). This high frequency is not due to an unusually large overall reservoir in this donor, as the phenotypically-similar (PD1+) disenriched population sequenced from this donor exhibited a frequency of only two per million. Our observation that this enriched population, but not the disenriched one, exhibited robust viral outgrowth, further suggests that the intact proviruses in this population are replication-competent. Interestingly, the intact sequences in this enriched population consisted of two large clusters of EIS, suggesting two major clonal expansions in this well-defined population of memory CD4+ cells sorted based on eight surface markers. As these two clusters of viral sequences likely integrated at different genomic sites, these observations support a host rather than virus-driven expansion of these latent cells.

That we could find such vastly different levels of intact and replication-competent reservoir cells in our enriched versus disenriched populations goes against the notion that the reservoir is ‘everywhere’ and randomly distributed in all subsets. But how do our observations fit with prior reports that the frequencies of replication-competent provirus, as determined from outgrowth assays, were similar between sorted subsets (Buzon et al., 2014; Kwon et al., 2020)? We believe that the previously sorted subsets were not ones that differ markedly in reservoir frequencies. Most prior subset analyses had focused on the Tem and Tcm subsets, and when focusing on just these subsets our PP-SLIDE results indeed support the notion that both Tem and Tcm were prominent among kNN latent cells. However, our finding that the proportions of Tem were greater among the kNN latent cells than among atlas cells suggests that latency is somewhat biased toward the Tem subset, a finding in line with the higher prevalence of intact provirus in Tem over Tcm (Hiener et al., 2017). Further supporting a non-random distribution of the reservoir is our identification of markers consistently expressed at higher levels on kNN latent cells relative to memory CD4+ T cells. Such markers include ones previously implicated in latency (PD1, CTLA4, Ox40) (Banga et al., 2016; Fromentin et al., 2016; Kuo et al., 2018; McGary et al., 2017), some activation markers (CD25, CD69), and markers of Th1, Th2, and Th17 cells (Tbet, CRTh2, and CCR6, respectively) suggesting preferential persistence of HIV in more differentiated cells. The reservoir may persist in these cells due to antigen- or homeostatically-driven expansion of these cells which can maintain the reservoir through repeated cycles of clonal expansion.

Our analysis of longitudinal specimens by PP-SLIDE also provided insights into the stability of the reservoir and its capacity for reactivation. By comparing kNN latent cells from different donors versus longitudinal samples, we found intra-individual variability to be less than inter-individual variability. This observation implies that the phenotypic properties of the reservoir are stable over time, a feature that may facilitate its targeting. In fact, longitudinal stability also held for the reservoir’s susceptibility to the LRAs we tested. Our observation that different stimulatory signals reactivate different latent cells further suggests that ‘shock and kill’ strategies may require multiple LRAs, each preferentially targeting different cell subsets, as recently suggested (Baxter et al., 2016; Grau-Expósito et al., 2019; Pardons et al., 2019).

Although our study focused on the blood reservoir for logistical and technical reasons, we also conducted PP-SLIDE analyses on tissue specimens. Perhaps not surprising given the known phenotypic differences between T cells from blood versus tissues (Wong et al., 2015), we found that blood and tissue kNN latent cells differ quite dramatically. High-dimensional distance calculations revealed that kNN latent cells were more similar between lymph node and gut than between the tissues and blood, suggesting phenotypic commonalities in the latent reservoir found in tissues. Indeed, kNN latent cells from both lymph node and gut expressed high levels of the Trm marker CD69, suggesting that Trm in these tissues, like those from the cervix (Cantero-Pérez et al., 2019), preferentially harbor the reservoir. Together with our observation that kNN latent cells from all sites examined expressed high levels of PD1, these results suggest PD1-expressing Trm as possible targets to eliminate the tissue reservoir. Given recent findings that blood, lymph nodes, and gut are the major sources of HIV dissemination in ART-suppressed individuals (Chaillon et al., 2020), targeting latent cells in the compartments analyzed in this study may be sufficient to make a meaningful reduction in the reservoir.

Our study has some limitations, in particular, a relatively small sample size of only eight blood and five tissue donors, although some donors were characterized longitudinally. Nonetheless, our findings do not support the existence of a single surface biomarker that can be used to target the entire HIV reservoir at once. However, PP-SLIDE enables in-depth charting of the latent reservoir residing in the blood and, likely, any tissue we can access. For therapeutic applications, surface antigens identified by PP-SLIDE as preferentially co-expressed on latent cells can be targeted with a series of bi- or multi-specific antibodies (Klein et al., 2016). More importantly, our ability to isolate highly enriched populations of replication-competent reservoir cells enables in-depth functional analyses of the reservoir and potentiates future implementation of discovery-based approaches such as scRNAseq and PCR-activated cell sorting (PACS; Bradley et al., 2018; Clark and Abate, 2017). These approaches hold great promise for identifying novel markers for HIV eradication but require a high frequency of latent cells that was not achievable previously. We envision that implementing PP-SLIDE on cells phenotyped by scRNAseq together with Antibody-seq approaches (Peterson et al., 2017; Stoeckius et al., 2017) will enable even higher-resolution mapping, with the added advantage that whole-transcriptome approaches do not require pre-defining markers of interest, and allow for the discovery of unanticipated—and perhaps unique—markers of latent cells.

Raw CyTOF datasets have been made publically available through the public repository Dryad: https://doi.org/10.7272/Q6KK991S. The following is the citation for this dataset: Neidleman et al. (2020), Phenotypic Analysis of the Unstimulated In Vivo HIV CD4 T Cell Reservoir, v2, UC San Francisco, dataset, https://doi.org/10.7272/Q6KK991S.

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Author details

1. Jason Neidleman

   1. Gladstone Institutes, San Francisco, United States
   2. Department of Urology, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Contributed equally with

   Xiaoyu Luo

   Competing interests

   No competing interests declared

2. Xiaoyu Luo

   Gladstone Institutes, San Francisco, United States

   Contribution

   Data curation, Formal analysis, Investigation, Visualization, Methodology, Writing - review and editing

   Contributed equally with

   Jason Neidleman

   Competing interests

   No competing interests declared

3. Julie Frouard

   1. Gladstone Institutes, San Francisco, United States
   2. Department of Urology, University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

4. Guorui Xie

   1. Gladstone Institutes, San Francisco, United States
   2. Department of Urology, University of California, San Francisco, San Francisco, United States

   Contribution

   Data curation, Formal analysis, Investigation, Writing - review and editing

   Competing interests

   No competing interests declared

5. Feng Hsiao

   1. Gladstone Institutes, San Francisco, United States
   2. Department of Urology, University of California, San Francisco, San Francisco, United States

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

6. Tongcui Ma

   1. Gladstone Institutes, San Francisco, United States
   2. Department of Urology, University of California, San Francisco, San Francisco, United States

   Contribution

   Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

7. Vincent Morcilla

   Centre for Virus Research, the Westmead Institute for Medical Research, The University of Sydney, Sydney, Australia

   Contribution

   Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8159-6090

8. Ashley Lee

   Centre for Virus Research, the Westmead Institute for Medical Research, The University of Sydney, Sydney, Australia

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

9. Sushama Telwatte

   San Francisco Veterans Affairs (VA) Medical Center and University of California, San Francisco, San Francisco, United States

   Contribution

   Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

   Competing interests

   No competing interests declared

10. Reuben Thomas

    Gladstone Institutes, San Francisco, United States

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

11. Whitney Tamaki

    Department of Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Formal analysis, Investigation, Methodology

    Competing interests

    No competing interests declared

12. Benjamin Wheeler

    Department of Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Investigation, Methodology

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-5310-7213

13. Rebecca Hoh

    Division of HIV, Infectious Diseases and Global Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

14. Ma Somsouk

    Department of Medicine, Division of Gastroenterology, San Francisco General Hospital and University of California, San Francisco, San Francisco, United States

    Contribution

    Resources, Data curation

    Competing interests

    No competing interests declared

15. Poonam Vohra

    Department of Pathology, University of California, San Francisco, San Francisco, United States

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

16. Jeffrey Milush

    Department of Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Resources, Methodology

    Competing interests

    No competing interests declared

17. Katherine Sholtis James

    Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Resources, Investigation, Methodology

    Competing interests

    No competing interests declared

18. Nancie M Archin

    Division of Infectious Diseases, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, United States

    Contribution

    Resources, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology

    Competing interests

    No competing interests declared

19. Peter W Hunt

    Division of Experimental Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Methodology

    Competing interests

    No competing interests declared

20. Steven G Deeks

    Division of HIV, Infectious Diseases and Global Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Resources, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0001-6371-747X

21. Steven A Yukl

    San Francisco Veterans Affairs (VA) Medical Center and University of California, San Francisco, San Francisco, United States

    Contribution

    Resources, Supervision, Funding acquisition, Writing - review and editing

    Competing interests

    No competing interests declared

22. Sarah Palmer

    Centre for Virus Research, the Westmead Institute for Medical Research, The University of Sydney, Sydney, Australia

    Contribution

    Resources, Formal analysis, Supervision, Funding acquisition, Methodology

    Competing interests

    No competing interests declared

23. Warner C Greene

    1. Gladstone Institutes, San Francisco, United States
    2. Department of Medicine, University of California, San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    Competing interests

    No competing interests declared

24. Nadia R Roan

    1. Gladstone Institutes, San Francisco, United States
    2. Department of Urology, University of California, San Francisco, San Francisco, United States

    Contribution

    Conceptualization, Resources, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Visualization, Methodology, Writing - original draft, Project administration, Writing - review and editing

    For correspondence

    nadia.roan@ucsf.edu

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-5464-1976

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