Raman Spectroscopy of Extracellular Vesicles: Toward Clinical Photodiagnosis

Raman spectroscopy provides specific chemical information about the proteins, lipids, nucleic acids, and metabolites associated with extracellular vesicles (EVs), making it a promising approach for non-invasive disease diagnosis. This review evaluates the applications of conventional Raman spectroscopy (RS), Raman Tweezers Spectroscopy (RTS), and Surface-Enhanced Raman Spectroscopy (SERS) in EV profiling. Diagnostic applications across major malignancies, including breast, lung, ovarian, osteosarcoma, oral, gastric, liver, pancreatic, and prostate cancers, are examined alongside emerging roles in neurodegenerative, cardiovascular, and haematological disorders. Direct SERS configurations measuring inherent vibrational signatures of EVs are distinguished from indirect setups utilizing functionalized nanotags, extrinsic Raman reporters, and biorecognition elements for targeted detection. Furthermore, the review considers microfluidic platforms for automated EV isolation and the application of artificial intelligence (AI) and machine learning (ML) architectures for automated spectral classification. Key translational bottlenecks are critically evaluated. The co-isolation of non-vesicular proteins and lipoproteins during ultracentrifugation compromises spectral reproducibility, while variations in acquisition parameters, preprocessing workflows, and isolation protocols limit inter-study comparisons. Overcoming these challenges will require standardized microfluidic purification, adherence to Minimal Information for Studies of Extracellular Vesicles (MISEV) reporting frameworks, and large prospective multicentre trials to validate clinical efficacy. Overall, this review provides a practical roadmap for developing Raman-based extracellular vesicle liquid biopsies for clinical translation.