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

/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 genome structures to reproduce the input experimental data. We will dedicate significant effort to improve user experience and enhance computational efficiency. Project Number: 1R01GM162977-01 | Fiscal Year: 2026 | NIH Institute/Center: National Institute of General Medical Sciences (NIGMS) | Principal Investigator: Frank Alber | Institution: UNIVERSITY OF CALIFORNIA LOS ANGELES, LOS ANGELES, CA | Award Amount: $363,570 | Activity Code: R01 | Study Section: Biodata Management and Analysis Study Section[BDMA] View on NIH RePORTER: https://reporter.nih.gov/project-details/11280100