DIFFERENTIAL CHARACTERIZATION OF GLYCOSAMINOGLYCAN TANDEM MASS SPECTROMETRY DATA

这个子项目是许多利用资源的研究子项目之一 由NIH/NCRR资助的中心拨款提供。子项目的主要支持 而子项目的主要调查员可能是由其他来源提供的， 包括其它NIH来源。 列出的子项目总成本可能 代表子项目使用的中心基础设施的估计数量， 而不是由NCRR赠款提供给子项目或子项目工作人员的直接资金。 硫酸乙酰肝素（HS）是一种存在于所有动物细胞表面的糖胺聚糖，并直接与无数细胞外信号分子相互作用。 HS是胚胎发育和每个成人生理系统功能所必需的。 许多生长因子家族和生长因子受体之间的相互作用取决于在细胞表面和细胞外基质上表达的HS的结构。 因此，理解HS结构生物化学对于理解包括肿瘤生长、血管生成、淀粉样蛋白沉积、组织重塑和修复以及宿主-病原体相互作用在内的疾病机制至关重要，这并不奇怪。 通常用于比较质谱数据的两种方法是聚类分析和主成分分析。 聚类分析包括将数据划分为组（聚类），以捕获数据的自然结构。 在质谱领域中，聚类的第一个参考文献之一被用于比较烷基硫醇酯和药物产品。 将串联质谱数据聚类分析应用于蛋白质组学领域，目的在于：（1）通过对相似串联质谱数据进行重组，减少分析的冗余，从而减少用于蛋白质鉴定的串联质谱的数量;（2）提高对裂解模式的理解（片段化对强度）以实现改进的蛋白质鉴定算法;和（3）促进用于发现生物标志物的无标记定量实验。 我们已经开发了一种新的全自动化的方法的基础上，从不同器官组织提取的HS寡糖串联MS数据的新的解释。 我们将这种方法应用于使用自动碰撞活化解离（CAD）串联MS采集参数采集的一组数据。 从四种牛组织（主动脉、肺、肠和肾）中提取的HS寡糖上获得12种靶向前体离子的串联质谱，一式三份自动获得是研究的基础。 相应数据集的大小约为2800个特征，手动比较不可行。 我们使用凝聚层次聚类（AHC）的串联MS数据，以证明有足够的信息，在四个器官样品中的异构糖型的分化。 该分析是有用的识别碎片模式对应于器官特异性HS结构。

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. Heparan sulfate (HS) is a glycosaminoglycan present on all animal cell surfaces and directly interact with myriad extracellular signaling molecules. HS is required for embryonic development and for the functioning of every adult physiological system. The interactions between many families of growth factors and growth factor receptors are modulated depending on the structures of HS expressed on cell surfaces and extracellular matrices. Thus, it is not surprising that understanding of HS structural biochemistry is central to understanding of disease mechanisms including tumor growth, angiogenesis, amyloid deposition, tissue remodeling and repair, and host-pathogen interactions. Two approaches commonly used to compare mass spectral data are clustering analysis and principal components analysis. Cluster analysis consists of dividing data into groups (clusters) in order to capture the natural structure of the data. One of the first references of clustering in the mass spectrometry field was used to compared alkyl thiolesters and pharmaceutical products. Clustering analysis of tandem MS data was applied in the proteomics field for the following purposes : (1) to reduce the number of tandem mass spectra used in the identification of proteins by regrouping similar tandem data to decrease the redundancies of the analyses; (2) to improve the understanding of fragmentation patterns (fragmentation vs intensity) to enable improved protein identification algorithms; and (3) to facilitate label free quantification experiments for the discovery of biomarkers. We have developed a novel fully automated approach based on the new interpretation of tandem MS data of HS oligosaccharides extracted from different organ tissues. We applied this approach to a set of data acquired using a automated collisionally activated dissociation (CAD) tandem MS acquisition parameters. Tandem mass spectrometry of 12 targeted precursor ions were acquired on HS oligosaccharides extracted from each of four bovine tissues (aorta, lung, intestine and kidney) acquired automatically in triplicate was the fundaments of the study. The size of the corresponding data set was approximately 2800 features, for which manual comparison was not feasible. We used agglomerative hierarchical clustering (AHC) on the tandem MS data to demonstrate that sufficient information for differentiation of isomeric glycoforms in the four organs samples was present. The analysis was useful for recognition of fragmentation patterns corresponds to organ-specific HS structures.