标题: 开发萃取与分离技术用于植物组织、微脂体及动物细胞悬浮液之质谱分析方法
Development of extraction and separation methods for mass spectrometric analysis of plant tissues, liposomes and animal cell suspensions
作者: 陈思颖
帕伟鄂本
Chen, Ssu-Ying
Urban, Pawel
跨领域分子科学国际硕士学位学程
关键字: 质谱分析;萃取;层析;生物性材料;mass spectrometry;extraction;chromatography;biological matrices
公开日期: 2016
摘要: 由于生物性基材皆由大量物质所组成,因此在多数的情形中,分析之前须经由多项步骤进行样品处理。尤其将复杂组成样品进行质谱分析时,离子抑制现象为一必然面对的难题。于此篇论文中,我们开发了三种线上分析平台,包含萃取与分离技术连接气相层析串联质谱仪或电喷洒质谱仪。
于第一项研究中,藉由使用索式萃取法连接气相层析串联质谱仪以及电喷洒质谱仪来萃取植物果实(即柠檬与小番茄)以侦测挥发及非挥发萃取物,其中更以可见光侦测器监控萃取过程,并用微控制器依照预先设定的时间点将萃取溶液运送至分析仪器中以进行分析。目标分析峰的底下面积随着时间的关系图将进一步以法勒模型和一级动力学模型定义此种萃取方法的特性,例如,藉由一级动力学模型可以得到柠檬萃取物:蒎烯、苎和松油烯于萃取过程中的质量转移系数分别为每小时0.540、0.507及 0.722莫耳浓度。
于第二项研究中,藉由使用水动力层析连接可见光分析器及电喷洒质谱仪直接揭示脂质体分子于微脂体、老鼠巨噬细胞(RAW 257.4)、人类乳癌细胞(T47D)、老鼠未分化脂肪细胞(3T3-L1)和人类乳癌细胞(Hs578T)悬浮液。藉由计算分析峰底下面积以测试,我们得到此分析系统之重复性,相对标准差于可见光分析器与质谱仪分别为百分之十二以及十七。 也藉由质谱图中目标峰讯杂比的计算结果,可以得到有进行分离步骤相对于无进行分离步骤之讯杂比可提高至少十倍以上,藉以证实此线上水动力层析连接电喷洒质谱仪技术的优点。
在此论文中的两项研究皆藉由电子组件(树梅派微电脑与奥杜宜诺微控制板)辅助以完成自动化操作化学分析过程,此自动化不仅可以提高系统重复性,更可以大幅渐少人为操作误差的发生。此三项线上质谱分析平台的开发可以用于帮助研究隐藏于复杂生物性基材的目标分析物,更能进一步应用于生物工程、分子工程与药剂学领域的研究。最后,我们感谢国立交通大学以及台湾科技部给予补助以俾利此研究的进行。
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.
URI: http://etd.lib.nctu.edu.tw/cdrfb3/record/nctu/#GT070252452
http://hdl.handle.net/11536/138803
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