Yu Huaxu, Villanueva Nathaniel, Bittar Thibault, Arsenault Eric, Labonté Benoit, Huan Tao
Department of Chemistry, Faculty of Science, University of British Columbia, Vancouver Campus, 2036 Main Mall, Vancouver, V6T 1Z1, BC, Canada.
Department of Psychiatry and Neurosciences, Faculty of Medicine, Université Laval, CERVO Brain Research Center, Québec, G1J 2G3, QC, Canada.
Anal Chim Acta. 2020 Nov 1;1136:168-177. doi: 10.1016/j.aca.2020.09.051. Epub 2020 Sep 29.
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.
对特定脑区的代谢组和脂质组进行全面分析,对于理解调节大脑活动的细胞和分子机制至关重要。鉴于起始材料的量有限,传统的比较脑区的小鼠研究主要针对大脑大区域(如大脑、皮层)中的一组已知代谢物。在这项工作中,我们开发了一种多模态分析流程,能够从蛋白质含量低于0.2mg的解剖学上不同的小鼠脑区并行分析代谢组和脂质组谱。该分析流程由以下部分组成:(1)基于超声处理的组织匀浆,(2)并行代谢物和脂质提取,(3)基于BCA的样品标准化,(4)基于超高效液相色谱-质谱的多模态代谢组和脂质组分析,(5)简化的数据处理,以及(6)基于弦图的数据可视化。我们将此流程应用于雄性小鼠四个脑区的研究,包括杏仁核、背侧海马体、伏隔核和腹侧被盖区。通过这种新方法,我们检测到5000多种代谢特征和6000多种脂质特征,其中134种代谢物和479种脂质通过自动质谱光谱匹配得到直接确认。有趣的是,我们的分析在每个脑区都鉴定出了独特的代谢和脂质谱。此外,我们确定了代谢和脂质亚类之间的功能关系,这可能是所有四个脑区细胞和功能差异的潜在基础。总体而言,我们的新工作流程使用极少量的脑亚区域组织样本生成了全面的区域特异性代谢组和脂质组谱,这些谱可以很容易地与区域特异性基因组、转录组和蛋白质组数据整合,以揭示不同脑区活动背后分子机制的新见解。