Sequential Detection of Lipids, Metabolites, and Proteins in One Tissue for Improved Cancer Differentiation Accuracy

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个体组织样本中收集的。在此，基于内部萃取电喷雾电离质谱（iEESI - MS），展示了一种新的策略，可将代谢组学、脂质组学和蛋白质组学数据之间潜在的材料损失以及不匹配降至最低。通过依次使用具有不同化学成分的萃取溶液，在不重新加载样本以及不对质谱仪器进行硬件改动的情况下，实现了对同一组织样本中脂质、代谢物和蛋白质的连续检测。就单个化合物类别分析而言，从13名肺癌患者的57个样本中获取的代谢物、脂质和蛋白质综合信息用于肺癌预测时，其灵敏度、特异性和准确性分别从54.0%、51.0%和76.0%大幅提高到100.0%。所建立的方法具有样本消耗量低（约2.0毫克）和操作简便的特点，这对于在精准分子诊断和系统生物学研究中最大限度地减少系统误差非常重要。