AI-Enabled MultiOmics Cancer Precision Medicine Platform

Mass spectrometry imaging (MSI), through matrix-assisted laser desorption ionization (MALDI-MSI), is a fast, label-free spatial omics approach that enables molecular pre-screening directly in tissue. By mapping the spatial distribution of lipids, metabolites, and proteins, it provides an integrative molecular snapshot that supports multi-omics discovery and context-aware interpretation of biological systems.

Laser microdissection (LMD) is a spatial omics technique that enables the targeted selection of specific tissue regions or cell populations. By isolating defined microscopic areas with high precision, LMD preserves spatial information while providing material for downstream molecular identification across lipids, metabolites, proteins, or nucleic acids.

Liquid chromatography–tandem mass spectrometry (LC-MS/MS) is a high-resolution analytical platform that enables deep peptide and protein identification with label-free quantification. By combining chromatographic separation with sensitive mass spectrometric detection, LC-MS/MS provides robust molecular profiling that supports comprehensive proteomic analysis.

Tumor gene expression analysis using qPCR provides a sensitive and robust approach for profiling transcriptional activity in cancer tissues. By quantifying gene expression and normalizing results against a reference bank of healthy tissues, this method enables the identification of tumor-specific molecular signatures, supporting biomarker discovery, validation, and translational research.

Our solution enables the exploitation of clinical samples in an optimal and specific way by identifying and quantifying several thousands of proteins following the spatial molecular profiling of the tumor micro-environment using multiple analytes such as proteins, metabolites or lipids, from a small amount of biopsy-compatible material. Our multidimensional Multiomics data input from the tumor and non-tumor tissues allows us to define the associated molecular pathophysiology of the patient's cancer.

Rosetta Omics harnesses advanced multiomics technologies integrating genomics and proteomics with AI-driven algorithms to deliver deeper molecular insights for cancer precision medicine. By analyzing differential expression across the transcriptome and proteome, our platform more accurately predicts treatment response, empowering clinicians to deliver the right therapy to the right patient at the right time.

With our label-free mass spectrometry approach, free from the limitations of antibody specificity, we can identify both known and novel biomarkers of clinical relevance. This enables the discovery of therapeutic targets, the development of new treatment strategies and combinations, and improved tumor classification and patient stratification for biopharmaceutical companies.