# Computational Methods for Emerging Spatially-resolved Transcriptomics with Multiple Samples

> **NIH NIH R35** · JOHNS HOPKINS UNIVERSITY · 2024 · $404,485

## Abstract

Project Summary/Abstract
Understanding the spatial landscape of gene expression in tissues is a fundamental question for human health
and disease. Applications range from identifying the spatial organization of cell types to dysregulation of
spatial-dependent gene expression associated with disease. Advances in technologies, such as
spatially-resolved transcriptomics (SRT), provide a wealth of data to investigate these questions. Furthermore,
SRT combined with advances in long-read RNA-sequencing enable applications such as identifying
spatial-dependent splicing variation and allele specificity in healthy and disease states, such as cancer or
neurodegenerative disorders. Recent SRT studies are generating datasets across multiple samples (different
donors or adjacent tissue sections), but researchers analyze samples independently because there lack
computational tools for datasets with multiple samples. In contrast, when samples are jointly analyzed together,
the statistical power is increased to detect differences with greater accuracy and precision. The lack of tools to
analyze SRT data with multiple samples is a significant knowledge gap that limits are ability to refine the
molecular causes and consequences of diseases that can be targeted for prevention and treatment.
My research program develops scalable computational methods and open-source software for biomedical data
analysis, in particular single-cell and spatial transcriptomics data, leading to an improved understanding of
human health and disease. Here, our goal is to focus on developing scalable computational methods and
software for data from spatial and long-read technologies with multiple samples and experimental conditions to
accurately (1) predict spatial domains of tissues across multiple samples, (2) identify differences in spatial gene
expression across experimental conditions or biological groups with multiple samples in each group, and (3)
identify differential splicing variation across spatial domains or experimental conditions.
The rationale for the proposed work is that the computational tools developed will enable substantial advances
in our understanding of the spatial landscape of gene expression on distinct scales from cells to tissues to
individuals. The significance of this proposal is substantial with broad impact for researchers increasingly using
these imaging and genomic data, such as large-scale consortia generating spatial atlases across multiple
samples, but also the proposed methods will be relevant to a wide variety of scientific disciplines that leverage
high-dimensional data in a spatial context, such as environmental and mobile health. The project builds on my
past experience in developing computational methods and open-source software for scalable clustering and
identifying differences in gene expression at the single-cell level. The creation of well-documented, open-source
software expands the impact of this work to other researchers aiming to understand th...

## Key facts

- **NIH application ID:** 10907560
- **Project number:** 5R35GM150671-02
- **Recipient organization:** JOHNS HOPKINS UNIVERSITY
- **Principal Investigator:** Stephanie Carinne Hicks
- **Activity code:** R35 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $404,485
- **Award type:** 5
- **Project period:** 2023-09-01 → 2028-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10907560

## Citation

> US National Institutes of Health, RePORTER application 10907560, Computational Methods for Emerging Spatially-resolved Transcriptomics with Multiple Samples (5R35GM150671-02). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10907560. Licensed CC0.

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