The single-cell landscape of immunological responses of CD4⁺ T cells in HIV versus severe acute respiratory syndrome coronavirus 2

Abstract

Purpose of review

CD4⁺ T cell loss is the hallmark of uncontrolled HIV-1 infection. Strikingly, CD4⁺ T cell depletion is a strong indicator for disease severity in the recently emerged coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. We reviewed recent single-cell immune profiling studies in HIV-1 infection and COVID-19 to provide critical insight in virus-induced immunopathogenesis.

Recent findings

Cytokine dysregulation in HIV-1 leads to chronic inflammation, while severe SARS-CoV-2 infection induces cytokine release syndrome and increased mortality. HIV-1-specific CD4⁺ T cells are dysfunctional, while SARS-CoV-2-specific CD4⁺ T cells exhibit robust Th1 function and correlate with protective antibody responses. In HIV-1 infection, follicular helper T cells (T_FH) are susceptible to HIV-1 infection and persist in immune-sanctuary sites in lymphoid tissues as an HIV-1 reservoir. In severe SARS-CoV-2 infection, T_FH are absent in lymphoid tissues and are associated with diminished protective immunity. Advancement in HIV-1 DNA, RNA, and protein-based single-cell capture methods can overcome the rarity and heterogeneity of HIV-1-infected cells and identify mechanisms of HIV-1 persistence and clonal expansion dynamics.

Summary

Single-cell immune profiling identifies a high-resolution picture of immune dysregulation in HIV-1 and SARS-CoV-2 infection and informs outcome prediction and therapeutic interventions.

INTRODUCTION

The emergence of unknown and widespread infections, both HIV-1 in the 1980s and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in 2019, creates a global public health crisis. Researchers race at breathtaking speed to study viral pathogenesis and stop surging deaths and socioeconomic distress during the pandemic. Through the past 30 years, the HIV research community has built infrastructure such as advanced biosafety-level laboratories, competence to work on small-volume and infectious clinical samples, multidisciplinary approaches, and collaborating platforms and provided critical contributions to rapid scientific advancement in viral pathogenesis, immune responses, outcome prediction, treatment, and vaccine design for coronavirus disease 2019 (COVID-19).

CD4⁺ T cells are the central orchestrator of cellular and humoral adaptive immunity. In cellular immunity, cytotoxic T lymphocytes (CTLs) recognize and kill infected cells, and block exponential viral production. In humoral immunity, B cells undergo rounds of affinity maturation and class switching to produce antibodies that neutralize viral particles and block new rounds of infection. CD4⁺ T cells provide critical help for the development of CTL and B cell function and memory [1–3]. Both HIV-1 [4] and SARS-CoV-2 (reviewed in [5^▪]), two RNA viruses causing widespread global infections, impair CD4⁺ T cell function and induce morbidity and mortality. The two viral infections are distinct in nature: acute HIV infection is characterized by virus-induced CD4⁺ T cell depletion, while chronic HIV infection, despite antiretroviral therapy (ART), still shows efficient immune escape, a lack of protective immunity, and hallmarks of chronic immune activation. COVID-19, on the other hand, shows greatest severity in acute infection when the innate immune system misfires. While there can be prolonged periods of virus shedding or rapid fading of protective immune responses in rare cases, most SARS-CoV-2 infected individuals develop protective immunity and do not develop chronic infection. The commonality between both diseases is the fact that a thorough understanding of immune cells is needed to advance treatment.

Recent advances in single-cell profiling of heterogeneous populations of immune cells, from flow cytometry and mass cytometry to single-cell RNAseq and multiomics profiling, have enabled in-depth understanding of virus-induced immune dysfunction in multiple dimensions including frequency, differentiation, function, and exhaustion phenotypes. While flow cytometry and mass cytometry provide high-dimensional protein profiling on a targeted selection of up to 50 proteins of interest, single-cell RNAseq provides genome-wide profiling of more than 20 000 genes in individual single cells. Yet, RNA content in single cell is sparse and hard to capture. It is estimated that a single cell contains ~10 pg of RNA. Among them, ~1–5% are mRNA [6]. Primary CD4⁺ T cells contain even less RNA. Recent advances in single-cell multiomic profiling methods (reviewed in [6]), their performance [7^▪▪], batch correction methods [8^▪▪], trajectory inference [9^▪▪], and bioinformatic analysis (reviewed in [10^▪▪]) have evolved rapidly to overcome the heterogeneity of immune cells, the rarity of specific cellular populations in different biological states, and the sparse RNA content in primary cells.

By contrasting the differences in HIV-1 versus SARS-CoV-2-induced immune dysfunction at the single-cell level (Fig. 1, Table 1) and by summarizing recent advances in single-cell profiling in HIV-1-infected cells (Fig. 2), we provide here mechanisms of immunopathogenesis and potential targets for therapeutic interventions.

FIGURE 1.

CD4⁺ T cell dysfunction in SARS-CoV-2 versus HIV-1 infection. (a) Both SARS-CoV-2 and HIV-1 infection cause CD4⁺ T cell depletion. CD4⁺ T cell counts are restored after SARS-CoV-2 clearance while untreated HIV-1 infection continues to deplete CD4⁺ T cells and eventually causes AIDS. Antiretroviral therapy controls HIV viral load to clinically undetectable levels and maintains relatively normal CD4⁺ T cell levels. The proliferation capacity of CD4⁺ T cells is relatively high in recovered COVID-19 patients. In contrast, the proliferation capacity is impaired during acute HIV-1 infection but can be partially restored with long-term antiretroviral therapy. (b) Cytokine dysregulation induces CD4⁺ T cell dysfunction during viral infection. SARS-CoV-2 infection is characterized by delayed IFNα response, cytokine release syndrome or cytokine storm (IL-6, IL-10, TNF-α), and misfiring of type 2 immune responses. SARS-CoV-2-specific CD4⁺ T cells remain functional, showing Th1 effector function (IFN-γ, IL-2, TNF-α), cytotoxic signatures, and robust proliferation potential. Acute HIV-1 infection is characterized by robust IFNα responses but impaired Th1 responses. HIV-1-specific CD4⁺ T cells are dysfunctional, exhibiting impaired IFN-γ and IL-2 secretion, exhaustion phenotypes, and decreased proliferation capacity. Despite antiretroviral therapy, CD4⁺ T cells remain dysfunctional and exhausted during chronic HIV-1 infection, secondary to chronic inflammation. HIV-1-infected cells are heterogeneous, upregulating cellular programs involving antiviral chemokines and cellular survival. A few surface markers have been reported to enrich for HIV-1-infected cells.

Table 1.

Cytokine production and CD4⁺ T cell dysfunction in SARS-CoV-2 versus HIV-1 infection

	SARS-CoV-2	HIV-1 (during viremia)	HIV-1 (after ART suppression)	HIV-1-infected cells
Cytokine response	↑ IL-1α, IL-1β, IL-6, IL-10, IL-18, TNF-α, IFNα [18▪▪,19,20,22]	↑ IFNγ, TNF-α, MCP-1, IL-2R, IL-6, IL-8, IL-10, IL-12 [11,12▪,13]	↑ IFNγ, TNF-α, TGF-β [13,14]	IRF4, JAK1, LTA, CCL3, CCL4, XCL1 [106▪,125▪]
CD4 depletion	Lymphopenia [19,39▪,40] Negatively correlated with IL-6 and TNF-α levels [39▪] ↑ Apoptotic programs [23▪,41,42]	Fas/FasL-mediated apoptosis through caspase 1 [4,27] Abortive infection-mediated pyroptosis through caspase 3 [28] GALT CD4+ T cell depletion and microbial translocation [30–34]	Lymphoid tissue fibrosis prevents CD4+ T cell homeostasis and reconstitution [35–38]	Survival-associated genes: MIR155HG and ZNF217 [106▪,125▪]
CD4 responses	↑ ISG expression [48,49,50▪▪] ↑ TNF, IL-1β [52▪▪] ↑ TFH [80▪] ↑ Activation, proliferation [46▪] ↑ Type 1 and type 3 responses [18▪▪] ↑ Misfiring of type 2 responses (IL-5, IL-13) in severe COVID-19 [18▪▪] ↑ Cytotoxic CD4 [47]	↑ ISG expression [44▪▪] ↑TNF-α, IL-1β response [44▪▪] ↑ TFH [57,90,91]	↑CTLA4, TIGIT [45▪]	↑Th1 [106▪] ↑TIGIT, HLA-DR [125▪] ↑ During viremia: activation, proliferation, exhaustion markers, CXCR5, α4β7, α4β1 [114,115] ↑ During suppression: exhaustion markers, α4β1 [114,115] ↑ CD2, CD30, CD161 enrichment [116–118] ↑Tem [114]
Antigen-specific CD4+ T cells	Frequency ~0.2–2% [73▪,74▪▪] ↑ IL-2, IFNγ, TNF-α, proliferation [23▪,39▪,41,42,73▪, 76▪▪,78,79] ↑ cytotoxic effectors [80▪]	Frequency ~0.1–0.7% [55,57] IFNγ but not IL-2 production [60,61] ↓ proliferation [69–71] ↑ exhaustion markers [66–68]	↓TFH signature [57] ↓IFNγ production [55,60]	HIV-specific CD4+ T cells are enriched in HIV-1 infection [55]

ART, antiretroviral therapy; COVID-19, coronavirus disease 2019; GALT, gut-associated lymphoid tissue; ISG, interferon-stimulated gene; SARS-CoV-2, severe acute respiratory syndrome coronavirus 2; TNF, tumor necrosis factor.

FIGURE 2.

Single-cell methods characterize HIV-1-infected CD4⁺ T cells. (a–d) Targeted capture of HIV-1-infected cells from HIV-1-infected individuals. (a) The PCR-activated cell sorting by the Abate group [128]: using a microfluidics device, droplets of single cells that are HIV-1 DNA PCR positive are sorted for single-cell RNAseq or single-cell DNAseq. This method is currently under development. (b) HIV-1 SortSeq by the Ho group [106^▪] identifies HIV-1 RNA⁺ cells using a combination of 192 fluorescently labeled probes targeting 5′ and 3′ HIV-1 RNA for single-cell RNAseq. HIV-1 integration site may be identified by mapping HIV-1-host chimeric RNA junction. (c) LURE by the Nussenzweig group [125^▪] identifies HIV-1 Env expressing cells using broadly neutralizing antibody-based magnetic enrichment and fluorescence-based cytometric sorting. (d) HIV-1 RNA flow-FISH by the Kaufmann group [115] identifies HIV-1 Gag expressing cells using a combination of HIV-1 RNA fluorescent in-situ hybridization (FISH) (targeting gag-pol) and HIV-1 p24 protein staining. (e and f) Large-scale single-cell profiling with paired HIV-1 RNA mapping. (e) Microwell-based profiling, such as Seq-Well by the Shalek group, encloses single cells with barcoded beads in microwells for single-cell RNAseq. (f) Droplet-based profiling, such as that commercialized by 10× Genomics, and single-cell multiomic method ECCITEseq developed by the Smibert group [134], was applied to HIV-1 clinical samples by the Ho group [137] and profiled surface protein expression, cellular transcriptome, T cell receptor, and HIV-1 RNA in the same cell. AAAA, poly(A) tail. TCR, T cell receptor. TSO, template switch oligonucleotide. rGrGrG, 3 riboguanosines (rGrGrG). UMI, unique molecular identifiers, are molecular tags used to detect and quantify unique RNA transcripts.

CYTOKINE DYSREGULATION DRIVES CD4⁺ T CELL RESPONSES

Acute viral infection induces innate immune responses through pattern recognition receptors, type I interferon, proinflammatory cytokines, and interferon-stimulated gene (ISG) expression. While these antiviral programs hinder viral replication, prolonged cytokinemia induces overactivation of immune cells and tissue damage. Acute HIV-1 infection induces a surge in type I interferon responses within the first 2 weeks of infection, along with increased IFNγ, tumor necrosis factor (TNF), monocyte chemoattractant protein-1 (MCP-1), soluble IL-2 receptor, IL-6, IL-8, IL-10, and IL-12 plasma levels [11,12^▪,13]. Early ART substantially reduces cytokinemia to basal levels during acute infection [11,12^▪]. During untreated chronic HIV-1 infection, IFNγ, TNF-α, TGF-β, IL-4, IL-6, and IL-10 plasma levels are elevated. After 12 months of ART suppression, IL-4, IL-6, and IL-10 plasma levels return to normal, but IFNγ, TNF-α, and TGF-β levels remain elevated [13,14]. Therefore, although HIV-1-infected individuals do not experience cytokine storms involving multiorgan dysfunction, chronic inflammation contributes to long-term complications such as accelerated atherosclerosis [15].

Unlike HIV-1 infection, cytokine dysregulation in SARS-CoV-2 infection induces cytokine release syndrome (or cytokine storm), an acute systemic inflammatory syndrome that involves multiorgan dysfunction [16,17]. SARS-CoV-2-induced cytokine release syndrome, involving increased IL-1α, IL-1β, IL-6, IL-10, IL-18, and TNF plasma levels, is strongly associated with disease severity [18^▪▪,19,20]. Unlike HIV-1 and influenza infections in which IFNα peaks early and initiates antiviral programs, IFNα peaks relatively late (around 2 weeks after symptom onset) in severe SARS-CoV-2-infected patients [18^▪▪,21]. This is consistent with the observation that the late IFNα peak fails to limit viral infection and induces CD4⁺ and CD8⁺ T cell depletion in SARS-CoV-1 infection [22]. In another study, IFNα levels are decreased in severe SARS-CoV-2-infected patients: the lower plasma level of IFNα in these severe patients is associated with lower ISG expression and higher levels of SARS-CoV-2 viremia [23^▪], potentially due to inborn errors in type I interferon immunity [24] or the development of autoantibodies against type I interferon [25]. Overall, SARS-CoV-2 infection drives a decrease in antiviral cytokines (such as IFNα) and an increase in proinflammatory cytokines (such as IL-6) as the hallmark of COVID-19 [20].

MECHANISMS OF CD4⁺ T CELL DEPLETION

Both SARS-CoV-2 and HIV-1 infection lead to CD4⁺ T cell depletion. However, the underlying causes are very different. CD4⁺ T cell loss is the hallmark of untreated HIV-1 infection. A peripheral CD4⁺ T cell count less than 200/μl defines AIDS in which overt opportunistic infections occur [26]. In HIV-1 infection, CD4⁺ T cells are profoundly depleted due to Fas/Fas-ligand mediated apoptosis during direct infections [4,27] and pyroptosis during abortive viral infections [28]. The depletion of CD4⁺ T cells in the gut-associated lymphoid tissue [29] compromises gut epithelial barrier integrity, increases bacterial translocation, and causes persistent immune activation [30–34]. Chronic immune activation induces lymphoid tissue fibrosis [35,36], limits T cell access to homeostatic cytokine IL-7, and causes CD4⁺ T cell homeostasis failure. As the structural damage to lymphoid tissues is irreversible, HIV-induced CD4⁺ T cell depletion can be restored in most but not all infected individuals despite ART [37,38].

Mechanisms of CD4⁺ T cell depletion remain incompletely understood in SARS-CoV-2 infection. In severe COVID-19 patients, the level of CD4⁺ T cell depletion can be as low as ~200/μl in the peripheral blood, approaching the level seen in advanced HIV-1 infection [19,39^▪,40]. The CD4⁺ T cell count is significantly lower in severe COVID-19 cases than in mild and moderate cases, indicating that CD4⁺ T cell reduction is associated with disease severity [19,39^▪]. Preliminary studies suggest that increased apoptosis [23^▪,41,42] secondary to systemic inflammation may be a cause of SARS-CoV-2-induced CD4⁺ T cell depletion. Unlike HIV-1 infection, CD4⁺ T cell depletion resolves after recovery in SARS-CoV-2 infection [39^▪,43].

SINGLE-CELL IMMUNE LANDSCAPE OF IMMUNOLOGIC RESPONSES OF HIV-1 VERSUS SARS-COV-2 INFECTION

Advancement in single-cell RNAseq methods allows genome-wide profiling of heterogeneous immune cells down to the single-cell level, including detailed characterization of novel or previously unknown cell types. Bioinformatic and computational analysis enables integration and correction of interindividual variabilities and identification of differentially expressed genes and immune signaling pathways. Thus, single-cell data sets from very few study participants from carefully designed clinical studies may provide unbiased discovery of the landscape of immune responses and disease mechanisms.

Consistent with the IFNα surge in the plasma during acute HIV-1 infection, single-cell RNAseq of peripheral blood mononuclear cells (PBMCs) reveals upregulation of an interferon response signature in multiple cell types in acute HIV-1 infection [44^▪▪]. CD4⁺ T cells upregulate a gene module of ISGs (such as ISG15, IFI44, IFI44L, IFI6, IFIT1, IFIT3, MX1, XAF1, S100A8, S100A9). Upstream regulator analysis reveals that the transcriptional program in CD4⁺ T cells is driven by IFNα, IFNγ, TNF, TGF-β, CD3, KRAS, IL-1β, IL-2, IL-4, IL-6, and oncostatin [44^▪▪], reflecting that plasma cytokines and T cell activation drive CD4⁺ T cell responses during acute HIV-1 infection. Of note, during acute HIV-1 infection, CD4⁺ T cells are the major mediators of TNF-α and IL-1β responses in the peripheral blood [44^▪▪]. During chronic HIV-1 infection, a subset of CD4⁺ T cells express exhaustion markers (TIGIT, CTLA4), although studies from more study participants may be needed to identify the upstream regulators of such immune exhaustion phenotypes [45^▪].

High-dimensional profiling of PBMCs from COVID-19 patients using flow cytometry reveals increased frequency of activated and proliferating CD4⁺ T cells (expressing human leukocyte antigen – DR isotype (HLA-DR), CD38, and Ki67) [46^▪]. By incorporating plasma cytokine levels in high-dimensional flow cytometry-based PBMC profiling, a longitudinal analysis showed that moderate COVID-19 patients exhibit type 1 antiviral immune responses which decrease as the patient recovers [18^▪▪]. However, severe COVID-19 patients not only exhibit type 1 (IFNα) and type 3 (IFNλ) immune responses but also type 2 immune responses (IL-5 and IL-13) [18^▪▪], suggesting that misfiring of type 2 immune responses directs the clinical trajectory toward severe COVID-19.

Single-cell profiling of PBMCs from COVID-19 patients reveals increased frequency of clonally expanded Th1-like cytotoxic CD4⁺ T lymphocytes (expressing GZMA, GZMB, GZMH, and GNLY) [47]. CD4⁺ T cells upregulate ISGs similar to those seen in acute HIV-1 infection, such as ISG15, IFI44L, IFIT3, MX1, XAF1, S100A8, and S100A9 [48,49,50^▪▪], which were not seen after recovery [51]. By comparing the single-cell transcriptome profile between COVID-19 patients and severe influenza-infected patients, TNF/IL-1β-driven inflammation characterizes COVID-19 but not severe influenza infection [52^▪▪]. Treatment with IL-6R blockade tocilizumab reduces S100A8 and S100A9 expression and potentially breaks the IL-6/S100A8/9 feedback loop [50^▪▪]. Overall, single-cell profiling identifies the unique immune effectors (cytotoxic CD4⁺ T cells) and drivers of immune dysfunction in COVID-19.

ANTIGEN-SPECIFIC CD4⁺ TH1 RESPONSES

Antigen-specific CD4⁺ T cells provide help to rescue exhausted CD8⁺ T cells during chronic viral infection [53] and correlate with viral control [54]. Antigen-specific CD4⁺ T cells are typically isolated by flow cytometry using effector cytokines (using intra-cellular IL-2, IFNγ, or TNF staining) [55], surface activation-induced markers (AIM) such as CD69, CD154 (CD40L), and CD137 (4-1BB) [56–58], or cellular proliferation [59] upon ex vivo antigen peptide stimulation. The frequency of HIV-1-specific CD4⁺ T cells in HIV-1-infected individuals is ~0.1–0.7% measured by AIM, and only 0.2% express IFNγ [55,57]. Viremia increases CD4⁺ T cell production of IFNγ but not IL-2, stunting antigen-specific T cell proliferation despite high antigen levels [60,61]. The frequency of IFNγ-secreting HIV-specific CD4⁺ T cells further declines after ART [55,60]. In contrast, the same HIV-1-infected individuals have a higher frequency of cytomegalovirus (CMV)-specific CD4⁺ T cells (1.5%) [57]. Although HIV-1-specific CD4⁺ T cells can last for decades due to persistent antigen stimulation [62–64], these HIV-1-specific CD4⁺ T cells are dysfunctional [65], expressing exhaustion markers [66–68] with impaired proliferation capacity [69–71]. Immune checkpoint inhibitors, such as program cell death-1 (PD-1) blockade, reverse immune exhaustion, and restore HIV-1-specific CD4⁺ T cell proliferation capacity [62,72].

The frequency of SARS-CoV-2-specific CD4⁺ T cells in the recovery phase is ~0.2–2% when defined by cytokine production [73^▪] and AIM [74^▪▪]. SARS-CoV-2-specific CD4⁺ T cells exhibit robust Th1 function, express IL-2, IFNγ, and TNFα, and maintain proliferative capacities [73^▪,74^▪▪,75,76^▪▪,77^▪,78,79]. In particular, SARS-CoV-2-specific CD4⁺ T cells are enriched in cytotoxic CD4⁺ T cells expressing PRF1, GZMB, GNLY, NKG7, XCL1, and XCL2 [80^▪]. SARS-CoV-2-specific CD4⁺ T cells from unexposed individuals, likely the result of cross-reactive stimulation from prior common cold coronavirus infection (such as OC43, HKU1, NL63, and 229E), are functional in IFNγ production and are inferred as a potential reason for cross-protection [77^▪,81]. Importantly, the frequency of SARS-CoV-2 Spike-specific CD4⁺ T cells correlates with antibody titers (anti-Spike receptor binding domain IgG) [74^▪▪,82^▪▪]. While it is unclear how long SARS-CoV-2-specific CD4⁺ T cells will last, long-term studies from SARS-CoV-1 infection showed that, although SARS-CoV-1-specific IgG vanished, 60% of infected individuals have SARS-CoV-1-specific T cell responses for up to 6 years [83] or even 17 years [77^▪]. One caveat is that while SARS-CoV-1 [84] specific CD4⁺ T cells can be polyfunctional, dysregulation of SARS-CoV-1-specific responses toward Th2 but not Th1 can be seen in some fatal cases [84]. This skewing from Th1 toward Th2 immune responses is concerning, as some SARS-CoV-1 vaccines induces immunopathologic Th2 responses instead of immune protective Th1 responses [85,86].

CD4⁺ T CELL HELP FOR B CELL-MEDIATED HUMORAL IMMUNE RESPONSES

Follicular helper CD4⁺ T cells (T_FH) provide critical help for B cell-mediated humoral immunity, particularly in the germinal center, by facilitating B cell proliferation, differentiation, antibody affinity maturation, and class-switching [87]. Although T_FH express CXCR5 and costimulatory molecules such as PD-1 and inducible T cell costimulator (ICOS), the definition of T_FH relies on transcription factor Bcl6 expression [88] and IL-21 production [89]. HIV-1 viremia drives the expansion of T_FH cells mainly in the lymphoid tissue [90], although T_FH-like CD4⁺ T cells in the peripheral blood can sometimes be identified [57,91]. HIV-1-specific T_FH are associated with protective antibody responses [91] and enhanced HIV-specific CD8⁺ T cell function measured by perforin production [92]. However, since CD8⁺ cytotoxic T cells lack the homing marker CXCR5 to reach T_FH in the B cell follicle of lymphoid tissues [93], T_FH become a source of the HIV-1 reservoir at the immune sanctuary site [94].

In COVID-19 patients who recovered, the frequency of Spike-specific T_FH cells in peripheral blood correlates with neutralizing antibody titer, suggesting protective immunity [95]. A remarkable proportion of SARS-CoV-2-specific CD4⁺ T cells in the peripheral blood exhibit T_FH phenotypes, such as IL21 expression [80^▪]. However, in COVID-19 patients who died early (within 10 days of infection), there is an absence of germinal center formation, a block of T_FH differentiation, and a remarkable reduction of Bcl6⁺ germinal center B cells in the lymphoid tissues [96^▪▪]. This suggests that a lack of germinal centers and T_FH is associated with deficient antibody responses and high mortality in COVID-19.

UNDERSTANDING HIV-1-INFECTED CD4⁺ T CELLS AT SINGLE-CELL RESOLUTION

To determine the transcriptional state induced by HIV-1 and to overcome the sparsity of in vivo samples, in vitro models and single-genome sequencing methods have been developed. Two groups applied single-cell RNA sequencing to in vitro-infected primary cells [97,98]. However, the findings are hampered by the heterogeneity associated between different individuals.

Studying HIV-1-infected cells from HIV-1-infected individuals provides the most clinically relevant understanding of HIV-1 persistence [99–105,106^▪]. However, this translational approach has been extremely challenging. First, HIV-1-infected cells are extremely rare: ~100/million (0.01%) CD4⁺ T cells harbor an inducible HIV-1 provirus, and ~1000/million (0.1%) CD4⁺ T cells harbor a defective HIV-1 provirus [103,107]. Second, no cellular marker that can distinguish the rare HIV-1-infected cells from the 99.9% uninfected cells has been identified [108–110]. Third, HIV-1-infected cells are extremely heterogeneous, each having an HIV-1 provirus at a different integration site [99,111] and a different T cell functional state [112,113]. An advancement in single-cell methods is required to capture these rare HIV-1-infected cells.

Flow cytometry-based approaches have enabled some inferences into the HIV-1-infected cell landscape. During viremia, HIV-1 primarily infects activated CD4⁺ T cells. These cells are more likely to express activation markers (CD25, HLA-DR), proliferation markers (Ki67), immune checkpoint molecules (PD-1, LAG-3, TIGIT, Tim-3), CXCR5, and integrins α4β7 and α4β1 [114,115]. After ART suppression, HIV-1-infected cells may upregulate PD-1, TIGIT, and integrin α4β1 [114]. Other markers, such as CD2 [116], CD30 [117], CD161 [118], and CCR5 [119], have been shown to be enriched in HIV-1-infected cells, although these markers are insufficient to distinguish HIV-1-infected cells from uninfected cells. The Roan group developed a machine learning algorithm PP-SLIDE and a 37-parameter mass cytometry technique to identify a combination of protein markers to enrich for latently infected cells [120,121]. Using this model, a combination of protein markers (such as CD45RA⁻, CD45RO⁺, CD62L⁻, CCR7⁺, PD1⁺, TIGIT⁺, CD49D⁺ (integrin α4), CD28⁺, CCR5⁺ cells) (likely reflecting memory CD4⁺ T cells expressing activation, exhaustion, and homing markers) may enrich HIV-1-infected cells up to 100 fold in two out of three study participants examined [121]. Of note, the combination of protein markers is individual-dependent. One caveat is that this protein-based detection and algorithms may not be sufficient to recapitulate the heterogeneity of cellular states related to the breadth of tens of thousands of HIV-1 integration sites and the T cell antigen specificity in vivo.

To identify drivers for the proliferation of HIV-1-infected cells [122], several groups have developed flow cytometric sorting-based methods to determine the T cell antigen specificity and clonality [using T cell receptor (TCR) sequencing] and HIV-1 integration site, inducibility, and genome integrity (using single-genome sequencing) [121,123,124]. These studies demonstrated that latently infected cells are predominantly in the central and effector memory cells [123], and that antigen is a major driver for the clonal expansion of HIV-1-infected cells [124].

Since HIV-1-infected cells lack a specific cellular marker for effective enrichment, a breakthrough in the field is to use HIV-1 DNA (Fig. 2a), HIV-1 viral RNA (Fig. 2b and c), or protein expression (Fig. 2c and d) for sorting and subsequent single-cell profiling. HIV-1 SortSeq uses fluorescent in-situ hybridization (FISH)-based HIV-1 RNA staining after 16 h of 13-phorbol-12-myristate acetate (PMA)/ionomycin induction of HIV-1 RNA expression (Fig. 2b) [106^▪]. Using a method similar to single-molecule FISH, cells are fixed and permeabilized for FISH probes to enter the cells and hybridize to HIV-1 RNA. The benefit of this approach is the use of RNA-preserving procedures and a total of 192 fluorophore-tagged probes to increase HIV-1 RNA signal intensity and to overcome HIV-1 sequence diversity in clinical samples. The caveat is that the fixation process reduces RNA quality. The use of dead cell staining and colocalization of both 5′ and 3′ HIV-1 RNA probes reduces nonspecific staining. Using SMARTseq-based library preparation, which involves DNase treatment, total RNA amplification, and ribosomal RNA depletion, HIV-1 SortSeq sequences a small number of HIV-1-infected cells but in depth, detecting early events during latency reversal such as HIV-1-driven aberrant transcription of cancer-related genes as a mechanism for integration site-related proliferation [106^▪].

LURE uses broadly neutralizing antibody (bNAb)-based magnetic capture and fluorescence-tagged antibody staining of HIV-1 Env after 30 h of phytohemagglutinin (PHA) activation to induce Env expression (Fig. 2c) [125^▪]. The protein capture-based LURE captures late activation markers such as TIGIT and HLA-DRB [125^▪]. The benefit of LURE is that the cells remain viable for downstream cell culture. The caveat is that the capture efficiency depends on whether the diverse HIV-1 Env can be captured by bNAbs, as up to 18% of HIV-1-infected individuals may have HIV-1 strains that are resistant to bNAbs [126].

In both HIV-1 SortSeq and LURE, a unique transcriptional profile differentiates HIV-1-induced cells from uninduced and likely uninfected cells from the same HIV-1-infected individuals. Although each study reports more than 400 differentially expressed genes, only 30 genes were identified in both studies, highlighting the heterogeneity of CD4⁺ T cells and different gene expression during early versus late reactivation. Among them, genes associated with antiviral responses (IRF4, CCL3, CCL4, and XCL1) and cell survival (MIR155HG and ZNF217) are significantly upregulated in HIV-1-infected cells in both studies [106^▪,125^▪].

By a combination of HIV-1 gag-pol RNA expression (as detected by branched DNA-based RNA FISH) and HIV-1 Gag protein expression on flow cytometry, HIV-1 RNA Flow-FISH [115] can isolate HIV-1-infected cells for HIV-1 DNA genome targeted amplification and TCR sequencing [123] (Fig. 2d) using a method similar to RNAScope [127]. The benefit is that the fluorescent signal intensity is enhanced by branched DNA amplification. The caveat is that the branched DNA amplification step, which involves repeated cycles of heating and denature, damages RNA integrity and prevents subsequent RNAseq.

Of note, these methods require a stimulation step to induce HIV-1 RNA or protein expression and may not reflect the cellular state of truly transcriptionally silent, HIV-1 latently infected cells. A method that can detect the cellular transcriptome in transcriptionally silent HIV-1-infected cells remains unavailable. A potentially feasible solution may involve PCR-amplification of HIV-1 DNA in single-cell droplets and sorting of these HIV-1 DNA-positive cells for transcriptome analysis proposed by Clark and Abate et al. (Fig. 2d) [128]. While this method is still under development, Clark et al. [128] have provided a custom-built platform allowing sorting of single-cell droplets that are positive for HIV-1 DNA PCR for single-cell RNAseq or DNAseq. Whether the RNA integrity after the DNA PCR amplification step is sufficient for genome-wide transcriptome profiling remains to be examined.

As opposed to targeted HIV-1-infected cell capture, high-throughput and large-scale single-cell profiling without the enrichment step may identify HIV-1-infected cells by mapping the transcriptome to HIV-1 RNA (Fig. 2e and f). Current methods include microwell-based [129], droplet-based [130], and split-pool based [131] platforms for profiling tens of thousands of cells. Among them, two platforms have been used in blood samples from HIV-1-infected individuals. A microwell-based platform Seq-Well [129] (and a subsequent and improved version Seq-Well S^3) [132] encloses individual single cells with barcoded beads (Fig. 2e). With a unique barcode on each bead (which serves as a cellular barcode) and unique molecular identifiers (which denote each copy of RNA and measure RNA count), small amounts of blood and cerebrospinal fluid samples from HIV-1-infected individuals can be profiled for single-cell RNAseq and TCR profiling [44^▪▪,133]. This platform allows HIV-1 RNA mapping in single cells, and the method is currently being developed.

Using a droplet-based platform, ECCITEseq [134] (improved from the original version of CITEseq [135]) applies the template-switching activity of Moloney murine leukemia virus (M-MLV) reverse transcriptase for 5′-based RNA synthesis. M-MLV reverse transcriptase switches the template from the polyadenylated RNA to template-switching oligonucleotides. This allows cDNA synthesis, 5′ RNA capture, and, most importantly, the addition of a PCR handle and increased PCR amplification efficiency at the same time [136]. After staining cell surface protein markers with DNA-barcoded surface protein antibodies, ECCITEseq captures surface protein expression, single-cell transcriptome, TCR sequence, and HIV-1 RNA sequence at the same time within the same single cell. Using ECCITEseq, our group identified HIV-1 RNA positive cells from HIV-1-infected individuals for surface protein expression, single-cell RNAseq, and T cell clonal expansion dynamics tracking [137].

CONCLUSION

In summary, both HIV-1 and SARS-CoV-2 infection drive CD4⁺ T cell dysfunction and evade adaptive immune responses. Advancement in single-cell methods enabled us to identify different mechanisms of virus-induced immune dysfunction in unique immune cell subsets, to nominate indicators of disease severity, and to design immunotherapeutic strategies to revert HIV-1 and SARS-CoV-2-induced immune dysfunction.

KEY POINTS.

• HIV-1-specific CD4⁺ T cells are few and dysfunctional, with impaired IFNγ and IL-2 expression, increased immune exhaustion, and decreased proliferation capacity.

• SARS-CoV-2-specific CD4⁺ T cells are functional and are enriched in a cytotoxic phenotype, expressing GZMA, GZMB, GZMH, and GNLY.

• The frequency of SARS-CoV-2 Spike-specific CD4⁺ T cells correlates with antibody responses as a potential indicator for protective immunity.

• Misfiring of SARS-CoV-2 immunity toward type 2 instead of type 1 (antiviral) immunity is associated with a clinical trajectory toward severe COVID-19.

• Single-cell RNAseq of HIV-1-infected cells using HIV-1 SortSeq and LURE identified upregulation of antiviral response genes (IRF4, CCL3, CCL4, and XCL1) and cell survival genes (MIR155HG and ZNF217) in HIV-1-infected cells upon latency reversal.