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

Abstract

Mass spectrometry (MS)-based omics methods have transformed biomedical research with accurate and high-throughput analysis of diverse molecules in biological systems. Recent technological advances also enabled multi-omics to be achieved from the same sample or on a single analytical platform. Sample normalization is a critical step in MS-omics studies but is usually conducted independently for each omics experiment. To bridge this technical gap, we evaluated different sample normalization methods suitable for analyzing proteins, lipids, and metabolites from the same sample for multi-omics analysis. We found that normalizing samples based on tissue weight or protein concentration before or after extraction generated distinct quantitative results. Normalizing samples first by tissue weight before extraction and then by protein concentration after extraction resulted in the lowest sample variation to reveal true biological differences. We then applied this two-step normalization method to investigate multi-omics profiles of mouse brains lacking the GRN gene. Loss-of-function mutations in the GRN gene lead to the deficiency of the progranulin protein and eventually cause neurodegeneration. Comparing the proteomics, lipidomics, and metabolomics profiles of GRN KO and WT mouse brains revealed molecular changes and pathways related to lysosomal dysfunction and neuroinflammation. In summary, we demonstrated the importance of selecting an appropriate normalization method during multi-omics sample preparation. Our normalization method is applicable to all tissue-based multi-omics studies, ensuring reliable and accurate biomolecule quantification for biological comparisons.