# Computational methods for studying single-cell 3D genome

> **NIH NIH R01** · CARNEGIE-MELLON UNIVERSITY · 2024 · $532,931

## Abstract

PROJECT SUMMARY
The three-dimensional (3D) genome organization in the nucleus is of vital importance to genome function. The
vast majority of the existing 3D genome studies, however, are based on population-based assays that are
unable to unveil the functional roles of 3D genome structures at single-cell resolution in complex tissues.
Recent advent in single-cell Hi-C (scHi-C) technologies has enabled genomic mapping of chromatin
interactions in individual cells, but the analysis of scHi-C data remains a significant challenge. In particular,
computational methods that can effectively analyze scHi-C data to extract multiscale 3D genome features are
significantly lacking, limiting our ability to reveal the variability of structure and function connections in
heterogeneous cell populations. The overall objective of this proposal is to develop state-of-the-art
computational tools for scHi-C data analysis that effectively identify multiscale single-cell 3D genome features
and connect them to genome function. Specifically, we will (1) develop algorithms for scHi-C data processing
and imputation to delineate multiscale 3D genome features; (2) develop computational methods to connect 3D
genome structure and function in heterogeneous cell population; and (3) develop an integrative visualization
platform to navigate single-cell 3D genome organization. The methods developed in this project can be applied
to all types of scHi-C data generated by different single-cell chromatin interaction assays to reveal 3D genome
features at multiple scales, quantifying their variability and predicting their functional outcomes. The new tools
and resources from this project will be publicly accessible through our new visualization platform that provides
integrative and interactive navigation of scHi-C data and other data types. Overall, our project will greatly
facilitate the use of scHi-C data by the broad scientific community and be of high value to a diverse group of
biomedical researchers.

## Key facts

- **NIH application ID:** 10771267
- **Project number:** 5R01HG012303-03
- **Recipient organization:** CARNEGIE-MELLON UNIVERSITY
- **Principal Investigator:** Zhijun Duan
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $532,931
- **Award type:** 5
- **Project period:** 2022-02-11 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10771267

## Citation

> US National Institutes of Health, RePORTER application 10771267, Computational methods for studying single-cell 3D genome (5R01HG012303-03). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10771267. Licensed CC0.

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