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GNPS - Investigating/Evaluating sample normalization methods for mass spectrometry-based multi-omics and the application to a neurodegenerative mouse model

ABSTRACT: Omics research targets biomolecules such as proteins, lipids, and metabolites, providing a comprehensive view of biological systems. The field has seen significant advancements with the development of mass spectrometry (MS). However, the accuracy of quantitative analyses is crucial due to the expansion of omics applications. Accurate comparisons depend on sample preparation and normalization methods. Some biological samples, like tissues and feces, present inherent variations that challenge accuracy. Normalization aims to reduce these variations through pre-acquisition methods that equalize biomolecule content and post-acquisition methods that adjust instrument signals. Most research has focused on single-omics data and post-acquisition normalization. However, multi-omics research, essential for understanding complex biological systems, has not thoroughly evaluated normalization methods. This study evaluates three pre-acquisition normalization methods using methanol-chloroform-water extraction to enhance multi-omics data analysis. Our multi-omics data shows that the data is significantly different depending on the normalization methods and our optimized normalization method showed that there were significant multi-omics profile changes even with young mice tissue samples. Based on these findings, we suggest sample normalization based on total protein amount to minimize the sample variation and get an accurate comparison for multi-omics.

INSTRUMENT(S): Q Exactive HF-X

ORGANISM(S): Mus Musculus (ncbitaxon:10090)

SUBMITTER: Ling Hao

PROVIDER: MSV000096533 | GNPS | Tue Nov 26 16:11:00 GMT 2024

REPOSITORIES: GNPS

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Project description:Multi-omics integrates diverse types of biological information from genomic, proteomic, and metabolomics experiments to achieve a comprehensive understanding of complex cellular mechanisms. However, this approach is also challenging due to technical issues such as limited sample quantities, complexity of data pre-processing, and reproducibility concerns. Although conventional pre-processing methods in multi-omics research are standardized to ensure consistency; their simultaneous application can obscure specific details. Here, findings obtained from various omics approaches were profiled using various extraction methods (methanol extraction, Folch method, and Matyash methods for metabolites and lipids) and two digestion methods (Filter-aided sample preparation (FASP) and suspension traps (S-Trap)) for resuspended proteins. FASP was found to be more effective for separation of membrane-related proteins, whereas S-Trap excelled in isolating nuclear-related and RNA processing proteins. Thus, ASP may be suitable for investigating the immune response and bacterial infection pathways, whereas S-Trap may be more effective for studies focused on the mechanisms of neurodegenerative diseases. Moreover, the choice of extraction method, either single-phase MeOH or two-phase using Folch and Matyash methods, significantly influences the types of compounds identified, reflecting distinct profiles in different omics data sets. Among metabolites, the single-phase methd identified organic compounds and compounds related to fatty acids, whereas the two-phase extraction identified more hydrophilic compounds such as nucleotides. Lipids with strong hydrophobicity, such as ChE and TG, were identified in the two-phase extraction results. These findings highlight that significant differences between small molecules identified are primarily due to varying polarities of extraction solvents. To address human error and batch effects, a strategy that optimizes the balance between efficiency and the quality of the results is also proposed here. Our study reaffirms the impact of choice of pre-processing method in multi-omics, and also provides specific profiles of several protein and metabolite clusters as well as lipid classes.

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GNPS - Investigating/Evaluating sample normalization methods for mass spectrometry-based multi-omics and the application to a neurodegenerative mouse model

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