Evaluating sample normalization methods for MS-based multi-omics and the application to a neurodegenerative mouse...

Area of Science:

• Biomedical Research
• Analytical Chemistry
• Genomics

Background:

• Mass spectrometry (MS)-based omics methods are crucial for high-throughput molecular analysis in biology.
• Multi-omics studies, analyzing proteins, lipids, and metabolites from single samples, are increasingly feasible.
• Sample normalization is critical but often performed independently for each omics type, posing a challenge for integrated multi-omics.

Purpose of the Study:

• To evaluate and identify optimal sample normalization methods for multi-omics analysis.
• To compare the impact of different normalization strategies on quantitative results.
• To establish a reliable normalization protocol for tissue-based multi-omics studies.

Main Methods:

• Evaluation of various sample normalization techniques for proteins, lipids, and metabolites.
• Comparison of normalization based on tissue weight versus protein concentration, before and after extraction.
• Application of a validated two-step normalization method (tissue weight then protein concentration) to GRN-deficient mouse brain multi-omics data.

Main Results:

• Normalizing by tissue weight or protein concentration yielded distinct quantitative outcomes.
• A two-step normalization (tissue weight pre-extraction, protein concentration post-extraction) minimized sample variation.
• Multi-omics profiling of GRN knockout mouse brains revealed pathways linked to lysosomal dysfunction and neuroinflammation.

Conclusions:

• The choice of normalization method significantly impacts multi-omics data interpretation.
• A two-step normalization strategy (tissue weight followed by protein concentration) enhances reliability in tissue-based multi-omics.
• This method ensures accurate biomolecule quantification for robust biological comparisons in multi-omics research.