# Spatial Multi-Omics to Profile Metabolic Pathways for Kidney Disease

> **NIH NIH U01** · UNIVERSITY OF TEXAS HLTH SCIENCE CENTER · 2022 · $823,826

## Abstract

Project Summary/Abstract
The Kidney Precision Medicine Project (KPMP) is an NIDDK funded program that, in part, aims to generate a
human Kidney Tissue Atlas. In the initial UG3/UH3 KPMP program, our Tissue Integration Site (TIS)
implemented and streamlined spatial metabolomics and lipidomics technologies based on matrix-assisted laser
desorption/ionization mass spectrometry imaging (MALDI-MSI) and set up a quality-controlled pipeline for high
throughput analysis of human kidney biopsies. We also developed new bioinformatic methods for confident
molecular annotations of our MALDI-MSI data, as well as new visualization tools that permitted molecular image
overlay on optical images (e.g., histopathological images) using METASPACE– a cloud software for molecular
annotation, visualization, and data sharing. As such, these three technologies were sufficiently validated for
KPMP data collection on kidney biopsy tissue and approved by the Tissue Interrogation Site Approval Committee
(TISAC). We also developed a new platform for spatial profiling the kidney glycome that is currently under review
for TISAC approval. In close collaboration with other TISs and the Data Visualization Center, our TIS has been
able to demonstrate the unique value of connecting hundreds of bioactive molecules to genes and proteins and
to infer functional relationships at the renal compartment and cellular level, capacities critically needed for the
human Kidney Tissue Atlas. Our team combines the expertise of the University of Texas Health San Antonio
and Pacific Northwest National Laboratory in nephrology and spatial omics, the European Molecular Biology
Laboratory in molecular data annotation, management, and visualization, and the Icahn School of Medicine at
Mount Sinai in integrating multi-omics data. We are uniquely positioned to generate spatial multi-omics
interpretation describing kidney metabolism and to contribute to the Kidney Tissue Atlas. The overarching goals
of the project are to generate spatial multi-omics data from KPMP kidney biopsies to elucidate pathways related
to acute kidney injury (AKI), hypertensive chronic kidney disease (H-CKD), and diabetic kidney disease (DKD),
to define disease subgroups on the spatial and single-cell level, and to interrogate drug metabolism. We will
achieve this through two main project aims: (1) scaling up, improving, and validating spatial omics technologies,
and (2) developing and validating bioinformatics for pathways analysis and multi-level data integration. For each
of the spatial omics platforms, we expect to reach throughput to analyze up to two kidney biopsies per week with
a spatial resolution of ≤10 µm. We will focus on developing the necessary bioinformatics and data management
methods and workflows to extract the molecular knowledge for integration into the Kidney Tissue Atlas in
collaboration with the Kidney Tissue Atlas Coordinating Center, and we will develop integrative bioinformatics
for pathway analysis and dr...

## Key facts

- **NIH application ID:** 10515213
- **Project number:** 2U01DK114920-06
- **Recipient organization:** UNIVERSITY OF TEXAS HLTH SCIENCE CENTER
- **Principal Investigator:** Theodore Alexandrov
- **Activity code:** U01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $823,826
- **Award type:** 2
- **Project period:** 2017-09-15 → 2027-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10515213

## Citation

> US National Institutes of Health, RePORTER application 10515213, Spatial Multi-Omics to Profile Metabolic Pathways for Kidney Disease (2U01DK114920-06). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10515213. Licensed CC0.

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