Rosetta Omics provides an AI-enabled multiomics platform integrating spatial omics, proteomics, and gene expression analysis for cancer precision medicine. The platform processes single tissue slides to deliver accurate diagnosis, patient stratification, and personalized first-line treatment guidance for clinicians. This solution also supports biopharmaceutical companies by enabling biomarker discovery and identifying novel therapeutic targets through label-free mass spectrometry.
Funding
$30K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Current cancer treatment selection often relies on non-quantitative methods for protein expression, limiting the ability to precisely stratify patients and predict treatment response. This can lead to suboptimal first-line therapy choices, impacting patient outcomes and increasing healthcare costs due to ineffective treatments.
Solution
Rosetta Omics provides a multi-omics platform that integrates with clinical workflows to offer comprehensive tumor characterization. Utilizing high-resolution mass spectrometry and AI, the platform quantifies thousands of proteins and analytes from a single tissue slide, offering a more detailed molecular profile than traditional methods. This data enables oncologists, clinicians, and pathologists to better diagnose, stratify patients, and rank treatment options. The AI-driven analysis also identifies potential biomarkers for drug development and predicts patient response to therapies, facilitating precision medicine.
Target Audience
The primary customers are oncologists, clinicians, and pathologists seeking to improve cancer diagnosis and treatment selection. The platform also serves biopharmaceutical companies looking to identify new therapeutic targets and biomarkers.
Features
- High-resolution mass spectrometry imaging (MALDI-MSI) for spatial omics pre-screening of proteins and metabolites.
- Laser Capture Microdissection (LMD) for precise selection of tissue areas for molecular analysis.
- Liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) for high-resolution peptide identification and label-free quantification.
- AI-powered algorithms for comprehensive analysis of tumor tissues and patient profiles, including therapeutic option rankings.
- Quantitative identification and quantification of thousands of proteins and analytes (metabolites, lipids, glycans) from small biopsy samples.
- Machine learning models to predict treatment response based on molecular profiles.
- Identification of known and novel biomarkers for diagnostic validation and therapeutic strategy development.
- Patented methodology for analyte study on common conductive slides while preserving protein integrity.