# Computational methods for in situ spatial transcriptomics

> **NIH NIH R35** · DUKE UNIVERSITY · 2024 · $363,384

## Abstract

Project Summary/Abstract
The explosive growth of spatial transcriptomics technologies has revolutionized the study of tissue
spatial architecture and development. In contrast to microdissection and spatial barcoding methods, which
may not always achieve single-cell resolution, in situ spatial transcriptomics provides unparalleled detail by
recording the spatial locations of individual RNA transcripts. Though specialized computational methods
have been developed to tackle the unique challenges of analyzing in situ spatial transcriptomics
data, substantial obstacles still exist in accurately identifying cell boundaries, distinguishing cell clusters
and cell types, and understanding cells' interactions with their microenvironment. In this project, we
propose to develop a suite of computational tools to address these challenges. First, we will develop a
generally applicable framework for optimized cell segmentation that integrates RNA spatial location
information with imaging information, capitalizing on the latest segmentation algorithms, such as those
based on transformers. To evaluate their performances, we will generate a benchmarking dataset by
manually annotating cell boundaries in real in situ data from various tissues and disease conditions.
Second, we will establish a framework of cell clustering and cell type annotations for in situ data,
blending gene expression information with cell morphology and cell density information learned from
images. We will also test different combinations of computational methods for data transformation,
dimension reduction, and clustering. The performance will be evaluated on simulated datasets derived from
single-cell RNA-seq data with ground truth cell clusters and cell types. Finally, we will develop a flexible
method to systematically study cellular microenvironment for data from both in situ and other types of spatial
profiling technologies, taking into account diverse cell types, molecules, and different types of cell-cell
interactions based on spatial proximity. These methods will facilitate deeper understandings of the spatial
distributions and interactions of different cell types, providing new biological insights into cell senescence,
tumor microenvironment, and more.

## Key facts

- **NIH application ID:** 10936632
- **Project number:** 1R35GM154865-01
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Zhicheng Ji
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $363,384
- **Award type:** 1
- **Project period:** 2024-08-01 → 2029-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10936632

## Citation

> US National Institutes of Health, RePORTER application 10936632, Computational methods for in situ spatial transcriptomics (1R35GM154865-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10936632. Licensed CC0.

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