The human tissue-resident CCR5+ T cell compartment maintains protective and functional properties during inflammation

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Science Translational Medicine  04 Dec 2019:
Vol. 11, Issue 521, eaaw8718
DOI: 10.1126/scitranslmed.aaw8718

Tissue-resident T cells evade eviction

CCR5 antagonism, explored in various disease settings, does not appear to disrupt barrier immunity. Woodward Davis et al. examined human oral mucosa to characterize CCR5+ T cells in healthy and inflamed tissues. These cells were present below the epithelia and accumulated during inflammation. They had a range of phenotypes, including TH1, TH17, and regulatory cells. Rectal biopsies from a clinical trial using a CCR5 antagonist revealed that CCR5+ T cells were retained even during treatment. These data show that tissue residency may not be perturbed by CCR5 antagonism, allowing barrier immunity to remain intact.


CCR5 is thought to play a central role in orchestrating migration of cells in response to inflammation. CCR5 antagonists can reduce inflammatory disease processes, which has led to an increased interest in using CCR5 antagonists in a wide range of inflammation-driven diseases. Paradoxically, these antagonists appear to function without negatively affecting host immunity at barrier sites. We reasoned that the resolution to this paradox may lie in the CCR5+ T cell populations that permanently reside in tissues. We used a single-cell analysis approach to examine the human CCR5+ T cell compartment in the blood, healthy, and inflamed mucosal tissues to resolve these seemingly contradictory observations. We found that 65% of the CD4 tissue-resident memory T (TRM) cell compartment expressed CCR5. These CCR5+ TRM cells were enriched in and near the epithelial layer and not only limited to TH1-type cells but also contained a large TH17-producing and a stable regulatory T cell population. The CCR5+ TRM compartment was stably maintained even in inflamed tissues including the preservation of TH17 and regulatory T cell populations. Further, using tissues from the CHARM-03 clinical trial, we found that CCR5+ TRM are preserved in human mucosal tissue during treatment with the CCR5 antagonist Maraviroc. Our data suggest that the human CCR5+ TRM compartment is functionally and spatially equipped to maintain barrier immunity even in the absence of CCR5-mediated, de novo T cell recruitment from the periphery.


Tissue inflammation is associated with an influx of leukocytes (1), and CCR5-expressing cells are recruited to sites of inflammation by responding to the ligands CCL3, CCL4, and RANTES (2). CCR5 antagonists are of great therapeutic interest to interrupt immune cell trafficking. Clinical trials that have used CCR5 antagonists include studies to prevent graft versus host disease (GvHD) (3) and cancer metastasis (4), but clinical applications could extend to other inflammation-mediated diseases (5). The outcome of phase 1/2 clinical trials targeting GvHD appears promising (6, 7) and indicates that CCR5 antagonist treatment is an effective therapeutic intervention to prevent immune cell trafficking to sites of inflammation. The most frequently used CCR5 antagonist is Maraviroc, which is a U.S. Food and Drug Administration–approved drug to treat patients infected with CCR5-tropic HIV-1 in combination with other antiretroviral agents (8). Maraviroc has seemingly little to no negative effects on host immunity in patients with HIV and is relatively well tolerated compared to other CCR5 antagonists whose use has been associated with hepatotoxicity (9). However, together, these observations seem paradoxical because they suggest that CCR5 is, on the one hand, a critical mediator of immune cell trafficking to sites of inflammation and, on the other hand, dispensable for host barrier immunity. An additional layer of complexity is revealed from mouse model studies, demonstrating that a lack of CCR5 expression could also lead to impaired resolution of inflammatory events (10). This is due to CCR5 guiding regulatory T (Treg) cells to sites of inflammation and subsequently controlling proximity to their CD4 and CD8 target cells in a CCR5-dependent manner by Treg secretion of CCL3 and CCL4 (10).

To provide an explanation for the unexpected maintenance of barrier immunity in CCR5 antagonist–treated patients (11), we characterized the CCR5+ T cell compartment in human mucosal barrier tissue. We reasoned that a CCR5+ T cell population in human barrier tissue may be sufficient to maintain barrier immunity in the absence of incoming (CCR5 ligand–responding) T cells if this population is abundant in tissues and encompasses more functional breadth than the well-established T helper 1 cell (TH1)–driven responses (12, 13). Some T cells form a population of tissue-resident memory T cells (TRM) that remain in the tissue either permanently or for prolonged periods of time (1, 1418). TRM in barrier tissues are considered to be important for maintaining the health of the tissue, including sustaining tolerance to commensals, while also mediating rapid responses to invading pathogens (1, 1820).

Given the promising yet seemingly paradoxical clinical results of CCR5 antagonist therapy, it is critical to understand the distribution and functional properties of CCR5+ T cells in barrier tissues. We developed an analysis pipeline that allowed us to define the phenotype, function, and location of CD4 T cell populations in healthy and inflamed human mucosal tissues. Our study was also designed to assess changes across a spectrum of inflammation, reflecting the typical processes of human mucosal tissues where inflammation is rarely an on/off switch. To define the function of the CCR5+ T cell compartment in healthy and inflamed tissues, we focused on oral mucosal tissues, which offered the unique opportunity to examine how human T cell subsets, especially those with prolonged tissue longevity, were affected by gradations of inflammation (in the absence of drug treatment) because inflammatory processes in the oral mucosa are regularly induced (2124). We also examined TRM cells after CCR5 antagonism.


CD4 and CD8 CD69+ cells are abundant in healthy oral mucosal tissue

We first broadly characterized conventional T cells (Tconv) in the blood and healthy mucosal tissue of humans obtained from routine periodontic surgeries (Fig. 1A). We chose to use oral mucosal tissue because this mucosal barrier tissue is readily available in both healthy and inflamed states in the absence of drug treatment that would interfere with inflammatory processes from patients undergoing routine oral surgery. To assess the health status of the samples, a pathologist performed blinded scoring (score of 1 to 5) on hematoxylin and eosin (H&E) slides and we included scores 1 and 2 in healthy analysis (fig. S1 and table S1). In contrast to the donor-matched blood, almost all T cells in the mucosa had an effector memory cell phenotype (CD45RACCR7) (Fig. 1A). We next examined markers indicative of transient versus TRM cell subsets. TRM are often defined by expression of the biomarkers CD69 and/or CD103, each thought to have a unique contribution toward establishing residency and displaying different expression patterns in human nonlymphoid tissues (16). Similar to what has been reported for other human nonlymphoid tissues (15), CD8 T cells expressed CD69 and CD103 in the oral mucosa (Fig. 1, A and B). Using the matched blood as a gating reference, we found that half of the CD4 T cells in healthy oral mucosa were CD69+ and a portion were CD103+, with very few of either population in the blood (Fig. 1, A and C). The CD103+CD69+ population for both CD4 and CD8 T cells was exclusively found in the mucosa but not in the blood (Fig. 1, B and C). We found that programmed cell death protein 1 was highly expressed on mucosal CD69+ cells (fig. S2A), and all CD4 T cells in the tissue had higher expression of CTLA4 than those in the blood (fig. S2B). Further, in 89% of patients, CTLA4 expression was higher on the CD69+ population compared to the CD69 population (mucosal Treg cells shown for comparison). We also determined the tissue localization of the CD103 (fig. S2, C and E) and CD69 (fig. S2, D and E) CD4 T cell subsets by immunofluorescence (IF) and found that neither was associated with preferred localization in the tissue. To determine spatial localization and assess cell subset recovery from the tissue digestion process (25), we combined IF staining with a digital pathology (IFDP; indicated by blue graphs in figures) analysis approach to increase the number of cells that can be analyzed and remove any subjective bias when enumerating cells. Specifically, we quantified cells in three relevant compartments: epithelium, subepithelium (300-μm area below the epithelium), and stroma (Fig. 1D). The subepithelial layer was defined after initial analysis indicated an enrichment of T cells in this area. For each donor, 176 to 14,734 CD4 T cells (mean, 2102) and 23,379 to 210,390 nucleated cells (mean, 70,926) were quantified.

Fig. 1 CD4 and CD8 TRM are abundant in healthy oral mucosal tissue.

Human gingival tissue was obtained from routine oral surgeries. A piece of each tissue was saved for histology, including evaluation by a pathologist (scoring criteria are listed in Material and Methods). The remaining tissue was processed for flow cytometry. (A) Gating strategy for CD8 and CD4 T cells (middle) in the blood (top) and mucosa (bottom). Memory (CCR7 and CD45RA) and tissue-resident (CD69 and CD103) phenotype of CD8 T cells (left) and CD4 Tconv cells (right). Numbers indicate percentages of the parent population. Percentage of CD69+CD103 (orange), CD69+CD103+ (blue), CD69CD103+ (pink), and CD69CD103 (green) on (B) CD8 T cells and (C) CD4 T cells across n = healthy tissues from 14 donors. Error bars show SD from the mean. ****P ≤ 0.0001. (D) Mucosal tissue with 4′,6-diamidino-2-phenylindole (DAPI; white) staining depicting how epithelium (Epi), subepithelium (SE), and stroma compartments were drawn in HALO. (E) Representative healthy tissue stained with CD4 (red), CD8 (green), and DAPI (blue). Dotted line indicates the epithelial boundary. (F) IFDP analysis ratio of CD4 to CD8 T cells across the tissue compartments, n = healthy tissues from 10 donors. **P ≤ 0.01 generated by repeated measures one-way ANOVA with Tukey’s posttest.

We next examined the overall distribution of CD4 and CD8 T cells (Fig. 1E). In healthy oral mucosa, we found a 1:1 ratio of CD4 and CD8 T cells in the epithelium, whereas this ratio averaged 3:1 and 2:1 in the subepithelium and stroma, respectively (Fig. 1F). We also assessed CD4 and CD8 T cells as a percentage of nucleated cells, a measurement indicative of the density of cells they would be surveying. By this metric, CD4 and CD8 T cells made up 6 and 2.7%, respectively, of nucleated cells in the subepithelium compared to the epithelium where they averaged 0.98 and 0.77% (fig. S3). Note that the nuclei per square millimeter density is higher in the epithelial compartment (fig. S3). Thus, CD4 T cells are most abundant in a 300-μm area below the epithelium.

CCR5 is expressed on resident and transient CD4 Tconv and Treg populations in healthy oral mucosa

We next wanted to specifically focus on defining the CCR5+ CD4 T cell population, given the critical role of CD4 T cells in maintaining barrier immunity (1). In healthy tissues, CCR5 was expressed by ~65% of the CD69+ CD4 Tconv cells (Fig. 2, A and B). The CCR5+CD69+ population made up about a third of the total CD4 T cell population (Fig. 2C) and could be readily identified by IF (Fig. 2D). Given that Treg are necessary to maintain tissue homeostasis and orchestrate effector function (26), we next defined their abundance and phenotype. Identification of Treg in humans is confounded by the fact that Foxp3 and other identifying markers can also be expressed by activated effector T cells (27). We chose a stringent previously described gating method (27) to minimize the possibility of misclassifying recently activated CD4 T cells as Treg in tissues. This strategy isolated the most suppressive and highest CD25-expressing cells using CD45RA expression as a reference after first gating out CD127+ cells (Fig. 2E). The conservative gating strategy yielded a lower frequency of Treg (as a percentage of CD4 T cells; Fig. 2, E and G) in the blood compared to the typically used gating schemes. Mucosal Treg had increased Foxp3 and CTLA4 expression compared to those in the circulation (Fig. 2F). Both of these markers were also higher on the Tconv population in the tissue compared to the blood. Treg cells were more prevalent in the tissue compared to the blood (Fig. 2G) and expressed TRM markers CD69 and, to a lesser extent, CD103 (Fig. 2H). Treg also displayed a pattern of CCR5 expression (Fig. 2I) similar to the Tconv population (Fig. 2B) across blood and tissue subsets.

Fig. 2 CCR5 is highly expressed by CD4 Tconv and Treg in healthy oral mucosa.

(A) Representative flow plots of CCR5 and CD69 expression on CD4 Tconv cells from the blood and healthy mucosa. (B) Quantification of CCR5 expression on CD69+ and CD69 populations, numbers indicate the mean percentage for each population (n = 17 donors). (C) CCR5+CD69+ percentage of CD4 Tconv cells in healthy mucosa (n = 17). (D) Representative image from a healthy tissue showing CD4 (red), CD69 (blue), and CCR5 (green) staining. Detailed set of four images shows combined and individual staining of cells from white box with DAPI (gray). Arrows indicate all triple-positive cells. (E) Gating strategy for Treg in the blood (top) and mucosa (bottom). Numbers indicate percentages of the parent population. (F) Representative histograms depicting Foxp3 (top) and CTLA4 (bottom) expression on CD25neg/lo (blue) and CD25hi Treg (red) in the blood (filled) and mucosa (open). (G) Treg percentage of CD4 T cells in the blood and mucosa (n = 9 donors). (H) Proportion of CD69+CD103 (orange), CD69+CD103+ (blue), CD69CD103+ (pink), and CD69CD103 (green) in Treg cells from the blood and mucosa (n = 9 donors). Error bars show SD from the mean. (I) Expression of CCR5 on Treg from the blood and healthy mucosa. Numbers indicate the mean percentage for each population (n = 9 donors). *P < 0.05, ***P ≤ 0.001, ****P ≤ 0.0001, generated with paired t test.

Inflammation drives spatially dependent changes in T cell subsets

We next wanted to determine the effect of inflammation on the CCR5+ CD4 T cell compartment to understand whether functionality and subset distribution are altered. Inflammation involves changes in vasculature, secretion of inflammatory mediators, and cellular infiltrate (28) that together alter tissue architecture and the local immune network (29). As expected, large T cell clusters were readily apparent in the stroma of inflamed tissues (score of 3 to 5; Fig. 3A). IFDP analysis revealed that overall CD4 T cell numbers (as a proportion of nucleated cells) were not markedly changed in the epithelium with worsening inflammation while increasing significantly in the subepithelium (P = 0.005) and stroma (P = 0.001; Fig. 3B). The proportion of CD69+ CD4 T cells was preserved with increasing inflammation by both flow cytometry and IFDP analysis (Fig. 3, C and D). We found a significant decrease in the proportion of CD103+ CD4 T cells by flow cytometry (P = 0.012; fig. S4A), and IFDP analysis revealed that this was occurring in the subepithelium (P = 0.040); there were no statistically significant differences in the stroma (fig. S4B). The same results were found for CD8 T cells to an even greater degree (fig. S4, C and D).

Fig. 3 Inflammation is associated with an increase in CD69+ and CD69 CD4 T cells in distinct tissue compartments.

(A) CD4 (green) T cells in healthy (left) and inflamed (right) representative oral mucosa tissues with DAPI (blue). Dotted lines indicate the base of the epithelium, and solid lines mark the divide between subepithelium and stroma compartments. (B) IFDP quantification of CD4 T cells in the epithelium (left), subepithelium (middle), and stroma (right) stratified by inflammation score. n = 5 to 7 donors per score (total n = 23 donors analyzed). (C) Flow data of CD69+ percentage of CD4 T cells stratified by inflammation. Numbers indicate how many tissues were analyzed for each score. (D) IFDP quantification of CD69+ percentage of CD4 T cells stratified by inflammation for each compartment, n = 4 donors per score. Slope, R2, and P values for slope indicated on each graph were generated with linear regression. Black symbols indicate flow cytometry, and blue symbols indicate IFDP analysis. NS, not significant.

In inflamed tissues, the pattern of CCR5 expression across blood and tissue compartments was maintained on CD4 T cells (Fig. 4A), and we observed no changes in the overall proportion of CCR5-expressing CD4 T cells with increasing inflammation by flow cytometry (Fig. 4B). However, IFDP analysis revealed that the proportion of CCR5+ CD4 T cells increased with worsening inflammation, specifically in the subepithelium (and considerably in the stroma) (Fig. 4, C and D), which is also where most of the increase in CD4 T cell numbers occurred. The proportion of CCR5+ CD4 T cells detected by IF was uniformly lower compared to flow cytometry (Fig. 4, A and B versus D), which most likely can be attributed to the fact that flow cytometry captures the full range of CCR5 expression, and parameters set for HALO analysis exclude the lower range of expression (to distinguish from background signal). Last, we wanted to determine whether inflammation status affected the frequency of the CCR5+CD69+ population. Inflammation did not significantly increase in the proportion of CCR5+CD69+ CD4 T cells below the epithelium (subepithelium, P = 0.07; stroma, P = 0.20; fig. S4E). We observed that the proportion of Treg cells was maintained regardless of inflammation status (Fig. 4E). Treg cells reflected the same patterns as the CD4 Tconv cells in regard to CCR5 expression across blood and tissue subsets (Fig. 4F), as well as the CD103 and CD69 populations (fig. S4, F and G).

Fig. 4 CCR5 is expressed on resident and transient CD4 Tconv and Treg populations in inflamed oral mucosa.

(A) Expression of CCR5 on CD4 Tconv cells from the blood and inflamed mucosa determined by flow cytometry. Numbers indicate the mean percentage for each population (n = 20). (B) Flow cytometry data depicting CCR5+ percentage of CD4 T cells stratified by inflammation score. Numbers indicate how many tissues were analyzed for each score. (C) Representative image from a healthy tissue showing CD4 (red), CD69 (blue), and CCR5 (green) staining. Detailed set of four images shows combined and individual staining of cells from white box with DAPI (gray). Arrows indicate all triple-positive cells. Dashed line indicates the epithelial boundary. (D) IFDP quantification of CD4 T cells expressing CCR5 in the epithelium (top), subepithelium (middle), and stroma (bottom) stratified by inflammation with n = 4 per score. (E) Treg percentage of CD4 T cells stratified by inflammation. Numbers indicate how many tissues were analyzed for each score. (F) Expression of CCR5 on CD4 Treg cells from the blood and inflamed mucosa determined by flow cytometry. Numbers indicate the mean percentage for each population (n = 17). (A and F) ***P ≤ 0.001, ****P ≤ 0.0001, generated with paired t test. (B, D, and E) Slope, R2, and P values for slope indicated on each graph were generated with linear regression. Black symbols indicate flow cytometry, and blue symbols indicate IFDP analysis.

CCR5+CD69+ CD4 T cells in the mucosa share a universal TRM transcriptome signature and have a TH17 profile

To define the transcriptional breadth of CCR5-expressing CD4 T cells, we examined several CD4 T cell subsets by bulk RNA sequencing (RNA-seq). From 10 individuals (with varying degrees of tissue inflammation), three CD4 T cell populations were sorted: blood CCR5+CD69, mucosa CCR5+CD69+, and mucosa CCR5+CD69 (Fig. 5A). We first stratified all mucosa samples by inflammation score, treating it as a continuous variable, and found that inflammation had a very modest effect on CCR5+ T cell transcriptomes. Even with a nonstringent false discovery rate (FDR) of 0.25, only 14 genes were differentially expressed (DE) (Fig. 5B). Therefore, although a clear pattern of transcriptional changes is apparent, the functional potential of CD4 T cells in the tissue is remarkably stable in the presence of increasing inflammation. In contrast, a large number of DE genes were found between cells in the circulation compared to both CD69 and CD69+ cells in the oral mucosa (913 DE genes with an FDR of <0.05 and an expression fold change of ≥2) (Fig. 5C and table S2). A Search Tool for Retrieval of Interacting Genes/Proteins (STRING) network analysis (30) within these DE genes revealed that tissue occupancy was associated with an increased functional specialization that included both effector and regulatory genes (fig. S5A). Our findings indicate that entry into the tissue, independent of CD69 expression, has a more profound impact on the transcriptional profile of CD4 T cells than inflammation status.

Fig. 5 Tissue localization imparts a distinct gene expression profile on CCR5-expressing CD4 T cells.

(A) Sorting scheme for CD4 T cells from the blood (CCR5+CD69, red) and oral mucosa (CCR5+CD69+, orange; CCR5+CD69, teal) for RNA-seq experiments. (B) Heatmap of genes differentially expressed with inflammation score (FDR, <0.25). Grayscale and numbers indicate inflammation score. (C) Heatmap of genes differentially expressed between the blood and oral mucosa (FDR, <0.05; expression fold change, ≥2). Letters designate individual donors; colors for sample origin correspond to those in (A). (D) Gene set enrichment analysis (GSEA) for the “TRM Core Signature” gene set in our oral mucosa CD69+ samples versus CD69 samples. The y axis is the enrichment score, and the x axis is genes ranked according to the absolute value of log fold change between the mucosa CD69+ and CD69 samples. P < 0.001 for TRM Core Signature enrichment. (E) Volcano plot showing genes up-regulated in CD69 (teal) and CD69+ (orange) CD4 T cells from the oral mucosa. Highly significant genes associated with a TH17 signature are denoted. Dotted lines show an FDR of 0.05 (horizontal) and a fold change of ±2 (vertical).

We next performed gene set enrichment analysis (GSEA) (31) to compare our CCR5+CD69+ population with previously defined TRM populations (32). For this analysis, we ranked genes according to the absolute values of expression log fold changes between the tissue CCR5+CD69+ and tissue CCR5+CD69 samples. Sample label permutations were used to assess significance. GSEA revealed enrichment of the TRM Core Signature in the DE genes in mucosa CCR5+CD69+ CD4 T cells (P < 0.001; Fig. 5D), indicating strong similarity to other human TRM populations. To extend the generality of this comparison beyond human CD4 T cells, we also performed GSEA with a well-defined murine CD8 T cell gene signature (33) and obtained a P value of 0.04 (fig. S5B). This suggests a trend toward enrichment even in the context of a different species and cell type. Therefore, in line with recent literature (3437) and because the mucosal CCR5+CD69+ CD4 T cell population maintains a signature indicative of tissue residency, we will refer to these CD69+ simply as TRM cells, although it is important to note that CD69 expression requires careful interpretation due to its transient expression on the cell surface of recently activated T cells. Given their divergent transcriptional profiles, we wanted to define potential functional differences between these tissue-resident and tissue-transient populations. Comparing the TRM with the CD69 tissue population, there were 19 DE genes (FDR, <0.05; fold change in expression, ≥2) (Fig. 5E). A notable difference between the transcriptional profiles of the two oral mucosal populations was the expression of IL17F, IL17A, IL26, and aryl hydrocarbon receptor (AhR), which suggest an enhanced TH17 signature in the TRM population (Fig. 5E). We did not detect DE genes indicative of recent activation or exhaustion/dysfunction, indicating that CD69 protein expression is mainly due to tissue residence and likely not driven by other events.

TH17 CD4 T cells with a TRM signature are a distinct subset within the mucosa CCR5+CD69+ population

To address whether TH17 and TRM transcriptional profiles could be simultaneously detected in individual cells, we used 10x Genomics for single-cell RNA sequencing (scRNA-seq). We sorted six defined populations from the blood and mucosa of a single donor (pathology score of 4 with some healthy and markedly inflamed areas). This nested sort approach retained marker information, ensuring that we could link the transcriptional signatures back to previously defined subsets. Three of the populations matched those sorted for bulk RNA-seq (denoted with an asterisk), with the other populations providing reference datasets: blood total CD4+, *blood CCR5+CD69, mucosa CCR5+, mucosa CCR5, *mucosa CCR5+CD69+, and *mucosa CCR5+CD69 (sorting strategy and sorted cell numbers are listed in fig. S6A).

Visualization of the single-cell transcriptome data using t-distributed stochastic neighbor embedding (tSNE) plots (Fig. 6A) and differential expression between the nonoverlapping populations (blood CCR5+CD69, mucosa CCR5, mucosa CCR5+CD69+, and mucosa CCR5+CD69) confirmed that localization to the tissue had a profound impact on CD4 T cell transcriptional profiles (Fig. 6B). CCR5 expression imparted a distinct transcriptional profile in the tissue (table S5), as CCR5+ T cells had higher expression of genes related to effector function and migration reflecting the diverse functional potential in this population (TH1, TH17, and Treg). This further illustrates the importance of focusing on a specific inflammatory-responsive CD4 T cell subset when comparing the blood and tissue.

Fig. 6 TH17 CD4 T cells with a TRM signature are a distinct subset within the tissue CCR5+CD69+ tissue population.

(A) tSNE plot of the single-cell RNA-seq data from six blood and oral mucosa subsets, each assigned a unique color: blood total CD4 (purple), blood CD4 CCR5+CD69 (red), mucosa CD4 CCR5 (green), mucosa CD4 CCR5+ (blue), mucosa CD4 CCR5+CD69 (teal), and mucosa CD4 CCR5+CD69+ (orange). Asterisks indicate samples that correspond to bulk sorted populations from previous experiments. (B) Heatmap of the differentially expressed genes defining each population (FDR, <0.01). (C) Mean expression of TH17 and TRM Core Signature (only using genes up in CD69+ cells in the TRM Core Signature) gene sets in individual CD4 T cells from blood CCR5+CD69 (left), mucosa CCR5+CD69 (middle), and mucosa CCR5+CD69+ (right). Cells were sorted from a single donor.

We next asked whether blood CCR5+CD69, mucosa CCR5+CD69, and mucosa CCR5+CD69+ cells coexpressed genes from the TRM Core Signature, indicative of TRM, and a TH17 signature (gene signature list is in table S3). The mean gene set expression of each signature revealed individual cells expressing both signatures specifically within the CCR5+CD69+ population but not in the CCR5+CD69 populations (blood or mucosa) expressing both signatures (Fig. 6C). We confirmed that these signatures are not a transcript abundance–related artifact due to capture of multiple cells because across the three samples, the cells coexpressing these signatures do not show an unusually high number of unique molecular identifiers (UMIs) per cell (fig. S6B).

CD4 TRM in inflamed tissue respond rapidly to T cell receptor stimulation ex vivo to produce cytokines

Because we had seen by both bulk and scRNA-seq a strong TH17 signature in the CCR5+ CD4 TRM population, we wanted to determine whether this actually resulted in enhanced interleukin-17 (IL-17) protein production. We therefore chose short restimulation assays [minimizing possible indirect effects from longer culture (38)] to determine the direct ex vivo T cell receptor (TCR) responsiveness of blood and mucosal CD4 T cells. Activating cells for 6 hours was brief enough to avoid substantially changing the percentage of CD69+ cells; however, it altered CCR5 expression as previously reported (39). The stimulation experiments echoed the transcriptional data since a higher percentage of the oral mucosa CD69+ population (that typically consists of ~65% CCR5+ T cells; Fig. 2B) produced IL-17A/F than the CD69 population (Fig. 7, A and B). In contrast, interferon-γ (IFNγ) expression was increased in both oral mucosa populations compared to the blood, regardless of CD69 expression (Fig. 7, A and C), also mirroring the transcriptional data (fig. S7A). Similar results were obtained after phorbol 12-myristate 13-acetate (PMA)/ionomycin restimulation, with overall increased cytokine production (fig. S7B). Thus, the data strongly suggest the existence of a CCR5+CD69+ CD4 T cell population with TH17 properties that is found predominantly in this tissue-resident population.

Fig. 7 CD4 TRM in human oral mucosa rapidly produce inflammatory cytokines.

(A) Blood and mucosa CD4 T cell production of IL-17A/F and IFNγ after 6 hours without stimulation or TCR stimulation (anti-CD3/CD28 beads) from a single donor. Mucosa stimulation data are shown for CD69 (left) and CD69+ (right) populations. (B) IL-17A/F and (C) IFNγ production from n = 12 donors. *P < 0.05, **P ≤ 0.01, generated with paired t test. NS, not significant.

CCR5+ CD4 TRM are stably maintained during treatment with the CCR5 antagonist Maraviroc

We next wanted to directly test whether the CCR5+ CD4 T cell population is maintained in mucosal tissue during treatment with Maraviroc, a CCR5 antagonist. To address this question, we needed a healthy study cohort with available tissue biopsies before and after treatment in a well-defined time span. The CHARM-03 clinical trial met all these criteria and included an elegant cross-over study design that even allowed us to compare effects of oral versus topical Maraviroc treatment for 1 week (with a washout period between the treatment arms) (Fig. 8A and see Materials and Methods). To determine whether the CD4 T cell compartment changes after oral (systemic) or topical (rectal) Maraviroc treatment, we examined rectal biopsies taken before and after treatment by flow cytometry and IF.

Fig. 8 CCR5+ CD4 TRM are stably maintained in human rectal mucosa during treatment with the CCR5 antagonist Maraviroc.

(A) Diagram of sample collection for rectal biopsies used from the CHARM-03 study. (B) Representative flow plots showing CD69 and CCR5 expression on live, CD3+CD4+ T cells. Numbers indicate a percentage of parent population. (C) Proportions of CCR5+CD69+ (dark orange), CCR5CD69+ (light orange), CCR5+CD69 (teal), and CCR5CD69 (gray) as a percentage of CD4 T cells. Pre-Tx, pretreatment; MVC, maraviroc. (D) Representative immunofluorescence images of CD4 (red) and CCR5 (green) from a male donor before treatment (left), after oral Maraviroc (middle), and after topical Maraviroc (right). n = 5 male and n = 5 female participants.

The frequency of the CCR5+CD69+ population among CD4 T cells was comparable between oral and rectal mucosa (Fig. 2, A and C, and Fig. 8, B and C); however, CCR5 expression is minimal in the rectal mucosal CD69 population (Fig. 8B). The CCR5+ TRM population remained stable with either oral or topical treatment (Fig. 8C). To help interpret these data, we also assessed the frequency of CD3+ cells (percentage of total lymphocytes) and the frequency of CD4+ cells (percentage of total CD3+ cells) and did not observe significant changes with treatment [control versus oral treatment (Tx), P = 0.78; control versus topical Tx, P = 0.90; fig. S8A]. We did, however, find alterations in the frequency of CCR5 expression on the CD69 population that resulted in a significant increase in the proportion of CD69CCR5+ (control versus oral Tx, P = 0.0008; control versus topical Tx, P = 0.0035; fig. S8B), which has been observed previously (40) and further confirmed that the treatment was working as expected. To complement the flow cytometry data, we examined whether Maraviroc treatment caused changes in the spatial distribution of CD4 T cells in the tissue. These tissue biopsies were taken with large-cup biopsy forceps, and thus, the orientation of these sections was different compared to our other IF images (fig. S8C). In the acquired epithelial layer, we observed heterogeneity in the overall CD4 T cell distribution regardless of treatment status and readily detected clusters of CD4 T cells expressing CCR5 before and after treatment (Fig. 8D). Likely because of the different initial processing of these tissues, we were unable to simultaneously stain for CD69, CCR5, and CD4. We therefore decided to stain for CCR5 and CD4 because CCR5+ CD4 T cells were almost all found within the CD69+ fraction by flow cytometry. Together, the flow cytometry and IF data suggest that a stable population of CCR5+ CD4 TRM is maintained in human mucosal tissue in the absence of CCR5-directed recruitment.


CCR5 is largely accepted as a unique biomarker of TH1 in the literature since a 1998 landmark study by Loetscher and colleagues (12). Here, we report that CCR5 expression is not exclusive to the TH1 population in human mucosal tissue but is also expressed by TH17 and Treg cells with a TRM phenotype (CD69+). Our data also demonstrate that the CCR5+CD69+ CD4 T cell population does not undergo substantial transcriptome changes in healthy compared to inflamed tissues, whereas the population increases in size. Further, CCR5+ T cells have classically been characterized as responding to inflammation, whereas we demonstrate that this population is part of the immune network in healthy barrier tissues. Together, these data suggest that CCR5+ CD4 TRM cells are stably maintained as a population across healthy and inflamed tissues and also maintain their functional properties.

Here, we discuss the main conceptual advances from our study in context of their implications for CCR5 antagonist treatment strategies. Overall, we found that the CCR5+ CD4 TRM population was abundant and distributed throughout the tissue. Specifically, we found that there are areas of the tissue where (combining CD4 and CD8 T cells), on average, 9% of all nucleated cells are T cells. CCR5 was expressed by ~65% of CD4 T cells with a TRM (CD69+) and about 35% of CD4 T cells with a transient (CD69) phenotype in healthy human oral mucosal tissues. If the half-life of the human TRM cells is comparable to that of circulating human memory T cells [estimated half-life of 8 to 15 years (41)], then barrier immunity and homeostasis could be maintained for decades without impairment during treatment. Although the maintenance of barrier immunity is encouraging in this clinical context, it also suggests that it is most likely futile to use a CCR5 antagonist treatment strategy in an effort to interfere with inflammatory responses in tissues with an intact, functional TRM compartment, as has been suggested for some inflammatory diseases (5).

Our data also demonstrate that the CCR5+ CD4 TRM compartment displayed previously unappreciated functional breadth encompassing TH1, TH17, and Treg subsets. Specifically, when we compared (CD69+) TRM and (CD69) migratory CCR5+ CD4 T cell populations to elucidate potential functional differences (extrapolated from transcriptional data), we found that the TRM population had a notable TH17 transcriptional signature. Further, the CD4 TRM population as a whole displayed rapid IL-17 production in response to TCR restimulation. We used a scRNA-seq approach to determine whether there was a mixed population or whether an individual cell can have both a TRM and a TH17 signature within the CCR5+CD69+ CD4 T cell compartment. Our scRNA-seq data demonstrate that TRM and TH17 signatures were expressed by the same cells, which were almost exclusively found within the CCR5+CD69+ population, indicating that TH17 TRM cells exist within the CCR5+ compartment. Given that this scRNA-seq method may not capture scarcely expressed transcripts, it might underestimate the prevalence of this population and is thus not suitable to quantify the exact abundance of TH17 TRM cells. However, these data demonstrate that such a TH17 population exists in the CCR5+ tissue-resident population and is at best scarce in the transient population. These data reveal that the typical association of CCR5+ CD4 T cells solely promoting TH1 responses (12, 13) does not hold true for human barrier tissues.

Data presented here from the CHARM-03 clinical trial indicated that the CCR5+ CD4 TRM compartment was stably maintained in the tissue even during treatment with the CCR5 antagonist Maraviroc. We reasoned that a treatment period of 1 week would be sufficient to detect changes in the CCR5+ population, given that migrating memory T cells are able to rapidly enter tissues and affect local memory composition (42). We did observe an increase in the CCR5+CD69 population. It has been reported that oral treatment with Maraviroc increases the percentage of CCR5+ CD4 T cells in the blood (40). This effect is likely the result of Maraviroc stabilizing expression of CCR5 rather than it being internalized after signaling with its ligands. We observed the same phenotypical changes after oral Maraviroc and the topical Maraviroc treatment, indicating that the topical Maraviroc treatment is functional.

These data suggest that Maraviroc treatment may have different effects depending on whether patients have an established TRM compartment or lack a TRM compartment. For example, the CHARM-03 study also included a topical treatment phase to evaluate Maraviroc as an inhibitor of a primary HIV infection. The maintenance of the CCR5+ TRM compartment suggests that Maraviroc does not attenuate CCR5+ CD4 TRM–mediated barrier immunity. This notion of maintaining barrier immunity in the absence of a CCR5-mediated T cell influx into tissue is in line with clinical data from patients with HIV that were treated with Maraviroc for 5 years without signs of declining immunity (43).

In contrast, it is possible that CCR5 antagonist treatment in transplant patients lacking an intact TRM compartment due to myeloablative treatment may have long-term negative effects on barrier immunity and tissue homeostasis. Our scRNA-seq data indicate that CCR5+ T cells in the tissue have a distinct transcriptome signature compared to the CCR5 population and suggest that a reseeding of the TRM compartment with CCR5+ T cells is necessary for a balanced composition of TH1 and TH17 cells. Furthermore, the risks of cytomegalovirus reactivation need to be critically assessed in CCR5 antagonist–treated transplant patients. Other chemotactic cues may be able to compensate for missing CCR5 signals allowing for intact antiviral immunity, but longer-term studies are needed because interfering with CCR5 chemotaxis may delay proper TRM reconstitution, thereby increasing the time frame needed to reach immunocompetence.

Last, we observed remarkable functional stability in the CCR5+ CD4 T cell population with increased inflammation status since few DE genes were found when samples were stratified by pathology score. Further, CCR5+ T cells have classically been characterized as responding to inflammation, whereas we report that this population is part of the immune network in healthy barrier tissues. One may conclude from our data that inflammation has a limited effect on T cells, which does not seem likely. We prefer the interpretation that T cells in this barrier tissue are exposed to inflammatory cues, even in healthy tissues, and thus, amplification of those cues during regular inflammatory processes does not further markedly alter their transcriptional program. However, we observed an inflammation-associated increase in the total number of CD4 T cells below, but not in the epithelial layer, indicating that inflammation-driven changes occur in a spatially dependent manner and are predominantly quantitative and not qualitative in nature. Of the expanding cells (recruited or proliferating), CCR5+ CD4 T cells made up an increasing proportion, confirming their important contribution to the inflammatory response in peripheral tissue. In contrast, the proportion of CD69+ cells was maintained. An increase in the CD69+ population (containing CCR5+ TRM phenotype cells) along with the CD69 population could ensure that sufficient numbers of Treg and TH17 cells (contained in the CCR5+CD69+ population) are maintained in relation to incoming or proliferating effector CD4 T cells, which may be needed to eventually resolve inflammation. We observed that the CCR5+ Treg population increased along with the conventional CCR5+ TRM compartment in inflamed tissues. We think that this is a critical finding because a disconnect between the conventional and regulatory T cell populations could lead to pathogenesis. It will be important to examine not only the effects of CCR5 antagonist treatment but also the cessation of this treatment on Treg abundance and location in immunocompromised patients, which could lead to an unbalanced restoration of the T cell tissue compartment in regard to the Tconv/Treg ratio.

A limitation to studies—including our data presented here—that are focused on T cells in human tissues is the reliance on biomarkers to extrapolate certain characteristics. In particular, expression of CD69 needs to be interpreted with caution because it is expressed by recently activated T cells and is also used as a biomarker indicating tissue residence. Mouse studies have revealed an antagonistic relationship and inverse cell surface expression of CD69 and the sphingosine 1-phosphate receptor (S1P1) (4446). Expression of S1P1 by memory T cells leads to reentry into circulation (47), and thus, expression of CD69 has been used as an indicator that T cells are tissue resident. New single-cell technologies such as cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) or Ab-seq that provide a combination of protein and transcript expression data could prove useful in distinguishing between recently activated and tissue-resident cells in inflamed tissues.

In summary, our data resolve the seemingly paradoxical observation that CCR5 antagonists can interfere with pathogenic disease processes without affecting barrier immunity. Our study reveals that CCR5+ CD4 T cells are the most abundant cell type in the mucosal TRM compartment and not limited to TH1 responses but feature all key CD4 T cell functions including TH17 and Treg responses. We found that in the context of preventing recruitment of T cells via CCR5 in vivo, human mucosa retained a CCR5+ CD4 TRM population. Our data, therefore, suggest that CCR5 antagonist treatment may have more severe and delayed side effects in regard to susceptibility to infections in patients who lack a TRM compartment because it would interfere with the formation of diverse tissue-transient and tissue-resident T cell populations.


Study design

This study was exploratory with the purpose of understanding CD4 T cell dynamics in human oral mucosal tissues. Human oral mucosa was obtained from patients undergoing oral surgery where gingival tissue was removed as a normal part of the procedure. All participants providing oral mucosal samples signed a written informed consent before inclusion in the study, and the protocols were approved by the institutional review board (IRB) at the Fred Hutchinson Cancer Research Center (8335). Surgical procedures included gingivectomy/gingivoplasty, osseous surgery, implant uncovering, and tooth extractions. Samples were collected between 11 a.m. and 4 p.m. Participants were between 14 and 83 years old (mean, 52 years), in line with previously published donor populations (48). See table S1 for more information. Samples sizes were based on availability, and because there was no intervention, no blinding or randomization was used. The number of participants included or represented is indicated for each figure. Human rectal tissue was obtained from healthy, HIV-uninfected participants (n = 5 male and n = 5 female) as part of the CHARM-03 study (Combination HIV Antiretroviral Rectal Microbicide; NCT02346084). All CHARM-03 participants signed a written informed consent before inclusion in the study, and the protocol was approved by the IRB (REN18050213) at the University of Pittsburgh. At the time of enrollment, pretreatment biopsies were collected with large-cup biopsy forceps. Participants then took oral Maraviroc (300 mg) for 8 days. Twenty-four hours after the last dose, rectal biopsies were taken. Participants then had a 14- to 21-day recovery period before beginning 8 days of topical rectal Maraviroc (1% gel), and 24 hours after the last dose, rectal biopsies were taken. For n = 3 of the female participants, the rectal topical treatment followed a washout period after vaginal topical use—these samples are indicated in the figures. Primary data are reported in data file S1.

Human oral mucosa processing

Fresh tissue was placed immediately into a 50-ml conical tube with complete media [RPMI 1640 supplemented with penicillin (100 U/ml), streptomycin sulfate (100 μg/ml), and 10% fetal bovine serum]. The blood was collected by flushing the exposed surgical site after elevation of the gingiva with sterile saline. Two sterile 5-ml syringes with 10-gauge blunt tips were used to collect about 10 ml of saliva and blood from the oral cavity. Both tissue and blood were kept on wet ice or at 4°C until being processed within 2 to 4 hours. Cells from mucosal tissue were extracted using an adapted version of a previously published method (49). Briefly, tissues underwent two rounds of enzymatic digestion with collagenase II (Sigma-Aldrich) at 37°C, each followed by mechanical digestion with 30 cc syringe and blunt 16-gauge needle. Oral blood was treated with ammonium-chloride-potassium (ACK) lysing buffer (Gibco) to remove red blood cells.

Human rectal biopsy processing

At each time point from each participant, n = 6 rectal biopsies were taken and immediately processed for flow cytometry. Biopsies (n = 2) were flash-frozen and archived. For the purposed of this study, one of those flash-frozen biopsies for each participant at three time points (before treatment, after oral treatment, and after topical treatment) was embedded in Tissue-Tek optimal cutting temperature (O.C.T.) compound (Sakura Finetek, Thermo Fisher Scientific).

Bulk RNA-seq library generation

We performed RNA-seq on 250 to 1000 sorted CD4+CCR5+ cells from blood or tissue samples, as previously described (50). In total, 28 samples were sequenced: 9 from CD69 blood samples, 9 from CD69 tissue samples, and 10 from CD69+ tissue samples from a total of 10 subjects. Briefly, cells were sorted directly into SMARTer v4 lysis reagents (Clontech). Cells were lysed and complementary DNA was synthesized. After amplification, sequencing libraries were prepared using the Nextera XT DNA Library Preparation Kit (Illumina). Barcoded single-cell libraries were pooled and quantified using a Qubit Fluorometer (Life Technologies).

Bulk RNA-seq statistical analysis

A quality filter was applied to retain libraries in which the fraction of aligned reads examined compared to total FASTQ reads was >75%, the median coefficient of variation of coverage was less than 0.8, and the library had at least 1 million reads. All sequenced samples passed these quality filters. Nonprotein coding genes, mitochondrial genes, and genes expressed at less than 1 count per million in fewer than 10% of samples were filtered out. Expression counts were normalized using the trimmed mean of M values (TMM) algorithm (51). For differential gene expression analysis, we used the linear models for microarray data (limma) R package (52, 53) after voom transformation (54); this approach either outperforms or is highly concordant with other published methods (55, 56). Linear models comparing blood and tissue sample gene expression and comparing CD69+ and CD69 cells from tissue samples were used. In both models, donor identity was included as a random effect. Genes with an FDR of less than 0.05 and an expression fold change of greater than 2 between two blood and tissue samples were considered DE. STRING was used to identify protein-protein interactions in DE genes. STRING connects genes according to known or predicted interactions to form a network and can be used to perform enrichment analysis to identify biological processes associated with these networks (30).

scRNA-seq library generation and sequencing

Freshly isolated cells from the blood and tissue were sorted into complete media and processed using the 10x Genomics Platform with the Single-Cell 3′ Reagent (v2), as described previously (57). Sorted populations and total cell numbers loaded into the Chromium Controller were as follows: blood CD4 T cells (4000), blood CCR5+CD69 CD4 T cells (3000), blood CD4 Treg (1000), tissue CCR5+ CD4 T cells (4000), tissue CCR5 CD4 T cells (4000), tissue CCR5+CD69 CD4 T cells (4000), tissue CCR5+CD69+ CD4 T cells (4000), and tissue CD4 Treg (2000). According to the manufacturer’s instructions, the target cell number for each population was calculated as half the number loaded.

scRNA-seq data analysis

Sequencing data were processed to sequence read counts and UMI counts per cell using the 10X Cell Ranger software, v. 2.1, using default parameters. Reads were converted from BCL to FASTQ format, demultiplexed, filtered, aligned to the genome, collapsed to UMIs, and assigned to barcodes. Barcodes were called as cells versus background using default settings in Cell Ranger, with target cell numbers as described above. Data from each sample were aggregated without normalizing, as normalization was conducted during downstream analysis.

Count data were analyzed using the Seurat toolset (58). Genes detected in <3 cells were removed. We also removed cells with <200 or >1800 genes detected, total UMI counts >6500, or with >10 percent mitochondrial reads based on observed breaks in the cell distributions. We log-transformed the UMI counts and regressed out total UMIs and percent mitochondrial reads per cell. We projected the samples using tSNE on the first 10 principal component axes from 1030 highly variable genes identified using default cuts. To summarize each cell’s expression of defined gene sets, we calculated the mean normalized expression across the set in each cell; to remove noise around 0 introduced by normalization, we restored 0 values for each gene in each cell with a raw UMI count of 0. We quantified differential expression among populations using the hurdle model implemented in model-based analysis of single-cell transcriptomics (MAST) (59), with total UMIs per cell as a covariate; MAST has high precision and low false-positive rates and generally identifies DE genes that overlap with those found by other methods (60).

Pathology assessment and scoring

Tissue was processed as described for IF staining (frozen in O.C.T. and 8-μm sections were cut), and slides were stained with H&E. Histologic sections of oral mucosa (sampled from each patient) were assessed for quality and orientation and excluded if most of the epithelium or a representative portion of the lamina propria was not present. All sections were evaluated blinded and scored according to the following criteria (fig. S1): severity of inflammation (score of 1 to 5), location of inflammation (lamina propria, diffuse or perivascular, or intraepithelial), type of inflammatory infiltrate (mononuclear or neutrophilic), presence of lamina proprial edema and epithelial lesions (degeneration/necrosis, erosion, or ulceration). From these criteria, each sample of oral mucosa was deemed as healthy (inflammation scores of 1 and 2) and unhealthy (inflammation scores of 3 to 5). In cases of more than one section representing the sample/patient, the scores were based on the most severely affected regions of all sections.

Statistical analyses

For flow cytometry and IFDP data, a paired t test, linear regression, or repeated-measures one-way analysis of variance (ANOVA) with Tukey’s posttest was performed as described in figure legends. Two-sided P > 0.05 was considered not significant, and values denoted with asterisk symbols reflect significance levels as follows: *P ≤ 0.05, **P ≤ 0.01, ***P ≤ 0.001, and ****P ≤ 0.0001.


Material and Methods

Fig. S1. Oral mucosa pathology assessment.

Fig. S2. CD4 TRM phenotype and localization in human oral mucosa.

Fig. S3. T cells as a percentage of total nucleated cells in healthy oral mucosal tissue.

Fig. S4. CD8 T cell and CD4 T cell changes with inflammation in human oral mucosa.

Fig. S5. Network analysis and GSEA for bulk RNA-seq of CD4 T cells from blood and oral mucosa samples.

Fig. S6. scRNA-seq.

Fig. S7. CD4 T cell functional potential.

Fig. S8. Impact of the CCR5 antagonist Maraviroc on CD4 T cell subsets in human rectal mucosa.

Table S1. List of donors used in this study.

Table S2. DE gene list comparing CCR5+ CD4 T cells from human blood and oral mucosa from bulk RNA-seq.

Table S3. List of TH17-associated genes used for scRNA-seq analysis.

Table S4. List of commercially available antibodies used for IF experiments.

Table S5. List of DE genes between CCR5+ (fold change up) and CCR5 (fold change down) cells from scRNA-seq in human oral mucosa.

Data file S1. Primary data.

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Acknowledgments: We thank V. Davé for input on statistical analysis, D. Zehn for thoughtful feedback on the manuscript, and Y.-W. Chiu and J. Hsu for assistance collecting oral mucosa samples. Funding: This work was supported by NIH grants DP2 DE023321 (to M.P.), R01 AI23323 (to M.P.), R21 DE026565 (to M.P. and D.R.D.), T32AI07140 (to A.S.W.D.), T32 AI007509-16 (to C.K.S.), and T32 GM007270 (to J.D.B.). A.S.W.D. is a Doug and Maggie Walker Fellow. F.M. is an ISAC scholar. The CHARM-03 study was funded by a U19 grant under the Integrated Preclinical-Clinical Program for HIV Topical Microbicides (IPCP-HTM), Division of AIDS, National Institute of Allergy and Infectious Diseases, NIH (AI082637). Author contributions: A.S.W.D., D.R.D., and M.P. conceived and designed experiments. A.S.W.D. and M.P. analyzed data and wrote the manuscript. A.S.W.D., C.K.S., J.D.B., F.M., and J.R.E. performed experiments. H.N.R. performed IF experiments and IFDP analysis and contributed to the writing of the manuscript. A.M.K. provided technical assistance with IF. H.A.D., M.J.D., and P.S.L. performed transcriptome analysis, helped with data interpretation, and contributed to the writing of the manuscript. M.A.D. assessed and scored tissues for pathology and contributed to the writing of the manuscript. M.M. provided the CCR5 antibody for IF. R.M.B. and I.M. organized the CHARM-03 clinical study, provided flow cytometry data and frozen biopsies, and contributed to the writing of the manuscript. D.R.D. led and supervised the clinical team and contributed to the study design and the writing of the manuscript. Y.L. and A.K. enrolled human subjects and acquired clinical samples. Competing interests: I.M. is the Chief Medical Officer at and has shares in Orion Biotechnology Canada and also receives consulting income from Aelix Therapeutics, BTM Products Inc., and Abivax. P.S.L. is a consultant for Bristol-Myers Squibb. All other authors declare that they have no competing interests. Data and materials availability: All data associated with this study are present in the paper or the Supplementary Materials. All sequencing data have been deposited to the GEO (accession no. GSE116141). Code files can be found at and on GitHub Data and tissues from the CHARM-03 clinical trial were covered by a material transfer agreement between the University of Pittsburgh and the Fred Hutchinson Cancer Research Center.
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