Authors’ Reply: Computational Lymphocyte Topology: A Roadmap to Mechanism and Clinical Translation?

Journal of the American Society of Nephrology · 2025-12-05

We thank Dr. Zhou et al.1 for their interest in our work2 in JASN and their thoughtful letter outlining important future directions for this research. Our study represents a paradigm shift in the quantitative assessment of inflammation—the first demonstration that graph-based topologic features of lymphocyte distribution provide prognostic information independent of traditional metrics in glomerular diseases. This initial exploratory investigation serves as a discovery tool, proof-of-concept, and a foundation on which anchoring molecular analysis can provide biologic plausibility to these observations. We fully agree that integrating sophisticated bulk and spatial omics with our topologic framework would significantly enrich our understanding of which cell type drives clinical outcome and response to therapy and provide the mechanistic insights needed to understand whether these patterns actively drive injury versus attempt to repair or represent secondary phenomena. Complementary spatial multiomics studies are currently being conducted across nephrology consortia. Although these rapidly evolving technologies have been proven to advance our understanding of kidney diseases, broad access to biopsy imaging and tissue blocks remains challenging, at least in North America, due to strict institutional and national regulatory bodies' policies. Thus, Dr. Zhou and colleagues' observation is very timely in offering the opportunity to raise awareness of the importance of the application of these methodologies to improve current knowledge and patient care and to call on the medical community, regulatory agencies, and patient advocacy groups to revise current biopsy practices and tissue access regulations. Translation of image analysis–derived discoveries should represent the logical next step toward clinical implementation. Our work intends to broaden the methodologic space by introducing graph topology among various methodologic configurations and by demonstrating the applicability of different topology metrics. The optimal metric may depend on the specific clinical question and context, as different applications may require distinct topologic metrics. Moreover, clinical implementation will require multiple validation steps beyond simplification, including prospective validation in independent cohorts; harmonization across different scanning platforms and institutions; sustained collaborative efforts from the nephrology, pathology, computational, and clinical research communities; and, ultimately, demonstration that such metrics can effectively guide treatment decisions and improve patient outcomes. In conclusion, we are grateful and feel encouraged by Dr. Zhou and colleagues' engagement and look forward to future studies aiming at advancing mechanistic understanding and clinical implementation.

Computational characterization of lymphocyte topology on whole slide images of glomerular diseases

medRxiv · 2025-04-14

The complexity of distribution of inflammatory cells in the kidney is not well captured by conventional semiquantitative visual assessment. This study aims to computationally quantify the topology of lymphocytic inflammation and tested its clinical relevance. N=333 NEPTUNE/CureGN participants (N=155 focal segmental glomerulosclerosis (FSGS) and N=178 Minimal Change Disease (MCD) with available clinical/demographic data and 1 Hematoxylin & Eosin-stained whole slide image (WSI), were included. Deep learning models were applied to segment cortex and lymphocytes. Graph modeling, where nodes were defined as lymphocytes and edges as the spatial connections between cortical lymphocytes, were applied to all WSIs. We then developed a novel graph-based habitat clustering algorithm to identify dense vs. sparse lymphocytic habitats. From each habitat, 26 high-throughput quantitative pathomic features were extracted to capture cell density, connectivity, clustering, and centrality. The association of these pathomic features with disease progression (40% eGFR decline or kidney replacement therapy) was assessed using LASSO-regularized Cox proportional hazards models. Clinical and demographic characteristics were added as potential confounders. Kaplan-Meier survival analysis with log-rank test was used to evaluate risk stratification. Two validation strategies were applied: (i) training on NEPTUNE with external validation on CureGN data, and (ii) using an 80/20 data partition of the combined datasets for training and validation, respectively. Multivariable Cox models integrating clinical/demographic variables with graph features achieved validation concordance index of 0.736±0.072 in the CureGN external validation and 0.757±0.071 in the combined validation dataset. The average degree feature (overall connectivity) in dense habitat and k-core feature (clustering pattern strength) in sparse habitat revealed consistent association with clinical outcome. The topological characterization of lymphocytic inflammation identifies immune habits, capturing the complexity of pattern of inflammation beyond human vision. These pathomic/topology signatures represent potential digital biomarkers that can enhance our ability to prognosticate/predict clinical outcome in MCD/FSGS.

HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes

ArXiv.org · 2025-12-23

High-throughput "pathomic" analysis of Whole Slide Images (WSIs) offers new opportunities to study tissue characteristics and for biomarker discovery. However, the clinical relevance of the tissue characteristics at the micro- and macro-environment level is limited by the lack of tools that facilitate the measurement of the spatial interaction of individual structure characteristics and their association with clinical parameters. To address these challenges, we introduce HistoWAS (Histology-Wide Association Study), a computational framework designed to link tissue spatial organization to clinical outcomes. Specifically, HistoWAS implements (1) a feature space that augments conventional metrics with 30 topological and spatial features, adapted from Geographic Information Systems (GIS) point pattern analysis, to quantify tissue micro-architecture; and (2) an association study engine, inspired by Phenome-Wide Association Studies (PheWAS), that performs mass univariate regression for each feature with statistical correction. As a proof of concept, we applied HistoWAS to analyze a total of 102 features (72 conventional object-level features and our 30 spatial features) using 385 PAS-stained WSIs from 206 participants in the Kidney Precision Medicine Project (KPMP). The code and data have been released to https://github.com/hrlblab/histoWAS.

Computational Characterization of Lymphocyte Topology on Whole Slide Images of Glomerular Diseases

Journal of the American Society of Nephrology · 2025-10-08

Key Points Graph topology features of inflammation can enhance prognostication of proteinuric glomerular diseases. Computational image analysis is useful for tissue interrogation and extraction of hidden tissue characteristics. Digital pathology and computer vision allowed for characterization of inflammation patterns beyond human vision. Background The distribution of inflammation in the kidney and its clinical relevance is understudied. This study aimed to computationally quantify lymphocyte topology and test its prediction of disease progression. Methods NEPhrotic syndrome sTUdy NEtwork (NEPTUNE) ( N =333)/Cure Glomerulonephropathy (CureGN) participants ( N =155 focal segmental glomerulosclerosis, N =178 minimal change disease) with available clinical/demographic data and one hematoxylin and eosin–stained whole slide image were included. Cortex and lymphocytes were automatically segmented. A novel graph-based clustering algorithm was applied to identify dense versus sparse lymphocytic habitats, from which 26 pathomic features were extracted to capture cell density, connectivity, clustering, and centrality. The association of these pathomic features with disease progression (40% eGFR decline or KRT) was assessed using ElasticNet-regularized Cox proportional hazards models. Clinical and demographic characteristics and percent of interstitial fibrosis and inflammation were added as potential confounders. Kaplan–Meier survival analysis with log-rank test was used to evaluate risk stratification. Two validation strategies were applied: ( 1 ) training on NEPTUNE with external validation on CureGN data and ( 2 ) using an internal bootstrap validation of the combined datasets for training and validation, respectively. Results Unadjusted analysis: 17 features (65%) retained significance after adjustment for standard clinicodemographic variables, Number of K-core in sparse habitat maintained significance (hazard ratio, 1.29; 95% confidence interval, 1.04 to 1.61) even after adjustment for lymphocyte density, and Average Degree in dense habitat had borderline significance (hazard ratio, 1.25; 95% confidence interval, 1.00 to 1.57) after adjustment for interstitial fibrosis. Multivariable models (clinical/demographic+graph features) achieved validation concordance index of 0.78±0.15 in the CureGN external validation and 0.77±0.06 in the combined internal validation dataset. Time-dependent discrimination showed consistent performance at 3-year (area under the time-varying receiver operating characteristic curve: 0.78 versus 0.76) and 5-year time points (area under the time-varying receiver operating characteristic curve: 0.74 versus 0.76) across validation strategies. Sparse habitat clustering patterns ( Maximum of K-core×Number of K-core in sparse habitat : 88% selection frequency) and dense habitat connectivity ( Average Degree in dense habitat: 47% selection frequency) were consistently identified as robust predictors alongside clinical variables. Conclusions The topologic characterization of lymphocytic inflammation identified immune habitats, capturing the complexity of patterns of inflammation.

Chronic Changes on Kidney Histology by a Multiclass Artificial Intelligence Model

Kidney International Reports · 2025-05-29 · 4 citations

Introduction: Chronic changes in kidney histology are often approximated by using human vision but with limited accuracy. Methods: An interactive annotation tool trained an artificial intelligence (AI) model for segmenting structures on whole slide images (WSIs) of kidney tissue. A total of 20,509 annotations trained the AI model with 20 classes of structures, including separate detection of cortex from medulla. We compared the AI model detections with human-based annotations in an independent validation set. The AI model was then applied to 1426 donors and 1699 patients with renal tumor to calculate chronic changes as defined by measures of nephron size (glomerular volume, cortex volume per glomerulus, and mean tubular areas) and nephrosclerosis (globally sclerotic glomeruli, increased interstitium, increased tubular atrophy (TA), arteriolar hyalinosis (AH), and artery luminal stenosis from intimal thickening). We then assessed whether chronic kidney disease (CKD) outcomes were associated with these chronic changes. Results: During the AI model validation step, the agreement between the AI detections and human annotations was similar to the agreement between human pairs, except that the AI model showed less agreement with AH. Chronic changes calculated solely from AI-based detections associated with low glomerular filtration rate (GFR) during follow-up after kidney donation and with kidney failure after a radical nephrectomy for tumor. A chronicity score based on AI detections was calculated from cortex per glomerulus, percent glomerulosclerosis, TA foci density, and mean area of AH lesions and showed good prognostic discrimination for kidney failure (cross-validation C-statistic = 0.819). Conclusion: A multiclass AI model can help automate quantification of chronic changes on WSIs of kidney histology.

HistoWAS: A Pathomics Framework for Large-Scale Feature-Wide Association Studies of Tissue Topology and Patient Outcomes

arXiv (Cornell University) · 2025-12-23

High-throughput "pathomic" analysis of Whole Slide Images (WSIs) offers new opportunities to study tissue characteristics and for biomarker discovery. However, the clinical relevance of the tissue characteristics at the micro- and macro-environment level is limited by the lack of tools that facilitate the measurement of the spatial interaction of individual structure characteristics and their association with clinical parameters. To address these challenges, we introduce HistoWAS (Histology-Wide Association Study), a computational framework designed to link tissue spatial organization to clinical outcomes. Specifically, HistoWAS implements (1) a feature space that augments conventional metrics with 30 topological and spatial features, adapted from Geographic Information Systems (GIS) point pattern analysis, to quantify tissue micro-architecture; and (2) an association study engine, inspired by Phenome-Wide Association Studies (PheWAS), that performs mass univariate regression for each feature with statistical correction. As a proof of concept, we applied HistoWAS to analyze a total of 102 features (72 conventional object-level features and our 30 spatial features) using 385 PAS-stained WSIs from 206 participants in the Kidney Precision Medicine Project (KPMP). The code and data have been released to https://github.com/hrlblab/histoWAS.

Cellular and Spatial Drivers of Unresolved Injury and Functional Decline in the Human Kidney

bioRxiv (Cold Spring Harbor Laboratory) · 2025-09-28 · 4 citations

Building upon a foundational Human Kidney resource, we present a comprehensive multi-modal atlas that defines spatially resolved versus unresolved repair states and mechanisms in human kidney disease. Homeostatic interactions between injured kidney epithelium and its surrounding milieu determine successful repair outcomes, while pathogenic signaling promotes unresolved inflammation and fibrosis leading to chronic disease. We integrated multiple single-cell and spatial modalities across ~700 samples from >350 patients (~250 research biopsies), analyzing ~1.7 million cells alongside complementary mouse multi-omic profiles spanning acute-to-chronic injury and aging (>300,000 cells) and spatial transcriptomic analysis of >150 human biopsies. This cross-species atlas delineates functional pathways and druggable targets across the nephron and defines gene regulatory networks and chromatin landscapes governing tubular, fibroblast, and immune cell transitions from injury to either recovery or failed repair states. We identified distinct cellular states associated with specific pathological features that show dynamic distributions between acute kidney injury (AKI) and chronic kidney disease (CKD), organized within unique spatial niches that reveal progression mechanisms from early injury to unresolved disease. Gene regulatory analyses prioritized key transcription factor activities (SOX4, SOX9, NFKB1, REL, KLFs) and their target networks establishing disease states and tissue microenvironments. These regulatory programs were directly linked to clinical outcomes, identifying molecular signatures of recovery and secreted biomarkers predictive of AKI-to-CKD progression, providing a key resource for therapeutic development and precision medicine approaches in kidney disease.

Inaxaplin for a Broad Population with APOL1-Mediated Kidney Disease and Comorbid Conditions: Phase 2 Proof-of-Concept Study

Journal of the American Society of Nephrology · 2025-10-01

Individualized treatment effects of corticosteroids in IgA nephropathy

medRxiv · 2025-10-09

IgA nephropathy (IgAN) is a leading cause of kidney failure with diverse clinical presentations and treatment responses, particularly to corticosteroids, with conflicting evidence. Due to potentially severe side effects and heterogenous responses more individualized approaches are needed. We developed a causal machine learning framework for predicting individualized treatment effects of corticosteroids in IgAN by integrating clinical variables, histopathological scores, and deep learning-based biomarkers from digitized kidney biopsies (pathomics) of 1,022 patients from eight retrospective international cohorts. At the cohort-level, corticosteroids showed no significant effect on five-year kidney survival. However, the framework identified subpopulations with and without significant treatment benefit, improving progression-free kidney survival and also reducing overtreatment in low-benefit patients. Pathomics highlighted tubulointerstitial inflammation and glomerular tuft deformation as predictors of corticosteroid response. Our framework offers a blueprint for precision therapy in IgAN, supporting clinical decision-making in the era of emerging targeted treatments.

FUSION: a web-based application for in-depth exploration of multi-omics data with brightfield histology

Nature Communications · 2025-09-25 · 1 citations

Spatial technologies examining the cell and tissue microenvironment at near single-cell resolution are revealing important molecular insights. However, few tools enable integrated, interactive analysis of spatial-omics with tissue morphology in the same functional tissue unit. Here, we present FUSION (Functional Unit State Identification in Whole Slide Images), a web-based platform for visualizing and analyzing spatial-omics data with high-resolution histology. FUSION provides workflows for assessing cell compositions, quantitative morphometrics, and comparative tissue analyses. We demonstrate applicability across spatial assays, including 10x Visium, Visium HD, 10x Xenium, Cell DIVE, and PhenoCycler, applied to healthy and diseased tissues from kidney, small intestine, lung, and skin in the Human BioMolecular Atlas Program. FUSION is cloud-based, open-source, and accessible at https://fusion.hubmapconsortium.org/ , hosting over 50 paired datasets and tutorials. In a series of use cases, we show its capacity to distinguish renal glomeruli injury states, quantify morphometric changes, and characterize fibrosis with immune infiltration. Border and colleagues present FUSION, an open-source, web-based platform designed to integrate spatial omics with histological context. FUSION enables intuitive, interactive exploration of cellular and tissue-level features across diverse organs and assay types.