Ion Mobility Derived Collision Cross Sections to Support Metabolomics Applications

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• 作者：Giuseppe Paglia ; Jonathan P. Williams ; Lochana Menikarachchi ; J. Will Thompson ; Richard Tyldesley-Worster ; Skarph茅dinn Halld贸rsson ; Ottar Rolfsson ; Arthur Moseley ; David Grant ; James Langridge ; Bernhard O. Palsson ; Giuseppe Astarita
• 刊名：Analytical Chemistry
• 出版年：2014
• 出版时间：April 15, 2014
• 年：2014
• 卷：86
• 期：8
• 页码：3985-3993
• 全文大小：461K
• 年卷期：v.86,no.8(April 15, 2014)
• ISSN：1520-6882

文摘

Metabolomics is a rapidly evolving analytical approach in life and health sciences. The structural elucidation of the metabolites of interest remains a major analytical challenge in the metabolomics workflow. Here, we investigate the use of ion mobility as a tool to aid metabolite identification. Ion mobility allows for the measurement of the rotationally averaged collision cross-section (CCS), which gives information about the ionic shape of a molecule in the gas phase. We measured the CCSs of 125 common metabolites using traveling-wave ion mobility-mass spectrometry (TW-IM-MS). CCS measurements were highly reproducible on instruments located in three independent laboratories (RSD < 5% for 99%). We also determined the reproducibility of CCS measurements in various biological matrixes including urine, plasma, platelets, and red blood cells using ultra performance liquid chromatography (UPLC) coupled with TW-IM-MS. The mean RSD was < 2% for 97% of the CCS values, compared to 80% of retention times. Finally, as proof of concept, we used UPLC鈥揟W-IM-MS to compare the cellular metabolome of epithelial and mesenchymal cells, an in vitro model used to study cancer development. Experimentally determined and computationally derived CCS values were used as orthogonal analytical parameters in combination with retention time and accurate mass information to confirm the identity of key metabolites potentially involved in cancer. Thus, our results indicate that adding CCS data to searchable databases and to routine metabolomics workflows will increase the identification confidence compared to traditional analytical approaches.