# Linking single-neuron morphology and gene expression using deep learning

> **NIH NIH F31** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2022 · $39,112

## Abstract

Abstract
Characterizing neuronal cell identity in terms of transcriptomics, electrophysiology, and morphology is an
essential component for understanding neural circuits and function. A multi-modal understanding of cell-to-cell
variation in both single-cell transcriptional profiles and morphological phenotypes is needed to understand
functional characteristics and the emergence of complexities in the brain. High-throughput single-cell
measurements of neuronal gene expression are available, but the relationship between morphology and gene
expression is not well explored due to the challenge of measuring both modalities from the same cells. We
hypothesize that gene expression influences neuronal morphology, and thus that single-cell gene expression
can be used to predict single-cell morphology. We propose to leverage recent advances in deep generative
models to predict the distribution of single-cell morphology images from single-cell gene expression.
We will: (1) develop deep generative to learn the relationship between single-cell gene expression and
morphology; (2) train MorphGAN to generate morphologies for unseen neuronal single-cell gene expression
profiles.; and (3) identify morphological axes of variation and key genes that predict morphology using
MorphGAN.
Completion of these aims will produce publicly available software tools and a public database of predicted
single-cell neuron morphology images. Ultimately, linking transcriptomic and morphological characteristics of
single neurons would be invaluable in capturing the diversity of brain cells and delineating neuronal cell types.

## Key facts

- **NIH application ID:** 10534571
- **Project number:** 1F31MH129026-01A1
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Hojae Lee
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $39,112
- **Award type:** 1
- **Project period:** 2022-12-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10534571

## Citation

> US National Institutes of Health, RePORTER application 10534571, Linking single-neuron morphology and gene expression using deep learning (1F31MH129026-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10534571. Licensed CC0.

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