He Qing, Koster Hanna J, O'Sullivan Justin, Ono Samantha G, O'Toole Hannah J, Leiserowitz Gary S, Heffern Marie C, Carney Randy P
Department of Biomedical Engineering, University of California, Davis, USA.
Department of Chemistry, University of California, Davis, USA.
Biosens Bioelectron. 2025 Nov 15;288:117800. doi: 10.1016/j.bios.2025.117800. Epub 2025 Jul 18.
We report a proof-of-concept diagnostic strategy that integrates multiplexed Raman-tagged antibody labeling with label-free surface-enhanced Raman spectroscopy (SERS) and machine learning (ML) to improve the detection of ovarian cancer via extracellular vesicles (EVs). EVs were isolated from patient plasma using size-exclusion chromatography and labeled with polyyne-based Raman tags targeting three ovarian cancer biomarkers: CA-125, HE4, and CA-19-9. Labeled and unlabeled EVs were deposited onto SERS-active substrates, and spectra were collected using a custom confocal Raman microscope. Incorporating the tag-derived signal into SERS analysis enhanced interpretability and added molecular specificity. We evaluated classification performance using various ML models applied to spectral datasets from a cohort of ovarian cancer patients and healthy controls. Combined use of the Raman tag and label-free regions improved classification accuracy compared to either modality alone. Notably, support vector machine (SVM) achieved over 95 % accuracy, sensitivity, and specificity. Compared to ELISA, our SERS platform demonstrated improved sensitivity in detecting EV-associated biomarkers from small sample volumes. This approach addresses a key limitation of SERS-based diagnostics by linking spectral features to known biomarkers, offering improved transparency and performance in ML-enabled liquid biopsy.
我们报告了一种概念验证诊断策略，该策略将多重拉曼标记抗体标记与无标记表面增强拉曼光谱（SERS）和机器学习（ML）相结合，以通过细胞外囊泡（EVs）改善卵巢癌的检测。使用尺寸排阻色谱法从患者血浆中分离出EVs，并用靶向三种卵巢癌生物标志物CA-125、HE4和CA-19-9的基于聚炔的拉曼标签进行标记。将标记和未标记的EVs沉积在SERS活性底物上，并使用定制的共聚焦拉曼显微镜收集光谱。将标签衍生信号纳入SERS分析可增强可解释性并增加分子特异性。我们使用应用于一组卵巢癌患者和健康对照的光谱数据集的各种ML模型评估分类性能。与单独使用任何一种模式相比，拉曼标签和无标记区域的联合使用提高了分类准确性。值得注意的是，支持向量机（SVM）的准确率、灵敏度和特异性均超过95%。与酶联免疫吸附测定（ELISA）相比，我们的SERS平台在从小样本量中检测EV相关生物标志物方面表现出更高的灵敏度。这种方法通过将光谱特征与已知生物标志物联系起来，解决了基于SERS的诊断的一个关键限制，在基于ML的液体活检中提供了更高的透明度和性能。
