MALDI Imaging of "Arabidopsis thaliana" for an integrative analysis of topological Metabolome and Proteome features

该提案的重点是在胁迫下植物组织的MALDI成像（MI）技术的发展。我们研究中心的目标化合物将是小分子（代谢物），特别是苯丙烷途径的化合物。我们将利用拟南芥作为模式系统，研究次生代谢产物在不同组织和不同发育阶段响应生物胁迫的定位。我们的假设是，不同的苯丙素类（和其他代谢产物）在植物组织内的应力（细菌感染，草食动物攻击）和发育阶段的性质依赖于一个复杂的和应力特定的模式。将合并基因表达数据和MA成像（MSI）数据。我们的策略将是在现有代谢组和蛋白质组数据的丰富知识背景之上处理MS成像（MSI）数据，以桥接生物化学知识和来自样本图像数据的结构空间信息的领域。虽然MSI的纯处理（包括基线校正、峰选择、过滤和分割）取决于明确定义的工作流程，但MS成像中的另一项可能更具挑战性的任务是将复杂的MS数据与已知代谢物的分布联系起来。这种方法的最终目的是了解空间和时间上的次级代谢产物的产生的调节。

The proposal is focused on the development of MALDI-Imaging (MI) techniques for plant tissues under stress. Target compounds in the centre of our research will be small molecules (metabolites), especially compounds of the phenylpropanoid pathway. We will use Arabidopsis thaliana as a model system to investigate the localization of secondary metabolites in different tissues and different developmental stages in response to biological stress. Our hypothesis is that the different phenylpropanoids (and other metabolites) are expressed in a complex and stress-specific pattern within the plant tissues in dependence of the nature of the stress (bacterial infection, herbivore attack) and of the developmental stage. Gene expression data and MA Imaging (MSI) data will be merged. Our strategy will be to process the MS Imaging (MSI) data on top of this rich knowledge background of the existing metabolome and proteome data, to bridge the domains of biochemical knowledge and spatial information on structure derived from the image data of the samples. While the pure processing of MSI including baseline correction, peak selection, filtering and segmentation depends on a well-defined work flow, another, maybe even more challenging task in MS-Imaging is to link the complex MS data to the distribution of known metabolites. Ultimate aim of this approach is to understand the regulation of secondary metabolite production over space and time.