Project Details
Abstract
Patient-specific disease modeling is crucial in biomedical research due to high failure rates in drug discovery and clinical trials. This proposal aims to enhance disease modeling by developing a scalable platform using MUSE cells, an efficient alternative to iPSCs. Discovered by Mari Dezawa in 2010, MUSE cells offer advantages over iPSCs, including no need for genetic reprogramming, reducing cost and time. They can differentiate into endodermal, ectodermal, and mesodermal lineages, making them ideal for in vitro disease modeling. Phase one focuses on optimizing MUSE cell isolation from peripheral blood using a microfluidic-based technique. This device will selectively capture MUSE cells, enhancing purity and viability through size-based separation and immunocytochemical confirmation. Phase two will compare MUSE cells with iPSCs in differentiation efficiency, cost-effectiveness, and scalability. This evaluation will determine their suitability for patient-specific disease modeling, therapeutic screening, and drug development. The long-term goal is to provide the biopharmaceutical industry with a scalable, cost-effective disease modeling platform, accelerating drug development through personalized therapeutic testing. This project aims to revolutionize disease modeling by offering a faster, more efficient alternative to traditional iPSC-based approaches.
Submitting Institute Name
Hamad Bin Khalifa University (HBKU)
| Sponsor's Award Number | RTBC01-0219-250013 |
|---|---|
| Proposal ID | EX-QNRF-RTBC-5 |
| Status | Active |
| Effective start/end date | 1/11/25 → 1/11/26 |
Collaborative partners
- Hamad Bin Khalifa University (lead)
- Hamad Bin Khalifa University (HBKU)
Primary Theme
- Precision Health
Primary Subtheme
- PH - Diagnosis Treatment
Secondary Theme
- Others
Secondary Subtheme
- Biopharma ethics regulation and management, and resource centralization
Keywords
- MUSE Cells
- Microfluidics-Based Cell Isolation
- Regenerative Medicine
Fingerprint
Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.