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.
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