整合氣液微流道與表面增強拉曼光譜基板進行吸附性代謝物分離之細菌鑑別

Abstract

表面增強拉曼光譜(SERS)在這近50年間常被應用於細菌鑑別的檢測，這項技術在這個領域提供了各種生物分子組成資訊，像是腺嘌呤、次黃嘌呤、脲嘧啶與其他嘌呤的代謝產物，但相同或是類似結構的分子光譜會重疊在一起或者被親和力較大的分子所覆蓋，所以我們提供了一種吸附性分離的晶片，結合了表面增強拉曼光譜的量測技術與氣液微流道分離複雜溶液中的多重分子，當使用壓力幫浦提供穩定的脈衝流速的同時，液珠會流進微流道中的液珠停留區中等待特定的時間和表面增強拉曼光譜基板相互作用進行吸附性分離，由於親和力不同，含有多個分子的溶液可以依序被分離到不同的井中並使用表面增強拉曼光譜檢測。在初步結果中，我們設計了不同的流道寬度設計，發現0.4 mm的流道寬度設計能減少溶液殘留，也優化3分鐘的反應時間以及300 μm的流道寬度能觀察10-5到10-6 M濃度的腺嘌呤衰減曲線。將腺嘌呤以及脲嘧啶做不同比例混和之後，可觀察到脲嘧啶的訊號在一開始被腺嘌呤的訊號所屏蔽，但在使用微流道系統之後，脲嘧啶的訊號會被顯現出來，驗證了吸附性分離的能力。在主成分分析之中，與傳統方法做相比，不同細菌上清液可以被清晰的分群，展現它細菌鑑別的能力。因此這個微流道平台可用於鑑別不同菌種的細菌，提供對含有多重分析物溶液的見解，突破了傳統表面增強拉曼光譜檢測的限制。

Surface-enhanced Raman Spectroscopy (SERS) has been used to detect and recognize bacteria for five decades. This technique supplies information about the components of different biomolecules, such as adenine, hypoxanthine, uracil, and other purine derivatives. However, the SERS signals of compounds with similar structures would overlap together or be surpassed by the one with strong affinity. We proposed an adsorptive separation method that combines the SERS technique with air-liquid microfluidics to separate multiple molecules in complex solutions. Adsorptive separation occurred when the droplet flowed into different wells in the air-liquid microfluidics, while the pressure pump supplied the stable pressure and impulse wave of flow rate. Thus, the solution containing multiple molecules could sequentially be separated into different wells due to the different affinity. In the results, we designed channels with different widths and found that a channel width of 0.4 mm can save the volume loss. We also discovered that a reaction time of 3 minutes and a channel width of 300 μm allowed us to observe the attenuation curve of adenine concentrations from 10-5 to 10-6 M. After mixing adenine and uracil in different proportions, we observed that the signal of uracil was initially masked by the signal of adenine. However, using our microfluidic system, the signal of uracil could be revealed, confirming the capability of adsorptive separation. In the principal component analysis (PCA), different kinds of bacterial supernatants could be clustered clearly compared to the traditional method, showing the ability of bacteria identification. Therefore, this microfluidic platform helps us to distinguish the different species of bacteria, and it also can provide insights into the multiplex analytes contained solution, breaking the limitation of traditional SERS detection.