Abstract
Tissue-resident memory T cells (TRM) provide frontline defense against infectious diseases and contribute to antitumor immunity; however, aside from the necessity of TGF-β, knowledge regarding TRM-inductive cues remains incomplete, particularly for human cells. Oxygen tension is an environmental cue that distinguishes peripheral tissues from the circulation, and here, we demonstrate that differentiation of human CD8+ T cells in the presence of hypoxia and TGF-β1 led to the development of a TRM phenotype, characterized by a greater than 5-fold increase in CD69+CD103+ cells expressing human TRM hallmarks and enrichment for endogenous human TRM gene signatures, including increased adhesion molecule expression and decreased expression of genes involved in recirculation. Hypoxia and TGF-β1 synergized to produce a significantly larger population of TRM phenotype cells than either condition alone, and comparison of these cells from the individual and combination conditions revealed distinct phenotypic and transcriptional profiles, indicating a programming response to milieu rather than a mere expansion. Our findings identify a likely previously unreported cue for the TRM differentiation program and can enable facile generation of human TRM phenotype cells in vitro for basic studies and translational applications such as adoptive cellular therapy.
Keywords: Immunology
Keywords: Hypoxia, T cells
Introduction
Tissue-resident memory T cells (TRM) are a recently defined subset of non-recirculating memory T cells that reside in peripheral tissues and are important in frontline defense against viral infections and associated with antitumor immunity. However, despite intense study, relatively little is known regarding TRM differentiation. Studies in mice have shown CD8+ TRM are seeded by early effector T cells that differentiate into resident memory in situ, likely in response to local cues (1, 2). These cues are thought to differ by tissue but remain poorly defined. TGF-β is known to be critical in TRM establishment, and attempts to identify other resident phenotype inductive factors have focused on cytokines (1–9). Studies using mouse splenocytes 4 to 5 days postactivation have shown that TNF-α and IL-33 synergize with TGF-β to induce an intestinal resident T cell phenotype (CD103+Ly6C−CD69+) as well as downregulation of the transcription factor Kruppel-like factor 2 (KLF2) and its target genes sphingosine-1 phosphate receptor 1 (S1PR1) and SELL (CD62L), which is required for TRM establishment (4, 10).
Oxygen tension is another factor that distinguishes peripheral tissues from the circulatory environment: typical physiological oxygen concentrations encountered by T cells can be as high as 10% to 12.5% in arterial blood and 3% to 6% in most healthy tissues and as low as 0.5% to 6% in the spleen and lymph nodes (11–13). Notably, TRM were first described in barrier tissues, which are known to be relatively hypoxic (1, 14–18). In addition, any area of local inflammation can become hypoxic because of the increased density of metabolically active cells (19, 20). It has also been shown that oxygen consumption by activated neutrophils is sufficient to cause microenvironmental hypoxia (21). Thus, it is conceivable that the change in oxygen tension experienced by T cells in peripheral tissues and local inflammation could serve as a cue for the tissue residency program. We hypothesized that hypoxia may contribute to a TGF-β1–induced TRM phenotype in human CD8+ T cells.
To date, the majority of studies on TRM have been conducted in mice as human studies are subject to more technical and regulatory constraints. Although there is considerable overlap between the phenotypes reported for mouse and human TRM, there also appear to be key differences, notably illustrated by the differing importance of Hobit in TRM differentiation (8, 22–24). Such differences highlight the necessity for studying TRM differentiation in human cells, both to further understanding of basic molecular mechanisms and to facilitate effective translational application. Here, we demonstrate that human T cells differentiated during exposure to hypoxia and TGF-β1 develop a TRM phenotype characterized by expression of protein markers and a transcriptional profile reminiscent of endogenous TRM.
Results
Human CD8+ T cells differentiated in hypoxia and TGF-β1 acquire a TRM phenotype.
The necessity of TGF-β in TRM formation is well documented, but additional cues are required, as TGF-β alone is not sufficient to induce TRM phenotype (4, 8). Previous studies in mouse splenocytes demonstrated a role for inflammatory cytokines in TRM phenotype induction when used in combination with TGF-β (4, 10). Given the relative hypoxia in inflamed tissues, we postulated that low oxygen tension could provide additional cues for TRM differentiation. To determine whether hypoxia can contribute to induction of a TRM phenotype, we sorted naive (CD45RA+CCR7+) CD8+ T cells from human peripheral blood mononuclear cells (PBMCs), activated them for 4 days under hypoxia (2% O2) or normal cell culture conditions (~20% O2) to generate “early effectors,” and then cultured an additional 2 days in the presence of TGF-β1 (25).
We used quantitative real-time PCR (qPCR) to assess the bulk populations for expression of a panel of TRM-associated genes. Cells differentiated in 2% O2 + TGF-β1 showed upregulation of most of the genes Kumar et al. recently identified as the human TRM core signature, namely CD69, ITGAE (CD103), PDCD1 (programmed cell death 1 [PD-1]), CD101, and CXCR6 (Figure 1A and ref. 22). We did not observe a difference in ITGA1 (CD49a) transcript levels. In addition, transcripts of genes important in T cell recirculation (S1PR1, KLF2, SELL) were downregulated, further suggesting a residency program (Figure 1B). The downregulation of S1PR1 and KLF2 was previously shown to be critical to murine TRM differentiation, and decreased levels of these genes have also been observed in endogenous human TRM (10, 22, 23, 26). Elevated expression of the canonical hypoxia-responsive genes SLC2A1 and VEGFA confirmed the cells were responding to hypoxic conditions (Figure 1C). Together, these results indicate that when human CD8+ T cells are differentiated under hypoxia in combination with TGF-β1, they acquire a TRM-like transcriptional profile.
We then evaluated protein-level expression of the core human TRM signature via flow cytometry (22). In all donors tested there was an increase in CD69+CD103+ cells in the 2% O2 + TGF-β1 condition compared with 20% O2 + TGF-β1 (Figure 1D). Notably, unlike a previous report in mice, circulating naive human CD8+ T cells did not appreciably express CD103 (ref. 27 and Supplemental Figure 1B; supplemental material available online with this article; https://doi.org/10.1172/jci.insight.138970DS1). Cell viability was comparable or better in 2% O2 + TGF-β1 versus 20% O2 + TGF-β1 (Supplemental Figure 1C). These CD69+CD103+ cells expressed CD49a, PD-1, and CD101; however, we did not observe CXCR6 surface protein expression despite transcriptional upregulation in the bulk population (Figure 1, A and E). Although CD69 and CD103 expression is commonly used to define TRM, contention remains regarding which of these markers best denotes tissue residency, partly due to their heterogeneous expression in endogenous TRM (28). Thus, we compared expression levels of the TRM-associated markers CD49a, PD-1, and CD101 among the CD69–CD103+, CD69+CD103+, and CD69+CD103– populations from the 2% O2 + TGF-β1 condition. As expected, the CD69+CD103+ population had the highest levels of PD-1 and CD101 surface expression (Supplemental Figure 1D). CD49a expression was also high but not significantly different from levels observed in the CD69+CD103– population. In comparing the 2 oxygen conditions, we found the most dramatic increase in population fold change to be in the CD69+CD103+ population (Supplemental Figure 1, E and F). Based on these results, we focused further analysis on the CD69+CD103+ population as our in vitro induced TRM cells (hereafter i-TRM).
Hypoxia and TGF-β1 are synergistic cues for TRM phenotype acquisition.
Since the atmospheric oxygen level of normal tissue culture conditions is higher than what T cells experience in vivo, we evaluated the effect of 10% O2, which represents a physiologically relevant, nonhypoxic oxygen level in the circulation (11). Although there was a slight increase in CD69+CD103+ T cells in 10% O2 + TGF-β1 compared with 20% O2 + TGF-β1, correcting for multiple comparisons showed no significant differences in TRM-associated gene expression between the 2 conditions (Supplemental Figure 2, A–C). In addition, the fold increase of the CD69+CD103+ population over 20% O2 + TGF-β1 was significantly greater for the 2% O2 + TGF-β1 condition versus 10% O2 + TGF-β1 (Supplemental Figure 2D).
To assess the individual contributions of hypoxia and TGF-β1 to generation of TRM phenotype cells, we performed in vitro differentiation experiments in 2% or 20% O2 with or without TGF-β1. Hypoxia primarily induced CD69+ cells whereas TGF-β1 induced CD103+ cells, in congruence with reports that hypoxia and TGF-β can drive expression of these markers, respectively (29–31). Although hypoxia or TGF-β1 alone induced a modest population of CD69+CD103+ cells, their combination appeared to synergize induction of TRM phenotype to levels markedly greater than the additive effects of either condition alone (Figure 1F). The CD69+CD103+ cells induced by hypoxia + TGF-β1 expressed the highest levels of CD49a, PD-1, and CD101, compared with the majority populations in the 20% O2 and 2% O2 conditions (CD69–CD103– and CD69+CD103–, respectively) (Figure 1G).
In vitro induced TRM exhibit enrichment for endogenous human TRM gene signatures.
Since differences in TRM marker expression among the CD69–CD103– (20% O2), CD69+CD103– (2% O2), and CD69+CD103+ (2% O2 + TGF-β1) cells suggested that these represent distinct populations, each was sorted and transcriptionally profiled via RNA sequencing (RNA-Seq). Principal component analysis (PCA) confirmed that these 3 populations were distinct (Figure 2A). Hierarchical clustering showed distinct gene signatures for CD69–CD103– and CD69+CD103+ cells, whereas CD69+CD103– cells had a somewhat intermediate transcriptional profile (Figure 2, B and C). Comparison of the top DEGs between CD69+CD103+ and CD69–CD103– cells revealed gene expression patterns consistent with those reported for endogenous human TRM, including increased expression of ITGAE, EGR2, granulysin (GNLY), interleukin 2 receptor subunit β (IL2RB), Bcl2 modifying factor (BMF), RasGEF domain family member 1B (RASGEF1B), and nuclear receptor subfamily 4 group A member 1 (NR4A1), and decreased expression of SELL, KLF2, and KLF3, indicating a non-recirculating transcriptional program (Figure 2C; Supplemental Table 1; and refs. 22, 23, 32). S1PR1, a KLF2 target gene, was downregulated 1.5-fold (P adj < 0.05), but this did not meet our fold change threshold. CD69+CD103+ cells demonstrated increased expression of ITGA1, PDCD1, CD101, and TNFRSF9, all of which are consistently reported as upregulated in endogenous human TRM (22, 23, 26, 33). We also observed elevated transcripts encoding the transcription factor NOTCH1, which is known to contribute to maintenance of lung TRM, and RBPJ, which is central to Notch signaling (Figure 2C and refs. 23, 34).
TRM often express various chemokines, perhaps as part of their “alarm function,” to recruit immune cells to local tissues (35). Consistent with the endogenous TRM profile, i-TRM upregulated CXCL13 and CCL20, as well as CCL4, CCL5, and CCL22 (Figure 2C; Supplemental Table 1; and refs. 22, 23, 26). Changes in expression of genes that have currently undefined roles but are consistently reported in endogenous human TRM were also observed, such as upregulation of MYO7A and RGS1 and downregulation of SERPINE2, RAP1GAP2, and RASGRP2 (Supplemental Table 1 and refs. 22, 23, 26).
To gain insight into the physiological relevance of our findings, we performed gene set enrichment analysis (GSEA) using gene signatures from published data sets of endogenous human CD103+ TRM compared with CD103– effector memory T cells (TEM) from peripheral blood (23, 36). We observed that the transcriptional profile of CD69+CD103+ cells versus CD69–CD103– cells was similar to those of TRM from human skin (Cheuk et al., ref. 36) and lung (Hombrink et al., ref. 23) tissues versus blood TEM (Figure 3A). Signatures of genes upregulated in TRM were more enriched, reflecting that increases in gene expression predominated the transcriptional differences between CD69+CD103+ and CD69–CD103– cells, with enrichment driven by genes such as ITGAE, ITGA1, EGR2, BMF, PERP, CCL20, NOTCH1, RBPJ, and DUSP4, among others (Figure 2B, Figure 3A, and Supplemental Table 2). Analysis of these data sets for overlap revealed that all 3 shared genes related to TGF-β, cell adhesion, motility, and migration and genes encoding regulators of T cell differentiation: EGR2 and NR3C1 (glucocorticoid receptor, Figure 3B, and refs. 37, 38). Importantly, NR3C1 has previously been shown to promote the formation of memory precursor T cells and can be upregulated by hypoxia (38, 39). In addition, there was considerable overlap between the transcriptional profiles of our i-TRM and human skin TRM, including a number of canonical hypoxia-responsive genes (e.g., LDHA, MMP9, CA9), as well as LGALS3, a newly identified marker of human skin TRM (40).
Multiple recent profiles of TILs in various solid tumor types have reported the presence of CD103+ TILRM (41–45). Therefore, we compared CD69+CD103+ cells (hypoxia + TGF-β1) with CD69+CD103– cells (hypoxia alone) or CD69–CD103– cells (normal culture conditions) and observed enrichment of the CD8+CD69+CD103+ human breast cancer TIL signature reported by Savas et al. (Figure 3A, Supplemental Figure 3, and ref. 41). As hypoxia and TGF-β are common features of the tumor microenvironment (TME), our results suggest they may contribute to CD103+ TIL generation in vivo. Additionally, our results may help explain recent observations that TILRM are more radioresistant, as hypoxia and TGF-β are known to contribute to radioresistance, and DEGs in radioresistant human skin TRM show enrichment in the hypoxia pathway, relative to tissue-infiltrating T cells (40, 46–48).
Since the immunosuppressive TME tends to drive T cells toward exhaustion, it is possible that cells differentiated in hypoxia + TGF-β1 could become exhausted. Despite reports that hypoxia and TGF-β1 can cause T cell exhaustion, we did not observe differential expression of the exhaustion-associated genes ENTPD1 (CD39), HAVCR2 (TIM3), LAG3, CTLA4, CD38, SLAMF6, CD244, TIGIT, and TOX (Supplemental Table 1 and refs. 49, 50). To further compare our i-TRM with exhausted T cells, we performed GSEA using published human T cell exhaustion signatures. The Quigley et al. signature showed an inverse enrichment pattern; that is, genes that are downregulated in exhausted T cells were upregulated (positively enriched) in our i-TRM (Supplemental Figure 4A and ref. 51). Genes that are upregulated in exhausted T cells showed a trend toward negative enrichment in our data set but did not reach statistical significance. The CD39+ exhaustion signature of upregulated genes (Gupta et al.) was enriched in our data set; however, aside from PDCD1, the shared genes driving enrichment were not associated with exhaustion and instead encoded transcription factors and enzymes spanning a broad array of biological functions (Supplemental Table 2 and ref. 52). In comparison, genes upregulated in TRM from the skin of healthy individuals (Cheuk et al.) were more enriched in our data set, in terms of both significance and proportion (Supplemental Figure 4B and ref. 36). These data show that, compared with cells from normal cell culture conditions, i-TRM do not exhibit an exhausted transcriptional profile.
Pathway analysis reveals biological commonalities between in vitro induced TRM and endogenous human TRM.
Ingenuity Pathway Analysis (IPA; QIAGEN) comparing CD69+CD103+ cells with CD69–CD103– cells revealed that many of the DEGs were in the glycolysis and gluconeogenesis pathways (Figure 4). Given that hypoxia is a major regulator of cellular metabolism, these results were expected. There was also an enrichment of genes in the Notch signaling pathway, which has been reported in human lung TRM (23). To better understand the functional relevance of the hypoxia + TGF-β–induced TRM transcriptional profile, we ran pathway analysis on the Cheuk et al. data set and found that many of the same biological pathways were differentially regulated. Mirroring our earlier overlap analysis, we observed enrichment of DEGs in the HIF-1α and TGF-β signaling pathways in both transcriptional profiles (Figure 4). Changes in leukocyte extravasation signaling, agranulocyte adhesion and diapedesis, epithelial adherens junction signaling, and integrin signaling pathways, all involved in focal adhesion and related to changes in migratory programming, were common to their endogenous TRM and our i-TRM. Multiple pathways involved in inositol phosphate signaling were also enriched, in agreement with a previous report that PI3K signaling is implicated in cytokine-induced downregulation of KLF2 and may play a role in generation of TRM in vivo (10). Remarkably, the axonal guidance pathway was also differentially regulated in both analyses. Axonal guidance, while at first seemingly unrelated to TRM and unreported in current TRM literature, is a process whereby environmental cues influence cell migratory patterns (53). Many of the same factors governing axon guidance are also known to regulate immune cell trafficking and can be regulated by hypoxia and/or TGF-β (53–59). Overall, these results show that our i-TRM display striking similarities to endogenous human TRM at the biological pathway level.
In vitro induced TRM maintain their phenotype in response to milieu.
To assess the stability of our i-TRM phenotype, we sorted CD69+CD103+ cells generated using hypoxia + TGF-β1 and cultured them in parallel in 20% O2 or 2% O2, with IL-2 or IL-15, and with or without TGF-β. After 48 hours of culture at 20% O2 with IL-2 and without TGF-β1, a slight majority of cells had lost CD69 expression (becoming CD69–CD103+) with a gradual decrease in CD49a expression; however, culture in hypoxia helped maintain CD69 expression, consistent with our earlier results (Figure 5). Culture with IL-2 and TGF-β1 at both oxygen levels seemed to drive the cells toward CD103 single positivity but also helped maintain CD49a expression. In contrast, culture with IL-15 enabled maintenance of CD69 expression that was further enhanced in hypoxia, although CD49a expression declined. Culture in hypoxia with IL-15 and TGF-β1 maintained expression of CD69, CD103, and CD49a in the majority of cells. IL-2 is the prototypical cytokine for effector T cell culture, whereas IL-15 is important for the maintenance of memory T cells and the development and/or maintenance of TRM in several organs (2, 24, 60–62). The dependence of our i-TRM on IL-15 may be related to their increased expression of IL2RB, which encodes the β chain of the IL-15 receptor (CD122), in line with murine studies on the importance of IL-15 responsiveness for skin TRM (3, 62). Further, it has been shown that exposure of human PBMCs to IL-15 and TGF-β1 can also induce a CD69+CD103+ phenotype in CD8+ T cells (8). Thus, these data demonstrate that in addition to TGF-β1, hypoxia can cooperate with other environmental cues, such as IL-15, to promote a TRM phenotype in T cells. Our results also indicate that maintenance of the i-TRM phenotype relies on environmental cues, mirroring the plasticity and environmental adaptability of endogenous (murine) TRM, which can alter their phenotype but reacquire TRM characteristics when exposed to the appropriate milieu (63, 64).
Hypoxia and TGF-β1 influence the human TRM differentiation program.
We showed earlier that hypoxia or TGF-β1 alone can generate CD69+CD103+ cells, but their combination results in the greatest induction (Figure 1F). This observation raised the question of whether CD69+CD013+ cells from the combination condition are simply greater in number or actually phenotypically distinct. Comparison of TRM core signature protein expression via flow cytometry indicated that CD69+CD103+ populations from the different conditions are not identical. Consistent with previous results, CD69+CD103+ cells generated in hypoxia displayed higher CD69 expression, and those that were exposed to TGF-β1 were marked by higher CD103 expression (Figure 6A). TGF-β1 exposure was also related to higher expression of PD-1 and CD101. Remarkably, hypoxia alone induced elevated CD103 and CD49a expression; however, the combination of hypoxia and TGF-β1 resulted in CD69+CD103+ cells with the highest levels of CD103 and CD49a expression (Figure 6A). While the influence of TGF-β1 on CD103 and CD49a has been reported, to our knowledge, this is the first implication of hypoxia in induction of these canonical TRM-associated integrins (5, 30, 31, 65). These observations also further support our findings that hypoxia and TGF-β1 are synergistic cues for TRM phenotype acquisition.
To compare the populations at the transcriptional level, we sorted CD69+CD103+ cells generated by hypoxia alone (2% O2), TGF-β1 alone (20% O2 + TGF-β1), and their combination (2% O2 + TGF-β1) and conducted RNA-Seq. PCA showed that all 3 groups of CD69+CD103+ cells clustered away from CD69–CD103– cells from normal cell culture conditions analyzed previously (20% O2, batch1), indicating relative similarity (Figure 6B). All data were normalized and corrected for batch variation, and we validated that this clustering pattern was not due to batch effects by including CD69+CD103+ samples from our previous analysis (batch1), confirming that they clustered with the 2% O2 + TGF-β1 group samples as expected. Cells exposed to TGF-β1 clustered closer to each other than to those cultured in hypoxia alone, suggesting that the effects of TGF-β1 predominate in our system. Differential expression analysis was performed only among samples from the same batch (batch2) in order to analyze matched samples. Gene Ontology (GO) analysis on the 1014 DEGs between the 3 group conditions (P adj < 0.001, determined by likelihood ratio test) revealed enrichment of genes involved in glycolysis and hypoxia response, regulation of TGF-β signaling (TGF-β receptor and SMADs), and cell migration (Figure 6C). Interestingly, genes encoding regulators of histone methylation were also enriched, suggesting an epigenetic component.
Closer examination of TRM signature genes revealed many that were primarily influenced by TGF-β1, including ITGAE, CD101, KLF2, NOTCH1, and IL2RB (Figure 7A). ITGA1 transcript levels were also similar between TGF-β1–treated groups, suggesting a posttranscriptional mechanism for the CD49a upregulation observed (Figure 6A and Figure 7A). Expression of CD69, S1PR1, SELL, and EGR3 was more heavily influenced by hypoxia, and CXCR4 and CCR5 transcript levels were highest only in CD69+CD103+ cells from the combination condition (Figure 7A). Examination of the genes driving enrichment in the GO analysis showed that hypoxia exposure also influenced expression of genes encoding a number of adhesion molecules and epigenetic regulators, including upregulation of several histone lysine demethylases (KDMs): KDM5B, KDM4C, KDM4B, and KDM3A (Figure 7B). Expression patterns of genes involved in hypoxia response and regulation of TGF-β signaling provided some insight into the interaction of these 2 pathways. TGF-β1 exposure upregulated HIF1A, which encodes the transcription factor HIF-1α, and downregulated several genes encoding negative regulators of HIF-1α: EGLN1, EGLN3, and CITED2 (66, 67). Hypoxia exposure resulted in increased expression of BNIP3 and BNIP3L, which was slightly dampened by TGF-β1 exposure. Apart from their roles in apoptosis, BNIP3 and BNIP3L also mediate autophagy, which is involved in generation and perhaps maintenance of memory T cells, such as human liver TRM, and this is the function we propose, given our earlier observation that cellular viability is not compromised by hypoxia (68–70). Similarly, hypoxia increased expression of SMAD3, which encodes an important TGF-β signaling mediator, and decreased expression of several negative regulators of TGF-β signaling (SMAD7, SMURF1, SMURF2, SKIL). However, cells exposed to hypoxia + TGF-β had higher levels of SMAD7, SMURF1, and SKIL transcripts. These observations illustrate how hypoxia and TGF-β might potentiate and attenuate each other’s signaling and activity in a complex molecular network promoting the TRM differentiation program.
Hypoxia-induced TRM phenotype is partially recapitulated by HIF stabilization.
HIFs are transcription factors governing the cellular response to hypoxia that are regulated by prolyl hydroxylase (PHD) enzymes, which require oxygen to function (71). To assess whether HIFs mediated the hypoxic induction of CD69+CD103+ cells, we used the HIF PHD inhibitor FG-4592 (roxadustat) to stabilize HIFs during differentiation experiments in 20% O2 with the addition of TGF-β1 (72). We observed a dose-dependent increase in CD69+CD103+ cells that reached frequencies similar to those in 2% O2 + TGF-β1 (Figure 8A). This increase was primarily driven by increased CD69 expression, consistent with a recent report showing CD69 is a HIF target gene (Supplemental Figure 5 and ref. 29). To further assess whether HIF stabilization by PHD inhibition reproduces the hypoxia-induced TRM phenotype, we compared expression of CD49a, PD-1, and CD101 on CD69+CD103+ cells generated via both routes. Although the addition of FG-4592 slightly decreased CD49a, PD-1, and CD101 expression when compared with TGF-β1 alone (0 μM), PD-1 and CD101 expression levels were not significantly different from those of cells differentiated in hypoxia + TGF-β1, suggesting that HIFs are involved in TRM phenotype acquisition (Figure 8B). HIF stabilization via PHD inhibition did not recapitulate CD49a expression; however, this may be due to off-target effects of FG-4592. PHDs also regulate collagen biosynthesis, and since CD49a is part of the collagen-binding complex very late antigen-1, its expression may be affected by PHD inhibition (73).
To gain insight into the contributions of hypoxia versus HIFs, we compared the transcriptional profiles of CD69+CD103+ cells induced by 2% O2 + TGF-β1 or FG-4592 + TGF-β1, using CD69+CD103+ cells induced by TGF-β1 alone as a common reference. While the DEGs did not completely overlap, there was considerable similarity (Figure 8C). TRM-associated genes generally were not among the DEGs, again likely owing to the dominant effect of TGF-β1. Direct comparison of CD69+CD103+cells induced by hypoxia or FG-4592 showed there was no significant difference in their expression of core TRM signature genes; however, genes encoding CCR5 and GNLY were upregulated only by hypoxia (Figure 8D). Interestingly, genes encoding several of the KDMs discussed earlier were among the overlapping DEGs, and direct comparison of CD69+CD103+ cells induced by hypoxia or FG-4592 revealed comparable expression; however, the activity of these enzymes may differ between the 2 conditions (Figure 8E). These KDMs belong to a class of enzymes known as 2-oxoglutarate–dependent oxygenases, which utilize oxygen in their functions and thus are also regulated by oxygen in a HIF-independent manner (74). As this is the same class of enzymes to which HIF PHDs belong, it is possible that their activity would be affected by FG-4592; however, this is unlikely at the doses used here (73). These demethylases vary in their targets and sensitivity to oxygen and chemical inhibitors, so specific changes in activity and their downstream consequences in this study are currently unclear. In light of the large degree of overlap in expression of TRM-associated genes between the TGF-β1–treated groups, it is plausible that alterations in chromatin accessibility are partially responsible for the increased frequency of CD69+CD103+ cells in hypoxia and hypoxia-mimetic conditions. Indeed, recent work characterizing murine small intestine TRM differentiation reported high KDM5B expression in a likely TRM precursor population, concordant with the early time point used in our study (75). Overall, these results suggest that HIFs are involved in TRM phenotype acquisition, with the possibility that HIF-independent effects of hypoxia may play a role as well.
Discussion
The well-documented heterogeneity among TRM is likely due to varying environments across tissue niches, and we propose that hypoxia is one of the many possible environmental cues for the TRM program (6, 28, 32, 33, 36, 63, 76–78). The intestine, skin, and reproductive tract, sites with well-characterized TRM populations, are known to be relatively hypoxic (14–16). This is further reflected in our findings of similarity between hypoxia-cultured i-TRM and human skin TRM, due in part to the enrichment of hypoxia-responsive genes. However, questions may be raised regarding the relevance of hypoxia as an environmental cue in the lung, which, in addition to being an important site of TRM accumulation, is the organ responsible for oxygen exchange. Interestingly, a study on human lung TRM reported increased expression of transcripts encoding HIFs and enrichment of hypoxia-inducible genes when compared with T cells from peripheral blood (23). These findings could be attributed to the sources of tissue samples used: patients with lung tumors or chronic obstructive pulmonary disease, which are both conditions that can create hypoxic environments (79–81). However, although oxygen levels in the alveoli are high, oxygen tensions in lung tissue can be comparable to those in other organs (median ~5%; refs. 80, 82, 83). A recent study comparing murine lung airway and interstitial TRM highlights how microenvironmental variation within the same organ can impact TRM heterogeneity (76). Varying degrees of vascularization create a range of oxygen levels within an organ, presumably making the epidermis more hypoxic than the dermis of the skin and the white pulp more hypoxic than the red pulp of the spleen (12, 13, 84). Small TRM populations have also been described in secondary lymphoid organs (SLOs), which are predominantly sites of recirculation (26, 85). In this case microenvironmental hypoxia could promote TRM differentiation as a function of distance from blood vessels within the organ, or through inflammation-induced hypoxia, which is relevant to any inflamed tissue. Moreover, it is believed that some SLO TRM are derived from emigrants of TRM originally differentiated in nonlymphoid tissues (85). Microanatomical availability and receipt of additional signals, such as from TGF-β, would also influence residency programming. It is possible that hypoxia is not a universal driver of TRM in all tissues, just as TRM in different organs vary in their requirement for IL-15 (60). Sources of active TGF-β for TRM differentiation may also differ between tissues: provided by keratinocytes in the skin, Kupffer cells in the liver, dendritic cells in lymphoid and other organs, and perhaps monocytes and/or macrophages. These cells express integrins necessary to release active TGF-β from the latent form present in the environment — either bound to extracellular matrix via latent TGF-β binding protein or anchored to immune cell membranes via glycoprotein-A repetitions predominant protein (GARP/LRRC32) or LRRC33 (27, 86–91).
Our findings also raise questions regarding the role of metabolism in TRM differentiation. Hombrink et al. suggested that a major role of Notch signaling in lung TRM is regulation of metabolic programs because inhibition of Notch signaling affected genes involved in glycolysis, oxidative phosphorylation, and fatty acid metabolism pathways (23). It has also been suggested that deletion of the purinergic receptor P2RX7, which is known to modulate glycolysis, impairs TRM formation via metabolic dysregulation, as P2RX7-deficient cells displayed decreased mitochondrial mass and function, defective aerobic glycolysis, and impaired glucose uptake (92–94). While hypoxia reportedly promotes effector differentiation in T cells via HIF-driven glycolysis, TGF-β seems to be important for memory T cell formation, likely via effects on mitochondria (95–97). In contrast to hypoxia, few reports describe the role of TGF-β in T cell metabolism. It has been shown that TGF-β can increase mitochondrial membrane potential (a measure of mitochondrial activity) in T cells and that CD8+CD103+ tumor-specific T cells have increased spare respiratory capacity; however, the connection to oxidative phosphorylation and/or fatty acid metabolism is unclear and may also be dependent on T cell differentiation status (97–100). Interestingly, P2RX7 activity induces HIF-1α (likely involved in P2RX7’s regulation of T cell metabolism) as well as TGF-β receptor II, thereby increasing sensitivity to TGF-β and providing another possible avenue of integration of these pathways in endogenous TRM (93, 97, 101).
How hypoxia and TGF-β synergize to induce CD69+CD103+ cells remains an open question, and our results indicate that HIF-mediated mechanisms are involved. HIFs are known to modulate several TRM phenotype features, including upregulation of CD69 and TNFRSF9, and downregulation of SELL and S1PR1, as reflected in our results (29, 95, 102, 103). Further, inhibition of HIF-1α/β complex formation by HIF-1β deletion in murine T cells maintained CD62L expression, causing their accumulation in lymph nodes and preventing migration to nonlymphoid tissues. (102). Our work and that of others suggests TGF-β may potentiate HIF activity by upregulating HIF1A and promoting HIF stabilization and activity via downregulation of the negative regulators PHD2 and CITED2 (104, 105). Notably, HIF-1α can also be induced by other stimuli reportedly involved in TRM generation and/or maintenance, namely IL-15, TNF-α, and P2RX7 stimulation by extracellular ATP (101, 106, 107). Moreover, IL-15 and TNF-α can induce CD69 expression similarly to hypoxia (8, 108). Thus, it is unclear to what extent hypoxia is a relevant cue in vivo or whether hypoxia in our system mimics the effects induced by other inflammatory stimuli. However, the cooperation we observed between hypoxia and IL-15 suggests these cues are complementary rather than redundant. We also observed changes in expression of several epigenetic regulators, suggesting hypoxia-induced chromatin remodeling may alter accessibility to hypoxia-inducible transcription factors such as HIFs and the glucocorticoid receptor as well as TGF-β–inducible transcription factors such as SMAD3 and EGR2 (109). Future studies are warranted to further dissect the mechanism via which hypoxia and TGF-β synergize to induce the TRM phenotype.
In recent years multiple reports have suggested that TILRM are important in antitumor immunity. Specifically, the accumulation of CD103+ TILs in a wide array of solid tumor malignancies, including breast, lung, ovarian, pancreatic, and melanoma, is associated with favorable prognosis (41–45). In addition, murine model studies have shown that skin TRM are protective against melanoma (110, 111). Thus, there is much interest in leveraging resident phenotype T cells for immunotherapy; however, current approaches focus on using vaccination to induce these cells in vivo because endogenous TRM are difficult to isolate and culture, and there is no reliable method to generate these cells in vitro (92, 112, 113). The method described in this study may enable in vitro generation of TRM phenotype cells and has the potential for application to multiple existing adoptive cellular therapy (ACT) modalities. The elevated PD-1 expression on i-TRM make them attractive for combination with PD-1 blockade. Indeed, a recent study in metastatic melanoma patients reported that CD103+ TILs significantly expanded during anti–PD-1 therapy (114). There is also potential for i-TRM ACT to treat viral diseases: the importance of TRM in antiviral immunity is well established, and a recent study found that elite HIV controllers have high magnitudes of HIV-specific TRM in their lymphoid tissues, suggesting that these cells may control chronic viral infections (26). We show that even in unfavorable conditions (20% O2 with IL-2) i-TRM can maintain their phenotype because by 48 hours approximately 30% of the cells remained CD69+CD103+. In addition, since preparation of ACT products for administration typically occurs within a 12-hour period, we believe the majority of i-TRM would retain their phenotype during processing, as well as in vivo upon receiving suitable environmental cues. We are applying the findings of this study to expansion protocols for production of i-TRM in sufficient quantity for therapeutic purposes.
To our knowledge this study is the first to recapitulate TRM phenotype and transcriptional signature in vitro from human peripheral blood–derived T cells, as well as the first to identify hypoxia as a cue for the human TRM differentiation program. While there are obvious limitations to experiments that can be conducted in humans, we believe the studies described provide compelling evidence that hypoxia is an environmental cue that can contribute to acquisition of a TRM phenotype, supported by the observation that hypoxia + TGF-β i-TRM recapitulate the transcriptional and proteomic landscape of endogenous TRM. These observations suggest that lower oxygen tensions such as those found in peripheral tissues, sites of inflammation, and tumors can promote the TRM differentiation program. In vivo studies will be necessary to determine whether hypoxia and/or HIFs can contribute to TRM differentiation in peripheral tissues and/or tumors and to determine whether i-TRM can establish tissue residency after adoptive transfer.
Methods
Cell isolation and in vitro cell culture
Healthy donor PBMCs were collected via leukapheresis and stored in liquid nitrogen until use. CD8+ T cells were enriched from PBMCs using STEMCELL Technologies EasySep kits. Cells were then stained with fluorochrome-conjugated antibodies against CD8 (SK1), CD45RA (HI100), and CCR7 (G043H7) (all BioLegend, clones in parentheses). Naive CD8+CD45RA+CCR7+ T cells were sorted using a FACSAria IIIu or Fusion cell sorter (BD Biosciences). Sorted naive CD8+ T cells were resuspended in T cell culture media (RPMI, 10% FBS, l-glutamine, penicillin-streptomycin) with 10 IU/mL rhIL-2 (Prometheus) and equilibrated overnight to 2% O2 in a hypoxic chamber (Coy Laboratory Products) or in a standard cell culture incubator (~20% O2, Thermo Fisher Scientific). Cells were then activated with anti-CD3/anti-CD28 beads (Gibco, Thermo Fisher Scientific) at 3 cells per bead for 4 days. On day 4, 1.25 ng/mL rhTGF-β1 (BioLegend) was added, and cells were cultured for an additional 2 days. For phenotype stability assays i-TRM were generated as described, sorted, and resuspended in media pre-equilibrated to 20% O2 or 2% O2 with 20 IU/mL rhIL-2 (Prometheus) or 10 ng/mL rhIL-15 (R&D Systems, Bio-Techne), with or without 1.25 ng/mL rhTGF-β1 (BioLegend).
Flow cytometry
For analysis of human TRM-associated markers, beads were removed, and cells were washed once in staining buffer and stained with Live/Dead Fixable Aqua (Life Technologies, Thermo Fisher Scientific) and fluorochrome-conjugated antibodies against CD8 (SK1), CD69 (FN50), CD103 (ber-ACT8), PD-1 (EH12.2H7), CD101 (BB27), CXCR6 (K041E5), and CD49a (TS2/7) (all BioLegend, clones in parentheses). After staining, cells were fixed with Fixation Buffer (BioLegend) and stored in staining buffer until analysis. Stained cells were analyzed using an ACEA Novocyte 3000 flow cytometer. Single fluorochrome-stained compensation beads (UltraComp, eBioscience, Thermo Fisher Scientific) and FMO samples were used as controls. Data were analyzed using FlowJo software (BD Biosciences).
qRT-PCR or qPCR
For analysis of human TRM-associated gene expression, cells were separated from beads and washed once in PBS. RNA was isolated using the QIAGEN RNeasy Plus Mini Kit. When necessary RNA was further purified/concentrated using the QIAGEN RNeasy MinElute Cleanup Kit. First-strand cDNA was synthesized using M-MLV Reverse Transcriptase (Thermo Fisher Scientific). qRT-PCR was performed using the QuantStudio 5 Real-Time PCR System and PowerUp SYBR Green Master Mix (Applied Biosystems, Thermo Fisher Scientific). Relative mRNA gene expression was normalized to the housekeeping gene RPL13A. Primers used are listed in Supplemental Table 3.
RNA-Seq transcriptome analysis
Cells were sorted using a FACSAria IIIu cell sorter before RNA isolation using the RNeasy Plus Mini Kit followed by the RNeasy MinElute Cleanup Kit.
Regarding batch1.
The library was constructed using the Illumina TruSeq Stranded mRNA kit. RNA-Seq was conducted by the Sequencing and Non-Coding RNA Program of UT MD Anderson Cancer Center using the Illumina NextSeq500 platform. Raw reads were mapped to the Homo sapiens reference genome and transcriptome (GRCh38, GENCODEV23) by HISAT2 (version 2.1.0; ref. 115). Htseq-count (version 2.1.0) was used to get the counts for genes. R and Bioconductor packages DESeq2 (version 1.14.1) were used to identify DEGs (116). Genes (mRNA only, taking the protein-coding genes for P value adjustment) with FDR < 0.05 and |fold change| ≥ 2 were considered differentially expressed. R and Bioconductor package fgsea (version 1.10.0) was used to conduct GSEA. Gene sets were derived from several previously published data sets. The Nath et al. TGF-β signatures, Savas et al. TIL signatures, and Gupta et al. and Quigley et al. exhaustion signatures were derived from published differential expression tables (41, 51, 52, 117). The human TRM signature was constructed from several studies (22, 23, 26, 36). For the lung and skin TRM signatures, gene expression data were obtained from the Gene Expression Omnibus database (accessions GSE61397 and GSE83637) and analyzed using GEO2R or DESeq2, respectively (23, 36). FDR-adjusted P values less than 0.05 were considered significant. For analysis of intersection/overlap between gene sets, fold change thresholds were adjusted to ±1.5 and FDR ≤ 0.5 to account for the reduced sensitivity of microarray technology.
Regarding batch2.
The library was constructed using the NEBNext Ultra RNA Library Prep Kit for Illumina (New England Biolabs). mRNA sequencing was conducted by Novogene Corporation Inc. using the Illumina NovaSeq platform. Raw reads were mapped to the Homo sapiens reference genome and transcriptome (GRCh38, GENCODEV23) by HISAT2 (version: 2.1.0). Htseq-count (version: 2.1.0) was used to get the counts for genes. For analysis relevant to Figures 6 and 7: DEGs were determined using the likelihood ratio test function offered by Bioconductor R package DESeq2 (version 1.28.1). The genes (mRNA only, taking the protein-coding genes for P value adjustment) with P adj < 0.001 were selected as candidates. In order to decrease any sample-specific variation between the 2 batches of RNA-Seq data sets, supervised surrogate variable analysis using Bioconductor R package sva (version 3.36.0) based on the expression of human housekeeping genes (3804 genes) was performed (118, 119). For analysis relevant to Figure 8: R and Bioconductor package DESeq2 (version 1.28.1) was used to identify DEGs. Genes (mRNA only, taking the protein-coding genes for P value adjustment) with FDR ≤ 0.05 and |fold change| ≥ 2 were considered differentially expressed.
Regarding both batches.
Downstream logical and statistical outputs were generated using customized R scripts with packages dplyr (version 1.0.2), tidyr (version 1.1.2), tibble (version 3.0.4), stringi (version 1.5.3), tidyverse (version 1.3.0), clusterProfiler (version 3.16.1), and ggplot2 (version 3.3.2). Data have been deposited in the European Genome-phenome Archive (EGA) under the EGA ID/accession number EGAS00001005286.
Pathway analysis.
Functional analysis of significant DEGs (FDR < 0.05 and |fold change| > 2) was done with IPA software (version 60467501, QIAGEN) using all genes in the Ingenuity Knowledge Base as the reference set and right-tailed Fisher’s exact test in a core analysis to determine if pathways were significantly altered between conditions (−log10[P value] > 1.3).
GO.
Differentially expressed gene lists were tested for enrichment of GO pathways using the PANTHER classification system web-based gene list analysis tool, version 16.0 (120). The statistical overrepresentation test (released 20200728) was performed using the GO biological processes complete annotation data set and Fisher’s exact test with FDR < 0.05 correction for multiple testing.
Statistics
Graphical presentation and statistical analysis of the data were performed using GraphPad Prism (Version 8). Data are displayed as mean ± SEM. Samples were matched/paired unless otherwise indicated. Results between experimental groups were compared using statistical tests described in the figure legends (t tests always 2-tailed and 1- or 2-way ANOVA always followed by Tukey’s multiple comparisons test, unless otherwise indicated). P < 0.05 was considered statistically significant.
Study approval
All human sample collection was performed with informed consent and approved by the institutional review board of UT MD Anderson Cancer Center.
Author contributions
FH conceptualized the study, designed and performed experiments, collected and analyzed data, and wrote the paper. YC analyzed RNA-Seq data. RMS performed experiments. JW analyzed data, provided intellectual contributions, and edited the paper. CY oversaw the study, provided intellectual contributions, and edited the paper.
Supplementary Material
Acknowledgments
We thank the South Campus Flow Cytometry & Cell Sorting Core Laboratory of UT MD Anderson Cancer Center, which is supported by National Cancer Institute/NIH P30CA016672. We thank Stephen C. Jameson and Kimberly S. Schluns for their feedback on the manuscript and Sirisha Yadugiri for her assistance in figure preparation. This research was supported by the Parker Institute for Cancer Immunotherapy. CY is a member of the Parker Institute for Cancer Immunotherapy.
Version 1. 05/24/2021
Electronic publication
Footnotes
Conflict of interest: CY is a consultant/advisory board member for AffyImmune Therapeutics, Inc.; Immatics; and Obsidian Therapeutics. A patent application based on this manuscript has been filed (FH and CY, PCT publication number WO 2020/081987 A1).
Copyright: © 2021, Hasan et al. This is an open access article published under the terms of the Creative Commons Attribution 4.0 International License.
Reference information: JCI Insight. 2021;6(10):e138970.https://doi.org/10.1172/jci.insight.138970.
Contributor Information
Farah Hasan, Email: fhasan1@mdanderson.org.
Yulun Chiu, Email: YChiu@mdanderson.org.
Rebecca M. Shaw, Email: RMShaw@mdanderson.org.
Junmei Wang, Email: JWang17@mdanderson.org.
Cassian Yee, Email: cyee@mdanderson.org.
References
- 1.Masopust D, et al. Dynamic T cell migration program provides resident memory within intestinal epithelium. J Exp Med. 2010;207(3):553–564. doi: 10.1084/jem.20090858. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Mackay LK, et al. The developmental pathway for CD103(+)CD8+ tissue-resident memory T cells of skin. Nat Immunol. 2013;14(12):1294–1301. doi: 10.1038/ni.2744. [DOI] [PubMed] [Google Scholar]
- 3.Mackay LK, et al. T-box transcription factors combine with the cytokines TGF-β and IL-15 to control tissue-resident memory T cell fate. Immunity. 2015;43(6):1101–1111. doi: 10.1016/j.immuni.2015.11.008. [DOI] [PubMed] [Google Scholar]
- 4.Casey KA, et al. Antigen-independent differentiation and maintenance of effector-like resident memory T cells in tissues. J Immunol. 2012;188(10):4866–4875. doi: 10.4049/jimmunol.1200402. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Zhang N, Bevan MJ. Transforming growth factor-β signaling controls the formation and maintenance of gut-resident memory T cells by regulating migration and retention. Immunity. 2013;39(4):687–696. doi: 10.1016/j.immuni.2013.08.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Bergsbaken T, et al. Local inflammatory cues regulate differentiation and persistence of CD8+ tissue-resident memory T cells. Cell Rep. 2017;19(1):114–124. doi: 10.1016/j.celrep.2017.03.031. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Sheridan BS, et al. Oral infection drives a distinct population of intestinal resident memory CD8(+) T cells with enhanced protective function. Immunity. 2014;40(5):747–757. doi: 10.1016/j.immuni.2014.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Pallett LJ, et al. IL-2(high) tissue-resident T cells in the human liver: sentinels for hepatotropic infection. J Exp Med. 2017;214(6):1567–1580. doi: 10.1084/jem.20162115. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 9.Wakim LM, et al. Antibody-targeted vaccination to lung dendritic cells generates tissue-resident memory CD8 T cells that are highly protective against influenza virus infection. Mucosal Immunol. 2015;8(5):1060–1071. doi: 10.1038/mi.2014.133. [DOI] [PubMed] [Google Scholar]
- 10.Skon CN, et al. Transcriptional downregulation of S1pr1 is requiredfor the establishment of resident memory CD8+ T cells. Nat Immunol. 2013;14(12):1285–1293. doi: 10.1038/ni.2745. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 11.Atkuri KR, et al. Culturing at atmospheric oxygen levels impacts lymphocyte function. Proc Natl Acad Sci U S A. 2005;102(10):3756–3759. doi: 10.1073/pnas.0409910102. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Caldwell CC, et al. Differential effects of physiologically relevant hypoxic conditions on T lymphocyte development and effector functions. J Immunol. 2001;167(11):6140–6149. doi: 10.4049/jimmunol.167.11.6140. [DOI] [PubMed] [Google Scholar]
- 13.Huang JH, et al. Requirements for T lymphocyte migration in explanted lymph nodes. J Immunol. 2007;178(12):7747–7755. doi: 10.4049/jimmunol.178.12.7747. [DOI] [PubMed] [Google Scholar]
- 14.Gillies RM, et al. Immunohistochemical assessment of intrinsic and extrinsic markers of hypoxia in reproductive tissue: differential expression of HIF1α and HIF2α in rat oviduct and endometrium. J Mol Histol. 2011;42(4):341–354. doi: 10.1007/s10735-011-9338-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Karhausen J, et al. Epithelial hypoxia-inducible factor-1 is protective in murine experimental colitis. J Clin Invest. 2004;114(8):1098–1106. doi: 10.1172/JCI21086. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Rezvani HR, et al. HIF-1α in epidermis: oxygen sensing, cutaneous angiogenesis, cancer, and non-cancer disorders. J Invest Dermatol. 2011;131(9):1793–1805. doi: 10.1038/jid.2011.141. [DOI] [PubMed] [Google Scholar]
- 17.Boyman O, et al. Spontaneous development of psoriasis in a new animal model shows an essential role for resident T cells and tumor necrosis factor-alpha. J Exp Med. 2004;199(5):731–736. doi: 10.1084/jem.20031482. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Gebhardt T, et al. Memory T cells in nonlymphoid tissue that provide enhanced local immunity during infection with herpes simplex virus. Nat Immunol. 2009;10(5):524–530. doi: 10.1038/ni.1718. [DOI] [PubMed] [Google Scholar]
- 19.Colgan SP, Taylor CT. Hypoxia: an alarm signal during intestinal inflammation. Nat Rev Gastroenterol Hepatol. 2010;7(5):281–287. doi: 10.1038/nrgastro.2010.39. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Nizet V, Johnson RS. Interdependence of hypoxic and innate immune responses. Nat Rev Immunol. 2009;9(9):609–617. doi: 10.1038/nri2607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Campbell EL, et al. Transmigrating neutrophils shape the mucosal microenvironment through localized oxygen depletion to influence resolution of inflammation. Immunity. 2014;40(1):66–77. doi: 10.1016/j.immuni.2013.11.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Kumar BV, et al. Human tissue-resident memory T cells are defined by core transcriptional and functional signatures in lymphoid and mucosal sites. Cell Rep. 2017;20(12):2921–2934. doi: 10.1016/j.celrep.2017.08.078. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Hombrink P, et al. Programs for the persistence, vigilance and control of human CD8(+) lung-resident memory T cells. Nat Immunol. 2016;17(12):1467–1478. doi: 10.1038/ni.3589. [DOI] [PubMed] [Google Scholar]
- 24.Mackay LK, et al. Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science. 2016;352(6284):459–463. doi: 10.1126/science.aad2035. [DOI] [PubMed] [Google Scholar]
- 25.Atkuri KR, et al. Importance of culturing primary lymphocytes at physiological oxygen levels. Proc Natl Acad Sci U S A. 2007;104(11):4547–4552. doi: 10.1073/pnas.0611732104. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Buggert M, et al. Identification and characterization of HIV-specific resident memory CD8+ T cells in human lymphoid tissue. Sci Immunol. 2018;3(24):eaar4526. doi: 10.1126/sciimmunol.aar4526. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Mani V, et al. Migratory DCs activate TGF-β to precondition naïve CD8+ T cells for tissue-resident memory fate. Science. 2019;366(6462):eaav5728. doi: 10.1126/science.aav5728. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 28.Thome JJ, et al. Spatial map of human T cell compartmentalization and maintenance over decades of life. Cell. 2014;159(4):814–828. doi: 10.1016/j.cell.2014.10.026. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Labiano S, et al. CD69 is a direct HIF-1α target gene in hypoxia as a mechanism enhancing expression on tumor-infiltrating T lymphocytes. Oncoimmunology. 2017;6(4):e1283468. doi: 10.1080/2162402X.2017.1283468. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Hadley GA, et al. Regulation of the epithelial cell-specific integrin, CD103, by human CD8+ cytolytic T lymphocytes. Transplantation. 1999;67(11):1418–1425. doi: 10.1097/00007890-199906150-00005. [DOI] [PubMed] [Google Scholar]
- 31.Mokrani M, et al. Smad and NFAT pathways cooperate to induce CD103 expression in human CD8 T lymphocytes. J Immunol. 2014;192(5):2471–2479. doi: 10.4049/jimmunol.1302192. [DOI] [PubMed] [Google Scholar]
- 32.Weisberg SP, et al. Tissue-resident memory T cells mediate immune homeostasis in the human pancreas through the PD-1/PD-L1 pathway. Cell Rep. 2019;29(12):3916–3932.e5. doi: 10.1016/j.celrep.2019.11.056. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.Snyder ME, et al. Generation and persistence of human tissue-resident memory T cells in lung transplantation. Sci Immunol. 2019;4(33):eaav5581. doi: 10.1126/sciimmunol.aav5581. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Kopan R, Ilagan MX. The canonical Notch signaling pathway: unfolding the activation mechanism. Cell. 2009;137(2):216–233. doi: 10.1016/j.cell.2009.03.045. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Schenkel JM, et al. Sensing and alarm function of resident memory CD8+ T cells. Nat Immunol. 2013;14(5):509–513. doi: 10.1038/ni.2568. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Cheuk S, et al. CD49a expression defines tissue-resident CD8+ T cells poised for cytotoxic function in human skin. Immunity. 2017;46(2):287–300. doi: 10.1016/j.immuni.2017.01.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 37.Du N, et al. EGR2 is critical for peripheral naïve T-cell differentiation and the T-cell response to influenza. Proc Natl Acad Sci U S A. 2014;111(46):16484–16489. doi: 10.1073/pnas.1417215111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Yu B, et al. Epigenetic landscapes reveal transcription factors that regulate CD8(+) T cell differentiation. Nat Immunol. 2017;18(5):573–582. doi: 10.1038/ni.3706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Leonard MO, et al. Potentiation of glucocorticoid activity in hypoxia through induction of the glucocorticoid receptor. J Immunol. 2005;174(4):2250–2257. doi: 10.4049/jimmunol.174.4.2250. [DOI] [PubMed] [Google Scholar]
- 40.Strobl J, et al. Long-term skin-resident memory T cells proliferate in situ and are involved in human graft-versus-host disease. Sci Transl Med. 2020;12(570):eabb7028. doi: 10.1126/scitranslmed.abb7028. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Savas P, et al. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat Med. 2018;24(7):986–993. doi: 10.1038/s41591-018-0078-7. [DOI] [PubMed] [Google Scholar]
- 42.Kim Y, et al. Prognostic significance of CD103+ immune cells in solid tumor: a systemic review and meta-analysis. Sci Rep. 2019;9(1):3808. doi: 10.1038/s41598-019-40527-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 43.Smazynski J, Webb JR. Resident memory-like tumor-infiltrating lymphocytes (TILRM): latest players in the immuno-oncology repertoire. Front Immunol. 2018;9:1741. doi: 10.3389/fimmu.2018.01741. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 44.Duhen T, et al. Co-expression of CD39 and CD103 identifies tumor-reactive CD8 T cells in human solid tumors. Nat Commun. 2018;9(1):2724. doi: 10.1038/s41467-018-05072-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 45.Ganesan AP, et al. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat Immunol. 2017;18(8):940–950. doi: 10.1038/ni.3775. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 46.Arina A, et al. Tumor-reprogrammed resident T cells resist radiation to control tumors. Nat Commun. 2019;10(1):3959. doi: 10.1038/s41467-019-11906-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 47.Rockwell S, et al. Hypoxia and radiation therapy: past history, ongoing research, and future promise. Curr Mol Med. 2009;9(4):442–458. doi: 10.2174/156652409788167087. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 48.Bouquet F, et al. TGFβ1 inhibition increases the radiosensitivity of breast cancer cells in vitro and promotes tumor control by radiation in vivo. Clin Cancer Res. 2011;17(21):6754–6765. doi: 10.1158/1078-0432.CCR-11-0544. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 49.Tinoco R, et al. Cell-intrinsic transforming growth factor-beta signaling mediates virus-specific CD8+ T cell deletion and viral persistence in vivo. Immunity. 2009;31(1):145–157. doi: 10.1016/j.immuni.2009.06.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 50.Scharping NE, et al. Mitochondrial stress induced by continuous stimulation under hypoxia rapidly drives T cell exhaustion. Nat Immunol. 2021;22(2):205–215. doi: 10.1038/s41590-020-00834-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 51.Quigley M, et al. Transcriptional analysis of HIV-specific CD8+ T cells shows that PD-1 inhibits T cell function by upregulating BATF. Nat Med. 2010;16(10):1147–1151. doi: 10.1038/nm.2232. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 52.Gupta PK, et al. CD39 expression identifies terminally exhausted CD8+ T Cells. PLoS Pathog. 2015;11(10):e1005177. doi: 10.1371/journal.ppat.1005177. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 53.Aberle H. Axon guidance and collective cell migration by substrate-derived attractants. Front Mol Neurosci. 2019;12:148. doi: 10.3389/fnmol.2019.00148. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 54.Takamatsu H, et al. Regulation of immune cell responses by semaphorins and their receptors. Cell Mol Immunol. 2010;7(2):83–88. doi: 10.1038/cmi.2009.111. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 55.Poznansky MC, et al. Active movement of T cells away from a chemokine. Nat Med. 2000;6(5):543–548. doi: 10.1038/75022. [DOI] [PubMed] [Google Scholar]
- 56.Pocock R, Hobert O. Oxygen levels affect axon guidance and neuronal migration in Caenorhabditis elegans. Nat Neurosci. 2008;11(8):894–900. doi: 10.1038/nn.2152. [DOI] [PubMed] [Google Scholar]
- 57.Hachim IY, et al. Transforming growth factor-beta regulation of ephrin type-A receptor 4 signaling in breast cancer cellular migration. Sci Rep. 2017;7(1):14976. doi: 10.1038/s41598-017-14549-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Yi JJ, et al. TGF-beta signaling specifies axons during brain development. Cell. 2010;142(1):144–157. doi: 10.1016/j.cell.2010.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 59.Tamagnone L. Emerging role of semaphorins as major regulatory signals and potential therapeutic targets in cancer. Cancer Cell. 2012;22(2):145–152. doi: 10.1016/j.ccr.2012.06.031. [DOI] [PubMed] [Google Scholar]
- 60.Schenkel JM, et al. IL-15-independent maintenance of tissue-resident and boosted effector memory CD8 T cells. J Immunol. 2016;196(9):3920–3926. doi: 10.4049/jimmunol.1502337. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Holz LE, et al. CD8+ T cell activation leads to constitutive formation of liver tissue-resident memory T cells that seed a large and flexible niche in the liver. Cell Rep. 2018;25(1):68–79. doi: 10.1016/j.celrep.2018.08.094. [DOI] [PubMed] [Google Scholar]
- 62.Adachi T, et al. Hair follicle-derived IL-7 and IL-15 mediate skin-resident memory T cell homeostasis and lymphoma. Nat Med. 2015;21(11):1272–1279. doi: 10.1038/nm.3962. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 63.Frizzell H, et al. Organ-specific isoform selection of fatty acid-binding proteins in tissue-resident lymphocytes. Sci Immunol. 2020;5(46):eaay9283. doi: 10.1126/sciimmunol.aay9283. [DOI] [PubMed] [Google Scholar]
- 64.Fonseca R, et al. Developmental plasticity allows outside-in immune responses by resident memory T cells. Nat Immunol. 2020;21(4):412–421. doi: 10.1038/s41590-020-0607-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 65.Bromley SK, et al. CD49a regulates cutaneous resident memory CD8+ T cell persistence and response. Cell Rep. 2020;32(9):108085. doi: 10.1016/j.celrep.2020.108085. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 66.Epstein AC, et al. C. elegans EGL-9 and mammalian homologs define a family of dioxygenases that regulate HIF by prolyl hydroxylation. Cell. 2001;107(1):43–54. doi: 10.1016/S0092-8674(01)00507-4. [DOI] [PubMed] [Google Scholar]
- 67.Yoon H, et al. CITED2 controls the hypoxic signaling by snatching p300 from the two distinct activation domains of HIF-1α. Biochim Biophys Acta. 2011;1813(12):2008–2016. doi: 10.1016/j.bbamcr.2011.08.018. [DOI] [PubMed] [Google Scholar]
- 68.Bellot G, et al. Hypoxia-induced autophagy is mediated through hypoxia-inducible factor induction of BNIP3 and BNIP3L via their BH3 domains. Mol Cell Biol. 2009;29(10):2570–2581. doi: 10.1128/MCB.00166-09. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 69.Puleston DJ, et al. Autophagy is a critical regulator of memory CD8(+) T cell formation. Elife. 2014 doi: 10.7554/eLife.03706. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 70.Swadling L, et al. Human liver memory CD8+ T cells use autophagy for tissue residence. Cell Rep. 2020;30(3):687–698.e6. doi: 10.1016/j.celrep.2019.12.050. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 71.Jaakkola P, et al. Targeting of HIF-alpha to the von Hippel-Lindau ubiquitylation complex by O2-regulated prolyl hydroxylation. Science. 2001;292(5516):468–472. doi: 10.1126/science.1059796. [DOI] [PubMed] [Google Scholar]
- 72.Besarab A, et al. Randomized placebo-controlled dose-ranging and pharmacodynamics study of roxadustat (FG-4592) to treat anemia in nondialysis-dependent chronic kidney disease (NDD-CKD) patients. Nephrol Dial Transplant. 2015;30(10):1665–1673. doi: 10.1093/ndt/gfv302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 73.Yeh TL, et al. Molecular and cellular mechanisms of HIF prolyl hydroxylase inhibitors in clinical trials. Chem Sci. 2017;8(11):7651–7668. doi: 10.1039/C7SC02103H. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 74.Islam MS, et al. 2-oxoglutarate-dependent oxygenases. Annu Rev Biochem. 2018;87(1):585–620. doi: 10.1146/annurev-biochem-061516-044724. [DOI] [PubMed] [Google Scholar]
- 75.Kurd NS, et al. Early precursors and molecular determinants of tissue-resident memory CD8+ T lymphocytes revealed by single-cell RNA sequencing. Sci Immunol. 2020;5(47):eaaz6894. doi: 10.1126/sciimmunol.aaz6894. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 76.Hayward SL, et al. Environmental cues regulate epigenetic reprogramming of airway-resident memory CD8+ T cells. Nat Immunol. 2020;21(3):309–320. doi: 10.1038/s41590-019-0584-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 77.FitzPatrick MEB, et al. Human intestinal tissue-resident memory T cells comprise transcriptionally and functionally distinct subsets. Cell Rep. 2021;34(3):108661. doi: 10.1016/j.celrep.2020.108661. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 78.Wong MT, et al. A high-dimensional atlas of human T cell diversity reveals tissue-specific trafficking and cytokine signatures. Immunity. 2016;45(2):442–456. doi: 10.1016/j.immuni.2016.07.007. [DOI] [PubMed] [Google Scholar]
- 79.De la Garza MM, et al. COPD-Type lung inflammation promotes K-ras mutant lung cancer through epithelial HIF-1α mediated tumor angiogenesis and proliferation. Oncotarget. 2018;9(68):32972–32983. doi: 10.18632/oncotarget.26030. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 80.Le QT, et al. An evaluation of tumor oxygenation and gene expression in patients with early stage non-small cell lung cancers. Clin Cancer Res. 2006;12(5):1507–1514. doi: 10.1158/1078-0432.CCR-05-2049. [DOI] [PubMed] [Google Scholar]
- 81.Polosukhin VV, et al. Association of progressive structural changes in the bronchial epithelium with subepithelial fibrous remodeling: a potential role for hypoxia. Virchows Arch. 2007;451(4):793–803. doi: 10.1007/s00428-007-0469-5. [DOI] [PubMed] [Google Scholar]
- 82.Carreau A, et al. Why is the partial oxygen pressure of human tissues a crucial parameter? Small molecules and hypoxia. J Cell Mol Med. 2011;15(6):1239–1253. doi: 10.1111/j.1582-4934.2011.01258.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 83.Koh MY, Powis G. Passing the baton: the HIF switch. Trends Biochem Sci. 2012;37(9):364–372. doi: 10.1016/j.tibs.2012.06.004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 84.Evans SM, et al. Oxygen levels in normal and previously irradiated human skin as assessed by EF5 binding. J Invest Dermatol. 2006;126(12):2596–2606. doi: 10.1038/sj.jid.5700451. [DOI] [PubMed] [Google Scholar]
- 85.Beura LK, et al. T cells in nonlymphoid tissues give rise to lymph-node-resident memory T cells. Immunity. 2018;48(2):327–338. doi: 10.1016/j.immuni.2018.01.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 86.Hirai T, et al. Keratinocyte-mediated activation of the cytokine TGF-β maintains skin recirculating memory CD8+ T Cells. Immunity. 2019;50(5):1249–1261. doi: 10.1016/j.immuni.2019.03.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 87.Kelly A, et al. Human monocytes and macrophages regulate immune tolerance via integrin αvβ8-mediated TGFβ activation. J Exp Med. 2018;215(11):2725–2736. doi: 10.1084/jem.20171491. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 88.Mohammed J, et al. Stromal cells control the epithelial residence of DCs and memory T cells by regulated activation of TGF-β. Nat Immunol. 2016;17(4):414–421. doi: 10.1038/ni.3396. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 89.Bourdely P, et al. Transcriptional and functional analysis of CD1c(+) human dendritic cells identifies a CD163(+) subset priming CD8(+)CD103(+) T cells. Immunity. 2020;53(2):335–352. doi: 10.1016/j.immuni.2020.06.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 90.Qin Y, et al. A milieu molecule for TGF-β required for microglia function in the nervous system. Cell. 2018;174(1):156–171. doi: 10.1016/j.cell.2018.05.027. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 91.Watanabe R, et al. Human skin is protected by four functionally and phenotypically discrete populations of resident and recirculating memory T cells. Sci Transl Med. 2015;7(279):279ra39. doi: 10.1126/scitranslmed.3010302. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 92.Borges da Silva H, et al. ARTC2.2/P2RX7 signaling during cell isolation distorts function and quantification of tissue-resident CD8(+) T cell and invariant NKT Subsets. J Immunol. 2019;202(7):2153–2163. doi: 10.4049/jimmunol.1801613. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 93.Amoroso F, et al. The P2X7 receptor is a key modulator of aerobic glycolysis. Cell Death Dis. 2012;3:e370. doi: 10.1038/cddis.2012.105. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 94.Borges da Silva H, et al. The purinergic receptor P2RX7 directs metabolic fitness of long-lived memory CD8. Nature. 2018;559(7713):264–268. doi: 10.1038/s41586-018-0282-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 95.Doedens AL, et al. Hypoxia-inducible factors enhance the effector responses of CD8(+) T cells to persistent antigen. Nat Immunol. 2013;14(11):1173–1182. doi: 10.1038/ni.2714. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 96.Phan AT, et al. Constitutive glycolytic metabolism supports CD8+ T cell effector memory differentiation during viral infection. Immunity. 2016;45(5):1024–1037. doi: 10.1016/j.immuni.2016.10.017. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 97.Borges da Silva H, et al. Sensing of ATP via the purinergic receptor P2RX7 promotes CD8(+) Trm cell generation by enhancing their sensitivity to the cytokine TGF-beta. Immunity. 2020;53(1):158–171. doi: 10.1016/j.immuni.2020.06.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 98.Dimeloe S, et al. Tumor-derived TGF-β inhibits mitochondrial respiration to suppress IFN-γ production by human CD4+ T cells. Sci Signal. 2019;12(599):eaav3334. doi: 10.1126/scisignal.aav3334. [DOI] [PubMed] [Google Scholar]
- 99.Priyadharshini B, et al. Cutting edge: TGF-β and phosphatidylinositol 3-kinase signals modulate distinct metabolism of regulatory T cell subsets. J Immunol. 2018;201(8):2215–2219. doi: 10.4049/jimmunol.1800311. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 100.Abd Hamid M, , et al Self-maintaining CD103+ cancer-specific T cells are highly energetic with rapid cytotoxic and effector responses. Cancer Immunol Res. 2020;8(2):203–216. doi: 10.1158/2326-6066.CIR-19-0554. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 101.Mascanfroni ID, et al. Metabolic control of type 1 regulatory T cell differentiation by AHR and HIF1-α. Nat Med. 2015;21(6):638–646. doi: 10.1038/nm.3868. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 102.Finlay DK, et al. PDK1 regulation of mTOR and hypoxia-inducible factor 1 integrate metabolism and migration of CD8+ T cells. J Exp Med. 2012;209(13):2441–2453. doi: 10.1084/jem.20112607. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 103.Palazon A, et al. The HIF-1α hypoxia response in tumor-infiltrating T lymphocytes induces functional CD137 (4-1BB) for immunotherapy. Cancer Discov. 2012;2(7):608–623. doi: 10.1158/2159-8290.CD-11-0314. [DOI] [PubMed] [Google Scholar]
- 104.McMahon S, et al. Transforming growth factor beta1 induces hypoxia-inducible factor-1 stabilization through selective inhibition of PHD2 expression. J Biol Chem. 2006;281(34):24171–24181. doi: 10.1074/jbc.M604507200. [DOI] [PubMed] [Google Scholar]
- 105.Chou YT, Yang YC. Post-transcriptional control of Cited2 by transforming growth factor beta. Regulation via Smads and Cited2 coding region. J Biol Chem. 2006;281(27):18451–18462. doi: 10.1074/jbc.M601720200. [DOI] [PubMed] [Google Scholar]
- 106.Coulibaly A, et al. Interleukin-15 signaling in HIF-1α regulation in natural killer cells, insights through mathematical models. Front Immunol. 2019;10:2401. doi: 10.3389/fimmu.2019.02401. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 107.Jung Y, et al. Hypoxia-inducible factor induction by tumour necrosis factor in normoxic cells requires receptor-interacting protein-dependent nuclear factor kappa B activation. Biochem J. 2003;370(pt 3):1011–1017. doi: 10.1042/BJ20021279. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 108.Lopez-Cabrera M, et al. Transcriptional regulation of the gene encoding the human C-type lectin leukocyte receptor AIM/CD69 and functional characterization of its tumor necrosis factor-alpha-responsive elements. J Biol Chem. 1995;270(37):21545–21551. doi: 10.1074/jbc.270.37.21545. [DOI] [PubMed] [Google Scholar]
- 109.Fang F, et al. The early growth response gene Egr2 (Alias Krox20) is a novel transcriptional target of transforming growth factor-β that is up-regulated in systemic sclerosis and mediates profibrotic responses. Am J Pathol. 2011;178(5):2077–2090. doi: 10.1016/j.ajpath.2011.01.035. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 110.Malik BT, et al. Resident memory T cells in the skin mediate durable immunity to melanoma. Sci Immunol. 2017;2(10):eaam6346. doi: 10.1126/sciimmunol.aam6346. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 111.Park SL, et al. Tissue-resident memory CD8(+) T cells promote melanoma-immune equilibrium in skin. Nature. 2019;565(7739):366–371. doi: 10.1038/s41586-018-0812-9. [DOI] [PubMed] [Google Scholar]
- 112.Nizard M, et al. Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nat Commun. 2017;8:15221. doi: 10.1038/ncomms15221. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 113.Sandoval F, et al. Mucosal imprinting of vaccine-induced CD8+ T cells is crucial to inhibit the growth of mucosal tumors. Sci Transl Med. 2013;5(172):172ra20. doi: 10.1126/scitranslmed.3004888. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 114.Edwards J, et al. CD103+ Tumor-resident CD8+ T cells are associated with improved survival in immunotherapy-naïve melanoma patients and expand significantly during anti-PD-1 treatment. Clin Cancer Res. 2018;24(13):3036–3045. doi: 10.1158/1078-0432.CCR-17-2257. [DOI] [PubMed] [Google Scholar]
- 115.Kim D, et al. HISAT: a fast spliced aligner with low memory requirements. Nat Methods. 2015;12(4):357–360. doi: 10.1038/nmeth.3317. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 116.Love MI, et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15(12):550. doi: 10.1186/s13059-014-0550-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 117.Nath AP, et al. Comparative analysis reveals a role for TGF-β in shaping the residency-related transcriptional signature in tissue-resident memory CD8+ T cells. PLoS One. 2019;14(2):e0210495. doi: 10.1371/journal.pone.0210495. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 118.Leek JT. svaseq: removing batch effects and other unwanted noise from sequencing data. Nucleic Acids Res. 2014;42(21):e161. doi: 10.1093/nar/gku864. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 119.Eisenberg E, Levanon EY. Human housekeeping genes, revisited. Trends Genet. 2013;29(10):569–574. doi: 10.1016/j.tig.2013.05.010. [DOI] [PubMed] [Google Scholar]
- 120.Mi H, et al. PANTHER version 16: a revised family classification, tree-based classification tool, enhancer regions and extensive API. Nucleic Acids Res. 2020;49(d1):D394–D403. doi: 10.1093/nar/gkaa1106. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.