Creating tools to predict gene expression from tissue images
Official title: Developing informatics tools to predict virtual spatial transcriptomics data with single-cell resolution in large-scale studies
Funded at University of Pennsylvania · NIH-10942156 · Data updated 2026-11-12
This study is working on new tools that use regular tissue images to predict how genes behave, which could help researchers understand more about diseases and improve patient care without the high costs of current methods.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Pennsylvania NIH-funded |
| Lab location | 1 site (Philadelphia, United States) |
| Project start | August 27, 2024 |
| Project end | July 31, 2028 |
| Funding year | 2024 |
| Award (this year) | $362,813 |
| NIH project number | 1R01LM014592-01 |
| Project ID | NIH-10942156 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals participating in biobanks or large-scale studies where tissue samples are available for analysis.
✕Less likely to help
Patients who do not have tissue samples available or those not involved in large-scale studies may not benefit from this research.
What this research studies
This research aims to develop innovative informatics tools that can predict gene expression patterns using low-cost histology images instead of expensive spatial transcriptomics platforms. By leveraging existing correlations between histological features and gene expression, the project seeks to generate virtual spatial transcriptomics data efficiently and affordably. This approach will enable large-scale studies involving numerous samples, allowing researchers to explore the relationship between gene expression and clinical outcomes. Patients may benefit from insights gained through this research, particularly in understanding how gene expression variations relate to their conditions.
Why it matters
Potential benefit: If successful, this research could significantly reduce the costs and time associated with gene expression analysis, making it more accessible for large-scale studies.
How similar studies have gone: Previous studies have shown promising results in correlating histological features with gene expression, indicating that this approach has potential for success.
Where this research is happening
-
University of Pennsylvania
Philadelphia, United States Active
Researchers
- Principal investigator: Li, Mingyao — University of Pennsylvania
- Study coordinator: Li, Mingyao
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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.