Faster protein testing of blood and tissue to help detect and understand cancer
A Real-Time AI-Driven High-Throughput Proteomics Data Acquisition Method for Clinical Applications
This project is building an AI-guided lab method to measure thousands of proteins quickly in blood and tissue to help cancer patients and their doctors get richer protein information sooner.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Massachusetts General Hospital NIH-funded |
| Lab location | 1 site (Boston, United States) |
| Project ID | NIH-11263677 on NIH RePORTER |
What this research studies
If you have cancer or a suspected tumor, this work aims to speed up lab tests that read proteins in blood and tissue using mass spectrometry combined with AI-guided data collection and barcoded sample tagging. The team plans to multiplex samples so many can be run together and use AI to drive how the machine collects data, targeting deep protein coverage (over 2,000 proteins from plasma and over 8,000 from tissue). The goal is to cut the time per sample down to about 10 minutes and raise throughput up to tenfold so proteomic testing could be practical for larger patient groups. Faster, lower-cost proteomics could make protein-based insights more available in clinical settings over time.
Who could benefit from this research
Good fit: Ideal candidates would be people with cancer or those undergoing diagnostic biopsies or blood draws who can provide blood or tissue samples for lab analysis.
Not a fit: People without relevant blood or tissue samples, those not being evaluated for cancer, or those seeking an immediate change in treatment should not expect direct personal benefit from this technology development phase.
Why it matters
Potential benefit: If successful, this could make detailed protein testing faster and cheaper so protein-based diagnostics and treatment guidance become more available to patients.
How similar studies have performed: Mass spectrometry and isobaric multiplexing have been used in research, but combining AI-driven acquisition to achieve clinical-scale, tenfold faster deep proteome mapping is largely novel.
Where this research is happening
Boston, United States
- Massachusetts General Hospital — Boston, United States (Active)
Researchers
- Principal investigator: Haas, Wilhelm — Massachusetts General Hospital
- Study coordinator: Haas, Wilhelm
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 below.