Browsing by Subject "Tissue analysis"

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    Label-free quantitative imaging of cholesterol in intact tissues by hyperspectral stimulated Raman scattering microscopy
    (Wiley, 2013-12) Wang, Ping; Li, Junjie; Wang, Pu; Hu, Chun-Rui; Zhang, Delong; Sturek, Michael; Cheng, Ji-Xin; Department of Cellular & Integrative Physiology, School of Medicine
    A finger on the pulse: Current molecular analysis of cells and tissues routinely relies on separation, enrichment, and subsequent measurements by various assays. Now, a platform of hyperspectral stimulated Raman scattering microscopy has been developed for the fast, quantitative, and label-free imaging of biomolecules in intact tissues using spectroscopic fingerprints as the contrast mechanism.
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    Multivariate statistical differentiation of renal cell carcinomas based on lipidomic analysis by ambient ionization imaging mass spectrometry
    (Springer, 2010) Dill, Allison L.; Eberlin, Livia S.; Zheng, Cheng; Costa, Anthony B.; Ifa, Demian R.; Cheng, Liang; Masterson, Timothy A.; Koch, Michael O.; Vitek, Olga; Cooks, R. Graham; Pathology and Laboratory Medicine, School of Medicine
    Desorption electrospray ionization (DESI) mass spectrometry (MS) was used in an imaging mode to interrogate the lipid profiles of thin tissue sections of 11 sample pairs of human papillary renal cell carcinoma (RCC) and adjacent normal tissue and nine sample pairs of clear cell RCC and adjacent normal tissue. DESI-MS images showing the spatial distributions of particular glycerophospholipids (GPs) and free fatty acids in the negative ion mode were compared to serial tissue sections stained with hematoxylin and eosin (H&E). Increased absolute intensities as well as changes in relative abundance were seen for particular compounds in the tumor regions of the samples. Multivariate statistical analysis using orthogonal projection to latent structures treated partial least square discriminate analysis (PLS-DA) was used for visualization and classification of the tissue pairs using the full mass spectra as predictors. PLS-DA successfully distinguished tumor from normal tissue for both papillary and clear cell RCC with misclassification rates obtained from the validation set of 14.3% and 7.8%, respectively. It was also used to distinguish papillary and clear cell RCC from each other and from the combined normal tissues with a reasonable misclassification rate of 23%, as determined from the validation set. Overall DESI-MS imaging combined with multivariate statistical analysis shows promise as a molecular pathology technique for diagnosing cancerous and normal tissue on the basis of GP profiles.