# Leveraging Spatial Location for Single-Cell Molecular and Morphological Characterization

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $32,345

## Abstract

Abstract
 Innovative developments in single-cell sequencing technologies and techniques are providing
increased resolution and novel ways to define and characterize cellular profiles. Despite this progress, linking
different aspects of a cell’s identity, such as transcriptome, spatial location, morphology, and physiological
response remains challenging. Spatial transcriptomic technologies, while providing transcriptomic data within a
spatial framework, frequently must compromise achieving single-cell resolution in order to survey a wider panel
of genes. Similarly, while techniques such as fluorescent micro-optical section tomography (fMOST) and
functional ultrasound imaging (fUSI) provide detailed reconstructions of neuron morphology and physiological
response, these data modalities lack the ability to simultaneously capture molecular information. As a result,
while technological advances for each distinct modality continue to resolve finer and more complex cell type
distinctions, cohesive cellular profiles that combine all aspects of a cell’s identity, from transcriptome to
physiological response, have yet to be captured. Thus, understanding how the transcriptomic and
morphological composition of a cell influences its physiological response is a key barrier for the field.
 This proposal aims to develop computational tools that will connect multiple facets of cellular identity. In
Aim 1, we propose the addition of graph-regularization into the integrative non-negative matrix factorization
algorithm (GRINMF). The use of GRINMF to include spatial information will result in more refined cell-type
characterizations for cells assayed with spatial transcriptomics technologies. In Aim 2, we will validate a spatial
deconvolution algorithm that leverages non-negative matrix factorization to calculate cell-type proportions
within spatially registered transcriptomic data. We will anchor our derived cell-type proportion voxels in the
same coordinate framework as a series of morphological and physiological datasets. By completing the
proposed research, I will gain extensive experience in the development of algorithms to synthesize
physiological, transcriptomic, and spatial data. This training will facilitate advancement of my communication,
critical thinking, and translational technical skills, providing me with the tools necessary to pursue my ambition
of becoming a research scientist at the interface of neuroscience and bioinformatics.

## Key facts

- **NIH application ID:** 10752607
- **Project number:** 5F31HG012715-02
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** April Rose Kriebel
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $32,345
- **Award type:** 5
- **Project period:** 2023-01-01 → 2024-08-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10752607

## Citation

> US National Institutes of Health, RePORTER application 10752607, Leveraging Spatial Location for Single-Cell Molecular and Morphological Characterization (5F31HG012715-02). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10752607. Licensed CC0.

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