Optimizing MS-Based Multi-Omics: Comparative Analysis of Protein, Metabolite, and Lipid Extraction Techniques

Area of Science:

• Multi-omics research
• Biotechnology
• Analytical Chemistry

Background:

• Multi-omics integrates genomic, proteomic, and metabolomic data for cellular mechanism understanding.
• Challenges include limited samples, complex data processing, and reproducibility issues.
• Existing studies often focus on technical performance rather than specific differences in omics data generation.

Purpose of the Study:

• To investigate the impact of different sample preparation methods on multi-omics data.
• To compare extraction and protein digestion techniques for comprehensive biological insights.
• To guide the selection of optimal pre-processing strategies in multi-omics studies.

Main Methods:

• Profiling omics data using methanol, Folch, and Matyash extraction methods for metabolites and lipids.
• Comparing filter-aided sample preparation (FASP) and suspension traps (S-Trap) for protein digestion.
• Analyzing small molecule identification based on extraction solvent polarity and lipid hydrophobicity.

Main Results:

• FASP excelled in identifying membrane-related proteins; S-Trap was superior for nuclear and RNA-processing proteins.
• Single-phase extraction identified organic compounds and fatty acids; two-phase extraction identified hydrophilic compounds like nucleotides.
• Hydrophobic lipids (ChE, TG) were identified using two-phase extraction, highlighting solvent polarity's role in small molecule profiling.

Conclusions:

• The choice of protein digestion method (FASP vs. S-Trap) influences the types of proteins identified, suggesting specific applications for studying immune responses or neurodegenerative diseases.
• Extraction solvent polarity is a critical factor determining the identification of small molecules, impacting the scope of metabolomic and lipidomic analyses.
• This study provides detailed insights into sample preparation variables, aiding researchers in selecting appropriate multi-omics pre-processing techniques to minimize error and batch effects.