All living cells release extracellular vesicles (EVs): small, membrane-bound particles that transport molecular cargo from inside the cell into the bloodstream. Unlike many circulating markers that reflect cell damage or systemic background, EVs are actively secreted by viable cells and carry intact proteins, RNAs, lipids, and metabolites that capture ongoing cellular functions such as metabolism, stress adaptation, and intercellular signaling.
Importantly, every organ continuously sheds its own population of EVs into blood, creating organ-specific molecular fingerprints that reflect real-time biological activity.
This naturally occurring process provides a powerful, non-invasive window into tissue-specific biology, accessible through a simple blood draw. Instead of relying on systemic biomarkers or invasive tissue biopsies, organ-derived EVs offer direct insight into the molecular state of internal organs like the liver, lung, brain, or heart.
Accessing these intact, multiomic signals has the potential to transform how we detect early disease, monitor therapeutic response, and map dynamic disease progression over time.
For precision medicine, this represents a highly enriched biological signal stream uniquely suited for modern data-driven approaches, including biomarker discovery, longitudinal patient monitoring, and AI-powered molecular profiling — all derived from a simple blood sample.
At the core of Mursla Bio’s platform is a first-in-class ability to isolate extracellular vesicles (EVs) secreted by specific organs directly from blood, generating biologically labeled, tissue-specific molecular input for precision medicine. This uniquely enriched signal enables high-fidelity multiomics from minimal plasma volumes, overcoming the biological noise that limits conventional bulk approaches.
Machine learning is applied throughout proteomics and small RNA sequencing to generate integrated multiomic profiles, supporting robust biomarker discovery even in smaller patient cohorts. Embedded pipelines optimize signal extraction, feature engineering, and multi-layer integration with clinical data to identify biologically meaningful signatures.
Validated biomarkers are then translated into scalable, cost-efficient in vitro diagnostic (IVD) assays using targeted detection technologies such as qPCR and plate-based formats suitable for clinical deployment.
The platform has already enabled the development of EvoLiver™, a blood-based test for early liver cancer detection, which recently received the first FDA Breakthrough Device Designation in that indication in over five years. The same architecture is now being expanded across multiple organ systems to support early detection, therapeutic monitoring, and longitudinal disease interception.
