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Abstract：
Principal component analysis (PCA) is a useful tool for omics analysis to identify underlying factors and visualize relationships between biomarkers. However, this approach is limited in addressing life complexity and further improvement is required. This study aimed to develop a new approach that combines mass spectrometry-based metabolomics with multiblock PCA to elucidate the whole-body global metabolic network, thereby generating comparable metabolite maps to clarify the metabolic relationships among several organs. To evaluate the newly developed method, Zucker diabetic fatty (ZDF) rats (n = 6) were used as type 2 diabetic models and Sprague Dawley (SD) rats (n = 6) as controls. Metabolites in the heart, kidney, and liver were analyzed by capillary electrophoresis and liquid chromatography mass spectrometry, respectively, and the detected metabolites were analyzed by multiblock PCA. More than 300 metabolites were detected in the heart, kidney, and liver. When the metabolites obtained from the three organs were analyzed with multiblock PCA, the score and loading maps obtained were highly synchronized and their metabolism patterns were visually comparable. A significant finding in this study was the different expression patterns in lipid metabolism among the three organs; notably triacylglycerols with polyunsaturated fatty acids or less unsaturated fatty acids showed specific accumulation patterns depending on the organs.
摘要：
主成分分析（PCA）是用于组学分析的有用工具，用于识别潜在因素并可视化生物标志物之间的关系。然而,这种方法在解决生活复杂性方面受到限制，需要进一步改进。这项研究旨在开发一种新方法，将基于质谱的代谢组学与多模块PCA相结合，以阐明全身全球代谢网络。从而产生可比较的代谢物图，以阐明几个器官之间的代谢关系。为了评估新开发的方法，Zucker糖尿病脂肪（ZDF）大鼠（n=6）用作2型糖尿病模型，SpragueDawley（SD）大鼠（n=6）用作对照。心脏中的代谢物，肾,和肝脏进行毛细管电泳和液相色谱质谱分析，分别,并对检测到的代谢产物进行多模块PCA分析。在心脏中检测到300多种代谢物，肾,还有肝脏.当从三个器官中获得的代谢物用多块PCA分析时，获得的评分图和负荷图高度同步,其代谢模式在视觉上具有可比性.这项研究中的一个重要发现是三个器官之间脂质代谢的不同表达方式；特别是具有多不饱和脂肪酸或较少不饱和脂肪酸的三酰甘油显示出特定的积累模式，这取决于器官。
