# Improving the Accuracy of Lupus Nephritis Diagnosis using Biomarkers Derived from Ultraviolet and Mid-infrared Spectroscopic Imaging

> **NIH NIH R01** · UNIVERSITY OF HOUSTON · 2024 · $629,842

## Abstract

PROJECT SUMMARY
Histology is the current standard for diagnosis and predicting long-term disease outcomes in lupus nephritis
(LN). However, diagnosis and prognosis are challenging due to significant inter-pathologist variance and multiple
pitfalls in histopathology. We propose combining conventional histology with independent information from two
complementary optical imaging modalities that provide additional morphological, biochemical and molecular
context to LN, thus overcoming current diagnostic challenges. We will utilize milling with ultraviolet surface
excitation (MUSE) to provide protein-specific histology and mid-infrared spectroscopic imaging (MIRSI) for label-
free biochemical identification of small molecules and metabolites. Acquiring co-registered imaging data with
high speed and good resolution from these imaging modalities is challenging, and we propose a new
experimental platform for comprehensive biopsy imaging that addresses this challenge. We will identify new
structural and molecular features across these modalities that are decisive for LN diagnosis. A deep learning
architecture will be used to combine information from across all modalities, optimize feature selection and
quantification. We present extensive preliminary data from kidneys of wildtype and LN murine models
demonstrating the efficacy of our techniques. We will validate the efficacy of LN diagnostic metrics from murine
models using archival human kidney biopsy samples. We also present data from human subjects with Class II
LN (non-proliferative), Class IV LN (proliferative) and minimal change disease (control) and demonstrate
statistically significant metrics derived from our imaging modalities that enable improved LN diagnosis.

## Key facts

- **NIH application ID:** 10803337
- **Project number:** 1R01DK135870-01A1
- **Recipient organization:** UNIVERSITY OF HOUSTON
- **Principal Investigator:** Rohith Reddy
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $629,842
- **Award type:** 1
- **Project period:** 2024-01-01 → 2028-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10803337

## Citation

> US National Institutes of Health, RePORTER application 10803337, Improving the Accuracy of Lupus Nephritis Diagnosis using Biomarkers Derived from Ultraviolet and Mid-infrared Spectroscopic Imaging (1R01DK135870-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10803337. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*