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Windows Scanning Multiomics: Integrated Metabolomics and Proteomics.

Jiachen Shi¹, Jialiang Zhao¹, Yu Zhang¹

¹State 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.

Analytical Chemistry

|December 14, 2023

Summary

We developed Windows Scanning Multiomics (WSM), a novel method for simultaneous metabolite and protein extraction. This multiomics approach enhances biological data analysis and correlation network generation for systems biology research.

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Area of Science:

Systems Biology
Biochemistry
Bioinformatics

Background:

Single omics analysis presents limitations in understanding complex biological systems due to high specimen requirements and intricate associations.
Metabolomics and proteomics provide valuable insights but are often analyzed separately, hindering comprehensive understanding.

Purpose of the Study:

To introduce a novel multiomics analytical method, Windows Scanning Multiomics (WSM), for simultaneous extraction and analysis of metabolites and proteins.
To develop an integrated data processing approach for enhanced bioinformation mining from multiomics data.
To improve the accuracy and comprehensiveness of biological correlation networks.

Main Methods:

Simultaneous extraction of metabolites and proteins from the same biological sample.
Ultra-high-performance liquid chromatography mass spectrometry (UPLC-MS) for both metabolomics and proteomics analysis without instrument conversion.
Development of an R-based program (WSM.R) for integrating and visualizing multiomics data into correlation networks.

Main Results:

Achieved a 10% increase in biomolecule coverage through simultaneous extraction.
Generated more focused and comprehensive correlation networks compared to separate extraction methods.
Successfully excluded six pairs of false-positive metabolite-protein relationships in the integrated network.

Conclusions:

WSM offers a novel and efficient approach for multiomics analysis, improving data coverage and accuracy.
The WSM.R program facilitates robust bioinformatic mining and network construction from multiomics data.
This integrated multiomics strategy is crucial for advancing systems biology research and understanding complex biological mechanisms.