Fan Fan
BME PhD Proposal Presentation

Date: 2026-10-13
Time: 9:30AM ～ 11:00AM
Location / Meeting Link: https://emory.zoom.us/j/8579990455 In person: HSRBII N657

Committee Members:
Janowczyk, Andrew, PhD (Advisor); Madabhushi, Anant, PhD(Co-advisor); Sinha, Saurabh, PhD; Farris III, Alton B, MD; Viswanath, Satish Easwar, PhD; Barisoni, Laura,MD


Title: Clinical and Biological Relevance of the Tubulointerstitium in Glomerular Diseases Through Multiscale Computational Pathology

Abstract:
Proteinuric glomerular diseases, including minimal change disease (MCD) and focal segmental glomerulosclerosis (FSGS), remain major causes of chronic kidney disease and kidney failure. Although diagnosis has focused on glomerular pathology, increasing evidence demonstrates that the tubulointerstitium is a critical determinant of disease progression and therapeutic response. However, current evaluation of the tubulointerstitium relies largely on semiquantitative visual assessment of a limited number of histologic features, which fails to capture the full spectrum of structural and spatial information encoded within kidney tissue7. Because periodic acid–Schiff (PAS)-stained kidney biopsies are routinely acquired as part of standard clinical care, they provide a widely available and cost-effective substrate for computational pathology, making this approach readily translatable across diverse clinical settings. Advances in computational pathology and spatial molecular profiling provide an opportunity to quantitatively characterize tubulointerstitial remodeling and relate tissue morphology to underlying biology and clinical outcomes. The overall objective of this project is to establish a multiscale computational pathology framework that links quantitative histologic morphology with molecular cell states and patient outcomes across MCD and FSGS. We hypothesize that computationally derived tubular pathomic features, when organized into biologically interpretable signatures and spatial niche representations, reveal patient-specific tubulointerstitial morphotypes that reflect disease mechanisms, reflect disease mechanisms, predict clinical progression, and define biologically interpretable disease states. To test this hypothesis, Aim 1 will develop and validate clinically relevant tubular pathomic features that comprehensively quantify structural alterations of renal tubules from whole-slide images (WSI). Aim 2 will integrate these features into biologically explainable tubular pathomic signatures and trajectories and validate their molecular states using bulk transcriptomics, single-nucleus RNA sequencing, and spatial transcriptomics. Aim 3 will integrate tubular signatures with neighboring interstitial components through spatial niche analysis to define tubulointerstitial morphotypes and evaluate their ability to predict disease progression and proteinuria remission across independent glomerular disease cohorts. Successful completion of this project will establish a biologically interpretable, multiscale framework for computational characterization of the tubulointerstitium that systematically links tissue morphology with molecular mechanisms and clinical outcomes. Beyond generating novel digital biomarkers for risk stratification, this work will also deliver standardized quantitative pathology workflows for quality control, as well as interactive visualization tools for large-scale exploration and interpretation of kidney pathomic data. Collectively, these advances will facilitate objective, reproducible, and scalable assessment of routine kidney biopsy specimens and accelerate the translation of morphology-guided precision medicine for glomerular diseases.