# Integrating imaging and multi-omics data to infer single-cell 3D genome structures

> **NIH GM R01** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2026 · $363,570

## Abstract

PROJECT SUMMARY/ABSTRACT
The three-dimensional organization of eukaryotic genomes plays a crucial role in transcriptional regulation and
cellular functions. However, current genome structure models, primarily derived from genomic data, have
significant limitations. They lack precise physical dimensions, fail to capture nuclear morphologies accurately,
and are constrained by a resolution limit of approximately 200 kb—insufficient for studying interactions
between regulatory control regions. These shortcomings hinder the use of 3D genome structures in
understanding gene regulation and cellular processes. Recent advances in imaging technologies have
provided powerful tools to explore 3D genome organization. In this project, we will develop a probabilistic
approach to integrate genomic and imaging data to reconstruct 3D genome structures from thousands of
imaged nuclei. We have three aims: (1) Develop integrative methods for inferring high-resolution single cell
genome structures from sparse imaging and multi-omics data. This integration minimizes experimental biases
and improves resolution and coverage by 100-fold compared to imaging alone. Our approach will offer
unprecedented insights into the structural basis of gene regulation, enhancer networks, and the role of
chromatin architecture in epigenetic memory formation—insights unattainable through single-cell genome-wide
imaging or genomics data alone. (2) Structure-Function Mapping by analyzing the 3D regulatory architecture.
We will analyze the 3D regulatory environment of genes in mouse embryonic stem cells and the reorganization
of the microenvironment surrounding cell-type-specific long genes in the mouse brain cortex. For the first time,
we will systematically classify genes based on their 3D regulatory microenvironment and investigate its role in
gene expression. (3) We will expand our Integrative Genome Modeling (IGM) platform to incorporate imaging-
based features. The platform generates a population of genom

## Key facts

- **NIH application ID:** 11280100
- **Project number:** 1R01GM162977-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES
- **Principal Investigator:** Frank  Alber
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** GM
- **Fiscal year:** 2026
- **Award amount:** $363,570
- **Award type:** 1
- **Project period:** 2026-03-15T00:00:00 → 2030-02-28T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11280100

## Citation

> US National Institutes of Health, RePORTER application 11280100, Integrating imaging and multi-omics data to infer single-cell 3D genome structures (1R01GM162977-01). Retrieved via AI Analytics 2026-07-04 from https://api.ai-analytics.org/grant/nih/11280100. Licensed CC0.

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