Developing new models to analyze tissue structure and gene expression in cells
Official title: Novel geometric deep learning models for tissue structure-aware spatial expression representations from spatially resolved single-cell transcriptomics data
Funded at University of Pittsburgh at Pittsburgh · NIH-10952364 · Data updated 2026-11-12
This study is looking at how genes behave in different parts of lung tissue, especially in relation to diseases like COVID-19, to help scientists understand how cells work together and how that might change when someone is sick.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pittsburgh at Pittsburgh NIH-funded |
| Lab location | 1 site (Pittsburgh, United States) |
| Project start | September 1, 2024 |
| Project end | August 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $198,750 |
| NIH project number | 1R21GM155774-01 |
| Project ID | NIH-10952364 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include patients with COVID-19 or other lung-related conditions who are undergoing tissue analysis.
✕Less likely to help
Patients with conditions unrelated to lung tissue or those not requiring tissue analysis may not benefit from this research.
What this research studies
This research focuses on advancing techniques to analyze single-cell spatially resolved transcriptomics (scSRT), which allows scientists to map the locations of gene expressions within tissues. By utilizing innovative computational models, the project aims to identify tissue structures and associated pathologies, particularly in the context of diseases like COVID-19. The approach involves analyzing lung tissue samples to better understand how cells interact within their microenvironment and how these interactions may change in disease states. This could lead to new insights into previously uncharacterized diseases and improve our understanding of tissue heterogeneity.
Why it matters
Potential benefit: If successful, this research could enhance the diagnosis and treatment of diseases by providing deeper insights into cellular behavior and tissue pathology.
How similar studies have gone: Previous research has shown promise in using similar computational approaches to analyze tissue structures, indicating potential for success in this novel application.
Where this research is happening
-
University of Pittsburgh at Pittsburgh
Pittsburgh, United States Active
Researchers
- Principal investigator: Huang, Yufei — University of Pittsburgh at Pittsburgh
- Study coordinator: Huang, Yufei
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.