Injured epithelial cell states impact kidney allograft survival after T-cell-mediated rejection

Domain: neuroscience
Published: 2026-01-28
Editorial outcome: major_revision

Abstract
T-cell–mediated rejection (TCMR) remains a major cause of kidney transplant failure, despite being considered treatable. Its impact reflects a limited understanding of the underlying molecular mechanisms and their clinical consequences. To address this, we induced acute TCMR in mouse kidney transplants and profiled molecular changes using single-nucleus RNA sequencing (snRNA-seq), spatial transcriptomics and immunofluorescence. Results were compared with human snRNA-seq data from TCMR and stable allografts, as well as single-cell deconvolution analysis of bulk transcriptomic data from kidney transplant biopsies. Here we show that TCMR induces injured epithelial cell states in mouse kidney allografts, particularly in proximal tubules and thick ascending limbs. Spatial transcriptomics of these injured epithelial states demonstrated heterogeneous localization, interactions with immune cells and cellular microenvironments. Cross-species analysis confirmed similar severely injured epithelial states in human samples, whose abundances correlated with transplant survival and persisted despite TCMR resolution. Collectively, our results identify epithelial injury cell states as a determinant of outcome after TCMR.

Introduction
T cell-mediated rejection (TCMR) is a severe complication after kidney transplantation associated with reduced graft survival1,2. Often occurring within the first year after kidney transplantation, TCMR represents an adaptive immune response triggered by donor antigens, primarily mediated by T cells3–6. 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, arteritis5,7,8. 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 cells9–11. Current therapeutic strategies for TCMR focus on suppressing the immune cell infiltrate through intensified immunosuppression, often including steroid pulses12–14. 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 antibody-mediated rejection (ABMR) despite TCMR being considered treatable15–18. This raises questions about whether current therapies adequately address all aspects of injury occurring during TCMR.

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 value17,19–21. 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 states22–25. 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 gene expression profiles23,25,26. Despite these insights, epithelial injury signatures in TCMR have yet to be systematically characterized or linked to clinical outcomes. 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 factors27–31.

In this work, we delineate the molecular changes associated with TCMR and evaluate their impact on allograft outcomes. We employ single-cell sequencing technologies 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 probing them in large bulk transcriptomic cohorts with clinical follow-up data, we can assess their clinical significance.

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, diverse donor and recipient pathologies and clinical conditions—are commonly observed32. 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 points33. 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. 1A). 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 model33. Allogeneic transplants induced strong rejection, showing all histological features of TCMR, including tubulitis and interstitial inflammation (Fig. 1B, and Suppl. Fig. S1 for Banff scoring, sample information and additional histology). Clinically, allogeneic mice suffered from acute kidney injury (AKI) and increased mortality (Fig. 1B, and Suppl. Fig. S2).

To gain molecular insights into TCMR, we performed single-nucleus RNA sequencing (snRNA-seq) and spatial transcriptomics (ST) using the Xenium platform (mouse multi-tissue panel plus 100 custom genes; see Suppl. Data 1) on kidneys from 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. 1C and D, Suppl. Fig. S5). Cell types in ST data were obtained by label transfer from snRNA-seq using the singleR package34,35. This led to expected marker gene expression (Suppl. Fig. S4A) and spatial localization of cell types (Fig. 1D, and Suppl. Fig S5). In snRNA-seq data, global gene expression was mainly determined by the presence or absence of allogeneic transplantation as derived from principal component analysis (Fig. 1E). Leukocyte abundance was significantly higher in allogeneic samples compared to syngeneic controls in snRNA-seq, correlating with the interstitial inflammation observed in histology (Fig. 1F).

Mouse TCMR induces pronounced 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. 2A, and Suppl. Data 2). Epithelial gene expression in TCMR was characterized by signatures of kidney injury (elevated expression of Havcr1 and Lcn2, Fig. 2B, C, Lcn2 particularly pronounced in C57BL/6 to BALB/c kidneys), accompanied by broad upregulation of pro inflammatory chemokines (e.g., Cxcl9, Ccl5, Cxcl10) and MHC class I and II molecules (e.g., H2-Q7, H2-Eb1, H2-Q6) across epithelial cell types, consistent with immune pathway activation (Fig. 2D, E), alongside downregulation of genes associated with epithelial differentiation and tubular transport (Suppl. Fig. S6 and Suppl. Data 2). Consistently, pathway enrichment analysis on differentially 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 (Fig. 2F).

Cell type-specific differentially upregulated genes for PT and TAL closely mirrored this broader spectrum of differentially expressed genes in all other cell types, encompassing interferon-inducible transcripts, chemokines and genes involved in antigen presentation, as well as pathways associated with epithelial stress, injury and damage (Suppl. Fig. S7).

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 (Suppl. Fig. S6B). 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 (see Suppl. Data 2 for all differentially expressed genes).

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 epithelium22,23,25. 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. 3A-D). PT Injury m1 cells showed early epithelial stress adaptation with increased expression of Cryab, Myo5b, Serpina10, Acsm3 and Stk39. Concurrent upregulation of Jag1, Wnt5a and Foxc1 may indicate engagement of developmental or regenerative transcriptional programs (Fig. 3C). PT Injury m2 exhibited elevated expression of MAPK pathway components (Map3k1, Map3k13) and stress-responsive genes including Egfr (Fig. 3C). Cells in the PT Injury m3 cluster expressed genes associated with complement and coagulation pathways (C3, Pros1, Sparc), along with immune and interferon-related transcripts such as Cd74, Stat1, B2m and Klf7 (Fig. 3C). The most dedifferentiated injury state (as determined by pseudotime analysis, Fig. 3A), PT Injury m4, exhibited a broad injury-associated transcriptional program. This included expression of injury markers Cd44 and Havcr1, pro-inflammatory mediators (Cxcl10, Nfkb1, Atf3) and genes associated with epithelial plasticity and epithelial–mesenchymal transition, such as Vim, Itgb1, Tnfaip3, Tpm1 and Vcam1. Markers such as Ccl2, Cd47, Icam1 and Anxa3 may indicate epithelial signals promoting immune cell recruitment and interaction (Fig. 3C). Many of these markers including Ccl2, Vim and Vcam1 are reported to be involved in maladaptive repair after AKI22,24.

Label transfer from snRNA-seq to Xenium spatial transcriptomics revealed a healthy PT cell distribution consistent with known anatomy (Supplementary Fig. S8). Marker gene expression showed strong concordance between platforms, confirming cross-platform alignment (Fig. 3C and Supplementary Fig. S8B). Spatial mapping of injury-associated PT states (Fig. 3B) demonstrated that PT Injury m1 localized primarily to the outer medulla and medullary rays, as confirmed by Xenium data and immunofluorescence staining for ACSM3 and STK39 (Supplementary Figs. S9 and S10). In contrast, PT Injury m4 exhibited a patchy distribution across cortex and medulla, aligning with the spatial expression domains of Havcr1 and Vcam1, which were validated by Xenium data and immunofluorescence staining (Fig. 3E, and Supplementary Figs. S9 and S10).

In the TAL, three TCMR-associated injury clusters (TAL Injury m1-3) and a proliferating cluster (TAL Prolif) could be observed (Fig. 3F-I). The TAL Injury m1 cluster showed a modest injury profile characterized by activation of interferon-stimulated genes and immune regulators, including Parp9, Parp14, Stat1, Stat2, Irf1, Xaf1, Epsti1 and Upp1 (Fig. 3H). TAL Injury m2 markers included genes involved in pro-inflammatory and interferon-driven transcriptional programs such as Icam1, Socs3, Gbp2, Cebpd, Edn1 and Cxcl10. Marker genes such as Dcdc2a and Sdc1 suggest subtle changes in cellular organization and epithelial remodeling (Fig. 3H). By contrast, TAL Injury m3 exhibited a broad and complex injury signature. Core injury markers such as Mmp7, Spp1 and Atf3 are co-expressed with EMT- and cytoskeleton-associated genes such as Pmepa1, Fstl1, Flna and Pdgfb, reflecting epithelial plasticity and tissue remodeling (Fig. 3H). As PT Injury m4, TAL Injury m3 exhibited similarities to severely injured TAL cells in AKI, which we identified in a previous study22.

Healthy TAL cells showed an expected spatial localization and marker gene expression was consistent across snRNA-seq and Xenium datasets (Fig. 3H and Supplementary Fig. S11). Among the injury-associated TAL states, TAL Injury m2 localized to the cortex, TAL Injury m1 to the medulla and the most severely injured population, TAL Injury m3 (as defined by pseudotime analysis, Fig. 3F), was predominantly restricted to the inner stripe of the outer medulla, with few occasional cells also detected in medullary rays (Fig. 3G). The presence and location of TAL Injury m3 cells was validated by immunofluorescence staining for the marker gene Mmp7 (Fig. 3J and Supplementary Fig. S12).

It is of note that subclustering analyses of the remaining major cell types revealed TCMR-associated injury clusters in the distal convoluted tubule (DCT), connecting tubule (CNT) and endothelial cells (EC) at much lower abundances than for PT and TAL (Suppl. Figs. S13 and S14).

TCMR-induced injured epithelial cell states show heterogeneous proximity to immune cells and surrounding cell environments
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, most of them significantly overrepresented in allogeneic kidneys (Fig. 4A–C). The majority of leukocytes consisted of macrophage subtypes and CD8+ T cells as confirmed by the expression of hallmark genes (Fig. 4A, C). Spatial analysis showed that most leukocytes were located in the cortex and around blood vessels (Fig. 4D). However, both, T cells and macrophages were also observed in the inner medulla.

For spatial proximity analysis, we analyzed the co-localization of leukocytes in the direct neighborhood (defined by ≤25 µm distance between cells) of injured cells from PT and TAL (Fig. 4E). The highest probability of directly neighboring leukocytes could be observed in the injured PT cell states and was particularly high in PT Injury m3 and m4 (Fig. 4E). The most severely injured TAL cell state, TAL Injury m3 and also TAL Injury m1 showed the strongest depletion of neighboring leukocytes. These results show a differential interaction of leukocytes with different injured cell states in PT and TAL during TCMR. This general pattern was unaffected by the distance threshold used to define direct neighbors (Suppl. Fig. S15) and persisted when assessing the spatial proximity of injured PT and TAL cell states to leukocyte subtypes (Suppl. Figs. S16 and S17). It is of note that proximity of activated/ lipid-associated macrophages with injured PT cells was particularly high (Suppl. Fig. S16).

To further characterize the cellular microenvironments in our mouse model, we performed unbiased spatial domain identification using the SpatialLeiden package36 (Fig. 5A-D). Coarse clustering revealed anatomically meaningful and distinct domains, including glomeruli (cluster 8), two medullary compartments (clusters 2 and 3) and several cortical clusters (remaining clusters) (Fig. 5A, B). To examine the microenvironments surrounding PT Injury m4 and TAL Injury m3, we subclustered cortical clusters 0, 1, 6 and medullary cluster 2, separately (Fig. 5C, D). In the cortex, three microdomains (clusters 0, 3, 4) were enriched in allogeneic kidneys (Fig. 5C). Clusters 0 and 4 consisted almost exclusively of endothelial, immune and interstitial cells, whereas cluster 3 contained a high proportion of injured PT cells (PT Injury m2–m4) alongside T cells, macrophages and interstitial cells, suggesting a potentially pro-inflammatory and pro-fibrotic niche. In the medulla, a single cluster enriched in allogeneic kidneys (cluster 0) comprised mainly immune and interstitial cells with only a small fraction of injured TAL cells and a large abundance of intact epithelium. Notably, TAL Injury m3 cells clustered with immune and interstitial cells, though less extensively than the patchy arrangement characteristic of PT Injury m4 and were more diffusely distributed (see also Fig. 3E, J). Immunofluorescence staining validated the spatial proximity of macrophages, T cells and fibroblasts with PT Injury m4 cells (defined by KIM1 expression) (Fig. 5E, and Suppl. Fig. S18 for TAL Injury m3).

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 Suppl. Table S1 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. 6A, B, and Suppl. Fig. S19). 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.

Leukocyte subclustering revealed similar cell types as in mouse data (Suppl. Fig. S20). Subclustering of the PT and TAL revealed the presence of injured PT and TAL states (Fig. 6C–H), characterized by reduced expression or absence of canonical markers and upregulation of injury markers, similar to those observed in the mouse model (Fig. 6C, F). 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, the variable timing of biopsy collection relative to TCMR onset and the limited number of samples in our human snRNA-seq data. To overcome these limitations, we performed single-cell deconvolution of microarray data from Molecular Microscope Diagnostic System (MMDx) signouts, comprising 3858 biopsies from 3210 patients (2155 without rejection, 1107 with antibody-mediated rejection, 298 with TCMR and 298 with mixed rejection), as previously described by us37. As expected, these analyses revealed a significant depletion of healthy PT and TAL cell types in TCMR, mixed rejections and ABMR (Fig. 6I). We could also observe a significant enrichment of injury clusters PT Injury h2 and TAL Injury h2 in TCMR, mixed rejection and ABMR when compared to no rejection and to ABMR (Fig. 6I). PT Injury h1 and TAL Injury h1 were depleted in TCMR, mixed rejection and ABMR. Microarray data confirmed that gene expression profiles of TCMR in protocol biopsies (used for our human snRNA-seq data) were highly similar to those in indication biopsies (Suppl. Fig. S21A).

To bridge the mouse and human datasets, we performed a cross-species analysis of PT and TAL injury states using label transfer from mouse to human cell types with singleR. As suggested from the marker gene expression (Fig. 6C, F), the most severely injured mouse PT and TAL cell states (PT Injury m4 and TAL Injury m3) corresponded to human PT Injury h2 and TAL Injury h2, respectively (Fig. 6E, H). Human PT Injury h2 expressed all markers of PT maladaptive repair, similar to PT Injury m4, including VCAM1, CD44 and VIM (Fig. 6C). The TAL Injury h2 cluster showed a pronounced EMT signature with markers such as MMP7, TPM and SPP1, associated with severe TAL injury in AKI and marker genes of TAL Injury m3.

We also leveraged microarray data from 3858 MMDx biopsies (the same population used in our single-cell deconvolution analysis) to systematically compare TCMR gene expression with our mouse and human snRNA-seq datasets, revealing significant correlations (Suppl. Fig. S21B, C).

We validated marker genes of PT Injury h2 and TAL Injury h2 shared with their mouse counterparts using immunofluorescence staining (Fig. 7A, B, compare to Fig. 3, Suppl. Figs. S9, S10 and S12). The data further confirmed that T cells, macrophages and interstitial cells frequently located around PT Injury h2, whereas such associations were much sparser around TAL Injury h2 (Fig. 7C and Suppl. Fig. S22), consistent with mouse kidney stainings (Fig. 5 and Suppl. Fig. S18). For immunofluorescence validation, we selected the mouse TAL Injury m3 marker gene MMP7 as a marker for TAL Injury h2. For PT Injury h2 we chose KIM1, ANXA3 and VCAM1 (all PT Injury m4 markers) as well as STK39 and ACSM3 (PT Injury m1 markers) (Suppl. Fig. S23). Although grouped within a single human snRNA-seq cluster, we observed mostly overlapping expression domains for KIM1 and VCAM1 that differed somewhat from those of STK39 and ACSM3, consistent with mouse data. This suggests potential heterogeneity within PT Injury h2 not fully resolved by clustering.

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. S24 and S25, Suppl. Data 3). 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 1061 patients (including 624 with no rejection, 297 antibody-mediated rejections, 95 TCMRs, 45 mixed rejections) (Fig. 8). 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 TCMR17.

For TCMR, 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. 8A). Conversely, high scores for PT Injury h1 (not specific for TCMR) 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. Similar correlations were observed for ABMR and mixed rejection (Suppl. Fig. S26).

In an exploratory analysis, we assessed whether gene set scores persist over time in patients initially diagnosed with TCMR. This analysis was limited to 12 cases with TCMR that had available follow-up biopsies with resolution to a no rejection diagnosis in the final biopsy (Fig. 8B). Despite the limited number of patients in this analysis, two observations emerged. First, the levels of injury-related gene set scores exhibited large variability among TCMR patients. This aligns with our findings in AKI, where heterogeneous distributions of injured epithelial cell states were observed among patients22. Second, for PT Injury h2 and TAL injury states in particular, a 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 elevated levels of outcome-relevant 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. S27)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-New3 and 4). PT Injury h1 corresponded mostly to a less severely injured PT cell state (PT-New1) which expresses genes involved in oxidative stress. PT Injury h2 was strongly associated with severe and potentially maladaptive PT injury in AKI (PT-New4).

Methods
Animal experiments
Male C57BL/6 and BALB/c mice, aged 10–12 weeks, were used for all the experiments to minimize variability related to hormonal cycles in female mice which can influence the immune responses to the transplant. The animals were sourced from Janvier (Le Genest St Isle, France) and maintained under standard housing conditions at 21–24°C temperature, 45–65% humidity and 12/12-h dark/light cycle, with ad libitum access to food and water. Owing to the exploratory nature of the study, randomization and blinding were not performed. All animal procedures were performed following the Directive 2010/63/EU, the German Tierschutz-Versuchstierverordnung. Ethical approval was obtained from the Regional Ethics Committee for Animal Research (Landesamt für Gesundheit und Soziales Berlin, approval number: G0236/18).

Human samples snRNA-seq
For human samples, data collection and analysis were performed with informed consent of the patients and with approval of the institutional review board of Hannover Medical School (no 2765). All human study participants provided written informed consent for publication of potentially identifiable medical information presented in supplementary table 1. This included variables such as age and sex. Exclusion criteria for the protocol biopsy program were lack of patient consent, relevant bleeding risks and anticoagulation therapy for an artificial heart valve. A total of 6 patient samples were included coming from 4 male and 2 female participants (age range 49-77, see Suppl. Table S1). Sex assigned at birth was extracted from clinical records. No gender identity data were collected. Sex-based analyses were not carried out because samples size in each subgroup was insufficient for meaningful stratification.

Statistical testing
All statistical tests were two-sided unless explicitly stated. The specific test used is indicated in the corresponding figure legends.

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 described33. 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 h 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 h 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 POD 7 and the surviving animals were sacrificed on POD 28.

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
Kidneys samples were fixed in 4% neutral-buffered paraformaldehyde for 22–24 h 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 classification46.

Immunofluorescence staining on mouse kidneys
For immunofluorescence staining (IF), 1.5 µm paraffin-embedded tissue sections were first deparaffinized by sequential incubations in xylene, ethanol and distilled water. Antigen retrieval was performed using 1× Target Retrieval Solution (TRS, Cat. #S1699, Agilent Technologies, Inc., USA) in a pressure cooker (WMF). After antigen retrieval, tissue sections were encircled with a PAP pen, blocked with 5% skimmed milk or 5% BSA in 1x TBS-T for 1 h at room temperature. Primary antibodies (see Table 1 below) diluted in Antibody Diluent (Cat. #S3022, Agilent Technologies, Inc., USA) were applied to the tissue. Incubation was carried out overnight at 4°C in the dark. After three washes in 1x TBS-T, secondary antibodies (see Table 1 below) and DAPI (1:250 and 1:2500 respectively, diluted in Antibody Diluent) were applied for 1 h at room temperature in the dark. Slides were again washed three times with 1x TBS-T before mounting with 60% glycerol in PBS supplemented with 584 mM sucrose. Sections were analyzed and photographed with an Eclipse Ti2-A microscope, DS-Qi2 camera and NIS-Elements software (Nikon, USA).

For antibody stripping, sections were washed once in 1x TBS-T, followed by renewed antigen retrieval as described above. Subsequent immunofluorescence staining was then repeated with alternative primary antibodies. Multichannel images were aligned and overlaid using BigStitcher in FIJI/ImageJ. After registration via Load & Stitch Multiple XMLs, fused overlays were exported as TIFFs using Export Transformed Images from BigDataViewer.

Immunofluorescence staining on human kidneys
Kidney biopsy sections were obtained from paraffinized biopsy samples stored at the pathology department of Hannover Medical School. TCMR biopsies were obtained due to clinical allograft function decline (indication biopsies). Stable allograft biopsies were obtained as part of the protocol biopsy program 12 months after transplantation. All biopsies were performed between 2014 and 2025. Paraffin-embedded sections were deparaffinized by sequential incubations in ROTI® Histol (Cat. #6640.4, Carl Roth GmbH + Co. KG, Germany), ethanol and distilled water. Heat-induced antigen retrieval was performed by immersion in 9,5 µM citric acid (Cat. #1.00244.0500, Sigma-Aldrich, USA) buffer (pH 6.0) and heating for 2 × 8 min in a microwave. After antigen retrieval, tissue sections were encircled with a PAP pen and blocked with 5% skimmed milk in 1x TBS-T for 1 h at room temperature. Primary antibodies (see Table 2) were incubated overnight at 4°C, followed by three washes in 1x TBS-T at room temperature. Secondary antibodies (see Table 2) were incubated for 1 h in the dark at room temperature and subsequently washed three times in 1x TBS-T at room temperature. Primary and secondary antibodies were diluted in 1% BSA in 1x TBS-T. Sections were mounted using Immunoselect Antifading Mounting Medium containing DAPI (Cat. #DNA-SCR-038448, dianova, Germany). Subsequent confocal imaging was performed on a Zeiss LSM980 laser scanning microscope (Carl Zeiss AG, Germany). Antibody stripping was performed as previously described by Gendusa et al.47. In brief, sections were washed once in 1x TBS-T followed by incubation in antibody elution buffer containing 2% SDS (Cat. #CN30.2, Carl Roth GmbH + Co. KG, Germany) and 0,8% 2-Mercaptoethanol (Cat. #M3148, Sigma-Aldrich, USA) in 62,5 µM Tris-HCL (Cat. #9090.3, Carl Roth GmbH + Co. KG, Germany) buffer for 30 min at 56°C. Subsequently, sections were washed in distilled water for 1 h at room temperature with water changes every 15 min. Successful removal of antibodies was confirmed via epifluorescence microscopy. Staining was then repeated with alternative primary antibodies. Multichannel images were aligned and overlaid using BigWarp from the BigDataViewer plugin in FIJI/ImageJ.

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 h before being stored at -80°C until nuclei isolation, following the protocol described by Leiz, Hinze et al.48.

All samples underwent single-cell sequencing using the 10x Genomics Chromium Next GEM Single Cell 3’ v3.1 chemistry protocol (#CG000204 Rev D), targeting 9000–10,000 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.

Xenium in Situ Profiling
In situ single cell RNA expression analysis was performed using the Xenium system (10X Genomics). 5 µm 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 h 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. Data 1). The 100 custom genes were selected based on previous mouse single cell sequencing experiments and markers published in the literature. They include canonical marker genes for all expected kidney major cell types as well as expected cell populations induced by kidney injury.

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.

Cell types were annotated by label transfer from snRNA-seq using SingleR v2.0.0. We first assigned major cell types with the function SingleR(test=as.SingleCellExperiment(spatialdata), ref=snRNA-seq data, labels=celltype annotations from snRNA-seq, aggr.ref=TRUE, de.method=“wilcox”) and retained only labels within pruned.labels. The same procedure was subsequently applied within each major cell type to refine the annotations.

Spatial proximity. For every injury cluster of interest (e.g., PT Injury h2), the co-occurence to each leukocyte (sub)cell type was calculated with squidpy (v1.4.1) using Python (v3.10.14)49. Briefly, squidpy.gr.co_occurrence was run with a distance interval of 5 to 50 µm with step size 5 (and a second longer distance from 25 to 250 µm with step size 25) based on the provided centroid information from the Xenium data. We chose 25 µm as indicative of being a direct neighbor to calculate a one sample t-test for the significant deviation of the co-occurrence from a random co-occurence (ratio 1) across the allogeneic samples. Multiple testing correction was performed with the Benjamini-Hochberg procedure.

Spatial domains. To identify spatial domains in an unsupervised manner SpatialLeiden (v0.2.0) was used36. First, a directed spatial nearest neighbor graph was built per sample using squidpy.gr.spatial_neighbors (v1.4.1) with coord_type ‘generic’ and 10 neighbors. The distance between cells was transformed to a connectivity using spatialleiden.distance2connectivity. The undirected gene expression nearest neighbor graph was generated across all samples with scanpy.pp.neighbors (v1.10.1)50 using the Harmony batch-corrected latent space. Domains were identified using spatialleiden with a resolution of 0.9 and a layer_ratio of 1.1 running for 10 iterations.

To gain insights in the localization of TAL subtypes, the medullary domain (domain 2) was subclustered with spatialleiden using a resolution for the latent space neighbor graph and the spatial neighbor graph of 0.4 and 0.8, respectively. Similarly, the cortical domains containing the majority of the PT cells and exhibiting similar composition but varying levels of immune infiltration (domain 0, 1 and 6) were subclustered with spatialleiden using resolutions of 0.5 and 0.7, respectively.

Bioinformatic analyses of single nucleus mRNA sequencing data
Celltype 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 in Seurat version 5.1.0: 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 previously22. For the PT, injury population PT Injury m1 was finally determined by increasing clustering resolution to 0.6.

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).

Pathway analysis of DGE genes using WGCNA modules. For both strain comparisons (C57BL/6 to BALB/c vs. C57BL/6 to C57BL/6 and vice versa), pathway analysis was performed on the same sets of significantly upregulated genes described in the main DGE analysis (Fig. 2A; log2FC > 1, adj. p < 0.05). To group DGE genes into functionally coherent clusters, we applied Weighted Gene Co-expression Network Analysis (WGCNA; blockwiseModules, networkType=“signed”, minModuleSize=20, deepSplit=3, mergeCutHeight=0.20). Genes were ordered by module and displayed in clustered heatmaps (cell types × genes; log2FC clipped to [0, 3] for color scaling). Each module was tested for pathway enrichment using clusterProfiler, with MSigDB Hallmark gene sets (Mus musculus) for a stringent run (q < 0.05) and Hallmark + Reactome for a broader overview (q < 0.1). Results were summarized as module × pathway dot plots to support interpretation of the DGE genesets at the pathway level.

Trajectory inference with PAGA. For PT and TAL mouse subclustering, trajectory inference was performed using partition-based graph abstraction (PAGA) in Scanpy version 1.11.4. For each dataset, selected healthy subclusters were grouped into a single starting population (healthy PT or healthy TAL) in the largersubct metadata field. Log-normalized expression values were used to identify the top 500 highly variable genes (flavor = “seurat”), followed by PCA, neighborhood graph construction and diffusion map embedding. PAGA graphs were computed on the largersubct groups and diffusion pseudotime was calculated with the root set to the “healthy” cluster and normalized to a 0–1 range for visualization. The resulting PAGA connectivity graphs were visualized with nodes colored by pseudotime.

Cross-species analysis. Human-to-mouse mapping was performed for PT and TAL subclusters using SingleR v2.0.0, with human single-cell datasets serving as reference. Mouse genes were converted to unique human orthologs via biomaRt (Ensembl, Dec 2021) and only intersecting orthologous genes were retained. Log-normalized expression values were extracted from both species and human annotations were used as reference labels. SingleR was run with default settings and pruning enabled to remove ambiguous matches. Mapping performance was evaluated by confusion matrices comparing original mouse subclusters with SingleR-derived human-based labels, followed by column-wise normalization and visualization as non-clustered heatmaps.

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. We included a total of 5086 kidney transplant biopsies. 3570 biopsies from 3995 patients were collected through International Collaborative Microarray Extension Study (INTERCOMEX; NCT01299168) and Trifecta-Kidney (NCT04239703) clinical trials and MMDx-Kidney sub-studies. An additional 1516 biopsies, included from 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 microarrays51. MMDx signouts of NR, TCMR, ABMR and Mixed were available for 4371 biopsies. Rejection and injury states were derived according to previously published methods52. Biopsies having ≤10% cortex were excluded leaving 3858 biopsies. From this set of 3858 biopsies, MMDx signouts and graft status were available for 1292 biopsies from 1061 patients. For survival analyses, we selected the first biopsy post-transplant for each patient, leaving 1061 biopsies for assessment. For deconvolution analyses, cell composition was estimated in the cohort of 3858 biopsies. 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’.

Conservation in gene expression profiles of TCMR in single nuclei and bulk transcriptomic data. Agreement between gene expression profiles for TCMR among mouse blk6 single nuclei, human single nuclei and human bulk transcriptomics assays was assessed using Spearman correlation coefficients in log2FC values for the top 500 increased and decreased genes in allogenic vs syngeneic (mouse single nuclei) TCMR vs stable (human single nuclei) and TCMR vs NR (human bulk).

Conservation in gene expression profiles of indication and protocol biopsy bulk transcriptomic data. Agreement between gene expression profiles for TCMR among human indication and protocol biopsies assessed by bulk transcriptomics was assessed using Spearman correlation coefficients in log2FC values for the top 500 increased and decreased genes in TCMR-indication vs NR and TCMR-protocol vs NR (N TCMR protocol=22, N TCMR indication=140).

Gene set scores in bulk transcriptomic data. Gene set scores for each human PT and TAL cell state were assigned to each biopsy in the microarray 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 for NR, TCMR, Mixed and ABMR populations, including a total of 1061 patients from 605 male, 353 female and 103 unreported sex participants (age range 2-92 years). Sex assigned at birth was voluntarily provided by each patient’s clinician. No gender identity data were collected. Scores were assessed individually with Kaplan-Meier estimates, stratified by their median value in the full population. Sex had no significant impact on survival in this cohort and was omitted from the final Kaplan-Meier analyses to reduce overstratification and increase the number of failures in each stratum. The transition of gene set scores following TCMR diagnosis was depicted in MMDx kidney biopsies in patients diagnosed with TCMR (n=12) which eventually progressed to no rejection (NR) in subsequent follow-up biopsies.

Cellular deconvolution in bulk transcriptomic data. Cell compositions were estimated using the CIBERSORTx platform53–55. A de novo signature matrix was produced using the gene expression profiles of the 28 cell types identified in the human single nuclei studies described above. The cibersortx/fractions module was used to produce the signature matrix with the following parameters: single_cell=TRUE, g_min = 300, g_max = 5000, q_value=0.05, filter = FALSE, k_max = 999, replicates=5, sampling=0.5, fraction=0.75 with all other parameters set to default. In cases where genes in the signature matrix were represented by multiple Affymetrix probe sets, the mean expression of all prob sets for each gene was used. CIBERSORTx was run using the cibersortx/fraction module with the following parameters; single_cell=TRUE, perm=1000, QN=FALSE, rmbatchSmode=TRUE, with all other parameters set to default. Deconvolution was run using S-mode batch correction to account for cross-platform technical variability. A total of 3858 biopsies from 3210 patient samples were included coming from 1019 male, 600 female and 1591 unreported sex participants (age range 2-92 years). Sex assigned at birth was voluntarily provided by each patient’s clinician. No gender identity data were collected. Sex-based analyses were not carried out due to data incompleteness in reported sex. All samples were fit appropriately (i.e., p<0.05 from 1000 permutation), amounting to a total of 2155 NR, 298 TCMR, 298 Mixed and 1107 ABMR biopsies available for assessment. Estimates of PT and TAL cells were compared across MMDx groups using arcsin-transformed Bayes-moderated t-tests56. Healthy PT and TAL cells were pooled prior to group testing.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript focusses on the evaluation of TCMR argument that the molecular pathways triggered by TCMR remain poorly understood. The authors state that although when TCMR is treatable, it associates with reduced allograft survival, suggesting current therapies are not adequately addressing all the aspects of TCMR-induced injury. This statement represents the scientific premise of the study. They evaluate using single cell approaches (snRNAseq and spatial transcriptomics) kidney tissues from two mouse models of TCMR (including allo- and iso-genic kidney transplant mouse models). Although the authors claim that the study aim is to delineate the molecular changes associated with TCMR and evaluate their impact on allograft outcomes, they mainly focused on the analyses of the epithelium response to TCMR-associated injury. Nevertheless, this is the more novel aspect of the study. They compared the findings from mouse data to molecular data from human biopsies. Finally, they use bulk transcriptomics from a large cohort of patients to assess potential clinical relevance for elucidating potential therapeutic targets. The study includes allo- and iso-genic kidney transplant mouse models. Overall, the manuscript is well presented, organized, and test an important hypothesis. The use of single cell approaches, interspecies comparison, and validation using bulk transcriptomics is a strength of the study. Focus on the response of the epithelium to the injury is also relevant. The study effectively links mouse and human datasets, demonstrating conserved injury states in PT and TAL cells. However, some points that deserve further evaluation were identified including:\n1- Most of the single cell transcriptomics evaluations seems superficially evaluated. Although cell identity was established for PT subclusters (PT injurym1-4 and PT proliferating), the results about specifics of these clusters are limited. There are no specific results defining these cells subclusters and/or analyses about characterization of cell type-specific pathways. The description of the PT injury m4 cell cluster (identified as most injured cluster by number of injury-associated marker genes), is limited to “The marker genes for PT Injury m4 include several associated with maladaptive repair cells in AKI, such as Vcam1, Cd44 and Vim22,24”. Overall, most the results rely on markers previously described in the literature for native kidneys with AKI. Likewise, the same limitations affect the evaluation of TAL cell subclusters.\n2- Although the main interest seems to focus on PT and TAL cell clusters, immune cells and epithelium-immune interactions are critical as part of resolution of injury or progression to impaired repair during TCMR. However, the cell type-specific characterization of immune cells is minimal. Specifically, what type of macrophages were involved in injured PT-immune cell interactions? Fig 4 A shows a subcluster analyses of immune cells with 4 macrophages populations. However, no characterization of the macrophage subpopulations is provided nor their associated profiles. Same point is valid for all the immune cells.\n3- In the cross-species evaluation, the study effectively bridges mouse and human datasets, demonstrating conserved injury states in PT and TAL cells. However, although injured PT and TAL cells were identified, their relative abundance was not significantly higher in TCMR samples compared to stable allografts. How alike were the PT injured specific profiles between conditions? This point deserves further evaluation/discussion.\n4- The human biopsies were obtained from protocol biopsy program at Hannover Medical School, meaning they were not necessarily taken at the time of acute rejection symptoms but rather as part of scheduled monitoring of kidney transplant patients. This contrasts with the controlled timing in mouse models, reducing direct comparability. This is likely reflected by the absence of the cluster of proliferating cells in the human samples. Further discussion about how this issue may affect other cell clusters and moreover, their specific transcriptome must be discussed. Also, this reviewer was not able to identify the Banff scoring for human samples with TCMR used in the snRNAseq study or the specific time of biopsy collection.\n5- The study focused primarily on PT and TAL injury states, but other cell types (e.g., immune cells, endothelial cells, fibroblasts) may contribute significantly to TCMR pathogenesis. Additional cell type-specific analyses could enhance the understanding of injury mechanisms in response to TCMR.\n6- The longitudinal analysis included only 12 patients, which is a small sample size to draw definitive conclusions about persistence of injury states post-TCMR.\n7- The significant variability in injury-related gene set scores among TCMR patients indicates heterogeneous injury responses, making it challenging to define a universal injury signature. This suggests that additional patient-specific factors (e.g., immune responses, comorbidities, treatment regimens) may influence epithelial injury progression and should be further investigated.\n8- Although TAL injury signatures were more pronounced in TCMR, they were not exclusive to it, as they also correlated with injury in AKI. This suggests that while TAL injury is relevant for TCMR outcomes, other forms of allograft stress (e.g., ischemia) may also contribute to its expression.", "reviewer_2": "T-cell-mediated rejection (TCMR) remains a significant hinderance to long-term kidney transplant survival, as it contributes to graft injury and eventual failure despite immunosuppression strategies. To better understand molecular mechanisms associated with TCMR the authors induced acute TCMR in mouse kidney transplant models and analyzed molecular changes using snRNA sequencing and spatial transcriptomics. These findings were compared with human biopsy data from rejected and stable allografts. The study revealed that TCMR causes significant gene expression changes and epithelial injury, particularly in proximal tubules (PT) and thick ascending limbs (TAL), with spatial heterogeneity and proximity to immune cells. Cross-species analysis confirmed similar injury patterns in human TCMR cases. Furthermore, kidney transplant outcomes were linked to the persistence of injured epithelial states, even after rejection resolution. The manuscript reports interesting data with potential value in expanding current understanding about TCMR and underlying molecular landscapes. The results are significant despite the small sample size in the validation cohort. There are a few aspects the authors could do to improve the quality of the manuscript's claims and conclusions and make it more useful to its readers.\nThe primary concern about the manuscript is that the conclusion of this study is inconclusive in terms of the utility of such gene set scores in enabling clinicians to triage transplant recipients into different bins of TCMR. The sample size is small, and despite the molecular score based on the snRNA seq data from mice and humans, other injury types in transplant have not been discussed at all. The authors have brought AKI and CKD studies, but have failed to discuss how to interpret the data in the context of ABMR and other transplant injuries such as mixed rejection.\nMinor:\nFor Fig 1D, top panel, please provide color codes for major cell types.\nFig 6A-- How would other phenotypes behave other than TCMR and nonTCMR in Kaplan Meier curves? Did nonTCMR cohort include ABMR? How did the gene set score perform for mixed rejection cases?\nFig 6B-- There is no mentioning of the significance of orange vs grey circles in the figure legend or the result section where the data is presented. Please provide the information both in the legend as well as the result section.", "reviewer_3": "In this manuscript, the authors employ a murine kidney transplantation model to investigate epithelial cell injury states during acute T cell–mediated rejection (TCMR). They performed snRNA-seq on kidneys from both syngeneic and allogeneic transplant recipients (BALB/c and C57BL/6), identifying distinct injury-associated subpopulations within proximal tubule (PT) and thick ascending limb (TAL) cells. Spatial transcriptomics (Xenium) was used to characterize immune cell subtypes within the rejecting allografts. In addition, the authors derived injury gene expression signatures from snRNA-seq analysis of human kidney transplant biopsies and correlated these injury-associated signatures with reduced graft survival in an independent bulk transcriptomic dataset.\nThe core hypothesis—that injured epithelial cell states contribute to allograft dysfunction—is conceptually important, and the integration of scRNA-seq with spatial technologies is a valuable strategy. However, this manuscript fails to meet the analytical and experimental standards necessary to support such claims. The analysis lacks rigor, fails to provide cell type–specific insights, and omits essential validation steps. Several figures are descriptive but not interpretable in their current form. I do not believe the manuscript is suitable for publication. Substantial re-analysis, validation, and clarification are needed to bring the study to the level expected by the field. Below are my specific comments.\n1. In figure 2A, if the BALB/c to C57BL/6 kidneys has milder rejection, why there are more DEGs in PC, EC, intC, PEC and IC-A when comparing to the C57BL/6 to BALB/c kidneys?\n2. The heatmap shown in Figure 2B provides minimal information beyond the number of DEGs in each cell type. The authors should highlight key cell type–specific genes, particularly in PT and TAL cells, to improve interpretability. In addition, experimental validation of select disease-associated genes in these cell types would considerably strengthen the study’s conclusions.\n3. Figure 2C basically shows that all these cell types share most of the disease pathways. Therefore, the authors should conclude that few cell type–specific pathways were identified, which makes the overall conclusion less compelling. The authors should focus more on highlighting any truly cell type–specific disease pathways to strengthen the impact of their findings.\n4. In Figure 3A, PT injury m2–3 cannot be classified as distinct injured states, as they share all marker gene expression and lack unique disease genes. If the authors want to define these as transitional injury states, pseudotime trajectory analysis would be more appropriate than clustering analysis.\n5. The accuracy of mapping scRNA-seq to Xenium data depends heavily on the gene panel used in Xenium—for example, whether markers for PT subtypes are included. As a result, it is difficult to assess the accuracy of the spatial cell mapping shown in Figure 3A. The authors should apply the same mapping algorithm to PT S1–S3 subtypes to determine whether PT S3 localizes to the corticomedullary junction and PT S1/2 to the outer cortex, as expected. This can server as internal validation of the approach.\n6. In Figure 3B, if PT injury m1 represents an injured cell type, it is unclear how to explain the lack of change in its proportion between allogeneic and syngeneic kidneys. The only marker used to define PT injury m1 is Nqo1. Since this is a novel marker in this context, the authors should provide additional information about the gene, including its primary function and potential role in tubular injury. Furthermore, immunofluorescent staining is needed to confirm the presence of this injury state in the allogeneic kidney.\n7. All of the concerns raised above regarding the PT subtypes (points #4–#6) also apply to the TAL subtypes shown in Figure 3C and 3D.\n8. Since the authors have generated Xenium data, they should perform subclustering analysis on the PT and TAL populations within the Xenium dataset to validate the injured cell states and assess their spatial distribution in allogeneic versus syngeneic kidneys.\n9. It is unclear why the authors focus exclusively on immune cells in Figure 4 for the Xenium data analysis. A more comprehensive analysis should include all cell types to determine whether the same populations identified by scRNA-seq can also be resolved using Xenium data alone. In Supplementary Figure S4A, the authors perform clustering on the Xenium dataset, but substantially fewer cell types are shown, and no subclustering analysis was conducted for PT and TAL.\n10. The authors need to provide details on how the 100 custom genes were selected for their Xenium gene panel design. For instance, if the panel lacks sufficient markers for injured PT or TAL subtypes, the entire study would be compromised. In such a case, the key findings from the scRNA-seq data regarding injured PT and TAL cells would be difficult to validate or reproduce using the Xenium platform, making integration between the two datasets unreliable. Furthermore, the absence of appropriate markers would preclude meaningful downstream analyses, such as determining the spatial roles of injured PT and TAL subpopulations. For example, niche analysis—which could reveal which injured states preferentially recruit immune cells—would not be possible, thereby limiting mechanistic insights into tubular injury during TCMR.\n11. It is unclear how the authors generated Figure 4D, as they did not perform subclustering of PT and TAL cells. It appears that computational methods were used to map snRNA-defined cell types onto the spatial data. however, this mapping relies on the set of genes shared between the snRNA-seq and Xenium platforms. If the gene panel design lacks sufficient markers for injured states, particularly for PT and TAL, the resulting cell mapping may be highly inaccurate.\n12. In addition, for the neighborhood analysis in Figure 4D, the authors simply report the percentage of neighboring PT and TAL cells, which is a descriptive rather than statistical approach. The authors should utilize publicly available tools designed for spatial neighborhood analysis to reanalyze the data more rigorously. To support their findings, they should also provide immunostaining data to confirm that the identified cell types are indeed in close spatial proximity.\n13. In Figure 5, the authors focus solely on demonstrating the similarity of cell types between human and mouse in TCMR. However, they fail to present more critical data—specifically, whether the gene expression changes observed between stable allograft and TCMR biopsies in humans are similar to, or distinct from, those observed between allogeneic and syngeneic kidneys in the mouse model. This comparison is essential to establish the translational relevance of the mouse findings to human disease.\n14. Since the authors selected different sets of genes in Figure 5C and 5F compared to those used in Figure 3A and 3C, it is unclear whether the human injured cell states truly correspond to the mouse injured cell states. The simple Pearson correlation analysis presented in Figure 5F and 5H is insufficient to support the conclusion that these are the same cell types. Additional analyses are needed to strengthen this claim. For example, the authors should identify injury markers shared between human and mouse tubular subtypes and perform immunostaining to validate the identity of these matched cell states.\n15. The authors should use the gene signatures derived from the human biopsy scRNA-seq data to perform cell type deconvolution on the bulk transcriptomic dataset presented in Figure 6. This analysis would allow them to determine whether the proportions of injured tubular subtypes are specifically increased in TCMR or mixed rejection biopsies, but not in ABMR biopsies. Such evidence would provide stronger support for the relevance of the identified injury signatures in distinguishing rejection types."}, "author_response": "Point-by-point responses\nFirst, we would like to thank the editorial board and the reviewers for the in-depth review of our\nmanuscript and the many constructive comments.\nIn response to these comments, we have made extensive revisions that we believe\nsubstantially strengthen the manuscript. To summarize, key additions include:\n1. Extensive immunofluorescence stainings validating proximal tubule (PT) and thick\nascending limb (TAL) injury clusters in both mouse and human samples.\n2. Immunofluorescence stainings confirming the spatial proximity of injured PT cells\nto macrophages, fibroblasts and T cells in mouse and human samples.\n3. Results from unbiased spatial domain identification.\n4. Single-cell deconvolution analysis of more than 3,800 kidney transplant biopsies\n(including 298 TCMR biopsies) with microarray data, demonstrating enrichment\nand specificity of injury-associated cell states in TCMR.\n5. A systematic comparison of gene expression changes in TCMR between indication\nand protocol biopsies.\n6. Fully revised main figures and many new supplemental figures supporting and\ninvestigating the correspondence between:\na. Xenium and single-cell transcriptomic data and (sub) cell type labels.\nb. mouse and human TCMR data.\nAll these points resulted in substantial revisions of the originally provided figures and\nsupplement, the addition of 2 new main figures and 17 supplemental figures. We believe that\nthese results further substantiate our original findings. All content-relevant additions to the\nmanuscript are highlighted in red font.\nPlease note that we needed to substantially shorten and fully re-write the abstract (previously\n242 words, initially accepted) to comply with the journal’s formatting requirement of 150 words\nor less. The content remained unchanged, except for the inclusion of additional experiments\nand analyses conducted during the revision.\nPlease find our point-by-point responses to the specific comments below:\nReviewer #1 (Remarks to the Author):\nThe manuscript focusses on the evaluation of TCMR argument that the molecular pathways\ntriggered by TCMR remain poorly understood. The authors state that although when TCMR is\ntreatable, it associates with reduced allograft survival, suggesting current therapies are not\nadequately addressing all the aspects of TCMR-induced injury. This statement represents the\nscientific premise of the study. They evaluate using single cell approaches (snRNAseq and\nspatial transcriptomics) kidney tissues from two mouse models of TCMR (including allo- and\niso-genic kidney transplant mouse models). Although the authors claim that the study aim is\nto delineate the molecular changes associated with TCMR and evaluate their impact on\nallograft outcomes, they mainly focused on the analyses of the epithelium response to TCMR-\nassociated injury. Nevertheless, this is the more novel aspect of the study. They compared the\nfindings from mouse data to molecular data from human biopsies. Finally, they use bulk\ntranscriptomics from a large cohort of patients to assess potential clinical relevance for\nelucidating potential therapeutic targets. The study includes allo- and iso-genic kidney\ntransplant mouse models. Overall, the manuscript is well presented, organized, and test an\nimportant hypothesis. The use of single cell approaches, interspecies comparison, and\nvalidation using bulk transcriptomics is a strength of the study. Focus on the response of the\nepithelium to the injury is also relevant. The study effectively links mouse and human datasets,\ndemonstrating conserved injury states in PT and TAL cells.\nReply: We thank the reviewer for this favorable assessment of our manuscript.\nHowever, some points that deserve further evaluation were identified including:\n1- Most of the single cell transcriptomics evaluations seems superficially evaluated. Although\ncell identity was established for PT subclusters (PT injurym1-4 and PT proliferating), the results\nabout specifics of these clusters are limited. There are no specific results defining these cells\nsubclusters and/or analyses about characterization of cell type-specific pathways. The\ndescription of the PT injury m4 cell cluster (identified as most injured cluster by number of\ninjury-associated marker genes), is limited to “The marker genes for PT Injury m4 include\nseveral associated with maladaptive repair cells in AKI, such as Vcam1, Cd44 and Vim22,24”.\nOverall, most the results rely on markers previously described in the literature for native\nkidneys with AKI. Likewise, the same limitations affect the evaluation of TAL cell subclusters.\nReply: To address this important comment, we now include and discuss relevant marker\ngenes of these clusters in the respective section (section “Mouse TCMR elicits spatially diverse\ninjured cell states in PT and TAL”). We also adapted the presentation of marker genes, which\nnow convincingly shows the overlap with the human marker genes and the Xenium data (Fig.\n3C, H (mouse) versus Fig. 6C, F (human) versus Suppl. Figs. S9B and 11B (Xenium)).\n2- Although the main interest seems to focus on PT and TAL cell clusters, immune cells and\nepithelium-immune interactions are critical as part of resolution of injury or progression to\nimpaired repair during TCMR. However, the cell type-specific characterization of immune cells\nis minimal. Specifically, what type of macrophages were involved in injured PT-immune cell\ninteractions? Fig 4 A shows a subcluster analyses of immune cells with 4 macrophages\npopulations. However, no characterization of the macrophage subpopulations is provided nor\ntheir associated profiles. Same point is valid for all the immune cells.\nReply: We thank the reviewer for this important comment. We characterized and re-annotated\nthe identified immune cell subclusters in the mouse and the human data using cell type-specific\nmarkers from the literature1-4 (see Fig. 4 and new Suppl. Fig. 20). Specifically, we were able to\ndistinguish between CD4+ T helper cells, CD8+ cytotoxic T cells, regulatory T cells, B cells\nand NK cells within the lymphoid cells in both the mouse and the human data as well as a\nplasmacytoid dendritic cell population in the human data. Within the myeloid cells, we could\ndifferentiate between dendritic cells and monocytes as well as resident, infiltrating and\nactivated/ lipid-associated macrophages, although some marker genes were expressed in\nmore than one macrophage population indicating some transitional macrophages within the\nidentified clusters. Resident macrophages were for example marked by expression of Mrc1\nand lipid associated macrophages by expression of Trem2, Spp1 as well as genes encoding\nproteins involved in lipid handling such as Fabp4 and Fapb5. The “Activated Macrophage”\npopulation showed expression of profibrotic genes such as Col1a1 and other collagen\nencoding genes, such as Sparc and Cxcl12. The cluster termed “Infiltrating Macrophage”\nretained slight expression of some monocyte-characteristic genes, but predominantly showed\nexpression of macrophage markers without a specific transcriptomic profile, possibly also\nindicating a transitional state.\nFollowing a comment from Reviewer 3, we re-analyzed spatial proximity of injured PT and TAL\nclusters using a published tool. This analysis confirmed the previously observed depletion of\nall broad leukocyte cell types in the vicinity of TAL Injury m3 and an increased presence around\nPT Injury m4 (Fig. 4E). We also performed this analysis for the leukocyte subtypes shown in\nnew Suppl. Fig. S16. Especially activated macrophages are highly overrepresented in the\nvicinity of PT Injury m4. This finding is now also included in the main body (section “TCMR-\ninduced injured epithelial cell states show heterogeneous proximity to immune cells and\nsurrounding cell type environments”).\n3- In the cross-species evaluation, the study effectively bridges mouse and human datasets,\ndemonstrating conserved injury states in PT and TAL cells. However, although injured PT and\nTAL cells were identified, their relative abundance was not significantly higher in TCMR\nsamples compared to stable allografts. How alike were the PT injured specific profiles between\nconditions? This point deserves further evaluation/discussion.\nReply: We thank the reviewer for raising this point. We acknowledge that the abundances of\ninjured epithelia were not significantly increased in TCMR versus no TCMR. This can result\nfrom several expected factors, but primarily since the abundances of these populations are not\nconstant over time in all patients. As we do not exactly know (in contrast to the mouse model)\nat which time point after TCMR onset the biopsies were taken, this might explain some\nvariability among patients (see also Fig. 8B).\nTo address this important reviewer comment, we have therefore performed and now include\nthe following analyses:\n1. New single-cell deconvolution analysis of microarray data from 3858 biopsies obtained\nfrom 3210 kidney transplant recipients including 298 TCMR biopsies, enabling us to\nmeaningfully compare abundances between conditions, which shows:\na. Significantly increased abundances of the most severely injured cell states in\nPT and TAL (PT Injury h2 and TAL Injury h2) (see Fig. 6I) and\nb. reduced abundances of PT Injury h1 and TAL Injury h1 indicating that these\ninjury states might originate from other sources of kidney transplant injury (Fig.\n6I).\nc. A systematic comparison of gene expression changes showing - strong and\nsignificant correlations – between mouse and human snRNA-seq TCMR data\nas well as human TCMR microarray data (Suppl. Fig. S21).\n2. Fully updated marker gene heatmaps better highlighting the correspondence of injury\nclusters between mouse and human (compare heatmaps in Figs. 3 and 6).\n3. Immunofluorescence validation of critical markers for injured PT and TAL clusters in\nmouse and human (Figs. 3 E, J and 7, Suppl. Figs. 9, 10 and 12).\nWe hope these new analyses more convincingly demonstrate the correspondence between\nmouse and human TCMR data, as well as the induction of severely injured PT and TAL states\nby TCMR in human.\n4- The human biopsies were obtained from protocol biopsy program at Hannover Medical\nSchool, meaning they were not necessarily taken at the time of acute rejection symptoms but\nrather as part of scheduled monitoring of kidney transplant patients. This contrasts with the\ncontrolled timing in mouse models, reducing direct comparability. This is likely reflected by the\nabsence of the cluster of proliferating cells in the human samples. Further discussion about\nhow this issue may affect other cell clusters and moreover, their specific transcriptome must\nbe discussed. Also, this reviewer was not able to identify the Banff scoring for human samples\nwith TCMR used in the snRNAseq study or the specific time of biopsy collection.\nReply: Thank you for this comment. To address it, we systematically compared TCMR protocol\nand indication biopsies in our microarray dataset and observed extremely high correlations in\ngene expression. These results are now included in Suppl. Fig. S21A and are mentioned in\nthe results section (section “Human TCMR kidney allografts exhibit injured cell states\nresembling those in mice”). In summary, our analyses do not indicate major differences\nbetween protocol and indication biopsies in TCMR.\nWe agree with the reviewer that the absence of the proliferating cluster is most likely\nattributable to the timing of the human biopsies, a phenomenon also observed in AKI and CKD\nsamples. We do not consider this to affect the interpretation of the other cell types of interest\nin this manuscript as supported by the parallel analyses in mouse and human datasets.\n5- The study focused primarily on PT and TAL injury states, but other cell types (e.g., immune\ncells, endothelial cells, fibroblasts) may contribute significantly to TCMR pathogenesis.\nAdditional cell type-specific analyses could enhance the understanding of injury mechanisms\nin response to TCMR.\nReply: Thank you for this important remark. Indeed, we focused on PT and TAL cells, as these\ncell types exhibit the most pronounced response to TCMR—both in terms of differential gene\nexpression and the abundance of TCMR-induced injury clusters.\nWherever applicable, we applied the same analytical approaches across all cell types,\nincluding differential gene expression, pathway enrichment and subclustering analyses.\nIn the revised manuscript, we have now included unbiased domain analyses (new Fig. 5) that\nencompasses all cell types. This analysis revealed, for example, that the severely injured PT\nInjury m4 cells predominantly reside in inflammatory microenvironments surrounded by\nimmune cells and fibroblasts. We validated the presence of these cell groups using our Xenium\ndata as well as new immunofluorescence stainings in mouse and human (see Figs. 5 and 7 as\nwell as Suppl. Fig. S18).\nWe hope that these additional data satisfactorily address the reviewer’s comment. While we\nacknowledge that many more analyses - particularly involving other cell types—could be\nperformed, we have intentionally kept the focus of the manuscript on epithelial injury impact\nand patterns. We hope the reviewer agrees with this approach.\n6- The longitudinal analysis included only 12 patients, which is a small sample size to draw\ndefinitive conclusions about persistence of injury states post-TCMR.\nReply: We are aware of this limitation and had highlighted this in the discussion section. We\nagree that the sample size is small, but it interestingly still displays that some patients keep\nsignatures of injured epithelia. Certainly, larger cohorts would enable to identify the amount\nand conditions under which patients would display such a phenotype. This, of course, cannot\nbe answered by our small cohort. As we find the plots in Fig. 8B (previously Fig. 6B) still to be\nintriguing, we kept them in the figure and hope that this is acceptable for the reviewer.\n7- The significant variability in injury-related gene set scores among TCMR patients indicates\nheterogeneous injury responses, making it challenging to define a universal injury signature.\nThis suggests that additional patient-specific factors (e.g., immune responses, comorbidities,\ntreatment regimens) may influence epithelial injury progression and should be further\ninvestigated.\nReply: Thank you for this important remark. We included these limitations in the discussion.\n8- Although TAL injury signatures were more pronounced in TCMR, they were not exclusive\nto it, as they also correlated with injury in AKI. This suggests that while TAL injury is relevant\nfor TCMR outcomes, other forms of allograft stress (e.g., ischemia) may also contribute to its\nexpression.\nReply: Thank you for raising this important point. In fact, it is unknown but not unlikely that\nother conditions, which trigger epithelial injury, might also entail this kind of TAL injury. In fact,\ntogether with our new data from deconvolutional analysis, we can now state that:\n1. The mere presence of the discovered injury cell states affects different conditions after\nkidney transplantation alike, e.g. ABMR, TCMR (Fig. 8, Suppl. Fig. S26).\n2. The abundance of certain injury cell states, namely PT Injury h2 and TAL Injury h2, is\nsignificantly increased during TCMR (when compared to no rejection or ABMR, Fig. 6I).\nWe thank the reviewer for prompting us to clarify this important point.\nReviewer #2 (Remarks to the Author):\nT-cell-mediated rejection (TCMR) remains a significant hinderance to long-term kidney\ntransplant survival, as it contributes to graft injury and eventual failure despite\nimmunosuppression strategies. To better understand molecular mechanisms associated with\nTCMR the authors induced acute TCMR in mouse kidney transplant models and analyzed\nmolecular changes using snRNA sequencing and spatial transcriptomics. These findings were\ncompared with human biopsy data from rejected and stable allografts. The study revealed that\nTCMR causes significant gene expression changes and epithelial injury, particularly in\nproximal tubules (PT) and thick ascending limbs (TAL), with spatial heterogeneity and\nproximity to immune cells. Cross-species analysis confirmed similar injury patterns in human\nTCMR cases. Furthermore, kidney transplant outcomes were linked to the persistence of\ninjured epithelial states, even after rejection resolution. The manuscript reports interesting data\nwith potential value in expanding current understanding about TCMR and underlying molecular\nlandscapes. The results are significant despite the small sample size in the validation cohort.\nReply: We thank the reviewer for this positive assessment of our manuscript.\nThere are a few aspects the authors could do to improve the quality of the manuscript's claims\nand conclusions and make it more useful to its readers. The primary concern about the\nmanuscript is that the conclusion of this study is inconclusive in terms of the utility of such gene\nset scores in enabling clinicians to triage transplant recipients into different bins of TCMR.\nReply: We thank the reviewer for this important comment. Based on our data, we propose that\nthe epithelial injury signatures presented here should be monitored and evaluated in TCMR\npatients during follow-up visits. We have previously demonstrated the general feasibility of\nquantifying epithelial cell states in urine in the context of AKI5. Applying similar approaches in\nthe context of TCMR may enable prospective correlation of these epithelial states with clinical\noutcomes.\nCurrently, no therapies exist to specifically target injured PT and TAL states. Thus, further\nresearch is needed to develop interventions aimed at these cell states or the mechanisms that\nsustain them, and to assess their impact on graft outcomes.\nIn summary, if these epithelial cell states can be validated as predictive markers of allograft\nprognosis, and if targeted therapies become available, patients experiencing TCMR could\npotentially be stratified according to their risk profile and treated in a personalized manner. As\nthese considerations pertain primarily to future directions, they were mentioned in the\ndiscussion section.\nThe sample size is small, and despite the molecular score based on the snRNA seq data from\nmice and humans, other injury types in transplant have not been discussed at all. The authors\nhave brought AKI and CKD studies, but have failed to discuss how to interpret the data in the\ncontext of ABMR and other transplant injuries such as mixed rejection.\nReply: Thank you for this comment. We have updated the manuscript accordingly and now\ninclude:\n1. Kaplan Meier curves for ABMR and mixed rejection (see Fig. 8A and Suppl. Fig. S26)\nand\n2. single-cell deconvolution analyses from microarray data on 3858 kidney biopsies\ninvestigating abundances of injured cell states in these data (Fig. 6I).\nIn summary, we can now show that:\n1. The mere presence of the discovered injury cell states affects different conditions after\nkidney transplantation alike, e.g. ABMR, TCMR or mixed (Fig. 8A, Suppl. Fig. S26).\n2. The abundance of certain injury cell states, namely PT Injury h2 and TAL Injury h2, is\nsignificantly increased during TCMR when compared to e.g. ABMR or no rejection (Fig.\n6I).\nWe also included these new results in the revised discussion.\nMinor:\nFor Fig 1D, top panel, please provide color codes for major cell types.\nReply: We apologize, the color code is identical to the color code of the UMAP. The figure\nlegend was modified accordingly to better emphasize this.\nFig 6A-- How would other phenotypes behave other than TCMR and nonTCMR in Kaplan\nMeier curves? Did nonTCMR cohort include ABMR? How did the gene set score perform for\nmixed rejection cases?\nReply: As stated above, we have now included analogous curves for the other subgroups in\nthe paper (see. Suppl. Fig. S26). The initial nonTCMR cohort included patients with ABMR.\nThese cohorts were now separated for more clarity (compare Fig. 8A, Suppl. Fig. S26).\nFig 6B-- There is no mentioning of the significance of orange vs grey circles in the figure legend\nor the result section where the data is presented. Please provide the information both in the\nlegend as well as the result section.\nReply: We apologize, this is now included.\nReviewer #3 (Remarks to the Author):\nIn this manuscript, the authors employ a murine kidney transplantation model to investigate\nepithelial cell injury states during acute T cell–mediated rejection (TCMR). They performed\nsnRNA-seq on kidneys from both syngeneic and allogeneic transplant recipients (BALB/c and\nC57BL/6), identifying distinct injury-associated subpopulations within proximal tubule (PT) and\nthick ascending limb (TAL) cells. Spatial transcriptomics (Xenium) was used to characterize\nimmune cell subtypes within the rejecting allografts. In addition, the authors derived injury gene\nexpression signatures from snRNA-seq analysis of human kidney transplant biopsies and\ncorrelated these injury-associated signatures with reduced graft survival in an independent\nbulk transcriptomic dataset.\nThe core hypothesis—that injured epithelial cell states contribute to allograft dysfunction—is\nconceptually important, and the integration of scRNA-seq with spatial technologies is a\nvaluable strategy. However, this manuscript fails to meet the analytical and experimental\nstandards necessary to support such claims. The analysis lacks rigor, fails to provide cell type–\nspecific insights, and omits essential validation steps. Several figures are descriptive but not\ninterpretable in their current form. I do not believe the manuscript is suitable for publication.\nSubstantial re-analysis, validation, and clarification are needed to bring the study to the level\nexpected by the field. Below are my specific comments.\nReply: We thank the reviewer for the thorough and detailed review of our manuscript. We have\ncarefully considered all comments and hope we have addressed each of the points raised, as\noutlined below.\nGeneral remark from the authors:\nWe thank the reviewer for highlighting the importance of reliable cell type annotation in spatial\ndatasets, a point we fully agree with and consider essential. As this concern was raised multiple\ntimes across the reviewer’s comments, we would like to present our general perspective here\nto avoid redundancy in the point-by-point responses that follow.\nWe agree that confirming the presence of key marker genes, providing diagnostic evidence\nsuch as expected anatomical localization and validating selected cell populations are crucial\nfor spatial transcriptomics datasets. In response, we have further strengthened these aspects\nthroughout the revised manuscript (please refer to the specific responses below for details).\nThe reviewer requested to validate clustering results from Xenium data by directly comparing\nthem to snRNA-seq–derived clusters. While some sub cell types, such as within PT and TAL,\ncan be recapitulated from unsupervised clustering in both data modalities, other compartments\n(notably leukocytes) do not resolve into matching subclusters in the Xenium data.\nIt is expected that clustering of spatial transcriptomic data does not necessarily reproduce all\nsubclusters obtained from single-cell or single-nucleus RNA sequencing, since these\ncomplementary approaches are based on different techniques (imaging versus sequencing-\nbased), target a different number of genes (479 genes in the Xenium panel in this study versus\nwhole transcriptome) and result a different gene coverage (hundreds compared to thousands\nof transcripts per cell)6-8. Reference-based annotation approaches, such as RCTD or SingleR,\nhave therefore become widely used in spatial transcriptomics, precisely because they leverage\nthe transcriptome-wide coverage and sequencing depth of scRNA-seq to annotate spatial\ndatasets more reliably.\nTo further illustrate this point, we have performed several additional analyses for the reviewer\nvalidating our label transfer approach. Specifically, for leukocytes, we downsampled our high-\nresolution snRNA-seq data to match both the gene panel and UMI distributions of the Xenium\ndataset (see RevFig. 1). When subjected to unsupervised clustering, both, the downsampled\nsnRNA-seq and the original Xenium leukocyte data separated into similar clusters\ncorresponding to broad leukocyte cell types (e.g. T cells, myeloid cells), while finer subdivisions\nwere not clearly recovered (see RevFig. 1A). Applying label transfer to the downsampled\nsnRNA-seq data produced however meaningful sub cell type annotations (RevFig. 1B). This\nsupports the use of reference-based approaches, even when clustering resolution is limited\nand enabled sub annotation of leukocytes, a particularly challenging population to annotate\ndue to its location between multiple other cell types in the tight interstitial space.\nIn this way, whole transcriptome-based sequencing approaches and high-resolution imaging-\nbased spatial transcriptomic approaches can complement each other, but we, of course, agree\nthat additional secondary validation using diagnostic plots and protein-based techniques such\nas immunofluorescence validation can further support conclusions from the transcriptomic-\nbased approaches.\nWe took the reviewers concerns very seriously and included additional marker gene\nexpression analyses, diagnostic spatial plots and validation experiments to support our\nannotations, all of which are detailed in the point-by-point responses and the revised\nmanuscript. In fact, we devoted a large portion of the new supplementary material to these\nconcerns (Suppl. Figs. S5, S8-12, S18, S22). We re-annotated all Xenium data by snRNA-seq\nlabel transfer using the SingleR package, as it is simpler to implement and interpret and has\nrecently been shown to achieve superior performance for Xenium cell type annotation. In\naddition, during revision, we noticed that technical support for RCTD is very limited. As\ndepicted in the figures, cell type annotation did not differ by much between the tools.\nA Downsampled snRNA-seq Xenium\nB\nReviewer Figure 1: Comparison of unbiased clustering results in leukocytes from\ndownsampled snRNA-seq and Xenium data. A. Gene expression from snRNA-seq\nleukocytes was downsampled to the genes included in the Xenium panel and to comparable\nsequencing depth (see histograms). Unbiased clustering identified major leukocyte cell types\nbut did not resolve all subtypes. B. SingleR label transfer applied to downsampled snRNA-seq\ndata (columns, known cell type) enabled refined annotation of leukocyte (sub) cell types.\nycneuqerF\nycneuqerF\n#genes detected #genes detected\nper cell per cell\n#transcripts #transcripts\ndetected per detected per\ncell cell\nTrue labels (from snRNA-seq)\n)refsnart\nlebal\nmorf(\nslebal\ndetciderP\nsllec\nfo\n%\nsllec\nfo\n%\n1. In figure 2A, if the BALB/c to C57BL/6 kidneys has milder rejection, why there are more\nDEGs in PC, EC, intC, PEC and IC-A when comparing to the C57BL/6 to BALB/c kidneys?\nReply: Thank you for this remark. Indeed, rejection and injury in the C57BL/6 to BALB/c model\nare more severe, as reflected by higher expression of injury markers (snRNA-seq, ST and\nimmunofluorescence), increased mortality and Banff scoring. We therefore believe that\nreferring to C57BL/6 to BALB/c as the harsher rejection model is justified. Additionally,\ndifferentially upregulated genes, for example in the CD-PC, were enriched for metabolic\npathways and may indicate strain-specific differences in TCMR. All DE genes are provided in\nthe supplemental tables. We acknowledge these findings in the main text but do not elaborate\nfurther, as they are beyond the scope of this study.\n2. The heatmap shown in Figure 2B provides minimal information beyond the number of DEGs\nin each cell type. The authors should highlight key cell type–specific genes, particularly in PT\nand TAL cells, to improve interpretability. In addition, experimental validation of select disease-\nassociated genes in these cell types would considerably strengthen the study’s conclusions.\nReply: Thank you for this remark. Indeed, much of the gene expression changes are, as\nexpected, observable across cell types which is likely induced by the cytokine environment\nand general (epithelial) responses to injury.\nFollowing the reviewer’s suggestion, we moved the previous Fig. 2B to the supplement (Suppl.\nFig. S6B) and now highlight key examples of deregulated genes between rejection and control\nin the main figure and in the supplement (Fig. 2B-E, Suppl. Fig. Suppl. Fig. 7D). Most of these\ngenes could be validated using our Xenium data. We analyzed all DEG modules identified by\nunbiased clustering using Weighted Gene Correlation Network Analysis (WGCNA) and provide\nthe top deregulated genes specific to PT and TAL (Suppl. Fig. S7). As stated in the main text\n(section “Mouse TCMR induces pronounced gene expression responses in kidney epithelial\ncells”), the overall pattern of enriched pathways across cell types is reflected in the top DEGs\nof individual cell types, although some genes, including injury markers and cytokines, show\nclear cell type-specific expression.\n3. Figure 2C basically shows that all these cell types share most of the disease pathways.\nTherefore, the authors should conclude that few cell type–specific pathways were identified,\nwhich makes the overall conclusion less compelling. The authors should focus more on\nhighlighting any truly cell type–specific disease pathways to strengthen the impact of their\nfindings.\nReply: Thank you again for these remarks to Figure 2. We tried to address this in the revised\nversion of the manuscript as stated in reviewer comment #2.\n4. In Figure 3A, PT injury m2–3 cannot be classified as distinct injured states, as they share\nall marker gene expression and lack unique disease genes. If the authors want to define these\nas transitional injury states, pseudotime trajectory analysis would be more appropriate than\nclustering analysis.\nReply: Thank you for raising this point. In the revised figure we included additional marker\ngenes to demonstrate that PT Injury m2 and m3 are indeed distinct clusters (Fig. 3C),\nanalogous for TAL (Fig. 3H). In addition, pseudotime analyses confirmed that they progress\ntowards the most severely injured states, PT Injury m4 for PT and TAL Injury m3 for TAL (Fig.\n3A, F). Please note that we now also include overlapping marker gene expression in the\nXenium data to support validity of the label transfer for PT and TAL (Suppl. Figs. S8B and\nS11B).\n5. The accuracy of mapping scRNA-seq to Xenium data depends heavily on the gene panel\nused in Xenium—for example, whether markers for PT subtypes are included. As a result, it is\ndifficult to assess the accuracy of the spatial cell mapping shown in Figure 3A. The authors\nshould apply the same mapping algorithm to PT S1–S3 subtypes to determine whether PT S3\nlocalizes to the corticomedullary junction and PT S1/2 to the outer cortex, as expected. This\ncan server as internal validation of the approach.\nReply: This is indeed a critical point. As suggested by the reviewer, we performed the same\nmapping for the healthy PT and TAL segments. This resulted in the expected localization and\nis now included in Suppl. Figs. S8 (for the PT) and S11 (for the TAL).\n6. In Figure 3B, if PT injury m1 represents an injured cell type, it is unclear how to explain the\nlack of change in its proportion between allogeneic and syngeneic kidneys. The only marker\nused to define PT injury m1 is Nqo1. Since this is a novel marker in this context, the authors\nshould provide additional information about the gene, including its primary function and\npotential role in tubular injury. Furthermore, immunofluorescent staining is needed to confirm\nthe presence of this injury state in the allogeneic kidney.\nReply: Thank you for this important remark. Upon reinvestigation of the clustering, we found\nthat the previous PT Injury m1 cluster comprised two biologically distinct subclusters, one\ncorresponding to healthy PT S3 medullary cells and the other to injured PT cells. Following the\nreviewer’s comment, we refined the clustering of PT Injury m1 (Fig. 3A-D). We also provide\nvalidation for the existence of this cluster through additional immunofluorescence stainings and\nXenium data (Suppl. Figs. S9 and S10). Please note that Nqo1 is no longer a marker of the\ninjured fraction of the revised PT Injury m1 cluster and is now correctly assigned as a marker\nof PT S3 medullary cells (Fig. 3C).\nPlease do also note that this new clustering slightly alters downstream analyses including\ncorrespondence of injury clusters between mouse and human (Fig. 6).\n7. All of the concerns raised above regarding the PT subtypes (points #4–#6) also apply to the\nTAL subtypes shown in Figure 3C and 3D.\nReply: We tried to address all points for PT and TAL in points #4-#6, including pseudotime\nanalysis, expression of marker genes and spatial localization of clusters.\nWe noticed that some clusters previously annotated as cTAL, specifically cTAL2 and cTAL3,\nalso extend to some degree into medullary regions. We therefore renamed them as cmTAL1\nand cmTAL2 (cm = corticomedullary). We hope the reviewer agrees that this revised\nannotation more accurately reflects their localization.\nWe also noted a plotting error in the previous Fig. 3C, where TAL Injury m1 was not sufficiently\ndisplayed due to overplotting. We apologize for this.\n8. Since the authors have generated Xenium data, they should perform subclustering analysis\non the PT and TAL populations within the Xenium dataset to validate the injured cell states\nand assess their spatial distribution in allogeneic versus syngeneic kidneys.\nReply: We thank the reviewer for this valuable suggestion. In response, we have performed\nsubclustering analyses of the PT and TAL populations within the Xenium dataset. This analysis\nrevealed comparable subclusters as discovered in the snRNA-seq data (RevFigs. 2 and 3).\nSmall differences were noted in cell abundances, for example clustering overestimated TAL\nInjury m3 abundance compared to immunofluorescence stainings which aligned more closely\nwith annotations generated from label transfer. In contrast, spatial distribution was highly\ncomparable. Moreover, critical findings such as spatial localization of PT Injury m1, PT Injury\nm4 and TAL Injury m3 were validated using our Xenium data and/or immunofluorescence\nstainings which confirmed our assignment of PT and TAL subclusters in the Xenium data from\nlabel transfer (Fig. 3E, J, Suppl. Figs. 9, 10 and 12).\nThe clustering approach of ST data was not able to differentiate between the different healthy\nTAL clusters, e.g. cTAL1-2, cmTAL1-2 and mTAL1-2. However, as shown by marker gene\nexpression and spatial expression domains, these clusters seem to exist (see Fig. 3 and Suppl.\nFig. S11, presence of e.g. Cldn16 in cTAL1 and cmTAL1 while absent or low in cTAL2 and\ncmTAL2, vice versa for Cldn10).\nA\nB\nClustering Label transfer\nB\nC\nClustering Label transfer\nReviewer Figure 2: Comparison of clustering and label transfer for Xenium PT cells. A.\nLeft plot: UMAP from unbiased clustering of Xenium PT cells using highly variable genes. Right\nplot: Confusion matrix showing overlap between cell type assignment from clustering and from\nlabel transfer. B. Spatial distribution of healthy PT clusters S1/2, S3 and S3 medullary based\ngniretsulc\nmorf\nlebaL\nLabel from label transfer\nsllec\nfo\n%\non unbiased clustering (left) or from snRNA-seq label transfer (right). C. Spatial distribution of\ninjured PT clusters based on unbiased clustering (left) or from snRNA-seq label transfer (right).\nA\nB\nClustering Label transfer\nB\nC\nClustering Label transfer\nReviewer Figure 3: Comparison of clustering and label transfer for Xenium TAL cells. A.\ngniretsulc\nmorf\nlebaL\nLabel from label transfer\nsllec\nfo\n%\nLeft plot: UMAP from unbiased clustering of Xenium TAL cells using highly variable genes.\nRight plot: Confusion matrix showing overlap between cell type assignment from clustering\nand from label transfer. Note that cTAL1/2 and cmTAL1/2 were merged into a single cTAL\ngroup to match the clustering resolution. Similarly, mTAL1/2 were combined into mTAL. B.\nSpatial distribution of healthy TAL clusters cTAL – cortical TAL and mTAL – medullary TAL\nbased on unbiased clustering (left) or from snRNA-seq label transfer (right). C. Spatial\ndistribution of injured TAL clusters based on unbiased clustering (left) or from snRNA-seq label\ntransfer (right).\n9. It is unclear why the authors focus exclusively on immune cells in Figure 4 for the Xenium\ndata analysis. A more comprehensive analysis should include all cell types to determine\nwhether the same populations identified by scRNA-seq can also be resolved using Xenium\ndata alone. In Supplementary Figure S4A, the authors perform clustering on the Xenium\ndataset, but substantially fewer cell types are shown, and no subclustering analysis was\nconducted for PT and TAL.\nReply: In Figure 4, we focused on immune cells as they represent a pivotal cell population\nduring rejection. They do not only represent a histological hallmark (interstitial nephritis and\ntubulitis), but their impact is also intensively discussed in the literature. For us, it was hence an\nintuitive question to ask which spatial proximity exists between different immune cell\npopulations and injured epithelial cell states. We agree that a more comprehensive spatial\nproximity analysis can be informative which we tried to address with an unbiased spatial\ndomain clustering approach now included in the revised manuscript (new Fig. 5).\nRegarding the discovery of cell types observed in the single cell data when compared to the\nXenium data, we kindly refer to our general remark at the beginning of this reply to the reviewer,\nincluding RevFig. 1 and the response to comment #8.\n10. The authors need to provide details on how the 100 custom genes were selected for their\nXenium gene panel design. For instance, if the panel lacks sufficient markers for injured PT or\nTAL subtypes, the entire study would be compromised. In such a case, the key findings from\nthe scRNA-seq data regarding injured PT and TAL cells would be difficult to validate or\nreproduce using the Xenium platform, making integration between the two datasets unreliable.\nFurthermore, the absence of appropriate markers would preclude meaningful downstream\nanalyses, such as determining the spatial roles of injured PT and TAL subpopulations. For\nexample, niche analysis—which could reveal which injured states preferentially recruit immune\ncells—would not be possible, thereby limiting mechanistic insights into tubular injury during\nTCMR.\nReply: Thank you for pointing this out. The Xenium panel consisted, as pointed out in the\nmethods section, of 479 genes of which 379 originated from a pre-designed mouse multi tissue\npanel (Mouse Tissue Atlassing v1) and 100 custom genes. The 100 custom genes were\ndesigned based on previous single cell experiments on mouse kidneys in the context of acute\nkidney injury and rejection. Therefore, these genes included canonical marker genes for all\nmajor kidney cell types and expected injury-induced cell populations. We furthermore added a\nsentence to the methods section commenting on the origin and selection of the 100 additional\ngenes.\n11. It is unclear how the authors generated Figure 4D, as they did not perform subclustering\nof PT and TAL cells. It appears that computational methods were used to map snRNA-defined\ncell types onto the spatial data. however, this mapping relies on the set of genes shared\nbetween the snRNA-seq and Xenium platforms. If the gene panel design lacks sufficient\nmarkers for injured states, particularly for PT and TAL, the resulting cell mapping may be highly\ninaccurate.\nReply: Thank you for this comment. Indeed, Figure 4D (now Fig. 4E) was created by\nannotating spatial transcriptomics data with clustering results and labels from snRNA-seq data.\nWe fully agree with the reviewer that overlapping marker gene presence in the Xenium panel\nis required for the correct annotation of the spatial data. As pointed out above, we highlighted\nand presented marker genes present in both modalities (snRNA-seq and Xenium) (Fig. 3,\nSuppl. Figs. S8 and S11).\n12. In addition, for the neighborhood analysis in Figure 4D, the authors simply report the\npercentage of neighboring PT and TAL cells, which is a descriptive rather than statistical\napproach. The authors should utilize publicly available tools designed for spatial neighborhood\nanalysis to reanalyze the data more rigorously. To support their findings, they should also\nprovide immunostaining data to confirm that the identified cell types are indeed in close spatial\nproximity.\nReply: Thank you for this comment. We completely revised our spatial proximity approach and\nused, as suggested by the reviewer, publicly available tools (co-occurence probabilities as\nimplemented by squidpy), which confirmed our previous results of co-localization of specific\ninjury cell types with immune cells (see revised Fig. 4E, Suppl. Figs. S15-17). This analysis\nconfirmed the enrichment of T cells and macrophages in proximity to PT Injury m4 cells, while\nthey were depleted in proximity of TAL Injury m3 cells.\nAdditional to the direct neighboring cell analysis in Fig. 4E (previously 4D), we now performed\nan unbiased spatial neighborhood analysis and immunostaining validation. We devoted an\nentirely new Figure to these analyses (new Fig. 5). Please note that the identification of spatial\ndomains did not rely on any cell type labeling from our side but was merely based on Xenium\ngene expression. Apart from the fact that this gene expression-driven analysis identified the\nexpected compartments (renal medulla, glomeruli etc.) with the expected cell types (based on\nlabel transfer), we were able to identify microenvironments comprised of heavily injured PT\ncells (mainly PT Injury m4) in proximity to immune cells (macrophages and T cells) as well as\nactivated fibroblasts. We validated this in mice and human by immunostaining (Figs. 5 and 7,\nSuppl. Figs. 18 and 22).\n13. In Figure 5, the authors focus solely on demonstrating the similarity of cell types between\nhuman and mouse in TCMR. However, they fail to present more critical data—specifically,\nwhether the gene expression changes observed between stable allograft and TCMR biopsies\nin humans are similar to, or distinct from, those observed between allogeneic and syngeneic\nkidneys in the mouse model. This comparison is essential to establish the translational\nrelevance of the mouse findings to human disease.\nReply: We thank the reviewer for suggesting this important validation. In response, we now\nprovide several new results further strengthening the reliability of the translational\ninterpretation:\n1. Fully updated marker gene heatmaps, indicating overlap between mouse and human\nsnRNA-seq (Figs. 3 and 6).\n2. Immunofluorescence validation of key PT and TAL injury marker genes with\nimmunofluorescence in, both, mouse and human samples (Fig. 3, 7 and Suppl. Figs.\n9, 10 and 12).\n3. Systematic comparison of mouse and human snRNA-seq data with a large cohort of\nhuman TCMR microarray samples supporting strong correlation between mouse and\nhuman but also with a much larger gene expression cohort (298 TCMR biopsies)\n(Suppl. Fig. S21).\nAll three analyses now confirm strong similarities between mouse and human snRNA-seq data\nand are in line with results from an additional, much larger TCMR cohort.\n14. Since the authors selected different sets of genes in Figure 5C and 5F compared to those\nused in Figure 3A and 3C, it is unclear whether the human injured cell states truly correspond\nto the mouse injured cell states. The simple Pearson correlation analysis presented in Figure\n5F and 5H is insufficient to support the conclusion that these are the same cell types. Additional\nanalyses are needed to strengthen this claim. For example, the authors should identify injury\nmarkers shared between human and mouse tubular subtypes and perform immunostaining to\nvalidate the identity of these matched cell states.\nReply: Thank you for this comment. As stated above (e.g. reply to reviewer comment #13),\nwe updated the heatmaps to reflect shared marker genes across species (Fig. 3C, H (mouse),\nFig. 6C, F (human)) and validated PT and TAL injury population markers by\nimmunofluorescence staining (Figs. 3 and 7, Suppl. Figs. S9, S10 and S12). We also replaced\nthe initial Spearman correlation analysis with a published label transfer approach using\nSingleR (Fig. 6E, H).\n15. The authors should use the gene signatures derived from the human biopsy scRNA-seq\ndata to perform cell type deconvolution on the bulk transcriptomic dataset presented in Figure\n6. This analysis would allow them to determine whether the proportions of injured tubular\nsubtypes are specifically increased in TCMR or mixed rejection biopsies, but not in ABMR\nbiopsies. Such evidence would provide stronger support for the relevance of the identified\ninjury signatures in distinguishing rejection types.\nReply: Thank you for this important comment. As our human snRNA-seq dataset is limited in\nsize, we performed single-cell deconvolution on a large kidney transplant cohort comprising\n3858 biopsies from 3210 patients (2155 without rejection, 1107 with ABMR, 298 with TCMR,\nand 298 with mixed rejection) (Fig. 6I).\nBriefly, we observed the expected depletion of healthy epithelia and a significant\noverrepresentation of PT Injury h2 and TAL Injury h2 in TCMR and mixed (TCMR + ABMR)\nsamples. Both clusters were also significantly more abundant in TCMR than in ABMR.\nInterestingly, PT Injury h1 and TAL Injury h1 were depleted across most rejection groups,\nsuggesting a potentially different origin of these injured states.\nWe would like to emphasize that, although PT Injury h2 and TAL Injury h2 are enriched in\nTCMR, we never claimed that they ultimately can distinguish between all rejection types.\nRather, we stated that i) TCMR induces these injured cell states, ii) they are clinically relevant\nbut currently not therapeutically targeted and iii) they persist in some patients despite\nsuccessful rejection treatment.\nReferences:\nRegression of Kidney Disease. J Am Soc Nephrol 31, 2833-2854, doi:10.1681/ASN.2020060806\n(2020).\nDependent Manner. Cell 178, 686-698 e614, doi:10.1016/j.cell.2019.05.054 (2019).\nand pro-fibrotic tubular cell phenotype. Nat Commun 16, 4745, doi:10.1038/s41467-025-\n59997-4 (2025).\nmediate inflammation and fibrosis in ANCA-associated glomerulonephritis. Nephrol Dial\nTransplant 40, 1416-1427, doi:10.1093/ndt/gfae292 (2025).\nhuman acute kidney injury. Kidney Int 102, 1359-1370, doi:10.1016/j.kint.2022.07.032 (2022).\nXenium spatial transcriptomics data. BMC Bioinformatics 26, 22, doi:10.1186/s12859-025-\n06044-0 (2025).\nsingle-cell, spatial and in situ analysis. Nat Commun 14, 8353, doi:10.1038/s41467-023-43458-\nx (2023).\nBiotechnol 40, 517-526, doi:10.1038/s41587-021-00830-w (2022)."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors made a great effort in revising the manuscript and added important new data, but several key issues remain:\n1-The specificity of the epithelial injury states is overstated, as the so-called “TCMR-associated” states are also enriched in ABMR, mixed rejection, AKI, and CKD. These should be reframed as general maladaptive epithelial programs with prognostic rather than diagnostic value.\n2-The issue of causality is not established, since the study relies on transcriptional correlations and spatial proximity analyses. Claims of therapeutic targetability without functional perturbation or mechanistic studies this remains speculative and therefore s should be tempered.\n3- The limitations of bulk transcriptomic analyses remain, because prognostic associations are presented without multivariable adjustment or controls for cell composition, which undermines the independence of the signal.\n4- The human snRNA-seq dataset is underpowered, with only three TCMR and three stable biopsies. Heterogeneity within PT-h2 (e.g., KIM1/VCAM1 vs. STK39/ACSM3 expression) remains unresolved.\n5- The ambiguity of the TAL-h1 state persists, as it is prognostically relevant but paradoxically depleted in TCMR and lacks a clear mouse counterpart. Its biological meaning requires clarification.\n6- The claims about persistence of injury states are insufficiently supported, as they are based on only 12 patients with follow-up biopsies. These findings should be described as exploratory rather than definitive.", "reviewer_2": "The authors have adequately responded to my questions and I do not have further concerns or questions about the manuscript.", "reviewer_3": "The authors have provided substantial data to address my previous concerns in this revised version. The manuscript has been significantly improved, and I have no further comments."}, "author_response": "We thank the editor and the reviewers for their very positive perception of our revised\nmanuscript. Please find below our replies to the remaining important remarks by reviewer 1.\nAs before, all changes to the text are in red font.\nReviewer #1 (Remarks to the Author):\nThe authors made a great effort in revising the manuscript and added important new data, but\nseveral key issues remain:\n1-The specificity of the epithelial injury states is overstated, as the so-called “TCMR-\nassociated” states are also enriched in ABMR, mixed rejection, AKI, and CKD. These should\nbe reframed as general maladaptive epithelial programs with prognostic rather than diagnostic\nvalue.\nReply: We thank the reviewer for the positive assessment of our revised manuscript and for\nthis important remark. While we fully agree with the reviewer’s perspective, we would like to\nemphasize two points: 1. It is indeed very likely that the observed epithelial cell states represent\ngeneral maladaptive programs. However, conclusively proving that these states are truly\nidentical across different conditions is a non-trivial task that would require additional studies\nbeyond the scope of this manuscript. Therefore, we prefer to remain cautious in making this\nclaim. 2. By using the terms TCMR-associated or TCMR-induced cell states, we did not intend\nto imply TCMR-specific. Our mouse data indeed indicate that these cell states can be induced\nby TCMR, but we fully acknowledge that they may also arise under other conditions.\nWe agree that our previous wording could potentially be misleading and have therefore revised\nthe terminology throughout the manuscript to clarify that these states are induced during TCMR\nbut are likely not specific to it. We also added corresponding clarifications to the discussion.\nWe hope the reviewer finds the revised wording more accurate and appropriate.\n2-The issue of causality is not established, since the study relies on transcriptional correlations\nand spatial proximity analyses. Claims of therapeutic targetability without functional\nperturbation or mechanistic studies this remains speculative and therefore s should be\ntempered.\nReply: We thank the reviewer for this important remark. We fully agree with this assessment\nand have accordingly toned down or removed the respective statements from the manuscript.\n3- The limitations of bulk transcriptomic analyses remain, because prognostic associations are\npresented without multivariable adjustment or controls for cell composition, which undermines\nthe independence of the signal.\nReply: We thank the reviewer for this important remark. We fully agree that a definitive\nstatistical assessment would ideally include multivariable adjustment with additional clinical\ncovariates to control for potential confounders, as well as formal correction for cell composition.\nHowever, detailed clinical information was not available for all samples, and the number of\nTCMR biopsies (n = 95) is likely underpowered for a robust multivariable model. We have now\nexplicitly acknowledged this limitation in the discussion and emphasize that the reported\nfindings should be interpreted as associative rather than fully independent prognostic effects.\n4- The human snRNA-seq dataset is underpowered, with only three TCMR and three stable\nbiopsies. Heterogeneity within PT-h2 (e.g., KIM1/VCAM1 vs. STK39/ACSM3 expression)\nremains unresolved.\nReply: Thank you for this important remark. Indeed, it is very likely that additional clusters\n(including a potential subdivision of the current PT Injury h2 cluster) may emerge with an\nincreased number of human snRNA-seq samples. We attempted to transparently address this\npotential heterogeneity by immunofluorescence staining (Fig. 7A). Nevertheless, Fig. 6C\nshows that most top marker genes of PT Injury h2 overlap with those of PT Injury m4, and Fig.\n6E further demonstrates the strongest correspondence between these two clusters. Based on\nthese observations, we consider it most likely that a substantial part of the PT Injury m4 gene\nexpression profile is indeed represented by PT Injury h2.\nWe fully agree, however, that the full complexity of PT subclustering in human TCMR may not\nbe captured with our current approach. We have therefore incorporated this important point as\na limitation and included respective statements in the discussion.\n5- The ambiguity of the TAL-h1 state persists, as it is prognostically relevant but paradoxically\ndepleted in TCMR and lacks a clear mouse counterpart. Its biological meaning requires\nclarification.\nReply: We thank the reviewer for highlighting this important point. As correctly noted, TAL\nInjury h1 is associated with adverse graft outcome but is relatively depleted in TCMR and lacks\na clear mouse equivalent. In the revised discussion, we now elaborate on this apparent\nparadox. We propose that TAL Injury h1 may represent a general stress- or recovery-\nassociated TAL program independent of alloimmune injury, potentially linked to hemodynamic\nor metabolic stress. In addition, we acknowledge that its lower relative abundance in TCMR\ncould reflect proportional effects in the deconvolution analysis, where the expansion of more\nseverely injured TAL Injury h2 states reduces the relative contribution of TAL Injury h1.\nWe therefore consider TAL Injury h1 a non-TCMR-induced injury phenotype with adverse\nprognostic implications that warrants further investigation.\n6- The claims about persistence of injury states are insufficiently supported, as they are based\non only 12 patients with follow-up biopsies. These findings should be described as exploratory\nrather than definitive.\nReply: Thank you for this comment. We now explicitly mention this as an exploratory effort\nwith a limited sample size throughout the manuscript."}, {"round": 3, "reviewer_comments": {"reviewer_1": "The authors were responsive to my critiques by acknowledging the study’s limitations and appropriately tempering the tone of their conclusions. While no additional conclusive data were provided to fully address my concerns, the revisions have satisfactorily improved the manuscript. At this stage, I have no further concerns."}, "author_response": ""}]

Decision letter