Author response: Phenotypic analysis of the unstimulated in vivo HIV CD4 T cell reservoir (2020)

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics Abstract The latent reservoir is a major barrier to HIV cure. As latently infected cells cannot be phenotyped directly, the features of the in vivo reservoir have remained elusive. Here, we describe a method that leverages high-dimensional phenotyping using CyTOF to trace latently infected cells reactivated ex vivo to their original pre-activation states. Our results suggest that, contrary to common assumptions, the reservoir is not randomly distributed among cell subsets, and is remarkably conserved between individuals. However, reservoir composition differs between tissues and blood, as do cells successfully reactivated by different latency reversing agents. By selecting 8–10 of our 39 original CyTOF markers, we were able to isolate highly purified populations of unstimulated in vivo latent cells. These purified populations were highly enriched for replication-competent and intact provirus, transcribed HIV, and displayed clonal expansion. The ability to isolate unstimulated latent cells from infected individuals enables previously impossible studies on HIV persistence. eLife digest 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. Introduction 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. Results Validation of PP-SLIDE as a method to trace reactivated cells back to their latent state PP-SLIDE was recently validated as an approach to trace HIV-remodeled cells to their original pre-infection states (Cavrois et al., 2017; Ma et al., 2020). To determine if it can trace reactivated cells to their original latent state, we tested it first in J-Lat cells, an in vitro model of HIV latency. J-Lat cells are Jurkat cells harboring a single integrated HIV provirus that can be reactivated upon stimulation. Multiple lines of J-Lats have been cloned (Chan et al., 2013; Jordan et al., 2003) that have diverged slightly over successive passages. We tested how well PP-SLIDE traces reactivated cells back to their original clone when given the choice of two parental clones (Figure 1A). Cells of clone 6.3 were reactivated by stimulation with PMA/ionomycin and phenotyped using a CyTOF T cell panel (Ma et al., 2020), along with unstimulated cells of clone 6.3 and another clone 5A8. Using PP-SLIDE (see Materials and methods), the high-dimensional CyTOF-generated information from reactivated 6.3 cells (identified by expression of HIV Gag) was matched against an atlas of concatenated unstimulated 5A8 and 6.3 cells to identify the most similar unstimulated cell for each reactivated 6.3 cell. These unstimulated cells, identified using a k-nearest neighbor (kNN) approach, are collectively termed ‘kNN latent cells’. The error rate, defined as the % of reactivated 6.3 cells that matched to unstimulated 5A8 cells, was 0.8% (Figure 1B). These results suggest that although reactivation greatly changes the phenotype of a latent cell, some of the original ‘identity’ of the cell is still retained and that PP-SLIDE can be used to capture this information and thereby identify precursors of reactivated latent cells. Figure 1 Download asset Open asset Validation of PP-SLIDE as a method to trace reactivated J-Lat clones to their parent. (A) Schematic. J-Lat clones 5A8 or 6.3 were phenotyped using CyTOF in the absence of stimulation (a); 6.3 was additionally stimulated for 40 hr with PMA/ionomycin and then phenotyped using CyTOF (b). The high-dimensional datasets of unstimulated 5A8 and 6.3 were merged to generate the ‘atlas’ of unstimulated J-Lat cells. The high-dimensional data for each reactivated (Gag+) 6.3 cell was matched against the atlas by PP-SLIDE (c). The error rate was defined as the % of reactivated 6.3 cells that matched to unstimulated 5A8 cells (d). (B) PP-SLIDE correctly identifies the unstimulated precursor of reactivated 6.3 cells, 99.2% of the time. tSNE plots of unstimulated J-Lat 6.3 (a), unstimulated J-Lat 5A8 (b), and PMA/ionomycin-stimulated J-Lat 6.3 (c). PP-SLIDE was used to identify the kNN latent cell of each reactivated 6.3 cell among a merged atlas of unstimulated 6.3 and 5A8 cells. Note marked overlap of kNN latent cells (d) with unstimulated J-Lat 6.3 (e, 99.2%) and not J-Lat 5A8 (f, 0.8%). Latent cells are not randomly distributed among memory CD4+ T cells in vivo Next, we applied PP-SLIDE to characterize latent cells from HIV-infected individuals, using a CyTOF panel (Supplementary file 1) that is directed at markers of the major subsets and differentiation states of CD4+ T cells and includes receptors and intracellular proteins associated with latency (Banga et al., 2016; Buzon et al., 2014; Chomont et al., 2009; Descours et al., 2017; Fromentin et al., 2016; Hogan et al., 2018; Iglesias-Ussel et al., 2013; Khoury et al., 2016; Li et al., 2019; Serra-Peinado et al., 2019; Sun et al., 2015). Since reactivation events are rare and reducing background was key, we conjugated different anti-Gag antibodies to three different metal lanthanides and only considered cells displaying a Gag signal in all three channels as true events. The full panel was validated in vitro (Figure 2—figure supplement 1). We collected via leukapheresis CD4+ T cells from the blood of four ART-suppressed HIV-infected individuals (Supplementary file 2), purified memory CD4+ T cells by negative selection, and separated the cells into two pools (Figure 2A). One pool was used to generate the atlas of unstimulated cells. This pool was phenotyped within 2 hours of cell isolation, and never went through any cryopreservation or ex vivo culture. The other pool was stimulated with PMA/ionomycin under fully suppressive ART (Figure 2—figure supplement 2A) for 40 hr, a time point that precedes mitogen-induced cellular proliferation (Figure 2—figure supplement 2B). The CyTOF profiles of reactivated cells were matched against those of the unstimulated atlas cells. Assuming each reactivated cell retained some of its original pre-stimulation identity and that a phenotypically similar cell was present in the atlas of unstimulated cells, we used PP-SLIDE to identify, for each reactivated cell, the nearest-neighbor within the atlas (Figure 2A). These identified cells, the ‘kNN latent cells’, harbor the predicted phenotypes of latent cells before their reactivation from latency. Figure 2 with 3 supplements see all Download asset Open asset PP-SLIDE charting of in vivo latent reservoir reveals non-random distribution of latent cells. (A) Schematic of PP-SLIDE analysis of in vivo reservoir. Memory CD4+ T cells from infected, ART-suppressed individuals were isolated and an aliquot immediately processed for CyTOF phenotyping (a), while the remaining cells were phenotyped after treatment with PMA/ionomycin for 40 hr in the presence of ART (b). PP-SLIDE identified a kNN latent cell (the most similar cell in the atlas of unstimulated cells) for each reactivated cell (c). An overlay of the kNN latent cells (red) onto the atlas (gray) reveals regions preferentially associated with latency (d). (B) Identification of ex vivo-reactivated latent cells from the blood of ART-suppressed individuals. Cells were phenotyped before or after stimulation for 40 hr with PMA/ionomycin in the presence of ART. Reactivated cells are shown in the gates. The frequencies of reactivated cells were 12.0 (PID1695), 9.9 (PID3010), 17.6 (PID5003), and 5.6 (PID2053) per million memory CD4+ T cells. (C) kNN latent cells are non-randomly distributed among memory CD4+ T cells. Left: Reactivated cells (red) visualized by tSNE alongside unstimulated memory CD4+ T cells (black) from the same patient. Due to phenotypic changes induced by stimulation and reactivation, the reactivated cells (stacked as tight populations) reside in distinct regions of each tSNE plot (red ovals). Center: Atlas of memory CD4+ T cells from each sample, clustered using FlowSOM. Each cluster is depicted in a different color. Right: The kNN latent cells are colored according to the cluster they belong to. (D) Pie graphs showing relative proportions of each cluster among the atlas. ‘D’ (Detectable) designates clusters harboring at least one kNN latent cell and ‘U’ (Undetectable) those lacking any. The D clusters are arranged in order of the frequency of kNN latent cells they harbor, with D1 clusters harboring the highest frequencies. The existence of small D clusters and large U clusters, along with the chi-squared values, demonstrate non-random distribution of the latent reservoir. We identified between 20 and 96 reactivated cells per specimen, corresponding to a mean frequency of 11.3 per million memory (CD45RA-CD45RO+) CD4+ T cells (range 5.6–17.6, Figure 2B, Figure 2—figure supplement 3). When visualized against the atlas of unstimulated cells by t-distributed stochastic neighbor embedding (tSNE; van der Maaten, 2009), Gag+ reactivated cells occupied a unique region, confirming their distinct phenotype relative to unstimulated cells (Figure 2C, left). kNN latent cells mapped to several distinct regions of the atlas (Figure 2C, right), suggesting a non-random distribution among memory CD4+ T cells. To verify this, we implemented FlowSOM (Van Gassen et al., 2015) to subdivide the atlases from each of the four donors into 20 clusters, and visualized the distribution of kNN latent cells on the tSNE maps or on pie graphs (Figure 2C, center, and 2D). We found large clusters lacking kNN latent cells as well as small clusters harboring kNN latent cells, consistent with a non-random distribution of latent cells, which was confirmed using a chi-squared test (Figure 2D). For a detailed description of the extent of enrichment of kNN latent cells in clusters and validation of non-random distribution of the reservoir, see Materials and methods. kNN latent cells from different donors share phenotypic features Next, we examined the composition of the kNN latent cells. We focused first on the Tcm, Tem, and Ttm subsets, as they have been the most commonly studied memory CD4+ T cells in the context of HIV latency (Chomont et al., 2009; Josefsson et al., 2013; Kwon et al., 2020; Pardons et al., 2019; Soriano-Sarabia et al., 2014; von Stockenstrom et al., 2015). In all four donors, the contribution of Tem to the reservoir was greater than its contribution to total memory CD4+ T cells (Figure 3—figure supplement 1). We did not identify any kNN latent cells with the Ttm phenotype, likely because this subset is relatively rare in blood and not efficiently captured in our samples, each of which harbored fewer than 100 kNN latent cells. When the three FlowSOM clusters of atlas cells with the highest frequencies of kNN latent cells were examined for the relative contributions of the Tcm, Tem, and Ttm subsets, we found that they were all comprised of multiple subsets (Figure 3—figure supplement 2). Collectively, these data demonstrate that although Tem are over-represented among kNN latent cells, the clusters most enriched for kNN latent cells – as classified by unbiased clustering based on ~40 markers – are a mixture of Tcm/Tem/Ttm subsets. These findings could suggest that the reservoir is quite different between individuals. Alternatively, they could mean that latency markers shared between individuals are not those used to define Tcm/Tem/Ttm cells. Consistent with the latter, when datasets from the four donors were combined and re-run within the same tSNE, the kNN latent cells from the four donors resided within a similar region of the plot (Figure 3A). These results argue that kNN latent cells between different donors are in fact similar. Consistent with this conclusion, relative to total memory CD4+ T cells, kNN latent cells consistently expressed higher levels of select markers of immune checkpoint (PD1, CTLA4), activation (CD69, CD25, HLADR), and T cell differentiation states (Tbet, CRTH2, CCR6; Figure 3B). We conclude that in vivo latent cells are not randomly distributed but instead share specific features between individuals. Figure 3 with 3 supplements see all Download asset Open asset kNN latent cells from different donors are phenotypically similar. (A) The atlas (gray) and kNN latent cells (red) for four donors are depicted within the same tSNE space. In all donors, the center region of the tSNE is devoid of kNN latent cells. The kNN latent cells depicted here are those identified in Figure 2. (B) Expression levels of select antigens on kNN latent cells as compared to the total population of memory CD4+ T cells. Top plots show the antigen expression levels as a heatmap within the tSNE space depicted in panel A. Bottom graphs show the mean signal intensity (MSI) of each antigen in the total population of memory CD4+ T cells (Total) compared to the kNN latent cells (Latent). Proteins consistently expressed at higher levels on kNN latent cells than on the total population included immune checkpoint molecules (PD1, CTLA4) and the HIV co-receptors CCR5 and CXCR4. Some activation markers (CD69, CD25, HLADR) but not others (CD38) were also upregulated, as were markers of Th1, Th2, and Th17 cells (Tbet, CRTH2, and CCR6, respectively). Some markers of Tfh (PD1, ICOS) but not others (CXCR5) were consistently elevated. By the was expressed at levels on kNN latent cells. as determined by a not latent cells are similar between individuals, the in latent cells could still be greater than the To test this we leukapheresis from at two time 2 and compared in an unbiased the kNN latent cells from those to kNN latent cells from (Figure 3—figure supplement For each kNN latent cell in the sample, we the on CyTOF to kNN latent cell in the were between kNN latent cells from the two time in (Figure 3—figure supplement 3A). The were between from the same than from different donors (Figure 3—figure supplement that the of kNN latent cells between donors, the kNN latent cells differ more between donors than within a ex vivo reactivate different latent cells Next, we of our ability to maps of the phenotypic features of latent cells to their reactivation by different ex vivo blood were from the same suppressed individual and stimulated with the PMA/ionomycin or (Figure 3—figure supplement revealed that kNN latent cells from different time were more similar to each other than to kNN latent cells from the same time point (Figure 3—figure supplement Next, we compared the latent cells by PMA/ionomycin to those by a of an with an using three blood each 2 tSNE of the kNN latent cells revealed a region of the plot that harbored kNN latent cells from the PMA/ionomycin but not from the (Figure (Figure 3—figure supplement were again used to verify that kNN latent cells by the same stimulation at different time were more similar than those by different within the same time point (Figure These data suggest that only a subset of latent cells by such as although these findings be confirmed with from Figure with 1 supplement see all Download asset Open asset of kNN latent cells by different and from blood tissues using specimens. (A) Atlas (gray) and kNN latent cells (red) from three blood from the same reactivation was induced by PMA/ionomycin or regions that harbor kNN latent cells in the PMA/ionomycin but not the of kNN latent cells in each (B) kNN latent cells by PMA/ionomycin at two different time are more similar to each other than to those by at the same time The distribution of between kNN cells was between point 1 PMA/ionomycin and point 2 PMA/ionomycin and point 2 and point 3 and point 2 PMA/ionomycin and point 2 (C) of memory CD4+ T cells from or the blood of the same with corresponding kNN latent cells point to regions of the that are in kNN latent cells. Bottom plots show each atlas onto the blood atlas. the of kNN latent cells in each (D) plots that but not blood kNN latent cells (red levels of and Atlas cells are in Pie show proportions of kNN latent cells within each kNN latent cells from different are similar. Atlas and kNN latent cells from four blood, two and four as depicted by distribution of between atlas or kNN cells as between blood or features of kNN latent cells Since most of the HIV reservoir persists within tissues et al., we applied the PP-SLIDE method of latent cell to specimens. One of the four suppressed had a leukapheresis specimen, also to the of two sets of via isolation, each was immediately or stimulated for 40 hr with PMA/ionomycin and then to with blood memory CD4+ T cells were not purified of time limited of cells. However, we on memory CD4+ T cells to with purified blood cells. The atlases from the three blood, two that blood and cells were phenotypically distinct (Figure The kNN latent cells from the similar to those from blood, were not randomly distributed and remained similar the as by their similar regions of tSNE space (Figure Several antigens kNN latent cells in the blood tissues. For instance, an antigen expressed on cells, and a of activation in blood but a T memory in tissues et al., 2019), were more highly expressed on kNN latent cells (Figure Figure supplement 1A). and markers of Tfh that are preferentially expressed in latent cells particularly in tissues (Banga et al., were also expressed at levels on kNN latent cells from (Figure Figure supplement 1A). The was expressed at levels on kNN latent cells in while the was more expressed (Figure Figure supplement 1A). These results marked differences between the phenotypic features of kNN latent cells in blood with the of which was in We also the reservoir in the the of viral during suppressive therapy et al., 2017). As were not we a analysis comparing from four donors to the blood previously by differences in the reservoir between the blood and were some also kNN latent cells from the were while those from blood exhibited a more expression of these two antigens (Figure supplement 1B). By kNN latent cells from the and blood expressed levels of but relatively levels of (Figure supplement 1B). was that and CCR6, previously to be preferentially expressed on latent cells from tissues et al., 2017; et al., were highly expressed in kNN latent cells from the and with the these analyses identify and as antigens expressed at levels in kNN latent cells from and suggesting shared features of the reservoir between different To the between kNN latent cells from the three we visualized the atlas and kNN latent cells of all donors combined using tSNE (Figure the atlas cells to the blood and atlas cells, an confirmed by By kNN latent cells from and to cluster more from those from blood, which