Kang Jianing, David Lisa, Li Yangyang, Cang Jing, Chen Sixue
College of Life Science, Northeast Agricultural University, Harbin, China.
Department of Biology, University of Florida, Gainesville, FL, United States.
Front Genet. 2021 Apr 15;12:635971. doi: 10.3389/fgene.2021.635971. eCollection 2021.
Elucidation of complex molecular networks requires integrative analysis of molecular features and changes at different levels of information flow and regulation. Accordingly, high throughput functional genomics tools such as transcriptomics, proteomics, metabolomics, and lipidomics have emerged to provide system-wide investigations. Unfortunately, analysis of different types of biomolecules requires specific sample extraction procedures in combination with specific analytical instrumentation. The most efficient extraction protocols often only cover a restricted type of biomolecules due to their different physicochemical properties. Therefore, several sets/aliquots of samples are needed for extracting different molecules. Here we adapted a biphasic fractionation method to extract proteins, metabolites, and lipids from the same sample (3-in-1) for liquid chromatography-tandem mass spectrometry (LC-MS/MS) multi-omics. To demonstrate utility of the improved method, we used bacteria-primed leaves to generate multi-omics datasets from the same sample. In total, we were able to analyze 1849 proteins, 1967 metabolites, and 424 lipid species in single samples. The molecules cover a wide range of biological and molecular processes, and allow quantitative analyses of different molecules and pathways. Our results have shown the clear advantages of the multi-omics method, including sample conservation, high reproducibility, and tight correlation between different types of biomolecules.
阐明复杂的分子网络需要对不同信息流和调控水平的分子特征及变化进行综合分析。因此,诸如转录组学、蛋白质组学、代谢组学和脂质组学等高通量功能基因组学工具应运而生,以进行全系统研究。不幸的是,分析不同类型的生物分子需要特定的样品提取程序,并结合特定的分析仪器。由于其不同的物理化学性质,最有效的提取方案通常仅涵盖有限类型的生物分子。因此,提取不同分子需要几组/几份样品。在这里,我们采用了一种双相分级分离方法,从同一样品中提取蛋白质、代谢物和脂质(三合一),用于液相色谱-串联质谱(LC-MS/MS)多组学分析。为了证明改进方法的实用性,我们使用经细菌预处理的叶片从同一样品中生成多组学数据集。总共,我们能够在单个样品中分析1849种蛋白质、1967种代谢物和424种脂质种类。这些分子涵盖了广泛的生物学和分子过程,并允许对不同分子和途径进行定量分析。我们的结果显示了多组学方法的明显优势,包括样品保存、高重现性以及不同类型生物分子之间的紧密相关性。