# Integrative analysis of spatial transcriptomics with histology images and single cells

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $540,739

## Abstract

PROJECT SUMMARY
The
function.
relative
disease
 tissues in our body consist of diverse cell t ypes with each cell type specialized to carry out a particular
The behavior of a cell is influenced by its surrounding environment within a tissue. Knowledge of the
locations of different cells in a tissue is critical for understanding the spatial organization of cell types and
pathology.Although single-cell RNA sequencing (scRNA-seq) has made it possible to characterize cell
types and states at an unprecedented resolution, the lack of physical relationships among cells has hindered the
study of cell-cell communications within tissue context. Recent technology advances in spatial transcriptomics
(ST) have enabled gene expression profiling while retaining location information in tissues. A popular ST
technology is based on spatial barcoding followed by next-generation sequencing in which transcriptome-wide
gene expression is measured in spatially barcoded spots. Data from such ST technologies often include a high-
resolution hematoxylin and eosin (H&E)-stained histology image of the tissue section from which the gene
expression data are obtained. Although ST is powerful, such data are still expensive to generate. On the other
hand, it is relatively cheaper to generate H&E-stained histology images and scRNA-seq data. The main
motivation of this project is to leverage information in ST to gain additional knowledge from the relatively easy-
to-obtain histology images and scRNA-seq data. Building upon our expertise in statistical genomics, we propose
to develop novel machine learning methods to address key computational challenges when performing
integrative analysis of ST, histology images, and single cells. Our methods will jointly model gene expression
and histology to characterize the spatial organization of tissues and predict spatial gene expression from
histology images. The resulting spatial map from these analyses will further enable the spatial mapping of single
cells back to tissues. The proposed methods will be applied to public data and data generated from ongoing
collaborations in various diseases to evaluate their performance. The successful completion of this project will
allow researchers to take advantage of advanced machine learning algorithms to integrate ST, histology, and
single-cell data to gain a holistic view of the spatial organization of tissues.

## Key facts

- **NIH application ID:** 10932432
- **Project number:** 5R01HG013185-02
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Mingyao Li
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $540,739
- **Award type:** 5
- **Project period:** 2023-09-20 → 2027-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10932432

## Citation

> US National Institutes of Health, RePORTER application 10932432, Integrative analysis of spatial transcriptomics with histology images and single cells (5R01HG013185-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10932432. Licensed CC0.

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