State Key Laboratory of Inorganic Synthesis and Preparative Chemistry, College of Chemistry , Jilin University , Changchun 130012 , P. R. China.
Jiangxi Key Laboratory for Mass Spectrometry and Instrumentation , East China University of Technology , Nanchang 330013 , P. R. China.
Anal Chem. 2019 Aug 20;91(16):10532-10540. doi: 10.1021/acs.analchem.9b01507. Epub 2019 Jul 30.
Traditionally, molecular information on metabolites, lipids, and proteins is collected from separate individual tissue samples using different analytical approaches. Herein a novel strategy to minimize the potential material losses and the mismatch between metabolomics, lipidomics, and proteomics data has been demonstrated based on internal extractive electrospray ionization mass spectrometry (iEESI-MS). Sequential detection of lipids, metabolites, and proteins from the same tissue sample was achieved without sample reloading and hardware alteration to MS instrument by sequentially using extraction solutions with different chemical compositions. With respect to the individual compound class analysis, the sensitivity, specificity, and accuracy obtained with the integrative information on metabolites, lipids, and proteins from 57 samples of 13 patients for lung cancer prediction was substantially improved from 54.0%, 51.0%, and 76.0% to 100.0%, respectively. The established method is featured by low sample consumption (ca. 2.0 mg) and easy operation, which is important to minimize systematic errors in precision molecular diagnosis and systems biology studies.
传统上，代谢物、脂质和蛋白质的分子信息是使用不同的分析方法从单独的组织样本中收集的。本文提出了一种新策略，基于内部提取电喷雾电离质谱（iEESI-MS），以最小化代谢组学、脂质组学和蛋白质组学数据之间的潜在物质损失和不匹配。通过顺序使用具有不同化学成分的提取溶液，无需重新加载样品和更改 MS 仪器硬件，即可从同一组织样品中连续检测脂质、代谢物和蛋白质。就单个化合物类别分析而言，从 13 位肺癌患者的 57 个样本中获得的代谢物、脂质和蛋白质综合信息对肺癌的预测的灵敏度、特异性和准确性分别从 54.0%、51.0%和 76.0%显著提高到 100.0%。所建立的方法具有低样品消耗（约 2.0mg）和易于操作的特点，这对于最小化精密分子诊断和系统生物学研究中的系统误差非常重要。
