由於胞外體(extracellular vesicles, EVs)攜帶豐富的生物訊息如蛋白質與核醣核酸(mRNA, microRNA, ncRNA)等，不只負責在細胞間溝通，在醫療診斷上也扮演重要角色，也因為如此，胞外體近年來在學界備受重視，然而，為了要明確定義胞外體所代表的醫療訊息，標準化的分離方法勢必要被建立，目前最多人採用的分離方式為超高速離心法(ultracentrifugation)，但在高轉速下會導致胞外體與蛋白質聚積甚至破裂，破壞原有的型態，相較之下，粒徑層析法(size exclusion chromatography, SEC)不但可以保有胞外體原本的特性，也可以在更短的時間內完成。
粒徑層析法是依據生物分子的大小來做分離，為了要讓粒徑層析法擁有成功的結果，必須要有適合的實驗設計，本篇文章提出了基於優化柱長、樣品體積、流動相黏滯度與固定相粒徑大小，以減少粒徑層析法中不理想的結果，藉由調整這些參數，可以讓我們了解對於不同大小的分子所需的洗出時間，並得到更高解析度、準確度與回收率的結果，理論上來說，所施加的樣品體積越小就能夠得到更好的解析度、固定相凝膠體的顆粒越小分離效能也會越高，然而樣品的狀態與固定相堆疊的方式，都會造成影響，使結果更難預測。實驗結果顯示，在優化柱長(Figure 21、Figure 22)、樣品體積(Figure 23、Figure 24)、移動相黏滯度(Figure 25-Figure 28)與縮小固定相粒子後(Figure 31-Figure 35)，回收率與準確度都有明顯提升，最後，我們應用優化過後的參數，成功的將血漿中的胞外體與蛋白質分離(Figure 36-Figure 39)。

Extracellular vesicles (EVs) not only facilitate intercellular communication but also play an important role in the medical diagnosis. As a result, EV research has become an attractive field among the scientific community. However, to characterize the biomedical message carried by EVs, we must establish the standardization of methods employed for the isolation of vesicles. Ultracentrifugation has been the most widely used method, but the protein aggregates may be generated at high ve-locities, and vesicles may clump and rupture, ruining their original morphology. In contrast, the size exclusion chromatography (SEC) retains the original characteris-tics of EVs as well as can be completed in a shorter period of time.
SEC is a size-based separation of biological molecules. For this technique to be successful, a suitable experimental design must be implemented. In order to reduce undesirable results in SEC, this study proposes an optimization on the column length, sample volume, viscosity of the mobile phase, and the size of stationary phase parti-cles. By adjusting these parameters, we have attained a more precise elution time for different sized molecules, improved the resolution, accuracy, and recovery rate. In general, the sample volume should be kept to a minimum for the optimal resolution in SEC; also the smaller the particle size, a higher separation efficiency will be. However, the results are difficult to be predicted, since the sample status and column packing also plays a major role in the process. The experimental results show that the recovery rate or accuracy has been improved by changing the column length, sample volume (Figure 23 and Figure 24), mobile phase viscosity (Figure 25-Figure 28) and size of stationary phase particles (Figure 31-Figure 35). Lastly, by using the optimized SEC setting, we successfully separate the EVs from proteins in human plasma (Figure 36-Figure 39).

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