Signatures and Specificity of Tissue-Resident Lymphocytes Identified in Human Renal Peritumor and Tumor Tissue : Journal of the American Society of Nephrology

Journal Logo

Basic Research

Signatures and Specificity of Tissue-Resident Lymphocytes Identified in Human Renal Peritumor and Tumor Tissue

Dornieden, Theresa1; Sattler, Arne1; Pascual-Reguant, Anna2; Ruhm, Annkathrin Helena1; Thiel, Lion Gabriel1; Bergmann, Yasmin Samira1; Thole, Linda Marie Laura1; Köhler, Ralf2; Kühl, Anja Andrea4; Hauser, Anja Erika2,3; Boral, Sengül5; Friedersdorff, Frank6; Kotsch, Katja1

Author Information
JASN 32(9):p 2223-2241, September 2021. | DOI: 10.1681/ASN.2020101528
  • Free
  • Infographic
  • SDC


Currently, the identification and description of CD8+ tissue-resident memory T (TRM) cells in various tissues are attracting increasing attention.12–3 Originally, TRM cells were identified primarily at barrier sites, such as mucosal tissue or skin, but their presence has meanwhile been documented in a number of nonmucosal sites, including lymphoid and peripheral tissues such as thymus, skin, spleen, liver, pancreas, heart, and brain.45–6 In general, CD8+ TRM cells have been characterized by expression of the integrin-αE (CD103), the activation/retention marker CD69, and the collagen-binding integrin-α1 (CD49a). They are described as a distinct memory population generated at the site of infection, where their main function relies on their strategic localization and scanning of barrier tissues, thus allowing early detection of reinfection by pathogens and possibly constituting the first line of cellular defense.78910–11

Although the human kidney can be affected by bacterial pathogens generally resulting from urinary tract infections, it does not represent a typical border tissue, such as lung or intestine. TRM cells have been already mentioned as being present in resected kidneys and transplant nephrectomies, illustrating that both donor-resident CD8+ T cells as well as graft-infiltrating recipient-derived CD8+ T cells display a TRM phenotype.12,13 However, no detailed investigation of CD8+ TRM cells in human kidneys and their potential relevance with respect to renal pathology is available so far. We, therefore, performed a comprehensive analysis of tissue-resident T cell subsets in the kidney and provide detailed information on their phenotype, effector profile, and antigen specificity.



Macroscopic portions of kidney tumor tissue and adjacent peritumor tissue as well as paired blood samples were collected from patients undergoing nephrectomy at the Clinic for Urology, Charité–Universitätsmedizin Berlin, Germany. The study was approved by the Ethics Committee of Charité–Universitätsmedizin Berlin (EA1/353/16 and EA4/066/19), and all experiments described in this study were conducted in compliance with the Declarations of Helsinki and Istanbul. In total, 62 patients were included in this study. GFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration creatinine equation. Patient demographics are summarized in Table 1.

Table 1. - Patient demographics
Variable n (%) Minimum/ Maximum Mean SD
Total no. of included patients 62 (100)
Age, yr 26/85 63.92 13.58
Sex, men/women 40 (64.52)/22 (35.48)
Cause of nephrectomy
 Renal cell carcinoma 46 (74.19)
 Ureter tumor 7 (11.29)
 Oncocytoma 1 (1.61)
 Nephrolithiasis 1 (1.61)
 Impaired function 4 (6.45)
 Cystic kidney 3 (4.84)
Preoperative kidney function (eGFR, CKD-EPI)
 Stage 1, ≥90 ml/min 8 (12.90)
 Stage 2, 60–90 ml/min 24 (38.71)
 Stage 3, 30–60 ml/min 17 (27.42)
 Stage 4, 15–30 ml/min 2 (3.23)
 Stage 5, <15 ml/min 5 (8.06)
 ns 6 (9.68)
Further diagnoses
 Hypertension 37 (59.98)
 CKD 17 (27.42)
 Diabetes DM1: 1 (1.61); DM2: 12 (19.35)
 Cardiac disease 11 (17.74)
Staging by size of tumor
 T1 (thereof 1a, 1b) 23 (50.00 a );11 (23.91 a ), 13 (28.26 a )
 T2 (all 2a) 8 (17.39 a )
 T3 (all 3a) 13 (28.26 a )
 T4 2 (4.35 a )
Relapse of patients with tumors
 Renal cell carcinoma remained kidney 1 (2.17 a )
 Lung + lymph nodes 1 (2.17 a )
 Urinary bladder 1 (2.17 a )
 Liver 1 (2.17 a )
 Larynx 1 (2.17 a )
 Prostate 1 (2.17 a )
 No 40 (86.96 a )
Metastases diagnosed before nephrectomy
 Pulmonary metastases 3 (6.52 a )
 Osseous metastases 2 (4.35 a )
 Adrenal gland metastases 1 (2.17 a )
 Lymph node metastases 2 (4.35 a )
 Lymph node + pulmonary metastases 1 (2.17 a )
 No 37 (80.43 a )
CKD-EPI, Chronic Kidney Disease Epidemiology Collaboration; ns, not significant; DM1, diabetes mellitus 1; DM2, diabetes mellitus 2.
aPercentage of n=46 (patients with tumors).

Cell Isolation and Flow Cytometry

Resected peritumor and tumor tissues were perfused with 0.9% NaCl and immediately processed. To obtain single-cell suspensions, tissue was dissected into small pieces. Digestion medium was added, consisting of RPMI 1640 medium supplemented with 0.3 mg/ml glutamine, 10% FCS, 1% penicillin-streptomycin, 1 mg/ml collagenase II, and 10 U/ml DNAse I. Samples were incubated for 45 minutes at 37°C in a shaking water bath followed by separation through a 100-µm cell strainer. Subsequently, mononuclear cells from kidney and blood were isolated with LEUCO Human Separating Solution (Genaxxon Bioscience GmbH, Ulm, Germany) by means of density gradient centrifugation. For analysis of surface molecules, 1 × 106 mononuclear cells were stained with the antibodies listed in Supplemental Table 1. For intracellular staining, cells were fixed, permeabilized (Fixation Permeabilization Kit; Thermo Fisher, Darmstadt, Germany), and stained with respective antibodies (Supplemental Table 1). Cells were measured with a BD FortessaX20 flow cytometer (BD Bioscience, Heidelberg, Germany). FACS data analysis was conducted using FlowJo software 10.0 (Tree Star Inc., Ashland, OR). FlowSOM analysis, viSNE plots, and heat maps were generated using the Cytobank platform (Cytobank Inc., Santa Clara, CA). Following dimensionality reduction with viSNE, eight to 11 metaclusters were generated in FlowSOM. As per Cytobank recommendations, the number of clusters and metaclusters was empirically honed until a good resolution and correspondence between the FlowSOM metaclustering and the viSNE islands was reached. Kidney-specific metaclusters were identified and annotated according to their marker expression. A manual gating strategy for identification of the various lymphocyte subsets is illustrated in Supplemental Figure 1A. All FACS datasets were uploaded in the public repository Zenodo (; DOI 10.5281/zenodo.4550735).

Analysis of Antigen-Specific T Cells

For detection of antigen-specific cells, PE-labeled HLA-A*0201 MHC I dextramers containing immunodominant epitopes of polyomavirus-type BK (BKV), influenza virus, Epstein–Barr virus (EBV), and cytomegalovirus (CMV; Immudex, Copenhagen, Denmark) were used (Supplemental Table 2). Peripheral blood and renal mononuclear cells of HLA-A*02–positive patients were stained with multimers according to the manufacturer’s protocol. For phenotypic analysis, cells were additionally stained with a mixture of surface antibodies (Supplemental Table 3). PE-labeled control multimers (Immudex) were used to determine gate limits.

Cell Stimulation and Functional Analysis

For functional analysis, 4 × 106 cells were stained with CD69 antibody for 20 minutes at room temperature (RT) before stimulation with PMA (50 ng/ml) and ionomycin (1 μg/ml) for 6 hours at 37°C and 5% CO2 in the presence of 10 μg/ml brefeldin A. After stimulation, cells were stained with a surface antibody mixture for 20 minutes at RT, fixed, permeabilized (Fixation Permeabilization Kit; BD Bioscience, Heidelberg, Germany), and stained with antibodies against intracellular molecules (Supplemental Figure 1B, Supplemental Table 4).

Tissue Preparation for Multiepitope Ligand Cartography

Multi-epitope ligand cartography (MELC) analysis was performed as described recently.14 Fresh frozen tissue was cut into 5-µm sections using an NX80 cryotome (ThermoFisher, Waltham, MA) on APES-coated cover slides (24 × 60 mm; Menzel-Gläser, Braunschweig, Germany). Samples were then fixed for 10 minutes at RT with 2% paraformaldehyde (methanol and RNAse free; Electron Microscopy Sciences, Hatfield, PA). After washing, samples were permeabilized with 0.2% Triton X-100 in PBS for 10 minutes at RT and blocked with 10% goat serum and 1% BSA in PBS for at least 20 minutes. Afterward, a fluid chamber holding 100 μl of PBS was created using “press-to-seal” silicone sheets (Life Technologies, Carlsbad, CA; 1.0-mm thickness), which were attached to the coverslip, surrounding the sample.

MELC Image Acquisition

A modified Toponome Image Cycler MM3 (MelTec GmbH & Co. KG, Magdeburg, Germany) was used. MELC image acquisition was performed as previously shown.15 This robotic microscopic system consists of (1) an inverted wide-field (epi-)fluorescence microscope (Leica DM IRE2) equipped with a CMOS camera and a motor-controlled XY stage, (2) a CAVRO XL3000 Pipette/Diluter (Tecan GmbH, Crailsheim, Germany), and (3) the MelTec TIC-Control software for controlling the microscope and pipetting system and for synchronized image acquisition. An MELC run consists of a sequence of cycles, each containing the following four steps: (1) incubation of the fluorescence-coupled antibody with subsequent washing, (2) crosscorrelation-based autofocusing followed by acquisition of the fluorescence image three-dimensional (3D) stack (plus or minus seven z steps), (3) photobleaching of the fluorophore, and (4) a second autofocusing step followed by acquisition of a 3D stack after bleaching the fluorescence image. In each four-step cycle, another fluorescence-labeled antibody is used. Steric hindrance issues that might appear due to this specific labeling order have been ruled out as previously shown.15 Antibodies used for multiplexed immunofluorescence histology of kidney samples were stained in the indicated order as listed in Supplemental Table 5 (Supplemental Figure 2).

Image Preprocessing

All images were aligned on the basis of the reference phase contrast image taken at the beginning of measurement. Thereafter, each fluorescence MELC image was processed by background subtraction and illumination correction on the basis of bleaching images.16 Then, an “Extended Depth of Field” algorithm was applied to the 3D fluorescence stack in each cycle.17 Images were then normalized in ImageJ,18 where a rolling ball algorithm was used for background estimation,19 edges were removed (accounting for the maximum allowed shift during the autofocus procedure), and fluorescence intensities were stretched to the full-intensity range (16 bit→216). The two-dimensional fluorescence images generated in this way were subsequently segmented and analyzed.

Cell Segmentation and Single-Cell Feature Extraction

Segmentation was performed in a two-step process—a signal classification step using Ilastik 1.3.220 followed by an object recognition step using CellProfiler 3.1.8,21 as described elsewhere.22 Ilastik was used to classify pixels in three classes (nuclei, membrane, and extracellular matrix) and to generate probability maps for each class. A combination of images was summed up and used to classify membranes and extracellular matrix, whereas only the DAPI image was used to classify nuclei. The random forest algorithm (machine learning; Ilastik) was trained by manual pixel classification in a small region of a dataset (approximately 6% of the image). The rest of the dataset as well as seven extra datasets analyzed here was classified without retraining using the random forest algorithm. CellProfiler was subsequently used to segment the nuclei and membrane probability maps and to generate nuclei and cellular object masks, respectively. These masks were superimposed on the individual fluorescence images acquired for each marker in order to extract single-cell information (i.e., mean fluorescent intensity of each marker per segmented cell).

Cluster Analysis

Segmented cells were analyzed in Orange 3.26.023 using several algorithms for dimensionality reduction that use the list of mean fluorescent intensity values as single-cell features. K-means clustering of the kidney single-cell data was performed for all segmented cells from eight kidney datasets pooled together (four tumor and four patient-matched peritumor samples); t-distributed stochastic neighbor embedding of CD69+CD103+ tissue-resident cells was performed with eight principal components and a perplexity of 30 and 1000 iterations.

Data Transformation and Normalization

Fluorescence intensities per pixel were normalized to the full 16-bit range in ImageJ and brought to a zero to one scale in CellProfiler. Data were transformed using the hyperbolic arcsine function with a scale argument of 0.2 previous to cluster analysis. All datasets for MELC were uploaded in the public repository Zenodo (; DOIs: 10.5281/zenodo.4434363, 10.5281/zenodo.4434454, 10.5281/zenodo.4434462, 10.5281/zenodo.4434473, 10.5281/zenodo.4434504, 10.5281/zenodo.4434522, 10.5281/zenodo.4434545, 10.5281/zenodo.4434560, and 10.5281/zenodo.4434596).

Statistical Analyses

Statistical analysis was performed using GraphPad Prism 8 (GraphPad Software, La Jolla, CA). Data were tested for normal distribution using the Kolmogorov–Smirnov test. For comparison of two groups, a paired t test or a Wilcoxon signed ranks test was performed for paired samples; for unpaired groups, a Mann–Whitney U test or an unpaired t test was performed. For comparison of three or four groups, a Friedman test or one-way ANOVA was used for paired samples, as well as ordinary one-way ANOVA or the Kruskal–Wallis test for unpaired groups (Supplemental Table 6). Statistical significance was considered for P=0.05.


Effector Memory T Cells Are Enriched in the Kidney Displaying a Proinflammatory Phenotype

First, we analyzed frequencies of bulk CD8+ and CD4+ T cells in resected peritumor kidney tissues and paired blood samples by flow cytometry. Significantly more CD3+CD8+ T cells and double-positive CD3+CD4+CD8+ T cell frequencies were detected in the kidney, whereas CD3+CD4+ T cells were enriched in the blood. No differences regarding the proliferation status of CD4+ and CD8+ T cells reflected by their Ki67 expression were identified (Supplemental Figure 3, A and B). Using a viSNE algorithm approach for dimensional reduction of selected markers analyzed, a clear differentiation between blood and kidney was detected. Both CD4+ and CD8+ T cells derived from blood or kidney were further highlighted for the various surface molecules (Figure 1A). A detailed focus on T cell subset differentiation revealed that both CD8+ and CD4+CD45RACD62L effector memory T (TEM) cells were enriched in the kidney, whereas significantly more naïve CD8+ and CD4+CD45RA+CD62L+ as well as CD8+ and CD4+CD45RACD62L+ central memory T cells were detected in blood samples (Figure 1, A and B, Supplemental Figure 3C). The viSNE map indicated that renal CD8+ was characterized by an elevated HLA-DR expression pointing toward an activated phenotype as well as by higher levels of the coinhibitory molecule PD-1 as compared with peripheral blood (Figure 1A). A detailed analysis using manual gating further revealed higher frequencies of the small CTLA-4+ population within kidney-derived CD8+ and CD4+ T cells and a higher percentage of CD4+ T cells negative for CD28 in the kidney (Figure 1C), being partially supported by viSNE (Figure 1A). We further applied FlowSOM,24 an unsupervised technique for clustering and dimensionality reduction of FACS data. Generated viSNE maps were overlayed with metaclusters identified in FlowSOM, allowing the identification of multiple T cell subsets present in the kidney but not in blood (e.g., on the basis of memory and/or tissue residency signatures) (Supplemental Figure 4). With respect to their functional profile, renal CD8+ and CD4+ T cells produced significantly more IL-17, and especially, CD4+ T cells produced significantly more IFNγ and TNFα than did T cells from the peripheral blood (Figure 1D). Kidney-derived CD8+ and CD4+ T cells displayed significantly increased expression of the α1-subunit of α1β1-integrin, CD49a, a marker originally described on a minor fraction of kidney-resident natural killer (NK) cells.25 Both CD49a+ subsets produced significantly more IL-17 and less granzyme B, whereas CD49a+CD4+ T cells were additionally characterized by higher IFNγ, IL-2, and TNFα expression than were their CD49a T cell counterparts (Supplemental Figure 5A and B).

Figure 1.:
Increased frequencies of effector memory T cells reside in the human kidney. (A) viSNE plots of flow cytometric analysis of viable CD8+ and CD4+ T cells illustrate the separate clustering of kidney and blood. Individual plots for measured markers (CD45RA, CD62L, CD28, HLA-DR, PD-1, CTLA4) are displayed for both kidney- and blood-derived CD8+ and CD4+ T cells using the viSNE algorithm. Expression is shown by color coding in relative intensity. viSNE plots were generated from concatenated FCS files and gated on CD8+ and CD4+ T cells. Displayed markers were measured in three different panels, where three different viSNE plots were generated (CD45RA/CD62L/CD28: n=19, considering CD103, CD28, CD45RA, CD49a, CD62L, CD69, NKG2D, Va7.2 [Panel 1]; HLA-DR/PD-1: n=19, considering CD103, CD161, CD45RO, CD69, HLA-DR, NKG2D, PD-1, Va7.2 [Panel 2]; CTLA4: n=8, considering CD103, CD161, CD25, CD45RA, CD62L, CD69, CTLA-4, FoxP3, HLA-DR, Ki67 [Panel 3]). (B) Frequencies of CD45RACD62L+ central memory T, CD45RA+CD62L terminally differentiated effector, CD45RACD62L TEM, and CD45RA+CD62L+ naïve CD8+ and CD4+ T cells isolated from paired blood and kidney samples (n=29). (C) Frequencies of HLA-DR+ (n=19), PD-1+ (n=19), CTLA4+ (n=8), and CD28 (n=19) CD8+ and CD4+ T cells derived from paired blood and kidney samples. (D) Cytokine effector profile (granzyme B [GrB], IFNγ, IL-2, IL-17, TNFα) of CD8+ (n=27) and CD4+ (n=28) T cells derived from kidney tissue and paired blood samples. Statistically significant differences were tested with the two-tailed paired t or the Wilcoxon signed ranks test and are presented as means ± SD. In all tests, a value of P=0.05 was considered significant.

TRM Cells Reside in the Kidney

The presence of tissue residency for CD8+ memory T cells has been associated with local protection after antigen exposure in multiple tissues.78–9,26 In order to clarify the presence of TRM cells in the kidney, we investigated classic tissue-resident markers, including KLRG1, CD103, and CD69, for CD4+ and CD8+ T cells. Compared with blood, significantly higher frequencies of renal KLRG1CD103+ and double-positive KLRG1+CD103+CD8+ T cells were detected, whereas frequencies of KLRG1+CD103CD8+ T cells were decreased. On the contrary, all investigated subsets for CD4+ T cells were enriched in the kidney (Supplemental Figure 6, A and B). It has been demonstrated that KLRG1+ effector CD8+ T cells lose KLRG1 expression after receiving activating signals.27 Especially KLRG1-activated CD8+ T cells can develop into CD103+CD8+ TRM cells in the skin.28 The analysis by viSNE revealed a clear induction of CD69 and CD103 on CD4+ and CD8+ T cells, thus documenting their TRM phenotype in the kidney (Figure 2A). Interestingly, an enrichment for CD69CD103+ single-positive cells was solely observed for renal CD8+ T cells (Figure 2, A and B, Supplemental Figure 6C). Applying MELC, we confirmed the presence of renal CD8+ and CD4+ TRM cells in situ on the basis of the coexpression of CD45RO, CD69, CD103, and CD49a in CD3+CD4+ and CD3+CD8+ T cell subpopulations (Figure 2C).

Figure 2.:
Tissue-resident (TRM) cells can be identified within the human kidney. (A) viSNE plots of flow cytometric analysis of viable CD8+ and CD4+ T cells showing separate clustering of kidney and blood. Individual plots of measured TRM markers (CD69 and CD103, n=19) are displayed for both kidney- and blood-derived CD8+ and CD4+ T cells using viSNE algorithm. Expression is shown by color coding in relative intensity. viSNE plots were generated from concatenated FCS files and gated on CD8+ and CD4+ T cells considering CD103, CD161, CD45RO, CD69, HLA-DR, NKG2D, PD-1, and Va7.2 (Panel 2). tSNE, t-distributed stochastic neighbor embedding. (B) CD8+ and CD4+ kidney-derived T cells express the tissue-resident markers CD103 and CD69 as compared with their blood-derived counterparts (n=28). Statistically significant differences were tested with the two-tailed Wilcoxon signed ranks test. (C) Overlay of MELC fluorescent images showing marker combinations that allow the identification of TRM cells in the peritumor tissue of the kidney. (Upper panel) CD3 (blue), CD8 (red), and DAPI (green) staining is shown for the whole field of view (FOV). DAPI (green) is exchanged for CD69, CD103, CD45RO, or CD49a in a region of interest (ROI), where CD8+ TRM cells are marked with white arrows. Scale bar: 100 µm in the left panel; 20 µm in the right panels. (Lower panel) CD3 (blue), CD4 (red), and DAPI (green) staining is shown for the whole FOV. DAPI (green) is exchanged for CD69, CD103, CD45RO, or CD49a in an ROI, where CD4+ TRM cells are marked with white arrows. Data represent one of four independent experiments.

Renal TRM Cells Exhibit an Activated Phenotype

Both heat map analysis and manual gating revealed that CD69+CD103+ TRM and CD69+ single-positive TRM cells were characterized by significantly stronger expression of HLA-DR and PD-1 than were CD69CD103 T cells (Figure 3A). Moreover, CD4+ and CD8+ TRM cells showed a loss of CD28, indicating their antigen experience and differentiation. Interestingly, HLA-DR expression on single-positive CD69+CD103CD8+ TRM cells discriminates this subset from double-negative CD69CD103CD8+ T cells, whereas PD-1 is significantly more frequently expressed on CD69+CD103CD4+ TRM versus CD69CD103CD4+ T cells (Figure 3, A and B). In comparison with the other subsets analyzed, CD69+CD103+CD8+ TRM cells showed a significant reduction in frequencies expressing the cytotoxic mediator granzyme B but an enrichment of IL-2–, IL-17–, and TNFα-expressing cells, whereas no difference was detected for IFNγ (Figure 3C). CD69+CD103+CD4+ TRM cells displayed a higher proliferative capacity as reflected by Ki67 expression than did CD69+CD103CD4+ TRM cells, an observation not confirmed for CD8+ TRM cells (Supplemental Figure 7A). Renal CD69+CD103+CD8+ TRM cells were negative for CD45RA and CD62L, being indicative for TEM cells, and expressed significantly higher levels of CD49a than did CD69+CD103CD8+ TRM cells (Supplemental Figure 7, B and C).

Figure 3.:
Tissue-resident CD8+ TRM cells in the kidney demonstrate an activated phenotype and accumulate in the aged kidney. (A) Heat map plot of flow cytometric analysis for renal peritumor CD8+ and CD4+ TRM cell populations illustrating mean relative expression levels of measured markers (CD8, CD4, CD69, CD103, HLA-DR, and PD-1 [all n=19]; CTLA4 [n=8]; CD28 [n=19]). (B) Renal peritumor CD69+CD103+, CD69+CD103, CD69CD103CD8+, and CD4+ T cells were analyzed for expression of HLA-DR (n=19), PD-1 (n=19), or CTLA-4 (n=8) or loss of CD28 (n=19). (C) Their functional profile after stimulation with PMA and ionomycin (granzyme B [GrB], IFNγ, IL-2, IL-17, and TNFα; n=21). Statistically significant differences were tested with one-way ANOVA or Friedman test and are presented as mean values ± SD. (D) Correlation analysis of both peritumor-derived CD8+CD69+CD103+ and CD8+CD69+CD103 cell frequencies with patient age (n=35). Linear regression analysis was performed, and Spearman rank-order coefficients were calculated. (E) Patients were divided according to their stages of CKD by their eGFR (stage 1: n=4; stage 2: n=18; stage 3: n=13; stage 4: n=0; and stage 5: n=3). Peritumor-derived CD8+ and CD4+CD69+CD103+ TRM cell frequencies were compared between the five groups. Statistically significant differences were tested with ordinary one-way ANOVA or the Kruskal–Wallis test and are presented as mean values ± SD. Additionally, a correlation analysis of the same cell population frequencies with patient eGFR (milliliters per minute; n=42) is illustrated. Linear regression analysis was performed, and Spearman rank-order coefficients were calculated.

Kidney-Resident CD8+ TRM Cells Accumulate in the Aged Kidney

As it has been proposed that TRM cells accumulate in tissues mediating local immunity,29 we hypothesized that advanced age leads to an accumulation of TRM in the kidney as with immunosenescence the pool of memory T cells increases. On the basis of n=35 patients in our cohort, we defined two age groups according to the calculated mean age of the patients (63 years): n=13 being ≤63 years (Group I) or n=22 being >63 years (Group II). Interestingly, kidneys with an advanced age (Group II) displayed significantly higher frequencies of peritumor-derived CD69+CD103+ as well as CD69+CD103CD8+ T cells than did Group I kidneys. In contrast, this result was not detected for CD69+CD103+CD4+ T cells (Supplemental Figure 8, A and B). An increase of intrarenal peritumor frequencies of both CD69+CD103+ and CD69+CD103CD8+ T cells significantly correlated with increased age (Figure 3D). Intriguingly, patients diagnosed with CKD stage 2 display significantly higher frequencies of both CD4+ and CD8+ TRM cells than patients diagnosed with stage 3 or 5 CKD. However, only CD4+CD69+CD103+ TRM cells significantly correlated with eGFR (Figure 3E).

Renal Mucosal-Associated Invariant T and NK Cells Express TRM-Associated Markers

We further identified the CD69 and CD103 signature for both renal CD56dim and CD56bright NK cells, indicating their tissue residency (tissue-resident natural killer [NKTR]). In addition, although it has already been reported that mucosal-associated invariant T (MAIT) cells displaying a tissue-resident phenotype were detected in renal tissue,30 we were able to confirm this observation, thus further demonstrating the presence not only of renal MAIT cells but also, of tissue-resident MAIT cells (Figure 4A). We confirmed the presence of these nonconventional tissue-resident populations in the kidney by MELC, demonstrating that CD3CD56+ NK cells and CD3+CD8+CD161+Va7.2+ MAIT cells coexpress CD69 and CD103 (Figure 4B).

Figure 4.:
MAIT and NK cells co-expressing CD69 and CD103 reside in renal tissue. (A) Identification of CD56dim and CD56bright NK cells (n=19) or CD3+CD8+/CD3+CD4CD8 T cell receptor Vα7.2+CD161+ MAIT cells (n=29) in the kidney expressing CD69 and CD103. Statistically significant differences were tested with the two-tailed Wilcoxon signed rank test. (B) Overlays of MELC fluorescent images showing marker combinations that allow the identification of NKTR cells (upper panels; white arrows) and tissue-resident MAIT cells (lower panels; red arrows) in the kidney. For NK cell analysis, CD3 is shown in red; CD56 is shown in blue; and DAPI, CD69, or CD103 is shown in green in the same region of interest (ROI). For MAIT cell analysis, CD8 is shown in cyan; CD3, CD161, or CD103 is shown in magenta; and DAPI, T cell receptor Vα7.2, and CD69 are shown in yellow in a different ROI. Data represent one of four independent experiments. Scale bars: 50 µm.

CD69+CD103+ Lymphocytes Accumulate in Renal Tumors

Tumor epithelium generally shows enrichment for CD103+CD8+ T cells, whereas CD103CD8+ T cells are found more frequently in tumor stroma.31,32 Cluster analysis of MELC data from pooled tumor and patient-matched peritumor samples (n=4) for 16 analyzed markers revealed the identification of the following clusters: vessels, activated monocytes/macrophages, CD69+CD103+ tissue-resident cells, CD69+CD103CD8+ TRM cells, CD69+CD103CD4+ TRM cells, and undefined cells (Figure 5A, Supplemental Figure 9). Especially CD69+CD103CD8+ TRM and CD69+CD103+ tissue-resident cells were found to be significantly enriched within tumors (Figure 5B). Compared with peritumor, CD69+CD103CD8+ and CD69+CD103CD4+ TRM cells were forming groups of at least more than ten cells along the vessel structures in tumor tissues. A microanatomical colocalization between T cells and CD14+CD16+CD163+ monocytes/macrophages was particularly observed for CD69+CD103CD4+ TRM, whereas CD69+CD103+ tissue-resident cells were identified being scattered through the tumor stroma (Figure 5C, Supplemental Figures 10 and 11). Cluster analysis for CD69+CD103+ lymphocytes identified four different CD69+CD103+ tissue-resident subclusters: NKTR cluster, TRM cluster, tissue-resident MAIT cluster, and the cluster of proliferating cells expressing Ki67 (Figure 5, D and E). Although all CD69+CD103+ tissue-resident subclusters accumulated within tumors, the differences were only statistically significant for NKTR cells (Figure 5F).

Figure 5.:
Tissue-resident T cells form clusters in renal tumors and colocalize in close proximity to antigen-presenting cells. (A) K-means clustering of pooled MELC data derived from patient-matched peritumor and tumor samples (n=4 per each) reveals distinct populations in the kidney. Heat map analysis illustrates the mean relative expression levels of the 16 markers analyzed for each cluster. (B) Bar graph comparing cell frequencies of selected tissue-resident (TR) clusters as (A) in tumor and peritumor samples. Statistically significant differences were tested applying a two-tailed paired t test. (C) Overlays of MELC fluorescent images depicting endothelial cells (CD31 in white; left panels) and monocyte/macrophages (CD14 in cyan, CD16 in magenta, and CD163 in yellow; right panels) together with color-coded dots that represent the centroid spatial coordinates of the three TR clusters identified in (A). The images depict the spatial distribution of the CD69+CD103CD4+ T cell cluster (light blue dots), the CD69+CD103CD8+ T cell cluster (purple dots), and the CD69+CD103+ TR cluster (red dots) in one representative tumor sample (upper panels) and the patient-matched peritumor sample (bottom panels). (D) The t-distributed stochastic neighbor embedding (t-SNE) map of the CD69+CD103+ TR population identified in (A). On the basis of CD3, CD56, CD161, Va7.2, and Ki67 expression, an NKTR cell cluster (red), a TRM cell cluster (green), a tissue-resident mucosal-associated invariant T (MAITTR) cell cluster (blue), and a proliferating TR cluster (orange) were defined. Each symbol represents one cell. (E) Heat map analysis of the relative expression levels of the eight markers analyzed for the TR cell clusters as in (D), where each row represents one single cell. (F) Bar graph comparing cell frequencies of selected clusters as in (D) in tumor and peritumor samples. Statistically significant differences were tested with the two-tailed paired t test and the two-tailed Wilcoxon test.

Tumor-Derived CD8+ TRM Cells Express PD-1 and TOX

Increased intratumor frequencies of CD69+CD103+ NKTR could also be confirmed for CD56dim and CD56bright NKTR cells by flow cytometry, a result that was not valid for MAIT cells (Figure 6A, Supplemental Figure 12). Both viSNE and FlowSOM analyses of FACS-derived data showed no clear differences between peritumor and tumor tissue (Figure 6B, Supplemental Figure 13). As opposed to CD69+CD103+CD4+ TRM cells, we were not able to demonstrate increased frequencies of CD69+CD103+CD8+ TRM cells in renal tumor tissue compared with adjacent peritumor tissue. However, we detected a significant accumulation of CD69+CD103CD8+ and CD69CD103+ CD8+ TRM cells in the tumor (Figure 6B), thereby confirming MELC data (Figure 5C). Higher frequencies of PD-1+ cells were demonstrated within both the CD8+ and CD4+ TRM compartments (Figure 6D). No difference was observed for CD49a expression (Supplemental Figure 14).

Figure 6.:
NKTR cells and TRM cells are enriched in renal tumors. (A) Expression of CD69 and CD103 by NK (CD56 dim and bright; n=12) and MAIT cells (n=10). (B) viSNE plots of flow cytometric analysis of viable CD8+ and CD4+ T cells showing separate clustering of peritumor and tumor tissue. Individual representation of measured markers (CD69, CD103, and PD-1 for n=7) is displayed for both peritumor- and tumor-derived CD8+ and CD4+ T cells using viSNE algorithm. Expression is shown by color coding in relative intensity. viSNE plots were generated from concatenated FCS files and gated on CD8+ and CD4+ T cells considering CD103, CD161, CD45RO, CD69, HLA-DR, NKG2D, PD-1, and Va7.2 (Panel 2). tSNE, t-distributed stochastic neighbor embedding. (C) CD69+CD103+, CD69+CD103, and CD69CD103+CD8+ and CD4+ T cell frequencies (n=16) derived from renal peritumor and tumor tissue. Statistically significant differences were tested with the two-tailed paired t test or the Wilcoxon signed ranks test. (D) PD-1 expression on CD69+CD103+CD4+ and CD8+ T cells (n=16). Statistically significant differences were tested with the paired t test.

In chronic infections and tumor diseases, exhausted CD8+ T cells have been identified that demonstrate limited effector function and express inhibitory receptors such as PD-1, illustrating transcriptional changes as compared with effector or memory CD8+ T cells.33 Recently, the HMG box transcription factor TOX as a central regulator of these exhausted T cells was identified.34,35 Here, we illustrate for the very first time the significantly induced coexpression of TOX and PD-1 in tumor-derived CD69+CD103+CD8+ and CD69+CD103CD8+ TRM cells as compared with peritumor tissue (Figure 7, A and B). Despite significantly higher granzyme B expression, functional assessment of tumor-derived CD8+ TRM and CD8+CD69+CD103 TRM cells showed reduced IL-2 and TNFα as compared with peritumor-derived TRM cells, suggesting at least a functional impairment of these cells (Figure 7C). We further compared patients diagnosed with metastases before nephrectomy with patients without metastases. Interestingly, tumor-derived CD8+ TRM and CD8+CD69+CD103 TRM cells in patients diagnosed with metastases were functionally impaired (IL-2, TNFα, IFNγ) compared with metastases-free patients (Figure 7D).

Figure 7.:
Tumor-derived renal TRM cells express the exhaustion marker TOX and are functionally impaired. (A) Exemplary flow cytometry dot plots of PD-1+TOX+ CD8+CD69+CD103+ and CD8+CD69+CD103 TRM cells in peritumor and tumor tissue. (B) Identification of PD-1+TOX+ CD8+CD69+CD103+ and CD8+CD69+CD103 TRM cells (n=8) in renal peritumor and tumor tissue. (C) Cytokine effector profile (granzyme B [GrB], IFNγ, IL-2, IL-17, and TNFα) of CD8+CD69+CD103+ and CD8+CD69+CD103 T cells (n=10). Statistically significant differences were tested with the two-tailed paired t test or the Wilcoxon signed ranks test. (D) Patients were divided into two groups according to presence of metastases (diagnosed before nephrectomy). The cytokine profile (IL-2, TNFα, and IFNγ) of tumor-derived CD8+ CD69+CD103+ and CD69+CD103 TRM cells was compared between the groups (no metastases, n=8; metastases, n=4). Statistically significant differences were tested with the two-tailed unpaired t test or the Mann–Whitney test and are presented as mean values ± SD.

Antigen-Specific T Cells Reside in the Kidney

Finally, in order to clarify the functional relevance of CD8+ TRM cells in the kidney, we aimed to address whether these cells are characterized by a certain antigen specificity. We, therefore, stained isolated cells with tetramers specific for CMV, EBV, and BKV. We further included influenza A as a representative for a nonpersisting virus. Intriguingly, we were able to identify the presence of CD8+ T cells specific for all four investigated antigens, namely not only in the blood of HLA-A*02–positive patients but also, in the peritumor and tumor tissue (Figure 8, A and B). Antigen-specific T cells made a considerable contribution to the overall memory pool with mean frequencies between 0.6% (BKV) and 2.5% (CMV and EBV) within peritumor tissue. T cells specific for all four antigens predominantly showed a terminally differentiated effector cell (CD45RA+CD62L) or TEM (CD45RACD62L) phenotype in the peritumor tissue. However, we detected, especially for EBV and CMV, significantly higher frequencies of antigen-specific CD69CD103CD8+ T cells in contrast to CD69+CD103+CD8+ TRM cells (Figure 8, C and D).

Figure 8.:
Kidney-derived CD8+ T cells possess a memory phenotype for recall antigens. (A) Representative FACS dot plots for HLA-A2 multimer staining specific for CMV, EBV, BKV, and influenza virus on peritumor and tumor tissue-derived T cells. Control multimer (CNT) was used for gate settings. neg., negative. (B) Detection of antigen-specific CD3+CD8+ T cells in peripheral blood, renal peritumor tissue, and tumor tissue (CMV: b: n=8, p-t: n=9, t: n=5; EBV: b: n=9, p-t: n=9, t: n=4; BKV: b: n=3, p-t: n=5, t: n=3; and influenza: b: n=4, p-t: n=7, t: n=3). Statistically significant differences were tested with the Kruskal–Wallis test. (C) Antigen-specific CD3+CD8+ T cells are mainly effector memory T cells in the peritumor tissue (CMV, n=9; EBV, n=9; BKV, n=5; and influenza, n=7). (D) CD69CD103 CD8+ T cells constitute the majority of antigen-specific T cells in peritumor renal tissue (CMV, n=9; EBV, n=9; BKV, n=5; and influenza, n=7). Statistically significant differences were tested with one-way ANOVA or the Friedman test and are presented as mean values ± SD. b, blood; p-t, peritumor; t, tumor.


Originally inspired by interest in characterizing tissue-resident cells in the kidney in order to acquire more insight into potential passenger leukocytes migrating out of the graft postkidney transplantation, we present here a detailed identification of TRM cells in the kidney in a further attempt to understand their functional aspects in the organ in relation to tumor pathology and antigen specificity.

Renal TRM cells were able to be identified through the expression of CD69 and the integrins CD103 and CD49a. Especially CD69 limits the egress of TRM cells from tissues by binding to sphingosine-1-phosphate receptor 1 as its signaling regulates effector T cell residence time in nonlymphoid tissues.36 Whereas CD69 has been shown to promote early migration and retention in the lung,37 recent experimental data demonstrated that its expression mainly contributes to TRM cell maintenance in the kidney, suggesting CD69 expression as a consistent marker of CD8+ TRM.38 Contrarily, it has been found that functional CD69 T cells can reside in murine kidneys, which led to the conclusion that CD69 is not a definitive marker for distinguishing recirculating cells from TRM cells.39 However, we were able to identify single-positive CD69CD103+CD8+ T cells in the human kidney, an observation not confirmed for CD4+ T cells. Although on the basis of experimental murine data,40 it has been stated that the majority of kidney TRM cells do not express CD103, our results and those of others show the opposite.13 However, renal CD103+ TRM cell frequencies are clearly lower than those of CD69+ TRM cells.

Kidney-derived CD69+ TRM cells express more HLA-DR and PD-1 and lower levels of CD28 in contrast to CD69CD8+ T cells, indicating an activated phenotype reflected by the stronger expression of IL-2 and IL-17. Within this context, a CD8+CD2hiCD28 T cell population has been described to contain high numbers of cells with polyfunctional cytokine production and cytotoxic effector molecule expression.41 In contrast, renal CD69+ TRM cells appear to be less cytotoxic on the basis of their granzyme B expression, an observation already made for hepatic CD69+ T cells.42 It has been shown that CD49a+CD8+ TRM cells preferentially localize to regions rich in collagen IV in the lung,11,43 whereas in nonmucosal organs, the localization within the tissue is less well understood. Here, we demonstrate the coexpression of CD69, CD103, and CD49a on CD8+ and CD4+ T cells in the kidney, showing their colocalization with myeloid cells in the renal parenchyma and suggesting potential interactions with antigen-presenting cells.

In addition to the “classical” CD8+ TRM cells, we were also able to identify CD4+ TRM cells in the kidney, which similar to CD8+ TRM cells, are characterized by the loss of CD28. CD4+CD28 T cells have been reported in a number of cardiovascular and autoimmune diseases44 but also, in the setting of renal transplant rejection.45 CD4+CD28 T cells can be defined as typical T helper 1 cells, which produce a large amount of IFNγ and express the cytotoxic molecules perforin and granzyme B.46 In general, we cannot rule out the possibility that the gradual loss of CD28 on both renal CD4+ and CD8+ TRM cells can be attributed to renal senescence.47 However, as we observed a clear correlation at least between CD69+CD8+ TRM and chronological age, but not for CD69+CD28CD8+ TRM cells (or for CD69+CD4+ TRM cells) (Supplemental Figure 8B), we propose that the loss of CD28 is a hallmark of this resident TRM subtype in the kidney. In this context, it appears conflicting that CD4+ TRM cells significantly correlate with better kidney function, indicating the necessity for further research on their functional relevance.

Intriguingly, we were also able to identify the combined expression of CD69 and CD103 on MAIT and NK cells, further illustrating that both markers are not unique for CD8+ T cells. Renal-resident MAIT cells have already been described in the kidney, where they contribute to the progression of fibrosis.30 We here confirm the presence of intrarenal CD69+CD103+Va7.2+CD161+ MAIT cells in a larger number of individuals. However, neither their frequencies nor the frequencies of NKTR correlated with chronological age (Supplemental Figure 8D). Although CD56bright NK cells have been identified as tissue-resident on the basis of their varying expression of CD69, CD49a, and CD103 in combination with NKp44 and the chemokine receptor CXCR6 in human lymphoid tissues, uterus, and liver,48,49 our data document, to the best of our knowledge, the first description of NKTR on the basis of the combined expression of CD69 and CD103 and their significant increase in renal tumors.

Tumor-derived CD8+ TRM cells have often been described as potential producers of IFNγ, paralleled with the upregulation of CD69, CD103, and CD49a most likely induced by TGFβ in the tumor microenvironment.50,51 The abundance of CD103+CD8+ T cells has been shown to correlate with longer disease-free and overall survival of patients5051–52 as CD103+ T cells are supposed to form more stable synapses with target cells than do their CD103 counterparts, thus resulting in efficient killing of tumor cells.50 We detected significantly higher percentages of both intratumor CD69+CD103CD8+ and CD69CD103+CD8+ TRM cells. In general, CD69+ TRM cells isolated from the tumor display a significantly strong expression of PD-1, an observation already made for other tumor entities.53 Renal tumor–derived CD69+ TRM cells are characterized by a significant induction of granzyme B, whereas other cytokines are not affected (IFNγ) or strongly downregulated (e.g., IL-2, IL-17). In principle, the formation of TRM cells in the kidney is promoted by TGFβ, whereas their long-term maintenance was described to be IL-15 dependent.54,55 Common TRM cell transcriptional programs are active in kidney TRM cells as deficiency in transcription factor Blimp-1, Hobit, or Runx3 leads to impaired maintenance of kidney TRM cells.55,56 Recently, the transcription factor TOX has been shown to be strongly expressed in dysfunctional tumor-derived T cells, preventing them from overstimulation and activation-induced cell death in settings of chronic antigen stimulation, including cancer.57 The identification of TOX+PD-1+CD69+ TRM cells in kidney tumors suggests an activated but not exhaustive phenotype as these cells are still producing cytokines, although to a lesser extent. Nonetheless, compared with patients without metastases, patients diagnosed with metastases display impaired functional intratumoral CD8+ TRM cells, suggesting that at least a loss of TRM function is indicative of tumor spreading.

Although the presence of virus-specific CD8+ T cells in human lung and colorectal cancer has been demonstrated,58 the relevance of antigens for TRM cell formation in the kidney has not yet been determined. Only one study reports BKV-specific CD69+CD103+ TRM cells in BKVN-affected kidney allografts lacking signs of effector differentiation.59 Another experimental study demonstrates that kidney TRM cells bind to T cell receptors with a 20-fold higher affinity than do splenic memory T cells, suggesting that local antigens may facilitate kidney TRM cell induction.60 Our data document the presence of TRM cells in human kidneys specific for EBV, CMV, and BKV as well for influenza. Interestingly, the majority of antigen-specific T cells for persistent viruses are negative for CD69 and CD103 expression, suggesting that the lack of both markers enables them to migrate easily as a consequence of ubiquitous virus infection, thus not being organ specific. Alternatively, although TRM cells are identified as effector cells in many tissues and in the kidney, they can maintain their differentiation state in the absence of persistent antigens.61,62 The role of antigen-specific TRM residing in tumors still remains to be elucidated, but recent research suggests that these T cells can be specifically reactivated via local delivery of adjuvant-free viral peptide for reactivation and tumor targeting.63

Although TRM cells are suggested to promote favorable immunity, it needs to be considered that the majority of aspects of TRM cell generation, maintenance, or functionality are derived from infectious mouse models, and data on human TRM cells are limited. We present here a comprehensive overview of TRM cells identified in the human kidney with respect to tumor pathology and viral infections, which is summarized in Figure 9. However, we are aware that our study has several limitations. First, our patient cohort was assembled following the diagnosis of a renal tumor and thus, displayed an advanced age (mean age 63 years) as there was understandably no opportunity to analyze healthy kidneys of a younger age. Second, a lack of sufficient cell numbers prevented us from analyzing the effector profile of antigen-specific TRM and TOX+ TRM. Third, renal cancer is a slow-growing cancer entity; therefore, we were not able to correlate our findings with long-term outcomes. Despite these limitations, our study reveals a number of novel aspects of tissue-resident lymphocytes in the kidney, and their exact role and relevance (e.g., for CKDs, tumor pathology, or at least transplantation) need to be comprehensively investigated in continuing studies.

Figure 9.:
Renal CD8+ TRM cells display an inflammatory phenotype reflected by enhanced IL-2, IL-17 and TNFa expression. Whereas CD8+ TRM cells correlate with increasing age, CD4+ TRM cells correlate with kidney function. Both, mucosa-associated invariant T (MAIT) and Natural Killer (NK) cells expressing CD69 and CD103 were identified in the kidney, with the latter being enriched in renal tumors. Tumor-derived CD8+ TRM cells express high levels of PD-1 and the transcription factor TOX and could be identified in close proximity to APCs. APC, antigen-presenting cell; GrB, granzyme B.


All authors have nothing to disclose.


This work was funded by Deutsche Forschungsgemeinschaft grant DFG-Ko2270/4-1 (to K. Kotsch) and a scholarship from Sonnenfeldstiftung (to A.H. Ruhm).

Published online ahead of print. Publication date available at


T. Dornieden, K. Kotsch, and A. Sattler were responsible for experiment design and acquisition, analysis, and interpretation of data; Y.S. Bergmann, S. Boral, T. Dornieden, F. Friedersdorff, A.E. Hauser, A.A. Kühl, A. Pascual-Reguant, A.H. Ruhm, A. Sattler, L.G. Thiel, and L.M.L. Thole performed experiments and data analysis; T. Dornieden and K. Kotsch were responsible for writing the manuscript; and all authors reviewed the manuscript before submission and agreed to be accountable for all aspects of the work.

Supplemental Material

This article contains the following supplemental material online at

Supplemental Table 1. Antibodies for phenotypic analysis.

Supplemental Table 2. Multimers.

Supplemental Table 3. Antibodies for phenotypic analysis of antigen-specific CD8+ T cells.

Supplemental Table 4. Antibodies for functional analysis.

Supplemental Table 5. Antibodies for MELC histology.

Supplemental Table 6. Used statistics.

Supplemental Figure 1. Gating strategy.

Supplemental Figure 2. Panel overview of a 19-marker MELC run in human kidneys shows different degrees of T cell infiltration.

Supplemental Figure 3. Quantification of PBMC- and tissue-derived T cell subsets.

Supplemental Figure 4. viSNE and FlowSOM analysis of FACS data.

Supplemental Figure 5. CD49a expression and related cytokine profiles.

Supplemental Figure 6. KLRG1 and CD103 expression of CD8+ and CD4+ T cells.

Supplemental Figure 7. Ex vivo proliferation of CD8+ and CD4+ TRM cell subsets.

Supplemental Figure 8. Patients were divided into two groups according to the calculated mean age of 63 years (n=35).

Supplemental Figure 9. Cluster analysis of MELC data from pooled tumor and patient-matched peritumor samples (n=4).

Supplemental Figure 10. Both CD69+CD103CD4+ and CD69+CD103CD8+ T cells form clusters, whereas CD69+CD103+ tissue-resident populations tend to be rather scattered across the tumor tissue.

Supplemental Figure 11. Overlays of MELC fluorescent images depicting endothelial cells and monocyte/macrophages together with color-coded dots that represent the centroid spatial coordinates of the three tissue-resident cell clusters identified in Figure 5A for n=3 peritumor/tumor-matched patient samples.

Supplemental Figure 12. Tissue residency of NK cells in peritumor and tumor kidney tissue.

Supplemental Figure 13. viSNE and FlowSOM analysis of FACS data for peritumor and tumor samples.

Supplemental Figure 14. CD49a expression of CD8+ TRM cell subsets.


1. Masopust D, Soerens AG: Tissue-resident T cells and other resident leukocytes. Annu Rev Immunol 37: 521–546, 2019
2. Park SL, Gebhardt T, Mackay LK: Tissue-resident memory T cells in cancer immunosurveillance. Trends Immunol 40: 735–747, 2019
3. Fan X, Rudensky AY: Hallmarks of tissue-resident lymphocytes. Cell 164: 1198–1211, 2016
4. Clark RA: Skin-resident T cells: The ups and downs of on site immunity. J Invest Dermatol 130: 362–370, 2010
5. Purwar R, Campbell J, Murphy G, Richards WG, Clark RA, Kupper TS: Resident memory T cells (T(RM)) are abundant in human lung: Diversity, function, and antigen specificity. PLoS One 6: e16245, 2011
6. Smolders J, Heutinck KM, Fransen NL, Remmerswaal EBM, Hombrink P, Ten Berge IJM, et al.: Tissue-resident memory T cells populate the human brain. Nat Commun 9: 4593, 2018
7. Hombrink P, Helbig C, Backer RA, Piet B, Oja AE, Stark R, et al.: Programs for the persistence, vigilance and control of human CD8+ lung-resident memory T cells. Nat Immunol 17: 1467–1478, 2016
8. Fernandez-Ruiz D, Ng WY, Holz LE, Ma JZ, Zaid A, Wong YC, et al.: Liver-resident memory CD8+ T cells form a front-line defense against malaria liver-stage infection. Immunity 45: 889–902, 2016
9. Gordon CL, Miron M, Thome JJ, Matsuoka N, Weiner J, Rak MA, et al.: Tissue reservoirs of antiviral T cell immunity in persistent human CMV infection. J Exp Med 214: 651–667, 2017
10. Wu T, Hu Y, Lee YT, Bouchard KR, Benechet A, Khanna K, et al.: Lung-resident memory CD8 T cells (TRM) are indispensable for optimal cross-protection against pulmonary virus infection. J Leukoc Biol 95: 215–224, 2014
11. Ray SJ, Franki SN, Pierce RH, Dimitrova S, Koteliansky V, Sprague AG, et al.: The collagen binding alpha1beta1 integrin VLA-1 regulates CD8 T cell-mediated immune protection against heterologous influenza infection. Immunity 20: 167–179, 2004
12. de Leur K, Dieterich M, Hesselink DA, Corneth OBJ, Dor FJMF, de Graav GN, et al.: Characterization of donor and recipient CD8+ tissue-resident memory T cells in transplant nephrectomies. Sci Rep 9: 5984, 2019
13. Park JG, Na M, Kim MG, Park SH, Lee HJ, Kim DK, et al.: Immune cell composition in normal human kidneys. Sci Rep 10: 15678, 2020
14. Pascual-Reguant A, Köhler R, Mothes R, Bauherr S, Hernández DC, Uecker R, et al.: Multiplexed histology analyses for the phenotypic and spatial characterization of human innate lymphoid cells. Nat Commun 12: 1737, 2021
15. Holzwarth K, Köhler R, Philipsen L, Tokoyoda K, Ladyhina V, Wählby C, et al.: Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections. Cytometry A 93: 876–888, 2018
16. Schubert W, Bonnekoh B, Pommer AJ, Philipsen L, Böckelmann R, Malykh Y, et al.: Analyzing proteome topology and function by automated multidimensional fluorescence microscopy. Nat Biotechnol 24: 1270–1278, 2006
17. Pertuz S, Puig D, Garcia MA, Fusiello A: Generation of all-in-focus images by noise-robust selective fusion of limited depth-of-field images. IEEE Trans Image Process 22: 1242–1251, 2013
18. Abràmoff MD, Magalhães PJ, Ram SJ: Image processing with ImageJ. J Biophotonics Int 11: 36–42, 2004
19. Sternberg SR: Biomedical image processing. Computer 16: 22–34, 1983
20. Berg S, Kutra D, Kroeger T, Straehle CN, Kausler BX, Haubold C, et al.: ilastik: Interactive machine learning for (bio)image analysis. Nat Methods 16: 1226–1232, 2019
21. Carpenter AE, Jones TR, Lamprecht MR, Clarke C, Kang IH, Friman O, et al.: CellProfiler: Image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7: R100, 2006
22. Schapiro D, Jackson HW, Raghuraman S, Fischer JR, Zanotelli VRT, Schulz D, et al.: histoCAT: Analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods 14: 873–876, 2017
23. Demšar J, Curk T, Erjavec A, Gorup C, Hocevar T, Milutinovic M, et al.: Orange: Data mining toolbox in python. J Mach Learn Res 14: 2349–2353, 2013
24. Van Gassen S, Callebaut B, Van Helden MJ, Lambrecht BN, Demeester P, Dhaene T, et al.: FlowSOM: Using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87: 636–645, 2015
25. Victorino F, Sojka DK, Brodsky KS, McNamee EN, Masterson JC, Homann D, et al.: Tissue-resident NK cells mediate ischemic kidney injury and are not depleted by anti-asialo-GM1 antibody. J Immunol 195: 4973–4985, 2015
26. Davies B, Prier JE, Jones CM, Gebhardt T, Carbone FR, Mackay LK: Cutting edge: Tissue-resident memory T cells generated by multiple immunizations or localized deposition provide enhanced immunity. J Immunol 198: 2233–2237, 2017
27. Herndler-Brandstetter D, Ishigame H, Shinnakasu R, Plajer V, Stecher C, Zhao J, et al.: KLRG1+ effector CD8+ T cells lose KLRG1, differentiate into all memory T cell lineages, and convey enhanced protective immunity. Immunity 48: 716–729, 2018
28. Mackay LK, Rahimpour A, Ma JZ, Collins N, Stock AT, Hafon ML, et al.: The developmental pathway for CD103(+)CD8+ tissue-resident memory T cells of skin. Nat Immunol 14: 1294–1301, 2013
29. Szabo PA, Miron M, Farber DL: Location, location, location: Tissue resident memory T cells in mice and humans. Sci Immunol 4: eaas9673, 2019
30. Law BMP, Wilkinson R, Wang X, Kildey K, Giuliani K, Beagley KW, et al.: Human tissue-resident mucosal-associated invariant T (MAIT) cells in renal fibrosis and CKD. J Am Soc Nephrol 30: 1322–1335, 2019
31. Webb JR, Milne K, Watson P, Deleeuw RJ, Nelson BH: Tumor-infiltrating lymphocytes expressing the tissue resident memory marker CD103 are associated with increased survival in high-grade serous ovarian cancer. Clin Cancer Res 20: 434–444, 2014
32. Workel HH, Komdeur FL, Wouters MC, Plat A, Klip HG, Eggink FA, et al.: CD103 defines intraepithelial CD8+ PD1+ tumour-infiltrating lymphocytes of prognostic significance in endometrial adenocarcinoma. Eur J Cancer 60: 1–11, 2016
33. Xia A, Zhang Y, Xu J, Yin T, Lu X-J: T cell dysfunction in cancer immunity and immunotherapy. Front Immunol 10: 1719, 2019
34. Khan O, Giles JR, McDonald S, Manne S, Ngiow SF, Patel KP, et al.: TOX transcriptionally and epigenetically programs CD8+ T cell exhaustion. Nature 571: 211–218, 2019
35. Wang X, He Q, Shen H, Xia A, Tian W, Yu W, et al.: TOX promotes the exhaustion of antitumor CD8+ T cells by preventing PD1 degradation in hepatocellular carcinoma. J Hepatol 71: 731–741, 2019
36. Skon CN, Lee J-Y, Anderson KG, Masopust D, Hogquist KA, Jameson SC: Transcriptional downregulation of S1pr1 is required for the establishment of resident memory CD8+ T cells. Nat Immunol 14: 1285–1293, 2013
37. Takamura S, Yagi H, Hakata Y, Motozono C, McMaster SR, Masumoto T, et al.: Specific niches for lung-resident memory CD8+ T cells at the site of tissue regeneration enable CD69-independent maintenance. J Exp Med 213: 3057–3073, 2016
38. Walsh DA, Borges da Silva H, Beura LK, Peng C, Hamilton SE, Masopust D, et al.: The functional requirement for CD69 in establishment of resident memory CD8+ T cells varies with tissue location. J Immunol 203: 946–955, 2019
39. Steinert EM, Schenkel JM, Fraser KA, Beura LK, Manlove LS, Igyártó BZ, et al.: Quantifying memory CD8 T cells reveals regionalization of immunosurveillance. Cell 161: 737–749, 2015
40. Ma C, Mishra S, Demel EL, Liu Y, Zhang N: TGF-β controls the formation of kidney-resident T cells via promoting effector T cell extravasation. J Immunol 198: 749–756, 2017
41. Lo DJ, Weaver TA, Stempora L, Mehta AK, Ford ML, Larsen CP, et al.: Selective targeting of human alloresponsive CD8+ effector memory T cells based on CD2 expression. Am J Transplant 11: 22–33, 2011
42. Stelma F, de Niet A, Sinnige MJ, van Dort KA, van Gisbergen KPJM, Verheij J, et al.: Human intrahepatic CD69 + CD8+ T cells have a tissue resident memory T cell phenotype with reduced cytolytic capacity. Sci Rep 7: 6172, 2017
43. Richter M, Ray SJ, Chapman TJ, Austin SJ, Rebhahn J, Mosmann TR, et al.: Collagen distribution and expression of collagen-binding alpha1beta1 (VLA-1) and alpha2beta1 (VLA-2) integrins on CD4 and CD8 T cells during influenza infection. J Immunol 178: 4506–4516, 2007
44. Broadley I, Pera A, Morrow G, Davies KA, Kern F: Expansions of cytotoxic CD4+CD28- T cells drive excess cardiovascular mortality in rheumatoid arthritis and other chronic inflammatory conditions and are triggered by CMV infection. Front Immunol 8: 195, 2017
45. Shabir S, Smith H, Kaul B, Pachnio A, Jham S, Kuravi S, et al.: Cytomegalovirus-associated CD4(+) CD28(null) cells in NKG2D-dependent glomerular endothelial injury and kidney allograft dysfunction. Am J Transplant 16: 1113–1128, 2016
46. Dumitriu IE, Baruah P, Finlayson CJ, Loftus IM, Antunes RF, Lim P, et al.: High levels of costimulatory receptors OX40 and 4-1BB characterize CD4+CD28null T cells in patients with acute coronary syndrome. Circ Res 110: 857–869, 2012
47. Pangrazzi L, Weinberger B: T cells, aging and senescence. Exp Gerontol 134: 110887, 2020
48. Melsen JE, Lugthart G, Lankester AC, Schilham MW: Human circulating and tissue-resident CD56(bright) natural killer cell populations. Front Immunol 7: 262, 2016
49. Lugthart G, Melsen JE, Vervat C, van Ostaijen-Ten Dam MM, Corver WE, Roelen DL, et al.: Human lymphoid tissues harbor a distinct CD69+CXCR6+ NK cell population. J Immunol 197: 78–84, 2016
50. Djenidi F, Adam J, Goubar A, Durgeau A, Meurice G, de Montpréville V, et al.: CD8+CD103+ tumor-infiltrating lymphocytes are tumor-specific tissue-resident memory T cells and a prognostic factor for survival in lung cancer patients. J Immunol 194: 3475–3486, 2015
51. Ganesan AP, Clarke J, Wood O, Garrido-Martin EM, Chee SJ, Mellows T, et al.: Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat Immunol 18: 940–950, 2017
52. Wang ZQ, Milne K, Derocher H, Webb JR, Nelson BH, Watson PH: CD103 and intratumoral immune response in breast cancer. Clin Cancer Res 22: 6290–6297, 2016
53. Gros A, Robbins PF, Yao X, Li YF, Turcotte S, Tran E, et al.: PD-1 identifies the patient-specific CD8+ tumor-reactive repertoire infiltrating human tumors. J Clin Invest 124: 2246–2259, 2014
54. Schenkel JM, Fraser KA, Casey KA, Beura LK, Pauken KE, Vezys V, et al.: IL-15-independent maintenance of tissue-resident and boosted effector memory CD8 T cells. J Immunol 196: 3920–3926, 2016
55. Mackay LK, Wynne-Jones E, Freestone D, Pellicci DG, Mielke LA, Newman DM, et al.: T-box transcription factors combine with the cytokines TGF-beta and IL-15 to control tissue-resident memory T cell fate. Immunity 43: 1101–1111, 2015
56. Milner JJ, Toma C, Yu B, Zhang K, Omilusik K, Phan AT, et al.: Runx3 programs CD8+ T cell residency in non-lymphoid tissues and tumours. Nature 552: 253–257, 2017
57. Scott AC, Dündar F, Zumbo P, Chandran SS, Klebanoff CA, Shakiba M, et al.: TOX is a critical regulator of tumour-specific T cell differentiation. Nature 571: 270–274, 2019
58. Simoni Y, Becht E, Fehlings M, Loh CY, Koo SL, Teng KWW, et al.: Bystander CD8+ T cells are abundant and phenotypically distinct in human tumour infiltrates. Nature 557: 575–579, 2018
59. van Aalderen MC, Remmerswaal EBM, Heutinck KM, Ten Brinke A, Feltkamp MCW, van der Weerd NC, et al.: Clinically relevant reactivation of polyomavirus BK (BKPyV) in HLA-A02-Positive renal transplant recipients is associated with impaired effector-memory differentiation of BKPyV-specific CD8+ T cells. PLoS Pathog 12: e1005903, 2016
60. Frost EL, Kersh AE, Evavold BD, Lukacher AE: Cutting edge: resident memory CD8 T cells express high-affinity TCRs. J Immunol 195: 3520–3524, 2015
61. Casey KA, Fraser KA, Schenkel JM, Moran A, Abt MC, Beura LK, et al.: Antigen-independent differentiation and maintenance of effector-like resident memory T cells in tissues. J Immunol 188: 4866–4875, 2012
62. Mackay LK, Minnich M, Kragten NA, Liao Y, Nota B, Seillet C, et al.: Hobit and Blimp1 instruct a universal transcriptional program of tissue residency in lymphocytes. Science 352: 459–463, 2016
63. Rosato PC, Wijeyesinghe S, Stolley JM, Nelson CE, Davis RL, Manlove LS, et al.: Virus-specific memory T cells populate tumors and can be repurposed for tumor immunotherapy. Nat Commun 10: 567, 2019

immunology; renal carcinoma; tissue-resident T cells; memory; antigen specific

Copyright © 2021 by the American Society of Nephrology