Parallel metabolomics and lipidomics enables the comprehensive study of mouse brain regional metabolite and lipid patterns.

Publication Type:

Journal Article


Anal Chim Acta, Volume 1136, p.168-177 (2020)


Animals, Brain, Lipidomics, Lipids, Male, Metabolome, Metabolomics, Mice, Proteomics


<p>Global profiling of the metabolome and lipidome of specific brain regions is essential to understanding the cellular and molecular mechanisms regulating brain activity. Given the limited amount of starting material, conventional mouse studies comparing brain regions have mainly targeted a set of known metabolites in large brain regions (e.g., cerebrum, cortex). In this work, we developed a multimodal analytical pipeline enabling parallel analyses of metabolomic and lipidomic profiles from anatomically distinct mouse brain regions starting with less than 0.2 mg of protein content. This analytical pipeline is composed of (1) sonication-based tissue homogenization, (2) parallel metabolite and lipid extraction, (3) BCA-based sample normalization, (4) ultrahigh performance liquid chromatography-mass spectrometry-based multimodal metabolome and lipidome profiling, (5) streamlined data processing, and (6) chord plot-based data visualization. We applied this pipeline to the study of four brain regions in males including the amygdala, dorsal hippocampus, nucleus accumbens and ventral tegmental area. With this novel approach, we detected over 5000 metabolic and 6000 lipid features, among which 134 metabolites and 479 lipids were directly confirmed via automated MS spectral matching. Interestingly, our analysis identified unique metabolic and lipid profiles in each brain regions. Furthermore, we identified functional relationships amongst metabolic and lipid subclasses, potentially underlying cellular and functional differences across all four brain regions. Overall, our novel workflow generates comprehensive region-specific metabolomic and lipidomic profiles using very low amount of brain sub-regional tissue sample, which could be readily integrated with region-specific genomic, transcriptomic, and proteomic data to reveal novel insights into the molecular mechanisms underlying the activity of distinct brain regions.</p>

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