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학술발표회초록보기

초록문의 abstract@kcsnet.or.kr

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제124회 대한화학회 학술발표회, 총회 및 기기전시회 안내 A single-injection with pre-column derivatization LC-MS/MS method for plasma metabolites

등록일
2019년 8월 29일 14시 23분 47초
접수번호
1811
발표코드
ANAL3-5 이곳을 클릭하시면 발표코드에 대한 설명을 보실 수 있습니다.
발표시간
금 14시 : 50분
발표형식
심포지엄
발표분야
Analytical Chemistry - Recent Trends in Mass Spectrometry
저자 및
공동저자
Kwangyoul Kim
Clinical pharmacology, Inha University, Korea
Metabolomics is a powerful emerging technology for studying the systems biology and biomarker discovery for various diseases. Targeted metabolomics, which focuses on the analysis of specific metabolites from biologically relevant metabolic pathways, can facilitate an overall understanding of the pathogenesis of diseases and the early diagnosis and treatment of the diseases. LC-MS/MS (MRM; multiple reaction monitoring) technique is the most widely approach used for targeted metabolomics, and features high selectivity and sensitivity, good reproducibility and wide dynamic range in quantitative analysis. Currently, targeted LC-MRM methods for polar metabolites are often limited by the number of analytes that can be measured, and/or require two or more LC platforms (HILIC or IPLC) including multiple injections. Here, we describe a selective precolumn derivatization LC-MRM based targeted metabolomics workflow for the quantitative analysis of polar metabolites, such as amino acids, biogenic amines, and carboxylic acids in central carbon metabolic pathways including an intermediate metabolite of the tricarboxylic acid (TCA) cycle. We developed a single-injection, targeted plasma metabolite quantification method on triple quadruple mass spectrometer. Analytical validation was conducted for the sensitivity, reproducibility, linearity, and carryover. The described LC-MRM method is accurate, reliable and reproducible with a wide dynamic range and also is capable of measuring polar metabolites in plasma in a single injection using small volume, resulting in significant improvement in throughput analysis. Therefore, the method might be a useful tool in helping the diagnosis of diseases and studying biomarker discovery.

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