轨道轨道
代谢组学
质谱法
脂类学
解吸电喷雾电离
化学
色谱法
分析化学(期刊)
电喷雾电离
再现性
计算机科学
代谢组
电离
化学电离
生物化学
离子
有机化学
作者
Andrew D. Southam,Ralf J. M. Weber,Jasper Engel,Martin R. Jones,Mark R. Viant
出处
期刊:Nature Protocols
[Nature Portfolio]
日期:2017-01-12
卷期号:12 (2): 310-328
被引量:142
标识
DOI:10.1038/nprot.2016.156
摘要
Metabolomic and lipidomic studies measure and discover metabolic and lipid profiles in biological samples, enabling a better understanding of the metabolism of specific biological phenotypes. Accurate biological interpretations require high analytical reproducibility and sensitivity, and standardized and transparent data processing. Here we describe a complete workflow for nanoelectrospray ionization (nESI) direct-infusion mass spectrometry (DIMS) metabolomics and lipidomics. After metabolite and lipid extraction from tissues and biofluids, samples are directly infused into a high-resolution mass spectrometer (e.g., Orbitrap) using a chip-based nESI sample delivery system. nESI functions to minimize ionization suppression or enhancement effects as compared with standard electrospray ionization (ESI). Our analytical technique-named spectral stitching-measures data as several overlapping mass-to-charge (m/z) windows that are subsequently 'stitched' together, creating a complete mass spectrum. This considerably increases the dynamic range and detection sensitivity-about a fivefold increase in peak detection-as compared with the collection of DIMS data as a single wide mass-to-charge (m/z ratio) window. Data processing, statistical analysis and metabolite annotation are executed as a workflow within the user-friendly, transparent and freely available Galaxy platform (galaxyproject.org). Generated data have high mass accuracy that enables molecular formulae peak annotations. The workflow is compatible with any sample-extraction method; in this protocol, the examples are extracted using a biphasic method, with methanol, chloroform and water as the solvents. The complete workflow is reproducible, rapid and automated, which enables cost-effective analysis of >10,000 samples per year, making it ideal for high-throughput metabolomics and lipidomics screening-e.g., for clinical phenotyping, drug screening and toxicity testing.
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