PROJECT SUMMARY Our overarching goal is to enable the AI-empowered 3D histopathological interpretation on routine digitized renal tissue biopsies, so as to (1) allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI), (2) advance the characterization of kidney-allograft rejection phenotypes on kidney transplant patients with cutting-edge 3D computer vision, and (3) equip clinical scientists with an advanced 3D spatial transcriptomics analytics tool to investigate the anatomical-molecular associated causes of chronic kidney disease (CKD). Our novel 3D histopathological interpretation, with 3D computer vision (Map3D toolkit) and 3D spatial transcriptomics, will open a new door for performing reproducible clinical phenotyping (Pheno3D toolkit), identifying and validating new 3D imaging and molecular biomarkers (GPS3D toolkit), and ultimately advancing the patient care with personalized diagnosis and prognosis options for a wide range of CKD. Despite more than 25 years of exploitation of digital pathology, the presence, significance and characteristics of 3D contextual information in renal histopathological assessment have been largely overlooked. The current 2D interpretation on renal histopathology is error-prone and less reproducible due to the heterogeneity of tissue morphologies (e.g., glomeruli, tubules, vessels) across 3D serial sections. For example, our previous study on segmental glomerulosclerosis (GS) in patients with nephrotic syndrome and idiopathic FSGS, the percent of GS increased from 31.5 +/- 6.8% to 48.0 +/- 6.6% (P < 0.025) in adults by replacing a 2D single section analysis with 3D serial section analysis. Moreover, 2D based phenotyping can also hinder the discovery of new biomarkers via state-of-the-art spatial transcriptomic techniques. As an example, a glomerulus with focal segmental glomerulosclerosis (FSGS) can have a normal appearance on a specific 2D section, which might lead to an opposite molecular finding using 2D spatial transcriptomics The core tenant of this proposal is NOT developing a new 3D imaging modality, but rather, to develop technologies that enable reproducible 3D characterization on routine 2D renal histopathological biopsies (with trivial added cost), so as to advance the care of future patients with renal diseases. To this end, we will: Aim 1. Develop novel 3D computer vision tools (Map3D) to facilitate renal pathologists in modeling, quantifying, and visualizing 3D renal histopathological tissues from routine 2D digital histopathology. Impact: Allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI). Aim 2. Develop 3D phenotyping tools (Pheno3D) to advance the characterization of kidney-allograft rejection for kidney transplant patients via 3D computer vision and self-supervised deep learning. Impact: Advance the characterization of kidney-allograft rejection phenotypes for kidney transplant patients. Aim 3. Develop 3D computer vision algorithms for 2D and 3D spatial transcriptomics (GPS3D toolkit). Impact: Equip clinical scientists an 3D spatial transcriptomics analytics tool to investigate image-omics interaction.

PROJECT NARRATIVE Computer-assisted interpretation of renal histopathology using whole slide imaging (WSI) has dramatically advanced both the research and practice for the diagnosis and scoring of specific causes of chronic kidney disease (CKD). However, current phenotyping approaches are restricted to investigating two dimensional (2D) sections, which are error-prone due to the heterogeneity of kidney tissue (e.g., glomeruli) across three dimensional (3D) serial sectioning. We seek to enable AI-empowered 3D interpretation of routine digital renal histopathology, so as to (1) allow renal pathologists to perform a reproducible 3D phenotyping on serial 2D whole slide images (WSI), (2) advance the characterization of kidney-allograft rejection phenotypes on kidney transplant patients with cutting-edge 3D computer vision, and (3) equip clinical scientists with an advanced 3D spatial transcriptomics analytics tool to investigate the anatomical-molecular associated causes of chronic kidney disease (CKD).