# DMS/NIGMS 1: Topological Study on Histological Images and Spatial Transcriptomics

> **NIH NIH R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2024 · $199,468

## Abstract

With the rapid advance of high-resolution transcriptomic profiling techniques, recent years have witnessed
an increased interest in the study of the tissue microenvironment (TM) arising in cancer research and
neuroscience, i.e., the collection of cells and structures in a tissue, such as neuron and glia in neural tissue
or immune, stroma, and epithelial cells in tumors. The spatial configuration of these cells and structures
plays pivotal roles in tissue function. Researchers have obtained high resolution transcriptomic and imaging
data for TM study. The two types of information complement each other. High resolution transcriptomic
profiling, such as single-cell RNA-seq (scRNA), provides cellular level molecular information, but does not
carry local contextual information of cell, while histology image analysis provides detailed context, but does
not provide corresponding cellular gene expression profiles. To combine them is challenging due to a lack
of one-to-one correspondence between cells in transcriptomics and cells in histology images.
This project intends to unify transcriptomics and bioimage informatics for a comprehensive study of
TM, applying the advanced topological data analysis (TDA) methodology on the newly emerged spatial
transcriptomic (ST) data. ST data provides localized spatial transcriptomics. TDA provides the foundation
for studying rich contextual information in multi-omics. This project will produce a spatial-context-aware
high-resolution mapping of TM transcriptomics. The outcome will be highly impactful. It will not only
promote normal tissue level functionality characterization and mechanism study, but will also boost various
types of diseases’ diagnosis, prognosis as well as their mechanistic studies.
The PI/Co-PIs will create new topological approaches to extract rich contextual information from cells
of multiple types in histology images. They will also propose new learning algorithms to integrate such
topological information into localized ST scRNA data analysis for better differentiation of cells of different
types and states, to build connection between spatial context and cell signaling gene activation, and to map
transcriptomics information into whole slide image for visualization.

## Key facts

- **NIH application ID:** 10896999
- **Project number:** 5R01GM148970-03
- **Recipient organization:** STATE UNIVERSITY NEW YORK STONY BROOK
- **Principal Investigator:** Chao Chen
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $199,468
- **Award type:** 5
- **Project period:** 2022-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10896999

## Citation

> US National Institutes of Health, RePORTER application 10896999, DMS/NIGMS 1: Topological Study on Histological Images and Spatial Transcriptomics (5R01GM148970-03). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10896999. Licensed CC0.

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