Thick ascending limb injury critically impacts kidney allograft survival after T-cell-mediated rejection

Thick ascending limb injury critically impacts kidney allograft survival after T-cell-mediated rejection | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Thick ascending limb injury critically impacts kidney allograft survival after T-cell-mediated rejection Christian Hinze, Anna Pfefferkorn, Lorenz Jahn, Patrick Gauthier, and 17 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5683198/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Abstract T-cell mediated rejection (TCMR) remains a significant challenge after kidney transplantation and is associated with reduced allograft outcome. Previous research highlighted the critical role of TCMR-induced renal epithelial injury. Yet, the detailed cellular origin of these injury responses and the associated clinical implications remain poorly understood. To induce acute TCMR, we used mouse models of allogeneic (C57BL/6 to BALB/c and BALB/c to C57BL/6) kidney transplantation and syngeneic controls (C57BL/6 to C57BL/6 and BALB/c to BALB/c). Molecular changes were analyzed 7 days post-transplant using single-nucleus RNA sequencing and spatial transcriptomics. Results were compared with snRNA-seq data from three human TCMR biopsies and three stable allografts without rejection. The clinical impact of TCMR-induced epithelial injury was evaluated using marker gene sets on bulk transcriptomic data from 1292 kidney allografts, including 95 TCMR samples, with allograft outcome. Mouse kidneys from allogeneic transplants exhibited all hallmark histological features of TCMR. Single-nucleus RNA sequencing revealed TCMR-induced injured cell states and significant gene expression changes particularly in proximal tubules (PT) and thick ascending limbs (TAL). Spatial transcriptomics showed a heterogeneous spatial distribution of these injured cell states and proximity to leukocytes. Cross-species analysis confirmed similar injured PT and TAL cell states in human TCMR. Kidney allograft outcomes strongly correlated with TCMR-induced injured epithelial cell states. Distinct from other transplant biopsies, severe TAL injury emerged as a key factor for allograft survival after TCMR and was associated with reduced leukocyte proximity, suggesting potential non-immune mechanisms of epithelial damage. Health sciences/Nephrology/Kidney diseases Health sciences/Nephrology/Kidney/Nephrons Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction T cell-mediated rejection (TCMR) is a severe complication after kidney transplantation associated with reduced graft survival 1 , 2 . Often occurring within the first year after kidney transplantation, TCMR represents an adaptive immune response triggered by donor antigens, primarily mediated by T cells 3 – 6 . This immune reaction causes injury to the transplanted kidney, resulting in inflammation and cellular damage, which can advance to chronic graft dysfunction. TCMR is defined histologically by interstitial leukocyte infiltration, tubulitis (inflammation of renal tubules) and, in some instances, arteritis 5 , 7 , 8 . Although the precise mechanisms driving kidney injury during TCMR are not fully understood, they likely involve direct cytotoxic T cell activity, a pro-inflammatory cytokine environment and interstitial edema leading to hypoxia in kidney cells 9 – 11 . Current therapeutic strategies for TCMR focus on suppressing the immune cell infiltrate through intensified immunosuppression, often including steroid pulses 12 – 14 . While these treatments achieve histological remission in most cases, TCMR remains associated with reduced allograft survival, with outcomes at least as poor as those in ABMR despite TCMR being considered treatable 15 – 18 . This raises questions about whether current therapies adequately address all aspects of TCMR-induced injury. Large-scale bulk transcriptomic studies have underscored the importance of epithelial injury signatures, especially in TCMR, noting that these signatures are most relevant for allograft outcomes, whereas immune cell infiltration signatures appear to have limited prognostic value 17 , 19 – 21 . Recent advances in high-resolution transcriptomic technologies have offered new perspectives on the molecular mechanisms underlying kidney diseases. These technologies, particularly in the context of acute kidney injury (AKI) and chronic kidney disease (CKD), have revealed the existence of injury-induced epithelial cell states 22 – 25 . Emerging evidence suggests that these injured cell states are not mere bystanders but actively contribute to further kidney damage through their pro-inflammatory and pro-fibrotic genes expression profiles 23 , 25 , 26 . Despite these insights, epithelial injury signatures in TCMR have yet to be systematically characterized or targeted therapeutically. The complexity of evaluating these injury states in transplanted human kidneys is compounded by multiple sources of injury, including drug toxicity, ischemia-reperfusion injury as well as host- and donor-specific factors 27 – 31 . The aim of this study is to delineate the molecular changes associated with TCMR and evaluate their impact on allograft outcomes. We employ single-cell sequencing techniques in mouse models of TCMR to establish a precise signature of TCMR-induced changes. These findings are then rigorously compared to molecular data from human biopsies. By identifying key molecular signatures in TCMR samples and analyzing them in large bulk transcriptomic cohorts with clinical follow-up data, we can assess their clinical significance and potential as therapeutic targets. Results Allogeneic kidney transplantation in mice induces acute TCMR Analyzing molecular signatures of TCMR in patient kidney biopsies poses significant challenges. Firstly, even when TCMR is present, various overlapping injury sources in human kidney allografts - such as drug toxicity, ischemia-reperfusion injury and diverse donor and recipient pathologies and clinical conditions - are commonly observed 32 . Secondly, the precise timing of TCMR onset in humans is often unknown, complicating inter-patient comparisons. Mouse models of syngeneic and allogeneic kidney transplantation offer a controlled setting to study TCMR at defined time points 33 . In this study, we therefore propose a cross-species approach by examining gene expression changes in mouse TCMR and systematically comparing them to human TCMR data and clinical outcomes (Fig. 1 A). For the mouse model, we transplanted kidneys from adult male mice of either C57BL/6 or BALB/c strains into syngeneic (C57BL/6 to C57BL/6 or BALB/c to BALB/c, further referred to as syngeneic mice) or allogeneic (C57BL/6 to BALB/c or BALB/c to C57BL/6, further referred to as allogeneic mice) recipients, with a cold ischemia time of 60 minutes. The BALB/c to C57BL/6 allogeneic transplantation was used to model milder rejection compared to the C57BL/6 to BALB/c group, while syngeneic transplants served as controls. Kidneys were harvested 7 days post-transplantation, a time point considered optimal for acute cellular rejection in this model 33 . Allogeneic transplants induced strong rejection, showing all histological features of TCMR, including tubulitis and interstitial inflammation (Fig. 1 B, Suppl. Fig. S1 for Banff scoring, sample information and additional histology). Clinically, allogeneic mice suffered from acute kidney injury (AKI) and increased mortality (Fig. 1 B, Suppl. Fig. S2). To gain molecular insights into TCMR, we performed single-nucleus mRNA sequencing (snRNA-seq) and spatial transcriptomics (ST) using the Xenium platform (mouse multi-tissue panel plus 100 custom genes; see Suppl. Table S1) on kidneys from, both, syngeneic and allogeneic transplants. Both, snRNA-seq (39706 nuclei from 3 syngeneic kidneys and 5 allogeneic kidneys, Suppl. Fig. S3) and ST (ca. 1.6 million segmented cells from 2 syngeneic and 5 allogeneic kidneys, Suppl. Fig. S4) produced high-quality transcriptomic data, enabling us to identify all expected major cell types (Fig. 1 C and D, Suppl. Fig. S4). Global gene expression was mainly determined by the presence or absence of allogeneic transplantation as derived from principal component analysis (Fig. 1 E). Leukocyte abundance was significantly higher in allogeneic samples compared to syngeneic controls in snRNA-seq, correlating with the interstitial inflammation observed in histology (Fig. 1 F). Mouse TCMR induces predominant gene expression responses in kidney epithelial cells Differential gene expression analysis between allogeneic and syngeneic mice revealed the strongest gene expression response in the kidney epithelium, predominantly in proximal tubules (PT) and thick ascending limbs (TAL) (Fig. 2 A, B, Suppl. Table S2). As expected, gene expression changes were most pronounced in allogeneic C57BL/6 kidneys transplanted into BALB/c mice in most cell types. We performed pathway enrichment analysis on up- and downregulated genes, separately (Fig. 2 C, Suppl. Fig. S5). Upregulated genes often showed pan-cell type enrichment in pathways likely associated with the overall inflammatory milieu in the allogeneic kidneys and AKI. This included interferon alpha, interferon gamma, interleukin 2, interleukin 6, TNF alpha and epithelial mesenchymal transition (EMT) signaling. Downregulated genes mostly enriched in pathways associated with metabolism and energy homeostasis (Suppl. Fig. S5). For most cell types, there was a large overlap of differential gene expression between the two different allogeneic models (C57BL/6 to BALB/c or BALB/c to C57BL/6) with a usually stronger gene expression response in C57BL/6 kidneys transplanted into BALB/c mice. Notably, the number of differentially upregulated genes in some cell types (CD-PC, EC, IntC, PEC, CD-IC-A) was higher in the BALB/c to C57BL/6 kidneys which represents the supposedly milder rejection model. Mouse TCMR elicits spatially diverse injured cell states in PT and TAL Single-cell studies in AKI have demonstrated the emergence of outcome-relevant AKI-associated cell states within the kidney epithelium 22 , 23 , 25 . To investigate whether similar phenomena occur in TCMR, we conducted subclustering analyses of the snRNA-seq data from PT and TAL cells, which exhibited the strongest gene expression changes. In the PT, four injury clusters (PT Injury m1-4) and a cluster of proliferating cells (PT Prolif) were identified (Fig. 3 A, B). Marker gene analysis showed that PT Injury m4 exhibits the highest expression of injury markers, while the other three injury populations appear to represent transitional cell states, displaying residual anatomical marker staining and lower levels of injury marker expression. The marker genes for PT Injury m4 include several associated with maladaptive repair cells in AKI, such as Vcam1, Cd44 and Vim 22 , 24 . The availability of ST data allows for the spatial mapping of PT injury cell states (Fig. 3 A). In congruence with the UMAP plot in Fig. 3 A, PT Injury m2 and m3 were predominantly found in the renal cortex, while PT Prolif and PT Injury m1 were enriched towards the medulla. The injury cluster PT Injury m4 could be found in, both, cortex and medulla. In the TAL, three TCMR-associated injury clusters (TAL Injury m1-3) and a proliferating cluster (TAL Prolif) could be observed (Fig. 3 C, D). Similar to PT, one injury cluster (TAL Injury m3) expressed marker genes observed in the most severely injured cell states in AKI, including Cd44, Met and Spp1 22 . In ST data, TAL injury cell states TAL Injury m1 and m2 were less abundant and seemed to occur in cortex as well as the medulla. However, the most abundant and dedifferentiated TAL injury cluster, TAL Injury m3, was predominant in the renal medulla, suggesting that this region is particularly vulnerable to severe TAL damage. The small TAL Prolif cluster was difficult to assign to a specific region due to the low cell count, with only sporadic occurrences in both the cortex and medulla in the ST data. Notably, as in AKI, some healthy PT and TAL cell types (e.g. PT S2, PT S3) were significantly depleted in allogeneic kidneys while most injury populations were significantly overrepresented in the allogeneic kidneys (Fig. 3 B, D). It is of note that subclustering analyses of the remaining major cell types revealed TCMR-associated injury clusters in DCT, CNT and EC at much lower abundances than for PT and TAL (Suppl. Fig. S6 and S7). TCMR-induced injured epithelial cell states show heterogeneous proximity to immune cells Although the exact mechanisms driving epithelial injury in TCMR remain unclear, it is widely recognized that host leukocytes play a central role, either by direct cytotoxicity or indirectly by cytokine production. The availability of snRNA-seq and ST data allows for estimating the spatial proximity of leukocyte subtypes to injured epithelial cells in the PT and TAL. Subclustering of leukocytes identified all expected subtypes, each significantly overrepresented in allogeneic kidneys (Fig. 4 A, B). The majority of leukocytes consisted of macrophage subtypes and CD8 + T cells (Fig. 4 A). Spatial analysis showed that most leukocytes were located in the cortex and around blood vessels (Fig. 4 C). However, both, T cells and macrophages were also observed in the inner medulla. For spatial proximity analysis, we analyzed the percentage of injured cells from PT and TAL directly neighboring a leukocyte cell type (Fig. 4 D). We also included randomly selected cells in this analysis to statistically test under- or overrepresentation of direct spatial proximity. The highest percentage of directly neighboring leukocytes could be observed in the injured PT cell states and was particularly high in PT Injury m3 (93%, 46% and 99% for CD8 + T cell, CD4 + T cell or a macrophage, respectively) (Fig. 4 D). The number of PT Injury m3 cells directly neighboring a CD8 + T cell was significantly higher than for randomly chosen cells. The number of directly neighboring leukocytes was lowest in the most severely injured TAL cell state, TAL Injury m3 and significantly lower than for randomly chosen cells (35%, 9% and 51% for CD8 + T cells, CD4 + T cells or macrophages, respectively). These results show a statistically differential interaction of leukocytes with different TCMR-induced injured cell states in PT and TAL. Human TCMR kidney allografts exhibit injured cell states resembling those in mice To compare the mouse TCMR data with human samples, we performed snRNA-seq on three TCMR and three stable allograft biopsies from archived samples of the protocol biopsy program at Hannover Medical School (see Supplementary Table S3 for patient and biopsy details). The snRNA-seq generated high-quality transcriptomic data, with 22183 nuclei used for analysis, allowing the identification of all major cell types (Fig. 5 A, B, Suppl. Fig. S8). Unlike the mouse data, we did not observe a distinct cluster of proliferating cells, likely due to the earlier time point of TCMR injury in the mouse samples. Subclustering of the PT and TAL revealed the presence of injured PT and TAL states (Fig. 5 C-H), characterized by reduced expression or absence of canonical markers and upregulation of injury markers, similar to those observed in the mouse model. However, the relative abundances of injured human PT and TAL cells was not significantly higher in TCMR samples when compared to stable allografts. This aligns with the broader spectrum of injury sources in human kidney allografts beyond TCMR and the variable timing of biopsy collection relative to TCMR onset. To bridge the mouse and human datasets, we performed a cross-species analysis of PT and TAL injury states. As suggested from the marker gene expression, the most severely injured mouse PT and TAL cell states corresponded to human PT Injury h2 and TAL Injury h1 and h2 (Fig. 5 E, H). Human PT Injury h2 expressed all markers of PT failed repair, similar to PT Injury m4, including VCAM1, CD44 and VIM (Fig. 5 C). For TAL cells, two distinct injury states were identified in the human samples, TAL Injury h1 and h2. The larger TAL Injury h2 cluster showed a pronounced EMT signature with markers such as CD44, TPM and SPP1, associated with severe TAL injury in AKI and marker genes of TAL Injury m3. The smaller human TAL injury cluster TAL Injury h1 also shares marker genes with TAL Injury m3 including IGBP1 and MET. Pathway enrichment analysis of TAL Injury h1 marker genes revealed a pronounced expression p53 signaling. Systematic cross-species analysis revealed that TAL Injury m3 from the mouse dataset closely corresponded to human TAL Injury h2, although it also shared features with TAL Injury h1 (Fig. 5 H). Injured epithelial cell states significantly impact kidney allograft survival after TCMR To assess the impact of injured human PT and TAL cell states, we generated biomarker gene sets which were highly specific for PT Injury h1 and h2 and TAL Injury h1 and h2 (Suppl. Fig. S9 and S10, Suppl. Table S4). To correlate these signatures with clinical outcome, we investigated their correlation with 3-year kidney allograft outcome in bulk transcriptomic data from a large kidney transplant bulk transcriptomics cohort comprising 1292 patients (including 624 with no rejection, 297 antibody-mediated rejections, 95 TCMRs, 45 mixed rejections, 50 probable antibody-mediated rejections or probable TCMRs) (Fig. 6 ). Scores for provided marker gene sets were derived from geometric mean expression of all marker gene sets in bulk data. We additionally included the IRRAT30 gene set representing injury-induced transcripts which were previously reported to be an important factor of kidney allograft survival after TCMR 17 . For all biopsies (including TCMR and all other biopsies), higher scores for PT Injury h2, TAL Injury h1 and TAL Injury h2 were significantly associated with an increased risk of allograft loss within 3 years post-biopsy (Fig. 6 A). Conversely, high scores for PT Injury h1 were associated with improved allograft survival after 3 years. This suggests that PT Injury h1, which we interpret as a less severely injured state, may represent a potentially regenerating PT cell phenotype. While the IRRAT30 and PT injury cell states exhibited a comparable influence on kidney allograft survival following TCMR and in other kidney transplant biopsies, the TAL injury signatures (TAL Injury h1 and h2) demonstrated a significantly greater effect on kidney allograft survival specifically after TCMR compared to all other transplant biopsies (Fig. 6 A). To evaluate the persistence of gene set scores over time in patients initially diagnosed with TCMR, we analyzed biopsies with available follow-up samples. These included biopsies with initial diagnosis of TCMR (and potentially TCMR in the follow-up samples), ultimately leading to a "no rejection" (NR) diagnosis in the last biopsy (n = 12, Fig. 6 B). Despite the limited number of patients in this analysis, two key observations emerged. First, the levels of injury-related gene set scores exhibited significant variability among TCMR patients. This aligns with our findings in AKI, where heterogeneous distributions of injured epithelial cell states were observed among patients 22 . Second, for PT Injury h2 and TAL Injury states in particular, a notable subset of patients demonstrated persistently high scores specific to these injured epithelial cell states, even after the resolution of the initial TCMR diagnosis. This suggests that, while kidneys may achieve remission from TCMR, they can still harbor outcome-relevant, elevated levels of injured epithelial cells months after the initial episode. It is of note that the gene sets specific for PT and TAL Injury h1 and h2 did also successfully label injured PT and TAL cells in AKI from our previous study (Suppl. Fig. S11) 22 . While the distinction between the different injured PT and TAL states was not as pronounced as for TCMR, we still observed correspondence of TAL Injury h1 and h2 with severely injured TAL cells from AKI (TAL-New 3 and 4). PT Injury h1 corresponded mostly with a less severely injured PT cell state (PT-New 1) which expresses genes involved in oxidative stress. On the other hand, PT Injury h2 was strongly associated with severe and potentially maladaptive PT injury in AKI (PT-New 4). Discussion In this study, we explored the molecular changes associated with TCMR and their clinical impact on kidney allograft survival. Using mouse models of TCMR, we identified molecular changes, including a significant gene expression response in most kidney cell types, with the most pronounced effects in the PT and TAL. TCMR was linked to the emergence of TCMR-associated cell states, primarily in PT and TAL. Cross-species analysis enabled us to correlate these injured cell states in mice with corresponding states in human biopsies from TCMR patients and stable allograft controls. Notably, all injured cell states in PT and TAL in humans had mouse counterparts. By deriving biomarker gene sets specific to each human injury cell state in PT and TAL, we calculated a score for these gene sets in a large kidney transplant bulk transcriptomics cohort and correlated these scores with clinical outcomes. We found that all identified injury clusters in humans were associated with allograft survival, with all but one gene set score (PT Injury h1) linked to reduced allograft survival. In TCMR samples, injured TAL cell states had the greatest impact on allograft survival, with a significantly stronger effect compared to all other kidney biopsies. This suggests that while similar injured cell states may occur in different settings post-kidney transplantation, their relative importance for clinical outcomes can differ substantially. The clinically most relevant cell states, PT Injury h2, TAL Injury h1 and h2, are characterized by marker genes previously identified in various kidney disease contexts beyond transplantation 22 – 25 , 32 , 34 . These cell states show gene expression changes indicative of EMT upregulation. Similar cell states have been observed in AKI and CKD, in, both, mouse and human samples 22 , 24 . Notably, VCAM1-positive PT cells, often referred to as maladaptive or “failed repair” cells, reflect significant dedifferentiation and exhibit pro-inflammatory and pro-fibrotic phenotypes. Injured TAL cell states, including the EMT-upregulated genes in TAL Injury h1 and h2 (e.g. MET, ITGB1, CD44, NNMT and SPP1), have been reported primarily in human AKI and CKD studies 22 , 23 . In non-transplant settings, VCAM1-expressing PT and CD44-expressing TAL cell states have been associated with reduced clinical outcomes 23 . Comparing cell states across studies is challenging due to variability in methodologies and limited single-cell studies in transplantation, which typically include fewer patients than studies on native kidneys. Nonetheless, similar cell types have been reported in other contexts including kidney transplantation 35 – 40 . Whether these represent identical cell states or share superficial similarities with profound underlying differences remains uncertain. However, it is evident that a range of kidney diseases and complications, in, both, native and transplanted kidneys, induces highly dedifferentiated EMT-like cell states in the PT and TAL. The key question is what these cell states signify. If they merely reflect nephron loss, their correlation with clinical outcomes is unsurprising. However, emerging evidence suggests that injured epithelial cell states may persist and interact with fibroblasts and immune cells, forming pro-fibrotic microenvironments that exacerbate renal damage 25 , 34 . To explore persistence, we analyzed follow-up biopsies from a subset of patients initially diagnosed with TCMR. Despite the limited sample size, we observed that elevated gene set scores indicative of PT and TAL injury states often persisted, even after the clinical resolution of TCMR. This challenges the notion that EMT-like states, such as PT Injury h2 and TAL Injury h2, simply represent dying tubules. Further research is needed to confirm these findings and elucidate their implications. Our data revealed the strong impact of TAL injury on kidney allograft survival after TCMR, contrasting with the results from all kidney transplant biopsies. PT injury showed comparable impact on transplant survival after TCMR and within all biopsies. The underlying reasons for these differences are unclear. Specifically, it is unknown why severe TAL injury has less impact on allograft survival in some transplant contexts when compared to TCMR. One hypothesis is that, for instance in TCMR, severe PT injury, beyond a certain threshold, might drive secondary TAL injury, making TAL injury an indicator of extensive cortical tubule damage. Alternatively, in other settings, TAL injury could arise independently under conditions that predominantly affect medullary regions (where most TAL injury cells reside) without causing substantial cortical PT damage. These hypotheses, however, remain speculative and require validation in future studies. Additionally, in TCMR, we observed that leukocyte proximity to TAL Injury m3 cells (the mouse counterpart to TAL Injury h1 and h2) is significantly less frequent than to PT Injury m4 cells (the mouse counterpart to PT Injury h2). This suggests that the pathogenesis of PT Injury h2 and TAL Injury h1/h2 may differ. Our human TCMR single-cell data, along with results from bulk transcriptomics follow-up biopsies, show that PT and TAL injury abundances vary between patients and appear to persist over time in some cases. This suggests that biomarkers indicative of these injured cell states could be used to stratify TCMR patients by risk and to monitor the effectiveness of rejection therapies longitudinally. Furthermore, if these injury states persist, PT and TAL injury cells could serve as therapeutic targets, paving the way for novel and personalized treatment approaches in TCMR. In summary, our study demonstrates the value of integrating high-resolution molecular data from mouse models with defined injury timelines and analogous data from humans, coupled with large cohorts featuring clinical follow-up data. This approach enables the identification of molecular signals specific to injury populations in single-cell transcriptomics and their correlation with clinical end points. It helps dissecting the diverse sources of injury in human kidney transplants and opens new avenues for precision medicine. Methods Animal experiments Male C57BL/6 and BALB/c mice, aged 10-12 weeks, were used for all the experiments. The animals were sourced from Janvier (Le Genest St Isle, France) and maintained under standard housing conditions, with ad libitum access to food and water. All animal procedures were performed following the Directive 2010/63/EU, the German Tierschutz-Versuchstierverordnung. Ethical approved was obtained from the Regional Ethics Committee for Animal Research (Landesamt für Gesundheit und Soziales Berlin, approval number: G0236/18). Mouse kidney transplantation Kidney transplantations were performed in syngeneic (C57BL/6 to C57BL/6 or BALB/c to BALB/c) and allogeneic (C57BL/6 to BALB/c or BALB/c to C57BL/6) mouse combinations under isoflurane inhalation anesthesia as previously described 33 . Briefly, after a midline abdominal incision, the left kidney, aorta and inferior vena cava of the donor mouse were exposed and carefully mobilized. The kidney was flushed in situ with histidine-tryptophane-ketoglutarate (HTK) solution (Custodiol ® , Dr. Franz Köhler Chemie GmbH, Bensheim, Germany), then procured en bloc with the renal vein, renal artery (with a small aortic cuff) and ureter. The harvested kidney was stored in ice-cold HTK solution for 1 hours and thereafter implanted in the left nephrectomized recipient mouse, below the level of native renal vessels. Blood supply to the graft was established through end-to-side anastomoses of the donor renal vessels to the recipient’s abdominal aorta and inferior vena cava. Reconstruction of the urinary tract was achieved by directly anastomosing the donor ureter to the recipient’s bladder. The contralateral native kidney of the recipient mouse was removed 24 hours prior sacrifice on post-operative day (POD) 7 to evaluate renal graft function. For animals undergoing survival analysis, the contralateral native kidney was removed on POD7 and the surviving animals were sacrificed on POD26. Assessment of mouse renal graft function Renal graft function was assessed by serum levels of creatinine and urea. Serum samples were collected terminally from the recipient mice and stored at -20°C until creatinine and urea were measured using the CREP2 Creatinine Plus version 2 and Urea/BUN assays, respectively, on a Roche/Hitachi Cobas C 701/702 system (Roche Diagnostics, Mannheim, Germany). Histopathology Kidney samples were fixed in 4% neutral-buffered paraformaldehyde for 22–24 hours before standard histological processing. Paraffin-embedded tissues were sectioned into 2 μm slices, deparaffinized and stained with periodic acid-Schiff (PAS) stain for histological evaluation. Lesions were evaluated and scored according to the criteria outlined in the Banff classification 41 . Single nucleus mRNA sequencing mouse and human specimens For single-nucleus RNA sequencing (snRNA-seq), the recipient mice were transcardially perfused with ice-cold PBS to clear blood from renal grafts. 1–2 mm middle slices was consistently extracted from the grafts and preserved in pre-cooled RNAlater (Invitrogen #AM7020) at 4°C for 24 hours before being stored at -80°C until nuclei isolation, following the protocol described by Leiz, Hinze et al. (2021) 42 . All samples underwent single-cell sequencing using the 10x Genomics Chromium Next GEM Single Cell 3’ v3.1 chemistry protocol (#CG000204 Rev D), targeting 9,000–10,000 nuclei per sample. Libraries were sequenced on Illumina HiSeq 4000 platforms (paired-end) and digital expression matrices were generated with the 10x Genomics Cell Ranger software (version 3.0.2) using the parameter `–force-cells 10000` against the mouse mm10 genome. Archived human kidney biopsy samples were prepared similarly, stored at -80°C in RNAlater and included from the archive of the Hannover Medical School protocol biopsy program. All subsequent steps, starting from the -80°C storage in RNAlater, were identical to those used for mouse tissue. For human samples, data collection and analysis were performed with informed consent of the patients and with approval of the institutional review board (no 2765). Exclusion criteria for the protocol biopsy program were lack of patient consent, relevant bleeding risks and anticoag­u­lation therapy for an artificial heart valve. Xenium in Situ Profiling In situ single cell RNA expression analysis was performed using the Xenium system (10X Genomics). 5μm thick FFPE tissue sections were placed on a Xenium slide according to the Xenium Tissue Preparation Guide (CG000578). Sections were dried at 37°C for 2 hours and placed overnight in a desiccator at room temperature, followed by deparaffinization and decrosslinking (CG000580). The Probe Hybridization Mix was prepared using a pre-designed 379 gene panel (Mouse Tissue Atlassing v1) and 100 gene custom add-on panel (Suppl. Table S1). Probe Hybridization, Ligation & Amplification were performed according to the Xenium In Situ Gene Expression user guide (CG000582). Raw data was processed in real-time with on-board Xenium Analyzer software v1.7.6. Bioinformatic analyses of spatial transcriptomic data Determination of major cell types in Xenium Data Raw data was analyzed using the xenium ranger v2.0 import-segmentation tool with parameter –expansion-distance=5. Results from this were directly imported into Seurat and clustered. In brief, data from all samples were normalized scaled and PCA was performed using highly variable genes. Harmony analysis was run on which basis clustering was performed with resolution 0.3. The so-generated clusters were inspected on the spatial histological images and for marker gene expression. Based on this, broad cell types were determined. All cells from the leukocyte, interstitial cell or endothelial cell compartment were first labeled as non-tubular cells. Non-tubular cells were further separated into endothelial cells, interstitial cells and leukocytes using the Robust Cell Type Decomposition tool 43 and the snRNA-seq data from our mouse experiments. Additionally, cell types (Prolif, CD-IC-A, CD-IC-B and PEC) were also determined by using RCTD and mouse snRNA-seq. RCTD was also used for sub cell type annotation (e.g. PT Injury h1 etc.). Spatial proximity For every injury cluster of interest (e.g. PT Injury h2), the minimum distance to each leukocyte sub cell type of interest was calculated per cell based on the provided centroid information from the Xenium data. We chose 25µm as indicative of being a direct neighbor as derived from average nearest neighbor distance analysis of Xenium data. Bioinformatic analyses of single nucleus mRNA sequencing data Cell type annotation and initial clustering Digital expression matrices were generated with the 10x Genomics Cell Ranger software (version 3.0.2 for mice and version 8.0.0 for human samples) using the parameter `–expect-cells 10000` against the mouse mm10 or human GRCh38-3.0.0 genome. Human samples were further filtered using cellbender remove-background with fpr 0.01 to remove ambient RNA. Data integration and clustering were performed using the following workflow: NormalizeData -> FindVariableFeatures -> SelectIntegrationFeatures -> FindIntegrationAnchors -> IntegrateData -> ScaleData -> RunPCA(npcs=30) -> harmony::RunHarmony(group.by.vars = c("group"), lambda = 2, tau = 1000, theta = 1, assay.use = "integrated", kmeans_init_nstart = 60, kmeans_init_iter_max=2000, max.iter.harmony = 30) -> ScaleData -> FindNeighbors(reduction=”harmony”, dims=1:15) -> FindClusters(resolution=0.5) Cell types were assigned based on marker gene expression as published previously 22 . Differential gene expression analysis For mouse samples, differential gene expression analysis was performed using Seurat’s FindMarkers function, comparing allogeneic kidneys from each group (C57BL/6 and BALB/c) to their syngeneic controls per cell type. Genes were considered differentially expressed in case of |log2 fold change|>1, adjusted p-value<0.05 and expressed in at least 10% of cells in allogeneic kidneys (for differentially upregulated genes in allogeneic kidneys) or 10% of cells in syngeneic kidneys (for downregulated genes in allogeneic kidneys). Cross-species analysis For this analysis, we first calculated marker genes for each mouse PT or TAL cluster using Seurat’s FindAllMarkers function with parameters only.pos = TRUE, logfc.threshold = 1. Markers were then filtered for pct.1>0.1 and adjusted p-value<0.05. Mouse genes were translated to human genes using the biomaRt package. Average expression per cluster on these genes was calculated for human and mouse samples using Seurat’s AverageExpression function. Correlation was calculated using R’s built-in cor() function with method=”spearman”. Determination of marker genes for injury clusters To get marker genes specific for the respective cell populations (further referred to as TP = “target population”), we first generated a list of potential marker genes for the TP by using the FindMarkers function with ident.1=TP and ident.2=”all other cells”. We then calculated the average expression of genes of interest in the TP. We also calculated the average expression of the mentioned genes in the adjacent injured cell population. E.g., if TP was TAL Injury h2, we also calculated average gene expression in TAL Injury h1. We only considered genes which show higher average expression in TP than in the adjacent injury population. This reduces the number of genes and the computational resources required downstream. The last filtering step included the generation of 100-cell neighborhoods of nearest neighbors around each cell not in TP. We furthermore excluded cells with 100-cell neighborhoods overlapping with the TP. We then calculated average expression of each of the remaining gene candidates in all 100-cell neighborhoods. Final genes only included genes which showed an average expression in the TP > 1.25 fold compared to all other 100-cell neighborhoods. Kidney transplant bulk transcriptomics cohort The 5086 kidney transplant biopsies previously described in detail 44 collected from 3995 patients through the MMDx-Kidney studies, as well as through the MMDx service laboratory in Portland, OR (Kashi Laboratories, submitted as anonymized files for this study) were processed for MMDx. Genome-wide assessment (49,495 probe set values representing 19,462 genes) of the 5086 biopsies was measured by microarrays 44 . MMDx signouts were available for most of the data set. Rejection and injury states were derived according to previously published methods 45 . Biopsies having ≤10% cortex were excluded leaving 4502 biopsies. From this set of 4502 biopsies, MMDx signouts and graft status were available for 1618 biopsies from 1292 patients. For survival analyses, we selected the first biopsy post-transplant for each patient, leaving 1292 biopsies for assessment. The MMDx-Kidney studies adhere to the Declaration of Helsinki. All biopsies were collected with informed consent per institutional review board review at each local center and approved in Edmonton by the University of Alberta (#Pro00022226). The clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the 'Declaration of Istanbul on Organ Trafficking and Transplant Tourism'. Gene set scores in bulk transcriptomic data Gene set scores for each human PT and TAL cell states were assigned to each biopsy in the K4502 population by taking the geometric mean expression of all marker genes for each biopsy, normalized to the geometric mean expression in 4 nephrectomy control biopsies (i.e., mean score in nephrectomy is 0). Risk of graft loss 3-years post biopsy associated with gene set scores was assessed across the entire population of biopsies. Scores were assessed individually with Kaplan-Meier estimates, stratified by their median value in the full population. The transition of gene set scores following TCMR diagnosis was depicted in MMDx-Kidney biopsies in patients diagnoses with TCMR (n = 12) which eventually progressed to no rejection (NR) in subsequent follow-up biopsies. Declarations Data availability Raw and processed data are accessible through Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE284742 Reviewer token: erkjcekgtfgnnqt Code availability All code is accessible through GitHub: https://github.com/LorenzJahnMHH/TCMR-induced-epithelial-injury-patterns-in-kidney-transplants- Material availability Mouse tissue: RNAlater-stored mouse kidney samples were completely used for snRNA-seq experiments. Paraffine-embedded tissue is available. Human tissue: RNAlater-stored kidney biopsy cores were completely used for snRNA-seq experiments. Paraffine-embedded tissue could be made available. Acknowledgement This study was supported by grants to M.I.A., I.M.S., F.I., K.M.S.O.: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project ID 394046635, SFB 1365 and grants to C.H.: DFG (grant HI 2238/2–1) and Ministry for Science and Culture of Lower Saxony as project of the “Center for Organ Regeneration and Replacement (CORE)”, Transplant Center, Hannover Medical School. Disclosure Authors have nothing to disclose. References Nankivell, B. J. & Alexander, S. I. Rejection of the kidney allograft. N Engl J Med 363 , 1451-1462, doi:10.1056/NEJMra0902927 (2010). Nankivell, B. J. et al. The natural history of chronic allograft nephropathy. N Engl J Med 349 , 2326-2333, doi:10.1056/NEJMoa020009 (2003). Venner, J. M. et al. Molecular landscape of T cell-mediated rejection in human kidney transplants: prominence of CTLA4 and PD ligands. Am J Transplant 14 , 2565-2576, doi:10.1111/ajt.12946 (2014). Tamargo, C. L. & Kant, S. 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Biopsy transcriptome expression profiling to identify kidney transplants at risk of chronic injury: a multicentre, prospective study. Lancet 388 , 983-993, doi:10.1016/S0140-6736(16)30826-1 (2016). Einecke, G. et al. Factors associated with kidney graft survival in pure antibody-mediated rejection at the time of indication biopsy: Importance of parenchymal injury but not disease activity. Am J Transplant 21 , 1391-1401, doi:10.1111/ajt.16161 (2021). Cippa, P. E. et al. Transcriptional trajectories of human kidney injury progression. JCI Insight 3 , doi:10.1172/jci.insight.123151 (2018). Hinze, C. et al. Single-cell transcriptomics reveals common epithelial response patterns in human acute kidney injury. Genome Med 14 , 103, doi:10.1186/s13073-022-01108-9 (2022). Lake, B. B. et al. An atlas of healthy and injured cell states and niches in the human kidney. Nature 619 , 585-594, doi:10.1038/s41586-023-05769-3 (2023). Kirita, Y., Wu, H., Uchimura, K., Wilson, P. C. & Humphreys, B. D. 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Gerhardt, L. M. S. et al. Lineage Tracing and Single-Nucleus Multiomics Reveal Novel Features of Adaptive and Maladaptive Repair after Acute Kidney Injury. J Am Soc Nephrol 34 , 554-571, doi:10.1681/ASN.0000000000000057 (2023). Liu, Y. et al. Single-cell analysis reveals immune landscape in kidneys of patients with chronic transplant rejection. Theranostics 10 , 8851-8862, doi:10.7150/thno.48201 (2020). Malone, A. F. et al. Harnessing Expressed Single Nucleotide Variation and Single Cell RNA Sequencing To Define Immune Cell Chimerism in the Rejecting Kidney Transplant. J Am Soc Nephrol 31 , 1977-1986, doi:10.1681/ASN.2020030326 (2020). Rashmi, P. et al. Multiplexed droplet single-cell sequencing (Mux-Seq) of normal and transplant kidney. Am J Transplant 22 , 876-885, doi:10.1111/ajt.16871 (2022). Wu, H. et al. Single-Cell Transcriptomics of a Human Kidney Allograft Biopsy Specimen Defines a Diverse Inflammatory Response. J Am Soc Nephrol 29 , 2069-2080, doi:10.1681/ASN.2018020125 (2018). McDaniels, J. M. et al. Single nuclei transcriptomics delineates complex immune and kidney cell interactions contributing to kidney allograft fibrosis. Kidney Int 103 , 1077-1092, doi:10.1016/j.kint.2023.02.018 (2023). Suryawanshi, H. et al. Detection of infiltrating fibroblasts by single-cell transcriptomics in human kidney allografts. PLoS One 17 , e0267704, doi:10.1371/journal.pone.0267704 (2022). Roufosse, C. et al. A 2018 Reference Guide to the Banff Classification of Renal Allograft Pathology. Transplantation 102 , 1795-1814, doi:10.1097/TP.0000000000002366 (2018). Leiz, J. et al. Nuclei Isolation from Adult Mouse Kidney for Single-Nucleus RNA-Sequencing. J Vis Exp , doi:10.3791/62901 (2021). Cable, D. M. et al. Robust decomposition of cell type mixtures in spatial transcriptomics. Nat Biotechnol 40 , 517-526, doi:10.1038/s41587-021-00830-w (2022). Halloran, P. F. et al. Subthreshold rejection activity in many kidney transplants currently classified as having no rejection. Am J Transplant 2024 Aug 6:S1600-6135(24)00461-1. doi: 10.1016/j.ajt.2024.07.034. Online ahead of print. , doi:10.1016/j.ajt.2024.07.034 (2024). Reeve, J. et al. Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. JCI Insight 2 , e94197, doi:10.1172/jci.insight.94197 (2017). Additional Declarations There is NO Competing Interest. Supplementary Files SupplTableS1Xeniumgenes.pdf List of custom genes for Xenium spatial transcriptomics SupplTableS2bl6bcDGEgenes.pdf Differentially expressed genes between allogeneic and syngeneic mouse kidneys SupplTableS3sampleinfohuman.pdf Sample information for human kidney biopsy cores SupplTableS4injurymarkergenesets.pdf Marker genes for injured epithelial cell states NMEDA138224rs.pdf Reporting Summary NMEDA138224epc.pdf Editorial Policy Checklist SupplFigs.pdf Collection of all supplemental figures Cite Share Download PDF Status: Published Journal Publication published 28 Jan, 2026 Read the published version in Nature Communications → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5683198","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":411152317,"identity":"e6908415-9e71-404d-b0a1-4f29c5df2f8d","order_by":0,"name":"Christian Hinze","email":"data:image/png;base64,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","orcid":"","institution":"Department of Nephrology and Hypertension, Hannover Medical School","correspondingAuthor":true,"prefix":"","firstName":"Christian","middleName":"","lastName":"Hinze","suffix":""},{"id":411152318,"identity":"1727000a-ac6a-4fa6-9351-292b215646af","order_by":1,"name":"Anna Pfefferkorn","email":"","orcid":"https://orcid.org/0000-0002-7126-9838","institution":"Department of Experimental Surgery","correspondingAuthor":false,"prefix":"","firstName":"Anna","middleName":"","lastName":"Pfefferkorn","suffix":""},{"id":411152319,"identity":"f1b6e7d4-a06a-4554-9da2-6659f209b315","order_by":2,"name":"Lorenz Jahn","email":"","orcid":"","institution":"Department of Nephrology and Hypertension","correspondingAuthor":false,"prefix":"","firstName":"Lorenz","middleName":"","lastName":"Jahn","suffix":""},{"id":411152320,"identity":"f177ee84-d15e-4134-8523-8955187e44df","order_by":3,"name":"Patrick Gauthier","email":"","orcid":"","institution":"Alberta Transplant Applied Genomics Centre","correspondingAuthor":false,"prefix":"","firstName":"Patrick","middleName":"","lastName":"Gauthier","suffix":""},{"id":411152321,"identity":"3a36728a-e8bf-4829-bb1a-ea1e2f74da74","order_by":4,"name":"Janna Leiz","email":"","orcid":"","institution":"Department of Nephrology and Hypertension","correspondingAuthor":false,"prefix":"","firstName":"Janna","middleName":"","lastName":"Leiz","suffix":""},{"id":411152322,"identity":"c7b8214f-156d-4aa2-bb2f-b921e99e8697","order_by":5,"name":"Sadia Safraz","email":"","orcid":"","institution":"Department of Experimental Surgery","correspondingAuthor":false,"prefix":"","firstName":"Sadia","middleName":"","lastName":"Safraz","suffix":""},{"id":411152323,"identity":"b18b7595-4044-4608-abfe-5b81b640dabe","order_by":6,"name":"Vera Kulow","email":"","orcid":"","institution":"Charité – Universitätsmedizin Berlin","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"Kulow","suffix":""},{"id":411152324,"identity":"1803d040-a7e0-42b6-966c-87e456b0d39c","order_by":7,"name":"Izabela Plumbom","email":"","orcid":"","institution":"Genomics Technology Platform","correspondingAuthor":false,"prefix":"","firstName":"Izabela","middleName":"","lastName":"Plumbom","suffix":""},{"id":411152325,"identity":"4fc9c17c-ffe1-414c-89b1-043cd39c1a98","order_by":8,"name":"Svjetlana Lovric","email":"","orcid":"https://orcid.org/0000-0003-3953-9794","institution":"Hannover Medical School","correspondingAuthor":false,"prefix":"","firstName":"Svjetlana","middleName":"","lastName":"Lovric","suffix":""},{"id":411152326,"identity":"79c82a17-f06b-48aa-b85e-9cb545c6087f","order_by":9,"name":"Jessica Schmitz","email":"","orcid":"https://orcid.org/0000-0001-9843-7339","institution":"Institute of Pathology, Nephropathology Unit, Hannover Medical School, 30625 Hannover, Germany.","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Schmitz","suffix":""},{"id":411152327,"identity":"611d3069-d135-48ba-8910-d8b3ddd31965","order_by":10,"name":"Jan Bräsen","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Bräsen","suffix":""},{"id":411152328,"identity":"a0c4a4cb-10fa-436f-861a-eb8bda6f210a","order_by":11,"name":"Irina Scheffner","email":"","orcid":"","institution":"Department of Nephrology and Hypertension","correspondingAuthor":false,"prefix":"","firstName":"Irina","middleName":"","lastName":"Scheffner","suffix":""},{"id":411152329,"identity":"21a48850-0ef4-4f32-bf3c-66310fd7326b","order_by":12,"name":"Michael Fähling","email":"","orcid":"https://orcid.org/0000-0003-1079-5049","institution":"Charité - 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A \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eScheme depicting the overall study design including mouse and human samples. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Upper panel: Representative hematoxylin and eosin staining of a C57BL/6 kidney transplanted into a BALB/c mouse 7 days after transplantation showing interstitial inflammation (red arrows) and tubulitis (black arrow). Lower panels: Significantly elevated serum creatinine levels in allogeneic kidneys and reduced survival. Kruskal- Wallis test followed by Dunn’ test for multiple comparisons. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Uniform manifold approximation and projection (UMAP) of snRNA-seq from mouse kidney transplants highlighting all major cell types and heatmap depicting marker gene expression. Selected markers are highlighted above the heatmap. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e ST data of a syngeneic kidney (C57BL/6 to C57BL/6 7 days after transplantation) showing the spatial distribution of major cell types. The lower panels show a magnification of a glomerulus from the same kidney with the periodic acid-Schiff staining on the left and cell type identities from ST data overlayed on the right. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eE\u003c/strong\u003e\u003c/em\u003e\u003cem\u003ePrincipal component analysis on PT pseudobulk data from snRNA-seq and PT-specific highly variable genes. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eF \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative abundances of major cell types in snRNA-seq samples. Student’s t test with Benjamini Hochberg correction for multiple testing. P-value: *\u0026lt;0.05, **\u0026lt;0.005. Prolif - proliferation, PEC - parietal epithelial cells, Uro – urothelium, IntC – interstitial cells, Leuko – leukocytes, EC – endothelial cells, CD-PC/IC-A/IC-B – collecting duct principal/type A and type B intercalated cells, CNT – connecting tubule, DCT – distal convoluted tubule, TAL –thick ascending limb, tL – thin limb, PT – proximal tubule, Podo – podocyte.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figures1.png","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/92e2c19dadd2f704bf2733a8.png"},{"id":78154552,"identity":"64db9e2f-7046-463c-a7fe-b4709e67ffd6","added_by":"auto","created_at":"2025-03-10 12:17:50","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":641845,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMouse allogeneic transplantation induces strong epithelial gene expression response. A\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Bar plots showing the number of differentially upregulated (left) and downregulated (right) genes across major kidney cell types for BALB/c to C57BL/6 (green) and C57BL/6 to BALB/c (blue) transplants when compared to the respective syngeneic control. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003cem\u003eHeatmaps displaying differentially upregulated (left) and downregulated (right) genes by cell type for both groups. Heatmaps show log2 fold changes of allogeneic kidneys with their respective syngeneic control. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Dot plot of pathway enrichment analysis of differentially upregulated genes for each major cell type and transplant group, e.g. BALB/c to C57BL/6 (green) and C57BL/6 to BALB/c (blue). Shown are all significant pathways (FDR \u0026lt; 0.05) from gene set enrichment analysis using hallmark gene sets.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figures2.png","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/95e4bf39607bc47fe6f143bc.png"},{"id":78154554,"identity":"a9562b69-84e9-40ec-8923-eda5ee8baa31","added_by":"auto","created_at":"2025-03-10 12:17:50","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":923038,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eTCMR induces spatially diverse injury cell populations in PT and TAL. A\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e SnRNA-seq UMAP plot of PT subclustering including anatomical segments (PT S1-3, PT S3 med.), a proliferative (PT Prolif) and injured cell clusters (PT Injury m1-4). Heatmap on the right displays expression of marker genes across PT subclusters, maximum-normalized on a per-gene basis. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Relative abundances of PT subclusters across different transplant groups. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e snRNA-seq UMAP plot of TAL subclusters (left) and heatmap of TAL marker gene expression (right). \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD\u003c/strong\u003e\u003c/em\u003e\u003cem\u003e Relative abundances of TAL subclusters across transplant groups. Student’s t test with Benjamini Hochberg correction for multiple testing. P-value: *\u0026lt;0.05, **\u0026lt;0.005, ***\u0026lt;0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figures3.png","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/9e6dfc5183878437a7b888d8.png"},{"id":78154046,"identity":"e02a359e-8198-4444-8f23-1d66ea99c05c","added_by":"auto","created_at":"2025-03-10 12:09:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1101081,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eLeukocyte subtypes exhibit varying proximities to TCMR-induced injured epithelial cell states within PT and TAL. A \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eSnRNA-seq\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eUMAP plot of subclustering analysis of all Leukocytes. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative abundances of leukocyte subtypes in individual samples. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eSpatial distribution of CD8+ cells and macrophages in ST data. Notably, the remaining broad leukocyte cell types are not shown and would be not visible due to the magnification and their low abundance. Student’s t test with Benjamini Hochberg correction for multiple testing. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eThis plot shows the percentage of cells (per cluster on the x-axis) with a direct neighboring leukocyte of the specified type. Statistical comparisons to random cells are indicated to highlight significant differences. Allogeneic transplantation C57BL/6 to BALB/c (triangle pointing downwards) and BALB/c to C57BL/6 (triangle pointing upwards). Student’s t test with Benjamini Hochberg correction for multiple testing. P-value: *\u0026lt;0.05, **\u0026lt;0.005, ***\u0026lt;0.001.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figures4.png","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/d996e3e28437d3258b97d19c.png"},{"id":78155487,"identity":"637a0421-b459-4e72-8a8c-a3d33ba51923","added_by":"auto","created_at":"2025-03-10 12:25:50","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":870564,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eMost severely injured mouse PT and TAL clusters correlate with injury clusters in human kidney allografts. A \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eUMAP plot of kidney biopsies from human TCMR samples and stable allografts (n=3) displaying major cell types and heatmap with marker gene expression. Selected marker genes are plotted right of the heatmap. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative abundances of major cell types in individual samples. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eC \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eSubclustering of human PT cells with corresponding marker gene heatmap. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eD \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eRelative abundances of PT subclusters per sample. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eE \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eCorrelation of mouse and human PT subclusters using mouse PT subcluster marker genes. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eF-H \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eAnalogous plots for TAL.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figures5.png","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/669c29573a0593b62ac742ad.png"},{"id":78154047,"identity":"f40d86b8-9e80-4d4d-a8f5-0d4cc3a8da4e","added_by":"auto","created_at":"2025-03-10 12:09:50","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":473586,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eInjured epithelial cell states impact kidney allograft survival. A \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eKaplan Meier curves for allograft survival in cohort of 1292 kidney transplant biopsies from 1292 patient including 95 TCMR biopsies. Survival is plotted for non-TCMR/TCMR patients below (dark and light blue) and above (orange, red) the median score of the respective gene set in the full cohort. False discovery rates (fdr) were derived from log-rank test. Identical letters on the right-hand side of the plot indicate no statistical difference. \u003c/em\u003e\u003cem\u003e\u003cstrong\u003eB. \u003c/strong\u003e\u003c/em\u003e\u003cem\u003eGene set scores over time for patients with initial diagnosis of TCMR and TCMR or no rejection in the follow up biopsies (n = 12).\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figures6.png","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/083121c84a8a0754b54f36d6.png"},{"id":101390543,"identity":"03630aad-cb97-40d4-8cbc-36c81a30594c","added_by":"auto","created_at":"2026-01-29 08:16:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8764347,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/4f6064e1-c5e7-440f-97e9-bd376f2cad31.pdf"},{"id":78152527,"identity":"285c1e16-f5cb-46a2-8c3a-288e18ce52ff","added_by":"auto","created_at":"2025-03-10 12:01:50","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":429839,"visible":true,"origin":"","legend":"List of custom genes for Xenium spatial transcriptomics","description":"","filename":"SupplTableS1Xeniumgenes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/944161157b8e7946b31c161a.pdf"},{"id":78154050,"identity":"4ecb9018-3a30-4f19-9f5c-ee570e4caa30","added_by":"auto","created_at":"2025-03-10 12:09:50","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":17601938,"visible":true,"origin":"","legend":"Differentially expressed genes between allogeneic and syngeneic mouse kidneys","description":"","filename":"SupplTableS2bl6bcDGEgenes.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/9bbde9755a5d019485d2b743.pdf"},{"id":78152533,"identity":"382756e7-259c-4951-8f01-0ac441191a3c","added_by":"auto","created_at":"2025-03-10 12:01:50","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":404921,"visible":true,"origin":"","legend":"Sample information for human kidney biopsy cores","description":"","filename":"SupplTableS3sampleinfohuman.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/6664df761203a882fa04ccd2.pdf"},{"id":78152538,"identity":"b2abaf2c-4275-4277-ac2e-0ed79356b2ad","added_by":"auto","created_at":"2025-03-10 12:01:50","extension":"pdf","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":426086,"visible":true,"origin":"","legend":"Marker genes for injured epithelial cell states","description":"","filename":"SupplTableS4injurymarkergenesets.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/459961f005aba74137666565.pdf"},{"id":78152535,"identity":"42535959-4b6b-4db3-bb0f-110db2a61929","added_by":"auto","created_at":"2025-03-10 12:01:50","extension":"pdf","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":1447831,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"NMEDA138224rs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/b6ab7b2878335198cf4ead58.pdf"},{"id":78155502,"identity":"7a7ceee6-a511-4afe-bc09-94a619689bde","added_by":"auto","created_at":"2025-03-10 12:25:51","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":1828841,"visible":true,"origin":"","legend":"Editorial Policy Checklist","description":"","filename":"NMEDA138224epc.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/ed5c3141b3b01c0fc397165d.pdf"},{"id":78152553,"identity":"484b1268-8331-42c7-b735-2b2a1f598216","added_by":"auto","created_at":"2025-03-10 12:01:51","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":39435415,"visible":true,"origin":"","legend":"Collection of all supplemental figures","description":"","filename":"SupplFigs.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5683198/v1/467d12750a883b464923edbd.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Thick ascending limb injury critically impacts kidney allograft survival after T-cell-mediated rejection","fulltext":[{"header":"Introduction","content":"\u003cp\u003eT cell-mediated rejection (TCMR) is a severe complication after kidney transplantation associated with reduced graft survival\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Often occurring within the first year after kidney transplantation, TCMR represents an adaptive immune response triggered by donor antigens, primarily mediated by T cells\u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. This immune reaction causes injury to the transplanted kidney, resulting in inflammation and cellular damage, which can advance to chronic graft dysfunction. TCMR is defined histologically by interstitial leukocyte infiltration, tubulitis (inflammation of renal tubules) and, in some instances, arteritis\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Although the precise mechanisms driving kidney injury during TCMR are not fully understood, they likely involve direct cytotoxic T cell activity, a pro-inflammatory cytokine environment and interstitial edema leading to hypoxia in kidney cells\u003csup\u003e\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. Current therapeutic strategies for TCMR focus on suppressing the immune cell infiltrate through intensified immunosuppression, often including steroid pulses\u003csup\u003e\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. While these treatments achieve histological remission in most cases, TCMR remains associated with reduced allograft survival, with outcomes at least as poor as those in ABMR despite TCMR being considered treatable\u003csup\u003e\u003cspan additionalcitationids=\"CR16 CR17\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. This raises questions about whether current therapies adequately address all aspects of TCMR-induced injury.\u003c/p\u003e \u003cp\u003eLarge-scale bulk transcriptomic studies have underscored the importance of epithelial injury signatures, especially in TCMR, noting that these signatures are most relevant for allograft outcomes, whereas immune cell infiltration signatures appear to have limited prognostic value\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. Recent advances in high-resolution transcriptomic technologies have offered new perspectives on the molecular mechanisms underlying kidney diseases. These technologies, particularly in the context of acute kidney injury (AKI) and chronic kidney disease (CKD), have revealed the existence of injury-induced epithelial cell states\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Emerging evidence suggests that these injured cell states are not mere bystanders but actively contribute to further kidney damage through their pro-inflammatory and pro-fibrotic genes expression profiles\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDespite these insights, epithelial injury signatures in TCMR have yet to be systematically characterized or targeted therapeutically. The complexity of evaluating these injury states in transplanted human kidneys is compounded by multiple sources of injury, including drug toxicity, ischemia-reperfusion injury as well as host- and donor-specific factors\u003csup\u003e\u003cspan additionalcitationids=\"CR28 CR29 CR30\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe aim of this study is to delineate the molecular changes associated with TCMR and evaluate their impact on allograft outcomes. We employ single-cell sequencing techniques in mouse models of TCMR to establish a precise signature of TCMR-induced changes. These findings are then rigorously compared to molecular data from human biopsies. By identifying key molecular signatures in TCMR samples and analyzing them in large bulk transcriptomic cohorts with clinical follow-up data, we can assess their clinical significance and potential as therapeutic targets.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eAllogeneic kidney transplantation in mice induces acute TCMR\u003c/h2\u003e \u003cp\u003eAnalyzing molecular signatures of TCMR in patient kidney biopsies poses significant challenges. Firstly, even when TCMR is present, various overlapping injury sources in human kidney allografts - such as drug toxicity, ischemia-reperfusion injury and diverse donor and recipient pathologies and clinical conditions - are commonly observed\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Secondly, the precise timing of TCMR onset in humans is often unknown, complicating inter-patient comparisons.\u003c/p\u003e \u003cp\u003eMouse models of syngeneic and allogeneic kidney transplantation offer a controlled setting to study TCMR at defined time points\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In this study, we therefore propose a cross-species approach by examining gene expression changes in mouse TCMR and systematically comparing them to human TCMR data and clinical outcomes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor the mouse model, we transplanted kidneys from adult male mice of either C57BL/6 or BALB/c strains into syngeneic (C57BL/6 to C57BL/6 or BALB/c to BALB/c, further referred to as syngeneic mice) or allogeneic (C57BL/6 to BALB/c or BALB/c to C57BL/6, further referred to as allogeneic mice) recipients, with a cold ischemia time of 60 minutes. The BALB/c to C57BL/6 allogeneic transplantation was used to model milder rejection compared to the C57BL/6 to BALB/c group, while syngeneic transplants served as controls. Kidneys were harvested 7 days post-transplantation, a time point considered optimal for acute cellular rejection in this model\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Allogeneic transplants induced strong rejection, showing all histological features of TCMR, including tubulitis and interstitial inflammation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Suppl. Fig. S1 for Banff scoring, sample information and additional histology). Clinically, allogeneic mice suffered from acute kidney injury (AKI) and increased mortality (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Suppl. Fig. S2).\u003c/p\u003e \u003cp\u003eTo gain molecular insights into TCMR, we performed single-nucleus mRNA sequencing (snRNA-seq) and spatial transcriptomics (ST) using the Xenium platform (mouse multi-tissue panel plus 100 custom genes; see Suppl. Table S1) on kidneys from, both, syngeneic and allogeneic transplants. Both, snRNA-seq (39706 nuclei from 3 syngeneic kidneys and 5 allogeneic kidneys, Suppl. Fig. S3) and ST (ca. 1.6\u0026nbsp;million segmented cells from 2 syngeneic and 5 allogeneic kidneys, Suppl. Fig. S4) produced high-quality transcriptomic data, enabling us to identify all expected major cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D, Suppl. Fig. S4). Global gene expression was mainly determined by the presence or absence of allogeneic transplantation as derived from principal component analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Leukocyte abundance was significantly higher in allogeneic samples compared to syngeneic controls in snRNA-seq, correlating with the interstitial inflammation observed in histology (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMouse TCMR induces predominant gene expression responses in kidney epithelial cells\u003c/h3\u003e\n\u003cp\u003eDifferential gene expression analysis between allogeneic and syngeneic mice revealed the strongest gene expression response in the kidney epithelium, predominantly in proximal tubules (PT) and thick ascending limbs (TAL) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA, B, Suppl. Table S2). As expected, gene expression changes were most pronounced in allogeneic C57BL/6 kidneys transplanted into BALB/c mice in most cell types. We performed pathway enrichment analysis on up- and downregulated genes, separately (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC, Suppl. Fig. S5). Upregulated genes often showed pan-cell type enrichment in pathways likely associated with the overall inflammatory milieu in the allogeneic kidneys and AKI. This included interferon alpha, interferon gamma, interleukin 2, interleukin 6, TNF alpha and epithelial mesenchymal transition (EMT) signaling. Downregulated genes mostly enriched in pathways associated with metabolism and energy homeostasis (Suppl. Fig. S5). For most cell types, there was a large overlap of differential gene expression between the two different allogeneic models (C57BL/6 to BALB/c or BALB/c to C57BL/6) with a usually stronger gene expression response in C57BL/6 kidneys transplanted into BALB/c mice. Notably, the number of differentially upregulated genes in some cell types (CD-PC, EC, IntC, PEC, CD-IC-A) was higher in the BALB/c to C57BL/6 kidneys which represents the supposedly milder rejection model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eMouse TCMR elicits spatially diverse injured cell states in PT and TAL\u003c/h3\u003e\n\u003cp\u003eSingle-cell studies in AKI have demonstrated the emergence of outcome-relevant AKI-associated cell states within the kidney epithelium\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. To investigate whether similar phenomena occur in TCMR, we conducted subclustering analyses of the snRNA-seq data from PT and TAL cells, which exhibited the strongest gene expression changes.\u003c/p\u003e \u003cp\u003eIn the PT, four injury clusters (PT Injury m1-4) and a cluster of proliferating cells (PT Prolif) were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B). Marker gene analysis showed that PT Injury m4 exhibits the highest expression of injury markers, while the other three injury populations appear to represent transitional cell states, displaying residual anatomical marker staining and lower levels of injury marker expression. The marker genes for PT Injury m4 include several associated with maladaptive repair cells in AKI, such as Vcam1, Cd44 and Vim\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe availability of ST data allows for the spatial mapping of PT injury cell states (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). In congruence with the UMAP plot in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, PT Injury m2 and m3 were predominantly found in the renal cortex, while PT Prolif and PT Injury m1 were enriched towards the medulla. The injury cluster PT Injury m4 could be found in, both, cortex and medulla.\u003c/p\u003e \u003cp\u003eIn the TAL, three TCMR-associated injury clusters (TAL Injury m1-3) and a proliferating cluster (TAL Prolif) could be observed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, D). Similar to PT, one injury cluster (TAL Injury m3) expressed marker genes observed in the most severely injured cell states in AKI, including Cd44, Met and Spp1\u003csup\u003e22\u003c/sup\u003e. In ST data, TAL injury cell states TAL Injury m1 and m2 were less abundant and seemed to occur in cortex as well as the medulla. However, the most abundant and dedifferentiated TAL injury cluster, TAL Injury m3, was predominant in the renal medulla, suggesting that this region is particularly vulnerable to severe TAL damage. The small TAL Prolif cluster was difficult to assign to a specific region due to the low cell count, with only sporadic occurrences in both the cortex and medulla in the ST data.\u003c/p\u003e \u003cp\u003eNotably, as in AKI, some healthy PT and TAL cell types (e.g. PT S2, PT S3) were significantly depleted in allogeneic kidneys while most injury populations were significantly overrepresented in the allogeneic kidneys (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB, D). It is of note that subclustering analyses of the remaining major cell types revealed TCMR-associated injury clusters in DCT, CNT and EC at much lower abundances than for PT and TAL (Suppl. Fig. S6 and S7).\u003c/p\u003e\n\u003ch3\u003eTCMR-induced injured epithelial cell states show heterogeneous proximity to immune cells\u003c/h3\u003e\n\u003cp\u003eAlthough the exact mechanisms driving epithelial injury in TCMR remain unclear, it is widely recognized that host leukocytes play a central role, either by direct cytotoxicity or indirectly by cytokine production. The availability of snRNA-seq and ST data allows for estimating the spatial proximity of leukocyte subtypes to injured epithelial cells in the PT and TAL. Subclustering of leukocytes identified all expected subtypes, each significantly overrepresented in allogeneic kidneys (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA, B). The majority of leukocytes consisted of macrophage subtypes and CD8\u003csup\u003e+\u003c/sup\u003e T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Spatial analysis showed that most leukocytes were located in the cortex and around blood vessels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). However, both, T cells and macrophages were also observed in the inner medulla.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor spatial proximity analysis, we analyzed the percentage of injured cells from PT and TAL directly neighboring a leukocyte cell type (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). We also included randomly selected cells in this analysis to statistically test under- or overrepresentation of direct spatial proximity. The highest percentage of directly neighboring leukocytes could be observed in the injured PT cell states and was particularly high in PT Injury m3 (93%, 46% and 99% for CD8\u003csup\u003e+\u003c/sup\u003e T cell, CD4\u003csup\u003e+\u003c/sup\u003e T cell or a macrophage, respectively) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). The number of PT Injury m3 cells directly neighboring a CD8\u003csup\u003e+\u003c/sup\u003e T cell was significantly higher than for randomly chosen cells. The number of directly neighboring leukocytes was lowest in the most severely injured TAL cell state, TAL Injury m3 and significantly lower than for randomly chosen cells (35%, 9% and 51% for CD8\u003csup\u003e+\u003c/sup\u003e T cells, CD4\u003csup\u003e+\u003c/sup\u003e T cells or macrophages, respectively). These results show a statistically differential interaction of leukocytes with different TCMR-induced injured cell states in PT and TAL.\u003c/p\u003e\n\u003ch3\u003eHuman TCMR kidney allografts exhibit injured cell states resembling those in mice\u003c/h3\u003e\n\u003cp\u003eTo compare the mouse TCMR data with human samples, we performed snRNA-seq on three TCMR and three stable allograft biopsies from archived samples of the protocol biopsy program at Hannover Medical School (see Supplementary Table S3 for patient and biopsy details). The snRNA-seq generated high-quality transcriptomic data, with 22183 nuclei used for analysis, allowing the identification of all major cell types (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, Suppl. Fig. S8). Unlike the mouse data, we did not observe a distinct cluster of proliferating cells, likely due to the earlier time point of TCMR injury in the mouse samples.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubclustering of the PT and TAL revealed the presence of injured PT and TAL states (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-H), characterized by reduced expression or absence of canonical markers and upregulation of injury markers, similar to those observed in the mouse model. However, the relative abundances of injured human PT and TAL cells was not significantly higher in TCMR samples when compared to stable allografts. This aligns with the broader spectrum of injury sources in human kidney allografts beyond TCMR and the variable timing of biopsy collection relative to TCMR onset.\u003c/p\u003e \u003cp\u003eTo bridge the mouse and human datasets, we performed a cross-species analysis of PT and TAL injury states. As suggested from the marker gene expression, the most severely injured mouse PT and TAL cell states corresponded to human PT Injury h2 and TAL Injury h1 and h2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE, H). Human PT Injury h2 expressed all markers of PT failed repair, similar to PT Injury m4, including VCAM1, CD44 and VIM (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). For TAL cells, two distinct injury states were identified in the human samples, TAL Injury h1 and h2. The larger TAL Injury h2 cluster showed a pronounced EMT signature with markers such as CD44, TPM and SPP1, associated with severe TAL injury in AKI and marker genes of TAL Injury m3. The smaller human TAL injury cluster TAL Injury h1 also shares marker genes with TAL Injury m3 including IGBP1 and MET. Pathway enrichment analysis of TAL Injury h1 marker genes revealed a pronounced expression p53 signaling. Systematic cross-species analysis revealed that TAL Injury m3 from the mouse dataset closely corresponded to human TAL Injury h2, although it also shared features with TAL Injury h1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eInjured epithelial cell states significantly impact kidney allograft survival after TCMR\u003c/h2\u003e \u003cp\u003eTo assess the impact of injured human PT and TAL cell states, we generated biomarker gene sets which were highly specific for PT Injury h1 and h2 and TAL Injury h1 and h2 (Suppl. Fig. S9 and S10, Suppl. Table S4). To correlate these signatures with clinical outcome, we investigated their correlation with 3-year kidney allograft outcome in bulk transcriptomic data from a large kidney transplant bulk transcriptomics cohort comprising 1292 patients (including 624 with no rejection, 297 antibody-mediated rejections, 95 TCMRs, 45 mixed rejections, 50 probable antibody-mediated rejections or probable TCMRs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Scores for provided marker gene sets were derived from geometric mean expression of all marker gene sets in bulk data. We additionally included the IRRAT30 gene set representing injury-induced transcripts which were previously reported to be an important factor of kidney allograft survival after TCMR\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor all biopsies (including TCMR and all other biopsies), higher scores for PT Injury h2, TAL Injury h1 and TAL Injury h2 were significantly associated with an increased risk of allograft loss within 3 years post-biopsy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). Conversely, high scores for PT Injury h1 were associated with improved allograft survival after 3 years. This suggests that PT Injury h1, which we interpret as a less severely injured state, may represent a potentially regenerating PT cell phenotype.\u003c/p\u003e \u003cp\u003eWhile the IRRAT30 and PT injury cell states exhibited a comparable influence on kidney allograft survival following TCMR and in other kidney transplant biopsies, the TAL injury signatures (TAL Injury h1 and h2) demonstrated a significantly greater effect on kidney allograft survival specifically after TCMR compared to all other transplant biopsies (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003eTo evaluate the persistence of gene set scores over time in patients initially diagnosed with TCMR, we analyzed biopsies with available follow-up samples. These included biopsies with initial diagnosis of TCMR (and potentially TCMR in the follow-up samples), ultimately leading to a \"no rejection\" (NR) diagnosis in the last biopsy (n\u0026thinsp;=\u0026thinsp;12, Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Despite the limited number of patients in this analysis, two key observations emerged. First, the levels of injury-related gene set scores exhibited significant variability among TCMR patients. This aligns with our findings in AKI, where heterogeneous distributions of injured epithelial cell states were observed among patients\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Second, for PT Injury h2 and TAL Injury states in particular, a notable subset of patients demonstrated persistently high scores specific to these injured epithelial cell states, even after the resolution of the initial TCMR diagnosis. This suggests that, while kidneys may achieve remission from TCMR, they can still harbor outcome-relevant, elevated levels of injured epithelial cells months after the initial episode.\u003c/p\u003e \u003cp\u003eIt is of note that the gene sets specific for PT and TAL Injury h1 and h2 did also successfully label injured PT and TAL cells in AKI from our previous study (Suppl. Fig. S11)\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. While the distinction between the different injured PT and TAL states was not as pronounced as for TCMR, we still observed correspondence of TAL Injury h1 and h2 with severely injured TAL cells from AKI (TAL-New 3 and 4). PT Injury h1 corresponded mostly with a less severely injured PT cell state (PT-New 1) which expresses genes involved in oxidative stress. On the other hand, PT Injury h2 was strongly associated with severe and potentially maladaptive PT injury in AKI (PT-New 4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we explored the molecular changes associated with TCMR and their clinical impact on kidney allograft survival. Using mouse models of TCMR, we identified molecular changes, including a significant gene expression response in most kidney cell types, with the most pronounced effects in the PT and TAL. TCMR was linked to the emergence of TCMR-associated cell states, primarily in PT and TAL. Cross-species analysis enabled us to correlate these injured cell states in mice with corresponding states in human biopsies from TCMR patients and stable allograft controls. Notably, all injured cell states in PT and TAL in humans had mouse counterparts. By deriving biomarker gene sets specific to each human injury cell state in PT and TAL, we calculated a score for these gene sets in a large kidney transplant bulk transcriptomics cohort and correlated these scores with clinical outcomes. We found that all identified injury clusters in humans were associated with allograft survival, with all but one gene set score (PT Injury h1) linked to reduced allograft survival. In TCMR samples, injured TAL cell states had the greatest impact on allograft survival, with a significantly stronger effect compared to all other kidney biopsies. This suggests that while similar injured cell states may occur in different settings post-kidney transplantation, their relative importance for clinical outcomes can differ substantially.\u003c/p\u003e \u003cp\u003eThe clinically most relevant cell states, PT Injury h2, TAL Injury h1 and h2, are characterized by marker genes previously identified in various kidney disease contexts beyond transplantation\u003csup\u003e\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. These cell states show gene expression changes indicative of EMT upregulation. Similar cell states have been observed in AKI and CKD, in, both, mouse and human samples\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Notably, VCAM1-positive PT cells, often referred to as maladaptive or \u0026ldquo;failed repair\u0026rdquo; cells, reflect significant dedifferentiation and exhibit pro-inflammatory and pro-fibrotic phenotypes. Injured TAL cell states, including the EMT-upregulated genes in TAL Injury h1 and h2 (e.g. MET, ITGB1, CD44, NNMT and SPP1), have been reported primarily in human AKI and CKD studies\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. In non-transplant settings, VCAM1-expressing PT and CD44-expressing TAL cell states have been associated with reduced clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eComparing cell states across studies is challenging due to variability in methodologies and limited single-cell studies in transplantation, which typically include fewer patients than studies on native kidneys. Nonetheless, similar cell types have been reported in other contexts including kidney transplantation\u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37 CR38 CR39\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Whether these represent identical cell states or share superficial similarities with profound underlying differences remains uncertain. However, it is evident that a range of kidney diseases and complications, in, both, native and transplanted kidneys, induces highly dedifferentiated EMT-like cell states in the PT and TAL.\u003c/p\u003e \u003cp\u003eThe key question is what these cell states signify. If they merely reflect nephron loss, their correlation with clinical outcomes is unsurprising. However, emerging evidence suggests that injured epithelial cell states may persist and interact with fibroblasts and immune cells, forming pro-fibrotic microenvironments that exacerbate renal damage\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e,\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. To explore persistence, we analyzed follow-up biopsies from a subset of patients initially diagnosed with TCMR. Despite the limited sample size, we observed that elevated gene set scores indicative of PT and TAL injury states often persisted, even after the clinical resolution of TCMR. This challenges the notion that EMT-like states, such as PT Injury h2 and TAL Injury h2, simply represent dying tubules. Further research is needed to confirm these findings and elucidate their implications.\u003c/p\u003e \u003cp\u003eOur data revealed the strong impact of TAL injury on kidney allograft survival after TCMR, contrasting with the results from all kidney transplant biopsies. PT injury showed comparable impact on transplant survival after TCMR and within all biopsies. The underlying reasons for these differences are unclear. Specifically, it is unknown why severe TAL injury has less impact on allograft survival in some transplant contexts when compared to TCMR. One hypothesis is that, for instance in TCMR, severe PT injury, beyond a certain threshold, might drive secondary TAL injury, making TAL injury an indicator of extensive cortical tubule damage. Alternatively, in other settings, TAL injury could arise independently under conditions that predominantly affect medullary regions (where most TAL injury cells reside) without causing substantial cortical PT damage. These hypotheses, however, remain speculative and require validation in future studies. Additionally, in TCMR, we observed that leukocyte proximity to TAL Injury m3 cells (the mouse counterpart to TAL Injury h1 and h2) is significantly less frequent than to PT Injury m4 cells (the mouse counterpart to PT Injury h2). This suggests that the pathogenesis of PT Injury h2 and TAL Injury h1/h2 may differ.\u003c/p\u003e \u003cp\u003eOur human TCMR single-cell data, along with results from bulk transcriptomics follow-up biopsies, show that PT and TAL injury abundances vary between patients and appear to persist over time in some cases. This suggests that biomarkers indicative of these injured cell states could be used to stratify TCMR patients by risk and to monitor the effectiveness of rejection therapies longitudinally. Furthermore, if these injury states persist, PT and TAL injury cells could serve as therapeutic targets, paving the way for novel and personalized treatment approaches in TCMR.\u003c/p\u003e \u003cp\u003eIn summary, our study demonstrates the value of integrating high-resolution molecular data from mouse models with defined injury timelines and analogous data from humans, coupled with large cohorts featuring clinical follow-up data. This approach enables the identification of molecular signals specific to injury populations in single-cell transcriptomics and their correlation with clinical end points. It helps dissecting the diverse sources of injury in human kidney transplants and opens new avenues for precision medicine.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eAnimal experiments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale C57BL/6 and BALB/c mice, aged 10-12 weeks, were used for all the experiments. The animals were sourced from Janvier (Le Genest St Isle, France) and maintained under standard housing conditions, with \u003cem\u003ead libitum\u003c/em\u003e access to food and water. All animal procedures were performed following the Directive 2010/63/EU, the German Tierschutz-Versuchstierverordnung. Ethical approved was obtained from the Regional Ethics Committee for Animal Research (Landesamt f\u0026uuml;r Gesundheit und Soziales Berlin, approval number: G0236/18).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMouse kidney transplantation\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eKidney transplantations were performed in syngeneic (C57BL/6 to C57BL/6 or BALB/c to BALB/c) and allogeneic (C57BL/6 to BALB/c or BALB/c to C57BL/6) mouse combinations under isoflurane inhalation anesthesia as previously described\u003csup\u003e33\u003c/sup\u003e. Briefly, after a midline abdominal incision, the left kidney, aorta and inferior vena cava of the donor mouse were exposed and carefully mobilized. The kidney was flushed \u003cem\u003ein situ\u003c/em\u003e with histidine-tryptophane-ketoglutarate (HTK) solution (Custodiol\u003csup\u003e\u0026reg;\u003c/sup\u003e, Dr. Franz K\u0026ouml;hler Chemie GmbH, Bensheim, Germany), then procured \u003cem\u003een bloc\u003c/em\u003e with the renal vein, renal artery (with a small aortic cuff) and ureter. The harvested kidney was stored in ice-cold HTK solution for 1 hours and thereafter implanted in the left nephrectomized recipient mouse, below the level of native renal vessels. Blood supply to the graft was established through end-to-side anastomoses of the donor renal vessels to the recipient\u0026rsquo;s abdominal aorta and inferior vena cava. Reconstruction of the urinary tract was achieved by directly anastomosing the donor ureter to the recipient\u0026rsquo;s bladder. The contralateral native kidney of the recipient mouse was removed 24 hours prior sacrifice on post-operative day (POD) 7 to evaluate renal graft function. For animals undergoing survival analysis, the contralateral native kidney was removed on POD7 and the surviving animals were sacrificed on POD26.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssessment of mouse renal graft function\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRenal graft function was assessed by serum levels of creatinine and urea. Serum samples were collected terminally from the recipient mice and stored at -20\u0026deg;C until creatinine and urea were measured using the CREP2 Creatinine Plus version 2 and Urea/BUN assays, respectively, on a Roche/Hitachi Cobas C 701/702 system (Roche Diagnostics, Mannheim, Germany).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHistopathology\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKidney samples were fixed in 4% neutral-buffered paraformaldehyde for 22\u0026ndash;24 hours before standard histological processing. Paraffin-embedded tissues were sectioned into 2\u0026thinsp;\u0026mu;m slices, deparaffinized and stained with periodic acid-Schiff (PAS) stain for histological evaluation. Lesions were evaluated and scored according to the criteria outlined in the Banff classification\u003csup\u003e41\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle nucleus mRNA sequencing mouse and human specimens\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor single-nucleus RNA sequencing (snRNA-seq), the recipient mice were transcardially perfused with ice-cold PBS to clear blood from renal grafts. 1\u0026ndash;2 mm middle slices was consistently extracted from the grafts and preserved in pre-cooled RNAlater (Invitrogen #AM7020) at 4\u0026deg;C for 24 hours before being stored at -80\u0026deg;C until nuclei isolation, following the protocol described by Leiz, Hinze et al. (2021)\u003csup\u003e42\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAll samples underwent single-cell sequencing using the 10x Genomics Chromium Next GEM Single Cell 3\u0026rsquo; v3.1 chemistry protocol (#CG000204 Rev D), targeting 9,000\u0026ndash;10,000 nuclei per sample. Libraries were sequenced on Illumina HiSeq 4000 platforms (paired-end) and digital expression matrices were generated with the 10x Genomics Cell Ranger software (version 3.0.2) using the parameter `\u0026ndash;force-cells 10000` against the mouse mm10 genome.\u003c/p\u003e\n\u003cp\u003eArchived human kidney biopsy samples were prepared similarly, stored at -80\u0026deg;C in RNAlater and included from the archive of the Hannover Medical School protocol biopsy program. All subsequent steps, starting from the -80\u0026deg;C storage in RNAlater, were identical to those used for mouse tissue.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor human samples, data collection and analysis were performed with informed consent of the patients and with approval of the institutional review board (no 2765). Exclusion criteria for the protocol biopsy program were lack of patient consent, relevant bleeding risks and anticoag\u0026shy;u\u0026shy;lation therapy for an artificial heart valve.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXenium in Situ Profiling\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn situ single cell RNA expression analysis was performed using the Xenium system (10X Genomics). 5\u0026mu;m thick FFPE tissue sections were placed on a Xenium slide according to the Xenium Tissue Preparation Guide (CG000578). Sections were dried at 37\u0026deg;C for 2 hours and placed overnight in a desiccator at room temperature, followed by deparaffinization and decrosslinking (CG000580). The Probe Hybridization Mix was prepared using a pre-designed 379 gene panel (Mouse Tissue Atlassing v1) and 100 gene custom add-on panel (Suppl. Table S1). Probe Hybridization, Ligation \u0026amp; Amplification were performed according to the Xenium In Situ Gene Expression user guide (CG000582). Raw data was processed in real-time with on-board Xenium Analyzer software v1.7.6.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatic analyses of spatial transcriptomic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of major cell types in Xenium Data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw data was analyzed using the xenium ranger v2.0 import-segmentation tool with parameter \u0026ndash;expansion-distance=5. Results from this were directly imported into Seurat and clustered. In brief, data from all samples were normalized scaled and PCA was performed using highly variable genes. Harmony analysis was run on which basis clustering was performed with resolution 0.3. The so-generated clusters were inspected on the spatial histological images and for marker gene expression. Based on this, broad cell types were determined. All cells from the leukocyte, interstitial cell or endothelial cell compartment were first labeled as non-tubular cells. Non-tubular cells were further separated into endothelial cells, interstitial cells and leukocytes using the Robust Cell Type Decomposition tool\u003csup\u003e43\u003c/sup\u003e and the snRNA-seq data from our mouse experiments. Additionally, cell types (Prolif, CD-IC-A, CD-IC-B and PEC) were also determined by using RCTD and mouse snRNA-seq. RCTD was also used for sub cell type annotation (e.g. PT Injury h1 etc.).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpatial proximity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor every injury cluster of interest (e.g. PT Injury h2), the minimum distance to each leukocyte sub cell type of interest was calculated per cell based on the provided centroid information from the Xenium data. We chose 25\u0026micro;m as indicative of being a direct neighbor as derived from average nearest neighbor distance analysis of Xenium data.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatic analyses of single nucleus mRNA sequencing data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell type annotation and initial clustering\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDigital expression matrices were generated with the 10x Genomics Cell Ranger software (version 3.0.2 for mice and version 8.0.0 for human samples) using the parameter `\u0026ndash;expect-cells 10000` against the mouse mm10 or human GRCh38-3.0.0 genome. Human samples were further filtered using cellbender remove-background with fpr 0.01 to remove ambient RNA.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eData integration and clustering were performed using the following workflow: NormalizeData -\u0026gt; FindVariableFeatures -\u0026gt; SelectIntegrationFeatures -\u0026gt; FindIntegrationAnchors -\u0026gt; IntegrateData -\u0026gt; ScaleData -\u0026gt; RunPCA(npcs=30) -\u0026gt; harmony::RunHarmony(group.by.vars = c(\u0026quot;group\u0026quot;), lambda = 2, tau = 1000, theta = 1, assay.use = \u0026quot;integrated\u0026quot;, kmeans_init_nstart = 60, kmeans_init_iter_max=2000, max.iter.harmony = 30) -\u0026gt; ScaleData -\u0026gt; FindNeighbors(reduction=\u0026rdquo;harmony\u0026rdquo;, dims=1:15) -\u0026gt; FindClusters(resolution=0.5)\u003c/p\u003e\n\u003cp\u003eCell types were assigned based on marker gene expression as published previously\u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential gene expression analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor mouse samples, differential gene expression analysis was performed using Seurat\u0026rsquo;s FindMarkers function, comparing allogeneic kidneys from each group (C57BL/6 and BALB/c) to their syngeneic controls per cell type. Genes were considered differentially expressed in case of |log2 fold change|\u0026gt;1, adjusted p-value\u0026lt;0.05 and expressed in at least 10% of cells in allogeneic kidneys (for differentially upregulated genes in allogeneic kidneys) or 10% of cells in syngeneic kidneys (for downregulated genes in allogeneic kidneys). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCross-species analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this analysis, we first calculated marker genes for each mouse PT or TAL cluster using Seurat\u0026rsquo;s FindAllMarkers function with parameters only.pos = TRUE, logfc.threshold = 1. Markers were then filtered for pct.1\u0026gt;0.1 and adjusted p-value\u0026lt;0.05. Mouse genes were translated to human genes using the biomaRt package. Average expression per cluster on these genes was calculated for human and mouse samples using Seurat\u0026rsquo;s AverageExpression function. Correlation was calculated using R\u0026rsquo;s built-in cor() function with method=\u0026rdquo;spearman\u0026rdquo;.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of marker genes for injury clusters\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo get marker genes specific for the respective cell populations (further referred to as TP = \u0026ldquo;target population\u0026rdquo;), we first generated a list of potential marker genes for the TP by using the FindMarkers function with ident.1=TP and ident.2=\u0026rdquo;all other cells\u0026rdquo;. We then calculated the average expression of genes of interest in the TP. We also calculated the average expression of the mentioned genes in the adjacent injured cell population. E.g., if TP was TAL Injury h2, we also calculated average gene expression in TAL Injury h1. We only considered genes which show higher average expression in TP than in the adjacent injury population. This reduces the number of genes and the computational resources required downstream.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe last filtering step included the generation of 100-cell neighborhoods of nearest neighbors around each cell not in TP. We furthermore excluded cells with 100-cell neighborhoods overlapping with the TP. We then calculated average expression of each of the remaining gene candidates in all 100-cell neighborhoods. Final genes only included genes which showed an average expression in the TP \u0026gt; 1.25 fold compared to all other 100-cell neighborhoods.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKidney transplant bulk transcriptomics cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 5086 kidney transplant biopsies previously described in detail\u003csup\u003e44\u003c/sup\u003e collected from 3995 patients through the MMDx-Kidney studies, as well as through the MMDx service laboratory in Portland, OR (Kashi Laboratories, submitted as anonymized files for this study) were processed for MMDx. Genome-wide assessment\u0026nbsp;(49,495 probe set values representing 19,462 genes)\u0026nbsp;of the 5086 biopsies was measured by microarrays\u003csup\u003e44\u003c/sup\u003e. MMDx signouts were available for most of the data set. Rejection and injury states were derived according to previously published methods\u003csup\u003e45\u003c/sup\u003e.\u0026nbsp;Biopsies having \u0026le;10% cortex were excluded leaving 4502 biopsies. From this set of 4502 biopsies,\u0026nbsp;MMDx signouts and graft status were available for 1618 biopsies from 1292 patients. For survival analyses, we selected the first biopsy post-transplant for each patient, leaving 1292 biopsies for assessment.\u0026nbsp;The MMDx-Kidney studies adhere to the Declaration of Helsinki. All biopsies were collected with informed consent per institutional review board review at each local center and approved in Edmonton by the University of Alberta (#Pro00022226). The clinical and research activities being reported are consistent with the Principles of the Declaration of Istanbul as outlined in the \u0026apos;Declaration of Istanbul on Organ Trafficking and Transplant Tourism\u0026apos;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene set scores in bulk transcriptomic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene set scores for each human PT and TAL cell states were assigned to each biopsy in the K4502 population by taking the geometric mean expression of all marker genes for each biopsy, normalized to the geometric mean expression in 4 nephrectomy control biopsies (i.e., mean score in nephrectomy is 0). Risk of graft loss 3-years post biopsy associated with gene set scores was assessed across the entire population of biopsies. Scores were assessed individually with Kaplan-Meier estimates, stratified by their median value in the full population.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe transition of gene set scores following TCMR diagnosis was depicted in MMDx-Kidney biopsies in patients diagnoses with TCMR (n = 12) which eventually progressed to no rejection (NR) in subsequent follow-up biopsies.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRaw and processed data are accessible through Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE284742\u003c/p\u003e\n\u003cp\u003eReviewer token: erkjcekgtfgnnqt\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCode availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll code is accessible through GitHub: https://github.com/LorenzJahnMHH/TCMR-induced-epithelial-injury-patterns-in-kidney-transplants-\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMaterial availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse tissue: RNAlater-stored mouse kidney samples were completely used for snRNA-seq experiments. Paraffine-embedded tissue is available. Human tissue: RNAlater-stored kidney biopsy cores were completely used for snRNA-seq experiments. Paraffine-embedded tissue could be made available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by grants to M.I.A., I.M.S., F.I., K.M.S.O.: Deutsche Forschungsgemeinschaft (DFG, German Research Foundation): Project ID 394046635, SFB 1365 and grants to C.H.: DFG (grant HI 2238/2\u0026ndash;1) and Ministry for Science and Culture of Lower Saxony as project of the \u0026ldquo;Center for Organ Regeneration and Replacement (CORE)\u0026rdquo;, Transplant Center, Hannover Medical School.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors have nothing to disclose.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNankivell, B. J. \u0026amp; Alexander, S. I. 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Online ahead of print.\u003c/strong\u003e, doi:10.1016/j.ajt.2024.07.034 (2024).\u003c/li\u003e\n\u003cli\u003eReeve, J.\u003cem\u003e et al.\u003c/em\u003e Assessing rejection-related disease in kidney transplant biopsies based on archetypal analysis of molecular phenotypes. \u003cem\u003eJCI Insight\u003c/em\u003e \u003cstrong\u003e2\u003c/strong\u003e, e94197, doi:10.1172/jci.insight.94197 (2017).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5683198/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5683198/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eT-cell mediated rejection (TCMR) remains a significant challenge after kidney transplantation and is associated with reduced allograft outcome. Previous research highlighted the critical role of TCMR-induced renal epithelial injury. Yet, the detailed cellular origin of these injury responses and the associated clinical implications remain poorly understood. To induce acute TCMR, we used mouse models of allogeneic (C57BL/6 to BALB/c and BALB/c to C57BL/6) kidney transplantation and syngeneic controls (C57BL/6 to C57BL/6 and BALB/c to BALB/c). Molecular changes were analyzed 7 days post-transplant using single-nucleus RNA sequencing and spatial transcriptomics. Results were compared with snRNA-seq data from three human TCMR biopsies and three stable allografts without rejection. The clinical impact of TCMR-induced epithelial injury was evaluated using marker gene sets on bulk transcriptomic data from 1292 kidney allografts, including 95 TCMR samples, with allograft outcome. Mouse kidneys from allogeneic transplants exhibited all hallmark histological features of TCMR. Single-nucleus RNA sequencing revealed TCMR-induced injured cell states and significant gene expression changes particularly in proximal tubules (PT) and thick ascending limbs (TAL). Spatial transcriptomics showed a heterogeneous spatial distribution of these injured cell states and proximity to leukocytes. Cross-species analysis confirmed similar injured PT and TAL cell states in human TCMR. Kidney allograft outcomes strongly correlated with TCMR-induced injured epithelial cell states. Distinct from other transplant biopsies, severe TAL injury emerged as a key factor for allograft survival after TCMR and was associated with reduced leukocyte proximity, suggesting potential non-immune mechanisms of epithelial damage.\u003c/p\u003e","manuscriptTitle":"Thick ascending limb injury critically impacts kidney allograft survival after T-cell-mediated rejection","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-10 12:01:45","doi":"10.21203/rs.3.rs-5683198/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5236dc59-5470-4ae9-901f-c7dca860b4a0","owner":[],"postedDate":"March 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":43838815,"name":"Health sciences/Nephrology/Kidney diseases"},{"id":43838816,"name":"Health sciences/Nephrology/Kidney/Nephrons"}],"tags":[],"updatedAt":"2026-01-29T08:16:04+00:00","versionOfRecord":{"articleIdentity":"rs-5683198","link":"https://doi.org/10.1038/s41467-026-68397-1","journal":{"identity":"nature-communications","isVorOnly":false,"title":"Nature Communications"},"publishedOn":"2026-01-28 05:00:00","publishedOnDateReadable":"January 28th, 2026"},"versionCreatedAt":"2025-03-10 12:01:45","video":"","vorDoi":"10.1038/s41467-026-68397-1","vorDoiUrl":"https://doi.org/10.1038/s41467-026-68397-1","workflowStages":[]},"version":"v1","identity":"rs-5683198","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5683198","identity":"rs-5683198","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}