開發萃取與分離技術用於植物組織、微脂體及動物細胞懸浮液之質譜分析方法

Abstract

由於生物性基材皆由大量物質所組成，因此在多數的情形中，分析之前須經由多項步驟進行樣品處理。尤其將複雜組成樣品進行質譜分析時，離子抑制現象為一必然面對的難題。於此篇論文中，我們開發了三種線上分析平台，包含萃取與分離技術連接氣相層析串聯質譜儀或電噴灑質譜儀。 於第一項研究中，藉由使用索式萃取法連接氣相層析串聯質譜儀以及電噴灑質譜儀來萃取植物果實(即檸檬與小番茄)以偵測揮發及非揮發萃取物，其中更以可見光偵測器監控萃取過程，並用微控制器依照預先設定的時間點將萃取溶液運送至分析儀器中以進行分析。目標分析峰的底下面積隨著時間的關係圖將進一步以法勒模型和一級動力學模型定義此種萃取方法的特性，例如，藉由一級動力學模型可以得到檸檬萃取物：蒎烯、苧和松油烯於萃取過程中的質量轉移係數分別為每小時0.540、0.507及 0.722莫耳濃度。 於第二項研究中，藉由使用水動力層析連接可見光分析器及電噴灑質譜儀直接揭示脂質體分子於微脂體、老鼠巨噬細胞(RAW 257.4)、人類乳癌細胞(T47D)、老鼠未分化脂肪細胞(3T3-L1)和人類乳癌細胞(Hs578T)懸浮液。藉由計算分析峰底下面積以測試，我們得到此分析系統之重複性，相對標準差於可見光分析器與質譜儀分別為百分之十二以及十七。 也藉由質譜圖中目標峰訊雜比的計算結果，可以得到有進行分離步驟相對於無進行分離步驟之訊雜比可提高至少十倍以上，藉以證實此線上水動力層析連接電噴灑質譜儀技術的優點。 在此論文中的兩項研究皆藉由電子組件(樹梅派微電腦與奧杜宜諾微控制板)輔助以完成自動化操作化學分析過程，此自動化不僅可以提高系統重複性，更可以大幅漸少人為操作誤差的發生。此三項線上質譜分析平台的開發可以用於幫助研究隱藏於複雜生物性基材的目標分析物，更能進一步應用於生物工程、分子工程與藥劑學領域的研究。最後，我們感謝國立交通大學以及台灣科技部給予補助以俾利此研究的進行。

Biological matrices contain numerous components. Thus, in most cases, it is necessary to carry out sample cleanup before chemical analysis of such matrices. Especially in the case of mass spectrometric analysis, ion suppression poses a serious problem, which needs to be addressed when analyzing complex biological matrices. In this work, three on-line platforms have been developed. They include extraction and separation techniques coupled with GC-MS or ESI-MS. In the first study, Soxhlet extraction was used to investigate volatile and non-volatile analytes extracted from plant tissues (i.e. lemon and cherry tomato) by GC-MS or ESI-MS. The extraction process was also monitored by an optical detector. The extract solution was transferred to the GC-MS instrument at pre-defined time points triggered by microcontroller. Temporal profiles of analyte signals were further fitted with the Peleg’s model and the first-order kinetic model to characterize the progress of this type of extraction. For example, the mass transfer coefficients of pinene, limonene and terpinene in the extraction of lemon samples, estimated using the first-order kinetic model, are 0.540 M h-1, 0.507 M h-1 and 0.722 M h-1, respectively. In the second study, hydrodynamic chromatography was coupled with ESI-MS to reveal the lipid fingerprints directly after separation of liposomes (Ø ≈ 11 µm), mouse macrophages (RAW 264.7; Ø ≈ 14 µm), human breast cancer cells (T47D; Ø ≈ 19 µm), mouse pre-adipocyte cells (3T3-L1; Ø ≈ 15 µm) and human breast cancer cells (Hs578T; Ø ≈ 20 µm) from buffer/culture medium components. Injection repeatabilities of the on-line HDC-UV and HDC-MS analyses are 12% and 17%, respectively, by using 5 mM caffeine standard solution (calculated based on the peak areas). ESI-MS signal-to-noise ratios with separation were at least 10 higher than without separation, proving usefulness of the HDC-ESI-MS approach. Electronic modules (i.e. Raspberry Pi microcomputer and Arduino microcontroller) were used in both studies. They automate repetitive operations in the chemical analysis. They help to eliminate human error. Overall, the three on-line mass spectrometric platforms allow one to study target analytes in complex biological matrices. We suggest the developed methodology can be used in the fields of biological engineering, molecular biology and pharmaceutics.