Monitoring Dynamic Changes in Lymph Metabolome of Fasting and Fed Rats by Electrospray Ionization-Ion Mobility Mass Spectrometry (ESI-IMMS)
Monitoring Dynamic Changes in Lymph Metabolome of Fasting and Fed Rats by Electrospray Ionization-Ion Mobility Mass Spectrometry (ESI-IMMS)
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DOI：
10.1021/ac901030k
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发表时间：
2009-10-01
影响因子：
7.4
通讯作者：
Hill, Herbert H., Jr.
中科院分区：
文献类型：
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作者：
Kaplan, Kimberly;Dwivedi, Prabha;Hill, Herbert H., Jr.
环境压力离子淌度飞行时间质谱 (IMMS) 最近已成为一种快速有效的分析技术，适用于代谢组学。代谢组学的一个重要应用是监测由于毒素暴露、营养变化或疾病造成的压力引起的代谢组变化。本文提出的研究使用 IMMS 来监测饮食压力随时间引起的大鼠淋巴液的代谢变化。通过在正离子和负离子检测模式下操作的电喷雾 (ESI) IMMS 对从饮食应激大鼠收集的淋巴液中发现的代谢物提取物进行分析。检测到的代谢物根据其质荷比 (m/z) 进行初步鉴定。在一份样品中，在负模式下检测到 1180 个可重现的暂定代谢物离子，在正模式下检测到 1900 个可重现的暂定代谢物离子。仅分析生物可重复离子（定义为在相同治疗下在不同大鼠中测量的代谢物离子），以降低数据的复杂性。使用为每个代谢组生成的代谢物峰列表（包括 m/z、迁移率和强度）来执行主成分分析 (PCA)。使用主成分 PC1 和 PC2 研究代谢组的动态变化，PC1 和 PC2 描述了正模式下系统变化的 62% 和负模式下系统变化的 810%。对 PC I 和 PC2 进行方差分析 (ANOVA)，并对平均值进行统计评估。比较大鼠喂食前后不同时间检测到的暂定代谢物离子的强度分布，以确定变化最大的代谢物。研究了不同类别化合物的迁移率-质量相关曲线（MMCC）。
Ambient pressure ion mobility time-of-flight mass spectrometry (IMMS) has recently emerged as a rapid and efficient analytical technique for applications to metabolomics. An important application of metabolomics is to monitor metabolome shifts caused by stress due to toxin exposure, nutritional changes, or disease. The research presented in this paper uses IMMS to monitor metabolic changes in rat lymph fluid caused by dietary stresses over time. Extracts of metabolites found in the lymph fluid collected from dietary stressed rats were subjected to analysis by electrospray (ESI) IMMS operated both in positive and negative ion detection mode. Metabolites detected were tentatively identified based on their mass to charge ratio (m/z). In one sample, 1180 reproducible tentative metabolite ions were detected in negative mode and 1900 reproducible tentative metabolite ions detected in positive mode. Only biologically reproducible ions, defined as metabolite ions that were measured in different rats under the same treatment, were analyzed to reduce the complexity of the data. A metabolite peak list including m/z, mobility, and intensity generated for each metabolome was used to perform principle component analysis (PCA). Dynamic changes in metabolomes were investigated using principle components PC1 and PC2 that described 62% of the variation of the system in positive mode and 810% of the variation of the system in negative mode. Analysis of variance (ANOVA) was performed for PC I and PC2 and means were statistically evaluated. Profiles of intensities were compared for tentative metabolite ions detected at different times before and after the rats were fed to identify the metabolites that were changing the most. Mobility-mass correlation curves (MMCC) were investigated for the different classes of compounds.