Developing advanced tools for analyzing single-cell genetic experiments
Official title: Robust and scalable Bayesian analysis tools for single cell CRISPR screens with sequencing- and imaging-based readouts
Funded at Harvard Medical School · NIH-10891780 · Data updated 2026-11-12
This study is working on new tools to help scientists better understand how genetic changes affect cell behavior, which could lead to improved treatments for conditions like Amyotrophic Lateral Sclerosis.
Quick facts
| Grant type | Fellowship grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Harvard Medical School NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project start | July 1, 2024 |
| Project end | June 30, 2027 |
| Funding year | 2024 |
| Award (this year) | $43,203 |
| NIH project number | 1F31HG013609-01 |
| Project ID | NIH-10891780 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals affected by genetic disorders, particularly those with Amyotrophic Lateral Sclerosis.
✕Less likely to help
Patients with non-genetic conditions or those not affected by diseases related to genetic perturbations may not benefit from this research.
What this research studies
This research focuses on improving the analysis of single-cell CRISPR screens, which allow scientists to study how genetic changes affect cell behavior at an unprecedented level of detail. The project will create two innovative tools that utilize Bayesian hierarchical models to efficiently analyze large datasets generated from these experiments, including both sequencing and imaging data. By enhancing the computational methods used to interpret complex genetic information, this research aims to provide clearer insights into cellular functions and responses. Patients may benefit from the findings as they could lead to better understanding and treatment options for conditions like Amyotrophic Lateral Sclerosis.
Why it matters
Potential benefit: If successful, this research could lead to more effective treatments for genetic diseases by providing deeper insights into cellular mechanisms.
How similar studies have gone: Previous research has shown promise in using Bayesian methods for complex data analysis, indicating potential success for this novel approach.
Where this research is happening
-
Harvard Medical School
Boston, United States Active
Researchers
- Principal investigator: Blaine, Logan — Harvard Medical School
- Study coordinator: Blaine, Logan
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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.