Department of Mechanical Engineering, Chung Yuan Christian University, Chung Li District, Taoyuan City 320314, Taiwan.
Biosensors (Basel). 2024 Jul 27;14(8):366. doi: 10.3390/bios14080366.
This study presents and compares two methods for identifying the types of extracellular vesicles (EVs) from different cell lines. Through SDS-PAGE analysis, we discovered that the ratio of CD63 to CD81 in different EVs is consistent and distinct, making it a reliable characteristic for recognizing EVs secreted by cancer cells. However, the electrophoresis and imaging processes may introduce errors in the concentration values, especially at lower concentrations, rendering this method potentially less effective. An alternative approach involves the use of quartz crystal microbalance (QCM) and electroanalytical interdigitated electrode (IDT) biosensors for EV type identification and quantification. The QCM frequency shift caused by EVs is directly proportional to their concentration, while electroanalysis relies on measuring the curvature of the I-V curve as a distinguishing feature, which is also proportional to EV concentration. Linear regression lines for the QCM frequency shift and the electroanalysis curvature of various EV types are plotted separately, enabling the estimation of the corresponding concentration for an unknown EV type on the graphs. By intersecting the results from both biosensors, the unknown EV type can be identified. The biosensor analysis method proves to be an effective means of analyzing both the type and concentration of EVs from different cell lines.
本研究提出并比较了两种从不同细胞系中鉴定细胞外囊泡（EVs）类型的方法。通过 SDS-PAGE 分析，我们发现不同 EVs 中 CD63 与 CD81 的比例一致且具有明显差异，这使其成为识别癌细胞分泌的 EVs 的可靠特征。然而，电泳和成像过程可能会导致浓度值出现误差，尤其是在较低浓度下，这使得该方法的效果可能会降低。另一种方法是使用石英晶体微天平（QCM）和电化学生物传感器（IDT）来识别和定量 EV 类型。EV 引起的 QCM 频率偏移与它们的浓度成正比，而电化学生物传感器则依赖于测量 I-V 曲线的曲率作为特征，这也与 EV 浓度成正比。各种 EV 类型的 QCM 频率偏移和电化学生物传感器曲率的线性回归线分别绘制，使得可以在图谱上估算未知 EV 类型的相应浓度。通过将两个生物传感器的结果相交，可以确定未知的 EV 类型。生物传感器分析方法被证明是一种有效分析不同细胞系来源的 EV 类型和浓度的方法。
