Department of Physics, Bogazici University, Istanbul, 34342, Turkey.
Department of Radiology Stanford School of Medicine, BioAcoustic MEMS in Medicine Lab (BAMM), Canary Center at Stanford for Cancer Early Detection, Palo Alto, CA, 94304, USA.
Small. 2023 Mar;19(9):e2205519. doi: 10.1002/smll.202205519. Epub 2023 Jan 15.
Exosomes, nano-sized extracellular vesicles (EVs) secreted from cells, carry various cargo molecules reflecting their cells of origin. As EV content, structure, and size are highly heterogeneous, their classification via cargo molecules by determining their origin is challenging. Here, a method is presented combining surface-enhanced Raman spectroscopy (SERS) with machine learning algorithms to employ the classification of EVs derived from five different cell lines to reveal their cellular origins. Using an artificial neural network algorithm, it is shown that the label-free Raman spectroscopy method's prediction ratio correlates with the ratio of HT-1080 exosomes in the mixture. This machine learning-assisted SERS method enables a new direction through label-free investigation of EV preparations by differentiating cancer cell-derived exosomes from those of healthy. This approach will potentially open up new avenues of research for early detection and monitoring of various diseases, including cancer.
外泌体是细胞分泌的纳米级细胞外囊泡 (EV)，携带反映其起源细胞的各种 cargo 分子。由于 EV 的内容物、结构和大小高度异质，通过确定其起源来通过 cargo 分子对其进行分类具有挑战性。在这里，提出了一种结合表面增强拉曼光谱 (SERS) 和机器学习算法的方法，以利用源自五种不同细胞系的 EV 的分类来揭示它们的细胞起源。使用人工神经网络算法，证明无标记拉曼光谱法的预测比例与混合物中 HT-1080 外泌体的比例相关。这种机器学习辅助的 SERS 方法通过区分癌细胞衍生的外泌体和健康细胞衍生的外泌体，为通过无标记研究 EV 制剂来区分癌症提供了新的方向。这种方法有可能为包括癌症在内的各种疾病的早期检测和监测开辟新的研究途径。
