Anthony Chang

This study generated a comprehensive kidney-wide single-cell transcriptomic dataset of a murine model of childhood nephrotic syndrome developed by the deletion of transcription factor 21 ( Tcf21) from early podocyte precursors. Our transcriptomic data provide further mechanistic insights into how TCF21 regulates podocyte health and development while enabling interrogation of how the tubulointerstitium responds to massive proteinuria.

Key Points: Pretransplant early acute rejection risk stratification offers a novel approach to personalized immunosuppression management in patients who received a kidney transplant. Stratifying immunologic risk independent of donor characteristics may help guide immunosuppression management in patients who received a kidney transplant. The pretransplant risk assessment gene set reflects metabolic and immune pathways involved in T-cell– and antibody-mediated mechanisms of transplant rejection. Background: Although donor and recipient characteristics are used to estimate graft failure risk, they remain limited in predicting early acute rejection (EAR). We developed and validated a next-generation sequencing assay targeting the pretransplant immunologic profile to predict EAR following kidney transplantation. Methods: This prospective, international study enrolled 321 kidney transplant participants across 13 sites, forming discovery and validation cohorts. Peripheral blood was collected before transplant and at 1, 3, 6, 12, and 24 months post-transplant, with protocol biopsies performed at 3 and 12 months, as well as any indication biopsies. Biopsies were assessed by a blinded central pathologist according to the 2019 Banff criteria. The pretransplant risk assessment (PTRA) test to evaluate RNA expression of a 29-gene signature algorithm designed to predict EAR risk was clinically validated using 122 deceased donor kidney transplant recipients. Results: PTRA classified 31 of 122 participants (25%) as high risk and 91 (75%) as low risk. There were nine EAR events in the first 60 days post-transplant: 6/31 (19%) in the high-risk group and 3/91 (3%) in the low-risk group. PTRA discrimination for EAR at 60 days post-transplant yielded an area under the curve of 0.78 (95% confidence interval [CI], 64.4 to 91.5), P < 0.001. A cutoff of ≤45 was defined as low risk and >45 as high risk for EAR within 60 days post-transplant. Sensitivity was 0.67 (95% CI, 0.36 to 0.97), specificity 0.78 (95% CI, 0.70 to 0.86), positive predictive value 0.19 (95% CI, 0.05 to 0.33), and negative predictive value 0.97 (95% CI, 0.93 to 1.00). The odds ratio comparing patients identified as high versus low risk by PTRA was 7.04 (95% CI, 1.64 to 30.2), P = 0.009. Conclusions: This study validates the ability of PTRA to stratify kidney transplant recipients as high or low risk for EAR using a pretransplant transcriptomic profile, with implications for graft health and personalized treatment management.

Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively assess spatial injury patterns, we collected high dimensional spatial proteomics data from 23 LuN, 33 RAR, and 8 kidney control (KC) biopsies. We developed a computational pipeline to segment and classify tubules, capillaries, and glomeruli in whole-slide images using three trained neural networks (Renal Damage diagnosis, RDDx). RDDx achieved high accuracy and generalizability, reliably identifying small capillaries and differentiating tubular and vascular inflammation in kidney tissues. Both LuN and RAR showed reduced tubular and capillary areas with expanded interstitial space. LuN displayed patchy clusters of stressed and inflamed tubules, whereas RAR exhibited diffuse injury. Within RAR, T cell–mediated rejection (TCMR) showed intense tubulitis while antibody-mediated rejection (ABMR) featured proliferating and inflamed capillaries near atrophic tubules. RDDx quantitative metric outputs correlated with histopathological scores, highlighting their reproducibility and clinical relevance. Stressed tubules in mildly inflamed LuN biopsies suggested they were a sensitive injury marker, while proliferating capillaries revealed microvascular remodeling in ABMR. These findings indicated RDDx can identify and quantify damage mechanisms specific to each renal disease thus facilitating future mechanistic studies and therapeutic target discovery.

BACKGROUND: In human lupus nephritis (LuN), tubulointerstitial inflammation (TII) is prognostically more important than glomerular inflammation. However, a comprehensive understanding of both TII complexity and heterogeneity is lacking. METHODS: Herein, we used high-dimensional confocal microscopy, spatial transcriptomics, and specialized computer vision techniques to quantify immune cell populations and localize these within normal and diseased renal cortex structures. With these tools, we compared LuN to renal allograft rejection (RAR) and normal kidney tissues on 54 deidentified biopsies. RESULTS: In both LuN and RAR, the 33 characterized immune cell populations formed discrete subgroups whose constituents covaried in prevalence across biopsies. In both diseases, these covariant immune cell subgroups organized into the same unique niches. Therefore, inflammation could be resolved into trajectories representing the relative prevalence and density of cardinal immune cell members of each covariant subgroup. Indeed, in any one biopsy, the inflammatory state could be characterized by quantifying constituent immune cell trajectories. Remarkably, LuN heterogeneity could be captured by quantifying a few myeloid immune cell trajectories, while RAR was more complex with additional T cell trajectories. CONCLUSIONS: Our studies identify rules governing renal inflammation and thus provide an approach for resolving LuN into discrete mechanistic categories. FUNDING: NIH (U19 AI 082724 [MRC], R01 AI148705 [MRC and ASC]), Chan Zuckerberg Biohub (MRC), and Lupus Research Alliance (MRC).

PurposeThe rapid development of highly multiplexed microscopy has enabled the study of cells embedded within their native tissue. The rich spatial data provided by these techniques have yielded exciting insights into the spatial features of human disease. However, computational methods for analyzing these high-content images are still emerging; there is a need for more robust and generalizable tools for evaluating the cellular constituents and stroma captured by high-plex imaging. To address this need, we have adapted spectral angle mapping—an algorithm developed for hyperspectral image analysis—to compress the channel dimension of high-plex immunofluorescence (IF) images.ApproachHere, we present pseudo-spectral angle mapping (pSAM), a robust and flexible method for determining the most likely class of each pixel in a high-plex image. The class maps calculated through pSAM yield pixel classifications which can be combined with instance segmentation algorithms to classify individual cells.ResultsIn a dataset of colon biopsies imaged with a 13-plex staining panel, 16 pSAM class maps were computed to generate pixel classifications. Instance segmentations of cells with Cellpose2.0 (F1-score of 0.83±0.13) were combined with these class maps to provide cell class predictions for 13 cell classes. In addition, in a separate unseen dataset of kidney biopsies imaged with a 44-plex staining panel, pSAM plus Cellpose2.0 (F1-score of 0.86±0.11) detected a diverse set of 38 classes of structural and immune cells.ConclusionsIn summary, pSAM is a powerful and generalizable tool for evaluating high-plex IF image data and classifying cells in these high-dimensional images.

Autoimmune diseases such as systemic lupus erythematosus (SLE) display a strong female bias. Although sex hormones have been associated with protecting males from autoimmunity, the molecular mechanisms are incompletely understood. Here we report that androgen receptor (AR) expressed in T cells regulates genes involved in T cell activation directly, or indirectly via controlling other transcription factors. T cell-specific deletion of AR in mice leads to T cell activation and enhanced autoimmunity in male mice. Mechanistically, Ptpn22, a phosphatase and negative regulator of T cell receptor signaling, is downregulated in AR-deficient T cells. Moreover, a conserved androgen-response element is found in the regulatory region of Ptpn22 gene, and the mutation of this transcription element in non-obese diabetic mice increases the incidence of spontaneous and inducible diabetes in male mice. Lastly, Ptpn22 deficiency increases the disease severity of male mice in a mouse model of SLE. Our results thus implicate AR-regulated genes such as PTPN22 as potential therapeutic targets for autoimmune diseases.