探索基于表面增强拉曼散射的生物感测技术在疾病诊断和治疗中的应用

Overview

This protocol presents a label-free analytical platform that integrates surface-enhanced Raman spectroscopy (SERS) with machine learning to detect and molecularly profile individual small extracellular vesicles (sEVs). The method enables high-specificity detection and classification of disease states, such as distinguishing gastric cancer from healthy controls, and quantifies drug loading in single vesicles, supporting both diagnostic and therapeutic applications.

Key Study Components

Area of Science

• Analytical chemistry
• Biomedical diagnostics
• Nanotechnology
• Machine learning applications in biosensing

Background

• Small extracellular vesicles (sEVs) are promising biomarkers for disease diagnosis and therapeutic monitoring.
• Traditional detection methods often require labeling or lysis, which can limit sensitivity and throughput.
• SERS provides molecular fingerprinting capabilities with high sensitivity.
• Machine learning enhances the analysis of complex, high-dimensional spectral data.

Purpose of Study

• To develop a label-free, high-throughput platform for single-vesicle analysis using SERS and machine learning.
• To distinguish between healthy and disease states (e.g., gastric cancer) using sEVs from various biofluids.
• To quantify drug (doxorubicin) loading in individual sEVs for therapeutic assessment.

Methods Used

• Isolation of sEVs via size exclusion chromatography or ultracentrifugation.
• Deposition of sEVs onto engineered plasmonic gold nanopyramid 2D array substrates.
• SERS data acquisition using a confocal Raman microscope with specific laser settings and mapping protocols.
• Preprocessing of spectra (noise filtering, normalization, signal-to-noise thresholding).
• Machine learning analysis using support vector classifiers (SVC) and linear discriminant analysis (LDA) for classification and visualization.
• Use of graphene-coated substrates as internal standards for drug quantification.

Main Results

• Distinct molecular signatures were observed for sEVs from tissue, blood, and saliva.
• Classification accuracies for distinguishing gastric cancer from healthy controls were 90.1% (tissue), 70.9% (blood), and 60.7% (saliva).
• LDA revealed clear clustering of sEVs by source and disease state, especially for tissue-derived samples.
• Quantification of doxorubicin loading in single sEVs was achieved, with graphene peaks serving as internal standards.

Conclusions

• The platform enables sensitive, non-invasive, and label-free detection of sEVs at the single-vesicle level.
• It allows for high-throughput analysis and captures population heterogeneity, aiding early disease detection.
• The method supports both diagnostic and therapeutic applications, including drug loading assessment.

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