# Automated detection of microstructural features that have unique protein markers and are prognostic for chronic kidney disease

> **NIH NIH R01** · MAYO CLINIC ROCHESTER · 2024 · $665,277

## Abstract

The primary microstructural attributes seen in “normal” kidneys are nephrosclerosis (arteriosclerosis, global
glomerulosclerosis, and interstitial fibrosis/tubular atrophy), nephron number, and nephron size. However
manual measures of these microstructures are impractical. Further, understanding their pathophysiology may
lead to new interventions for kidney disease. Automated morphometry with deep learning (DL) networks may
be able to rapidly measure nephrosclerosis, nephron number, and nephron size. Novel morphometry of
structures impacted by glomerular hyperfiltration (podocyte and parietal epithelial cell (PEC) density,
Bowman’s space, and the diameter of proximal and distal tubule) and of microvasculature are needed to better
understand the pathophysiology of early disease. Proteomic analysis of specific microstructures that differ
between kidneys that do versus do not develop CKD outcomes has the potential to identify prognostic or even
pathogenic proteins for early kidney disease. The multi-discipline multi-site Aging Kidney Anatomy study has
unique resources for the study of microstructure in “normal” kidneys. This includes data and specimens on
living kidney donors including needle core biopsies at donation with digitized whole slide images (WSI) and
long-term CKD outcomes in the donor and recipient. This also includes data and specimens on patients who
had a radical nephrectomy for tumor including digitized WSI of kidney wedge sections and annual eGFR
testing for CKD outcomes during follow-up. Aim 1 will determine if automated morphometry of nephrosclerosis,
nephron number, and nephron size predicts CKD outcomes to test the hypothesis that DL tools allows for
efficient quantification of these clinically relevant microstructural attributes. This aim will use both previously
developed and new DL networks, develop models to predict CKD outcomes from automated morphometry,
and compare prediction of CKD outcomes between automated and manual morphometry. Aim 2 will
characterize novel microstructural attributes that associate with kidney function, CKD risk factors, and CKD
outcomes to test the hypothesis that encoded in the kidney tissue are unexplored structural attributes that
reflect the glomerular hyperfiltration and interstitial microvascular status that are prognostic for CKD. This aim
will automatically quantify podocytes, PECs, peritubular capillaries (PTC), Bowman’s space (volume), and
proximal and distal tubules (diameter) on WSI using previously developed and newly developed DL tools and
associate these structures with kidney function, CKD risk factors, and CKD outcomes. Aim 3 will discover
protein markers linked to the microstructural attributes that are prognostic for CKD outcomes to test the
hypothesis that differentially expressed proteins contained within kidney microstructures predict CKD
outcomes. This aim will use laser capture microdissection, mass spectroscopy-based proteomics (both
discovery and targeted validation approac...

## Key facts

- **NIH application ID:** 10772154
- **Project number:** 5R01DK090358-13
- **Recipient organization:** MAYO CLINIC ROCHESTER
- **Principal Investigator:** ANDREW David RULE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $665,277
- **Award type:** 5
- **Project period:** 2011-02-10 → 2027-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10772154

## Citation

> US National Institutes of Health, RePORTER application 10772154, Automated detection of microstructural features that have unique protein markers and are prognostic for chronic kidney disease (5R01DK090358-13). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10772154. Licensed CC0.

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