# Multiplexed imaging of chromatin folding and RNA profiles in cancer

> **NIH NIH R33** · YALE UNIVERSITY · 2020 · $1,237,899

## Abstract

Project Summary/Abstract
In the cell nucleus, the three-dimensional (3D) folding of the genome regulates many genomic functions,
ranging from gene expression regulation to DNA replication, recombination, and repair. 3D genomic structures
exist at a variety of length scales, and changes in genome structures are known to be associated with cancer,
but a true physical picture of genome folding in cancer cells within their heterogeneous tumor
microenvironment is still elusive. It is also unknown how variations of the 3D genome organization may affect
single-cell gene expression in tumors. Existing sequencing-based omics approaches cannot address these
questions due to technical limitations. Here we propose the advanced development and validation of a new
imaging method that can directly trace the spatial folding of the genome across multiple length scales and
image numerous RNA species with single-molecule resolution in the same single cells in heterogeneous
tumors. We will test this method with mouse tumor models that enable lineage tracing of cancer clones during
progression and with clinical samples from human patients. We expect this technologic advance to transform
3D genome investigation in cancer biology and lead to new biomarkers for cancer diagnosis, prognosis, and
treatment.

## Key facts

- **NIH application ID:** 10025857
- **Project number:** 1R33CA251037-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Mandar Deepak Muzumdar
- **Activity code:** R33 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,237,899
- **Award type:** 1
- **Project period:** 2020-08-01 → 2024-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10025857

## Citation

> US National Institutes of Health, RePORTER application 10025857, Multiplexed imaging of chromatin folding and RNA profiles in cancer (1R33CA251037-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10025857. Licensed CC0.

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