# NIH grant: Developing integrative tools to link nuclear structure to gene regulation in situ (1R35GM165492-01) — retained record

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[original source ↗](https://reporter.nih.gov/project-details/1R35GM165492-01)

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NIH grant 1R35GM165492-01: Developing integrative tools to link nuclear structure to gene regulation in situ
Institute: {'code': 'GM', 'abbreviation': 'NIGMS', 'name': 'National Institute of General Medical Sciences', 'admin_org_id': '12500024', 'admin_funding_url': ''}. PI: Kadir  Akdemir. Award: $451000.
Start: 2026-08-07T00:00:00.
Project Summary
Scientific progress in biology and medicine has repeatedly been driven by the development of transformative
technologies. The invention of the microscope first revealed the cellular basis of life, while single-cell
sequencing uncovered the transcriptional diversity of cell types. Building on these advances, image-based
spatial single-cell transcriptomics now enables simultaneous measurement of high-resolution morphology and
gene expression directly within intact tissues. However, the computational frameworks needed to integrate
nuclear architecture, genomic instability, and transcriptional programs at scale remain underdeveloped. This
research program seeks to create novel computational tools and proof-of-concept datasets that leverage the
imaging component of spatial transcriptomics platforms. Nuclear morphology features such as micronuclei,
ploidy variation, and chromatin organization will be systematically measured and linked with matched
transcriptomic and genomic data. By integrating multiple modalities—including DNA FISH, spatial
transcriptomics, and nuclear segmentation data—we will uncover how genome instability and chromatin
topology shape gene regulation in situ. These efforts will establish generalizable frameworks for (i) detecting
nuclear anomalies and correlating them with transcriptional states, (ii) aligning multimodal spatial data to
connect nuclear architecture with gene expression, and (iii) developing interactive visualization tools for multi-
layered datasets. By bridging nuclear morphology with transcriptional and genomic profiles, this project will
advance fundamental understanding of how genome instability manifests within tissues and create
computational resources broadly applicable to cancer biology, developmental biology, neuroscience, and
environmental health research.
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