Toward 鈥極mic Scale Metabolite Profiling: A Dual Separation鈥揗ass Spectrometry Approach for Coverage of Lipid and Central Carbon Metabolism
详细信息 查看全文
- 作者：Julijana Ivanisevic ; Zheng-Jiang Zhu ; Lars Plate ; Ralf Tautenhahn ; Stephen Chen ; Peter J. O鈥橞rien ; Caroline H. Johnson ; Michael A. Marletta ; Gary J. Patti ; Gary Siuzdak
- 刊名：Analytical Chemistry
- 出版年：2013
- 出版时间：July 16, 2013
- 年：2013
- 卷：85
- 期：14
- 页码：6876-6884
- 全文大小：517K
- 年卷期：v.85,no.14(July 16, 2013)
- ISSN：1520-6882
文摘
Although the objective of any 鈥榦mic science is broad measurement of its constituents, such coverage has been challenging in metabolomics because the metabolome is comprised of a chemically diverse set of small molecules with variable physical properties. While extensive studies have been performed to identify metabolite isolation and separation methods, these strategies introduce bias toward lipophilic or water-soluble metabolites depending on whether reversed-phase (RP) or hydrophilic interaction liquid chromatography (HILIC) is used, respectively. Here we extend our consideration of metabolome isolation and separation procedures to integrate RPLC/MS and HILIC/MS profiling. An aminopropyl-based HILIC/MS method was optimized on the basis of mobile-phase additives and pH, followed by evaluation of reproducibility. When applied to the untargeted study of perturbed bacterial metabolomes, the HILIC method enabled the accurate assessment of key, dysregulated metabolites in central carbon pathways (e.g., amino acids, organic acids, phosphorylated sugars, energy currency metabolites), which could not be retained by RPLC. To demonstrate the value of the integrative approach, bacterial cells, human plasma, and cancer cells were analyzed by combined RPLC/HILIC separation coupled to ESI positive/negative MS detection. The combined approach resulted in the observation of metabolites associated with lipid and central carbon metabolism from a single biological extract, using 80% organic solvent (ACN:MeOH:H2O 2:2:1). It enabled the detection of more than 30,000 features from each sample type, with the highest number of uniquely detected features by RPLC in ESI positive mode and by HILIC in ESI negative mode. Therefore, we conclude that when time and sample are limited, the maximum amount of biological information related to lipid and central carbon metabolism can be acquired by combining RPLC ESI positive and HILIC ESI negative mode analysis.