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Mass spectral profiling of glycosaminoglycans from histological tissue surfaces.

ABSTRACT: Glycosaminoglycans (GAGs) are found in intracellular granules, cell surfaces, and extracellular matrices in a spatially and temporally regulated fashion, constituting the environment for cells to interact, migrate, and proliferate. Through binding with a great number of proteins, GAGs regulate many facets of biological processes from embryonic development to normal physiological functions. GAGs have been shown to be involved in pathologic changes and immunological responses including cancer metastasis and inflammation. Past analyses of GAGs have focused on cell lines, body fluids, and relatively large tissue samples. Structures determined from such samples reflect the heterogeneity of the cell types present. To gain an understanding of the roles played by GAG expression during pathogenesis, it is very important to be able to detect and profile GAGs at the histological scale so as to minimize cell heterogeneity to potentially inform diagnosis and prognosis. Heparan sulfate (HS) belongs to one major class of GAGs, characterized by dramatic structural heterogeneity and complexity. To demonstrate feasibility of analysis of HS, 15 ?m frozen bovine brain stem, cortex, and cerebellum tissue sections were washed with a series of solvent solutions to remove lipids before applying heparin lyases I, II, and III on the tissue surfaces within 5 mm × 5 mm digestion spots. The digested HS disaccharides were extracted from tissue surfaces and then analyzed by using size exclusion chromatography/mass spectrometry (SEC-MS). The results from bovine brain stem, cortex, and cerebellum demonstrated the reproducibility and reliability of our profiling method. We applied our method to detect HS from human astrocytoma (WHO grade II) and glioblastoma (GBM, WHO grade IV) frozen slides. Higher HS abundances and lower average sulfation level of HS were detected in glioblastoma (GBM, WHO grade IV) slides compared to astrocytoma (WHO grade II) slides.

SUBMITTER: Shao C

PROVIDER: S-EPMC3872031 | biostudies-literature | 2013 Nov

REPOSITORIES: biostudies-literature

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Mass spectral profiling of glycosaminoglycans from histological tissue surfaces.

Shao Chun C Shi Xiaofeng X Phillips Joanna J JJ Zaia Joseph J

Analytical chemistry 20131029 22

Glycosaminoglycans (GAGs) are found in intracellular granules, cell surfaces, and extracellular matrices in a spatially and temporally regulated fashion, constituting the environment for cells to interact, migrate, and proliferate. Through binding with a great number of proteins, GAGs regulate many facets of biological processes from embryonic development to normal physiological functions. GAGs have been shown to be involved in pathologic changes and immunological responses including cancer meta ...[more]

PMID: 24099043

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Project description:Characterization of the molecular attributes and spatial arrangements of cells and features within complex human tissues provides a critical basis for understanding processes involved in development and disease. Moreover, the ability to automate steps in the analysis and interpretation of histological images that currently require manual inspection by pathologists could revolutionize medical diagnostics. Toward this end, we developed a new imaging approach called multidimensional microscopic molecular profiling (MMMP) that can measure several independent molecular properties in situ at subcellular resolution for the same tissue specimen. MMMP involves repeated cycles of antibody or histochemical staining, imaging, and signal removal, which ultimately can generate information analogous to a multidimensional flow cytometry analysis on intact tissue sections. We performed a MMMP analysis on a tissue microarray containing a diverse set of 102 human tissues using a panel of 15 informative antibody and 5 histochemical stains plus DAPI. Large-scale unsupervised analysis of MMMP data, and visualization of the resulting classifications, identified molecular profiles that were associated with functional tissue features. We then directly annotated H&E images from this MMMP series such that canonical histological features of interest (e.g. blood vessels, epithelium, red blood cells) were individually labeled. By integrating image annotation data, we identified molecular signatures that were associated with specific histological annotations and we developed statistical models for automatically classifying these features. The classification accuracy for automated histology labeling was objectively evaluated using a cross-validation strategy, and significant accuracy (with a median per-pixel rate of 77% per feature from 15 annotated samples) for de novo feature prediction was obtained. These results suggest that high-dimensional profiling may advance the development of computer-based systems for automatically parsing relevant histological and cellular features from molecular imaging data of arbitrary human tissue samples, and can provide a framework and resource to spur the optimization of these technologies.

Dataset Information

Mass spectral profiling of glycosaminoglycans from histological tissue surfaces.

Publications

Mass spectral profiling of glycosaminoglycans from histological tissue surfaces.

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