Laura Barisoni

BACKGROUND: Acute interstitial nephritis (AIN) is a common cause of acute kidney injury (AKI), but the diagnosis may be missed as kidney biopsies are rarely obtained when acute tubular injury (ATI) is suspected. METHODS: The Kidney Precision Medicine Project is a cohort study that obtains kidney biopsies from individuals with AKI, which undergo pathologic and molecular interrogation. We compared ATI and AIN cases among the first 60 AKI participants. RESULTS: On clinicopathologic adjudication, 30 patients (50%) had a primary adjudicated diagnosis of ATI, 13 (22%) patients had AIN, 9 (15%) had diabetic nephropathy, and 3 (5%) had other conditions. There were increased interstitial white blood cells and tubulitis ( P < 0.05 for both) in AIN compared with ATI. Prior to biopsy, the treating clinician suspected ATI in 83% of the cases with adjudicated ATI, while the treating clinician suspected AIN in 54% of the cases with AIN. Tissue transcriptomic signatures showed enrichment of proinflammatory signaling and increased expression of CXCL9 , a chemokine induced by IFN-γ, in myeloid cells of participants with AIN. CXCL9 localized to inflammatory infiltration in spatial transcriptomic data. CONCLUSION: Adjudication of kidney biopsies revealed distinct pathologic and molecular profiles between ATI and AIN. Kidney biopsy should be considered more frequently in AKI, as AIN is clinically underrecognized. TRIAL REGISTRATION: ClinicalTrials.gov NCT04334707. FUNDING: National Institute of Diabetes and Digestive and Kidney Diseases grants U01DK133081, U01DK133091, U01DK133092, U01DK133093, U01DK133095, U01DK133097, U01DK114866, U01DK114908, U01DK133090, U01DK133113, U01DK133766, U01DK133768, U01DK114907, U01DK114920, U01DK114923, U01DK114933, U24DK114886, UH3DK114926, UH3DK114861, UH3DK114915, and UH3DK114937.

Key Points: Several years of follow-up are necessary to accurately estimate long-term eGFR slopes in patients with glomerular disease. Moderately sized gaps between eGFR measurements are acceptable when estimating long-term eGFR slopes. Background: Slope of eGFR is an important measurement of kidney disease progression and clinical outcome in glomerular disease. However, the length of time required to reliably estimate long-term eGFR slopes is unknown. Furthermore, the impact of missing data on eGFR slope estimation is unclear. Methods: Data from the Cure Glomerulonephropathy (CureGN) study were used to answer two questions. A Total Duration question compared eGFR slope estimates based on shorter intervals of follow-up (FU) with true long-term slopes using descriptive statistics, overall and within patient subgroups based on age, disease diagnosis, disease activity at study enrollment and during FU, nonlinearity of the eGFR trajectory, and eGFR measurement frequency. A Duration after Missingness question simulated missing data to examine different lengths of FU time after initial gaps between biopsy and study enrollment to compare eGFR slope estimates with true complete-data eGFR slopes, overall and within patient subgroups. Results: For the Total Duration question, eGFR slope estimates gradually became more accurate with longer FU, with 86% of slopes based on 3.5–4 years of FU within 5 ml/min per 1.73 m ² per year of true long-term slopes. For the Duration after Missingness question, 84% of eGFR slopes based on over 2 years of FU were within 5 ml/min per 1.73 m ² per year of true complete-data slopes after initial missing data gaps between 6 and 24 months. For both questions, eGFR slopes stabilized faster for adults compared with children and for those diagnosed with membranous nephropathy or IgA nephropathy compared with minimal change disease or FSGS. For the Total Duration question, participants with nonlinear trajectories required much longer time to achieve stable eGFR slopes compared with those with linear trajectories. Conclusions: Several years of FU are necessary to accurately estimate long-term eGFR slopes in patients with glomerular disease, but moderately sized gaps between eGFR measurements are acceptable.

Acute kidney injury (AKI) and chronic kidney disease (CKD) are two interconnected clinical conditions, both defined by degree of functional impairment, but with heterogeneous clinical trajectories. Using new transcriptomic technologies, recent studies have described the cellular diversity in the healthy and injured kidney at the single cell level. Here, we used single nucleus transcriptomics to investigate the molecular diversity and commonalities in kidney biopsies from over 150 participants with AKI and CKD enrolled within the Kidney Precision Medicine Project (KPMP) and did so at the patient participant level. Using an unsupervised approach, we identified two multi-cellular programs associated with clinical and histopathological features of acute injury and chronic damage, respectively. We found that these programs are expressed across patients with AKI and CKD, supporting shared, rather than distinct, underlying molecular mechanisms. These programs capture tissue-level compositional changes towards adaptive and failed-repair states in tubular epithelial cells, as well as intra-cellular molecular changes characteristic of stress in all cell types. We identified subunits of the NFkB and AP-1 complexes, as well as members of the STAT family, as putative upstream regulators of the acute and chronic programs. We were able to map these continuous molecular measures of acute injury and chronic damage to urine and plasma protein profiles obtained at time of biopsy. These non-invasive protein signatures were predictive of renal outcomes in an independent cohort of 44 thousand participants from the UK biobank. In summary, unbiased identification of cellular programs in kidney disease biopsies defined molecular programs of injury cutting across conventional disease categorization and established a non-invasive molecular link to long term patient outcomes. Graphical

Background The current semi-qualitative methods used to score sclerosis and hyalinosis in arteries and arterioles in clinical practice are limited in standardization and reproducibility. We developed a computational pipeline designed to accurately and consistently quantify prognostic arterial and arteriolar characteristics in digital kidney biopsies of patients with focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) through segmentation and pathomic feature extraction. Methods We utilized one trichrome-stained WSI from 225 participants in the NEPTUNE/CureGN studies, comprising 127 cases of focal segmental glomerulosclerosis (FSGS) and 98 cases of minimal change disease (MCD). We developed, validated, and quality-controlled deep learning models to segment muscular vessels and their internal compartments (lumen, intima, media, and hyalinosis), including (i) arcuate arteries, (ii) interlobular arteries, and (iii) arterioles with two muscle layers. Arterioles, interlobular, and arcuate arteries were visually scored for sclerosis and hyalinosis on a scale of 0 to 3. Area- and thickness-based pathomic feature extraction was performed on each compartment (lumen, intima, media, and hyalinosis) through radial sampling and ray casting. A correlation study was performed between pathomic and visual semiquantitative visual scores, and the association of both visual scores and pathomic features with disease progression (40% eGFR decline or renal failure) was assessed. Summary statistics (maximum, median, and 75th percentile) were computed for each WSI and analyzed using LASSO-regularized Cox proportional hazards models, adjusted for clinical and demographic factors. Results A total of 1,499 arterioles, 686 interlobular arteries, and 131 arcuate arteries were segmented. Statistically significant correlations were found between pathologists visual scores and the average intima-media thickness ratio (Spearman ρ = 0.27, p < 0.001 for arterioles; ρ = 0.69, p < 0.001 for interlobular arteries; and ρ = 0.80, p < 0.001 for arcuate arteries) and arteriolar hyalinosis (ρ = 0.46, p < 0.001). Incorporating pathomic features from trichrome-stained WSIs improved the prediction of disease progression, enhancing the concordance index from 0.70 to 0.75 in arterioles and from 0.69 to 0.74 in arcuate arteries, compared to using demographics and clinical characteristics alone. Conclusion Our computational approach offers a novel and reliable method for segmenting and analyzing the pathomic features of sclerosis and hylalinosis in arteries and arterioles. This technique has demonstrated potential as a valuable tool for enhancing the clinical assessment performed by pathologists. Key Points A computational pipeline was developed and validated to segment arteries and arterioles and to quantify lumen, intima, media, and hyalinosis in kidney biopsies from patients with FSGS and MCD. Pathomic features, such as intima-media thickness ratio and hyalinosis area, significantly correlated with pathologists’ semi-quantitative sclerosis and hyalinosis scores. Integrating pathomic features into clinical models improved disease progression prediction accuracy

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.

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.

Background Conventional assessment of Focal Segmental Glomerulosclerosis and Minimal Change Disease focuses on the presence/extent of segmental (SS) and global (GS) glomerulosclerosis. While SS and GS represent ongoing and terminal process, encoded in non-SS/GS glomeruli is prognostic information that can be extracted before structural changes are visually discernable. This study applies computational image analysis to (a) automate the segmentation and classification of glomeruli into GS, SS and non-GS/SS, (b) extract subvisual pathomic characteristics from non-GS/SS glomeruli, and (c) assess their clinical relevance. Methods Leveraging the NEPTUNE/CureGN Periodic acid Schiff-stained whole slide images, we (i) developed deep learning (DL) models for the segmentation and classification of glomeruli into GS, SS and non-GS/SS; (ii) compared the association with disease progression and proteinuria remission of DL-derived percent of GS and SS vs. human scoring; (iii) extracted pathomic features from non-GS/SS; (iv) assessed their prognostic value using ridge-penalized Cox regression, with pathomic features ranked by Maximum Relevance Minimum Redundancy algorithm; and (v) estimated associations between selected pathomic features and clinical outcomes using Cox proportional hazard models. Results Agreement between computer-aided and visual scoring was good for %GS (ICC = 0.889) and moderate for %SS (ICC = 0.592). The prognostic performance of Cox models of computer-aided visual scoring approaches was comparable (iAUCs 0.779 vs. 0.776 for disease progression and 0.811 vs. 0.817 for complete proteinuria remission, respectively). For non-GS/SS glomeruli, 3 and 4 pathomic features were selected and demonstrated modest prognostic performance for disease progression (iAUC = 0.684) and proteinuria remission (iAUC = 0.661), respectively. After adjusting for demographics, clinical characteristics, %GS and %SS, 2 pathomic features remained statistically significantly associated with proteinuria remission. Conclusion Computational pathology allows for automatic quantification of SS/GS glomeruli that is comparable to manual assessment for outcome prediction, and the uncovering of previously under-recognized clinically useful information from non-GS/SS glomeruli.

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.

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.