State Key Laboratory of Food Science and Technology, School of Food Science and Technology, National Engineering Research Center for Functional Food, National Engineering Laboratory for Cereal Fermentation Technology, Collaborative Innovation Center of Food Safety and Quality Control in Jiangsu Province, Jiangnan University, 1800 Lihu Road, Wuxi 214122, Jiangsu, People's Republic of China.
Department of Food Technology, Faculty of Food Science and Technology, Universiti Putra Malaysia, Serdang, Selangor 43400, Malaysia.
Anal Chem. 2023 Dec 26;95(51):18793-18802. doi: 10.1021/acs.analchem.3c03785. Epub 2023 Dec 14.
Metabolomics and proteomics offer significant advantages in understanding biological mechanisms at two hierarchical levels. However, conventional single omics analysis faces challenges due to the high demand for specimens and the complexity of intrinsic associations. To obtain comprehensive and accurate system biological information, we developed a multiomics analytical method called Windows Scanning Multiomics (WSM). In this method, we performed simultaneous extraction of metabolites and proteins from the same sample, resulting in a 10% increase in the coverage of the identified biomolecules. Both metabolomics and proteomics analyses were conducted by using ultrahigh-performance liquid chromatography mass spectrometry (UPLC-MS), eliminating the need for instrument conversions. Additionally, we designed an R-based program (WSM.R) to integrate mathematical and biological correlations between metabolites and proteins into a correlation network. The network created from simultaneously extracted biomolecules was more focused and comprehensive compared to those from separate extractions. Notably, we excluded six pairs of false-positive relationships between metabolites and proteins in the network established using simultaneously extracted biomolecules. In conclusion, this study introduces a novel approach for multiomics analysis and data processing that greatly aids in bioinformation mining from multiomics results. This method is poised to play an indispensable role in systems biology research.
代谢组学和蛋白质组学在理解两个层次的生物学机制方面具有显著优势。然而，传统的单一组学分析由于对样本的高需求和内在关联的复杂性而面临挑战。为了获得全面准确的系统生物学信息，我们开发了一种名为 Windows Scanning Multiomics（WSM）的多组学分析方法。在该方法中，我们从同一样品中同时提取代谢物和蛋白质，从而使鉴定生物分子的覆盖率提高了 10%。代谢组学和蛋白质组学分析均通过超高效液相色谱-质谱联用（UPLC-MS）进行，无需仪器转换。此外，我们设计了一个基于 R 的程序（WSM.R），将代谢物和蛋白质之间的数学和生物学相关性整合到一个相关网络中。与分别提取的生物分子相比，从同时提取的生物分子创建的网络更加集中和全面。值得注意的是，我们在同时提取的生物分子建立的网络中排除了 6 对代谢物和蛋白质之间的假阳性关系。总之，本研究介绍了一种新的多组学分析和数据处理方法，极大地有助于从多组学结果中挖掘生物信息。该方法在系统生物学研究中具有不可或缺的作用。
