Gorzolka Karin, Kölling Jan, Nattkemper Tim W, Niehaus Karsten
Proteome and Metabolome Research, Faculty of Biology, Center for Biotechnology (CeBiTec), Bielefeld, Germany.
Biodata Mining, Faculty of Technology, Center for Biotechnology (CeBiTec), Bielefeld, Germany.
PLoS One. 2016 Mar 3;11(3):e0150208. doi: 10.1371/journal.pone.0150208. eCollection 2016.
MALDI mass spectrometry imaging was performed to localize metabolites during the first seven days of the barley germination. Up to 100 mass signals were detected of which 85 signals were identified as 48 different metabolites with highly tissue-specific localizations. Oligosaccharides were observed in the endosperm and in parts of the developed embryo. Lipids in the endosperm co-localized in dependency on their fatty acid compositions with changes in the distributions of diacyl phosphatidylcholines during germination. 26 potentially antifungal hordatines were detected in the embryo with tissue-specific localizations of their glycosylated, hydroxylated, and O-methylated derivates. In order to reveal spatio-temporal patterns in local metabolite compositions, multiple MSI data sets from a time series were analyzed in one batch. This requires a new preprocessing strategy to achieve comparability between data sets as well as a new strategy for unsupervised clustering. The resulting spatial segmentation for each time point sample is visualized in an interactive cluster map and enables simultaneous interactive exploration of all time points. Using this new analysis approach and visualization tool germination-dependent developments of metabolite patterns with single MS position accuracy were discovered. This is the first study that presents metabolite profiling of a cereals' germination process over time by MALDI MSI with the identification of a large number of peaks of agronomically and industrially important compounds such as oligosaccharides, lipids and antifungal agents. Their detailed localization as well as the MS cluster analyses for on-tissue metabolite profile mapping revealed important information for the understanding of the germination process, which is of high scientific interest.
采用基质辅助激光解吸电离质谱成像技术对大麦萌发前七天的代谢产物进行定位。共检测到多达100个质量信号,其中85个信号被鉴定为48种不同的代谢产物,具有高度的组织特异性定位。在胚乳和发育中的胚的部分区域观察到了寡糖。胚乳中的脂质根据其脂肪酸组成共定位,在萌发过程中二酰基磷脂酰胆碱的分布发生变化。在胚中检测到26种潜在的抗真菌大麦碱,其糖基化、羟基化和O-甲基化衍生物具有组织特异性定位。为了揭示局部代谢物组成的时空模式,对一个时间序列的多个质谱成像数据集进行了批量分析。这需要一种新的预处理策略来实现数据集之间的可比性,以及一种新的无监督聚类策略。每个时间点样本的空间分割结果在交互式聚类图中可视化,并能够同时对所有时间点进行交互式探索。使用这种新的分析方法和可视化工具,发现了具有单质谱位置精度的代谢物模式的萌发依赖性发展。这是第一项通过基质辅助激光解吸电离质谱成像技术呈现谷物萌发过程中代谢物谱随时间变化的研究,鉴定出了大量具有农学和工业重要性的化合物,如寡糖、脂质和抗真菌剂的峰。它们的详细定位以及组织上代谢物谱图的质谱聚类分析揭示了对理解萌发过程很重要的信息,具有很高的科学价值。