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

> **NIH NIH R01** · STATE UNIVERSITY NEW YORK STONY BROOK · 2024 · $67,592

## Abstract

SUMMARY: In response to NOT-GM-24-021, we apply for a supplement to our ongoing NIGMS award 5R01GM148970-
02, “DMS/NIGMS 1: Topological Study on Histological Images and Spatial Transcriptomics”. We propose to extend the
parent award by integrating the developed methods with state-of-the-art deep learning methodology, in order to produce
more powerful topology- and gene-informed feature representation for spatial transcriptomics data. The method will use
topological features and gene features as guidance for contrastive learning and teacher-student consistency learning so that
histology image patches are mapped into a powerful topology- and gene-informed features. To achieve the goal, we will
develop contrastive learning or teacher-student consistency loss, in which we ensure that both gene-based and topology-
based similarity measure will be preserved. We will use public ST data (10 Visium) on breast cancer and brain cancer, as
well as proprietary ST data (4 triple negative breast cancer samples, from Indiana University). We will use gene correlation,
as well as topological feature similarity, to determine similarities between different spots, and learn the feature mapping.
To evaluate the quality of the obtained imaging feature, we will test on two downstream tasks: (1) direct image-to-gene
mapping; (2) downstream tasks on whole slide prediction. For the first task, we will use the learnt feature to train a classifier
to directly predict spot-level gene expression. We expect that the learnt new features have better prediction power of gene
expression. Furthermore, for downstream tasks, we will combine the learnt gene features with whole slide prediction models
such as Multiple Instance Learning (MIL). We expect that the new topology- and gene-informed features will achieve better
prediction power in these downstream tasks. To perform the proposed tasks, however, we require a powerful GPU server.
We expect such features will demonstrate strong prediction power in downstream tasks.

## Key facts

- **NIH application ID:** 11100515
- **Project number:** 3R01GM148970-03S1
- **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:** $67,592
- **Award type:** 3
- **Project period:** 2022-09-01 → 2025-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11100515

## Citation

> US National Institutes of Health, RePORTER application 11100515, DMS/NIGMS 1: Topological Study on Histological Images and Spatial Transcriptomics (3R01GM148970-03S1). Retrieved via AI Analytics 2026-06-02 from https://api.ai-analytics.org/grant/nih/11100515. Licensed CC0.

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