# Data Analysis Core

> **NIH NIH U54** · VANDERBILT UNIVERSITY · 2024 · $609,012

## Abstract

PROJECT SUMMARY – Data Analysis Core.
The characterization pipelines built by the VU Biomolecular Multimodal Imaging Center (BIOMIC) Data Analysis
Core (DAC) will produce a multimodal molecular atlas of kidney across multiple scales in 2-D and 3-D, comprising
rich and varied molecular data from MALDI IMS, MxIF, CODEX, spatial and non-spatial proteomics/
transcriptomics, united by common spatial coordinate space. The DAC will build on our previous developments,
including spatial segmentation for both acquisition and analysis, comprehensive molecular identification across
modalities, automated data mining across modalities by spatial masks using clinical and temporal cues, and
situating data within an “average” human kidney developed from 1000s of medical images. In Aim 1 we will
further develop and scale our modality-specific processing methods to prepare the different measurement types
for subsequent spatial integration, multimodal analysis, and content/cross-omic relationship mining. Aim 2 will
focus on expanding and scaling our characterization pipeline, bringing together the broad array of distinct
imaging and -omics measurement types individually processed by the methods in Aim 1. By integrating these
varied datasets spatially, temporally, as well as content-wise, we will empirically mine them for cross-modal and
molecular-functional relationships. These technologies will establish both 2-D and 3-D multimodal tissue maps
that concurrently report hundreds to thousands of biomolecules at cellular resolutions, together with
corresponding functional and cell type annotations. In Aim 3 we will compose all spatial and molecular
information into a reference atlas by expanding the spatio-temporal mapping of molecular variation, organization,
and function beyond single tissue block analyses (as in Aim 2) to multi-block and multi-donor tissue cohorts.
Finally, Aim 4 will enable continued coordination with the HuBMAP consortium and HIVE team members by
integrating our analyses into the HIVE’s ASCT+B tables for interactive exploration, visualization, and searchable
analyses.

## Key facts

- **NIH application ID:** 10884879
- **Project number:** 5U54DK134302-03
- **Recipient organization:** VANDERBILT UNIVERSITY
- **Principal Investigator:** Jeffrey M Spraggins
- **Activity code:** U54 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $609,012
- **Award type:** 5
- **Project period:** 2022-09-15 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10884879

## Citation

> US National Institutes of Health, RePORTER application 10884879, Data Analysis Core (5U54DK134302-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10884879. Licensed CC0.

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