Desorption electrospray ionization mass spectrometry (DESI-MS) imaging provides valuable information on disease state and grade based on lipid profiles in tissue sections. Hyperspectral imaging (HSI) and principal component analysis (PCA) provide a comprehensive exploration of the spatial and multivariate chemical information contained in MS images, allowing efficient characterizations of biological tissues [1]. In this study, a multivariate class-modelling tool has been deployed after data compression by principal components to identify neoplastic conditions in biopsied human tissues. Normal and tumor tissue sections of kidney, bladder, germ and prostate human tissue were imaged using a commercial LTQ linear ion trap mass spectrometer and a lab-built prototype DESI-MS ion source. Experiments were carried out in the negative ion mode, using a 5 kV spray voltage, a flow rate of 1.5 μL/min, and 150 psi nebulizing gas (N2) pressure. The spray solvent was acetonitrile:water (50:50 v/v). Mass spectra were acquired over the m/z range 50-1000. Reduction of the spectral variables (1:4) by a consecutive-window averaging algorithm, normalization with respect to the total ion current (TIC) and column-centering were performed. For each organ, representative training images for normal and tumor tissues were built and efficiency of class models in recognizing tumor tissue was evaluated on new DESI-MS imaged samples, which were independently characterized as tumor or normal tissue by pathological examination. In more detail, the class-modelling method unequal dispersed classes (UNEQ) was applied on the PC-reduced data. Preliminary results show the suitability of such a supervised strategy to identify tumor tissue in biopsied human tissues, which can be used as a valuable tool in cancer diagnosis, complementary to traditional histopathological tissue examination.
Identification of neoplastic condition in biopsied human tissues by supervised analysis of hyperspectral desi-ms images
PIRRO, VALENTINA;
2013-01-01
Abstract
Desorption electrospray ionization mass spectrometry (DESI-MS) imaging provides valuable information on disease state and grade based on lipid profiles in tissue sections. Hyperspectral imaging (HSI) and principal component analysis (PCA) provide a comprehensive exploration of the spatial and multivariate chemical information contained in MS images, allowing efficient characterizations of biological tissues [1]. In this study, a multivariate class-modelling tool has been deployed after data compression by principal components to identify neoplastic conditions in biopsied human tissues. Normal and tumor tissue sections of kidney, bladder, germ and prostate human tissue were imaged using a commercial LTQ linear ion trap mass spectrometer and a lab-built prototype DESI-MS ion source. Experiments were carried out in the negative ion mode, using a 5 kV spray voltage, a flow rate of 1.5 μL/min, and 150 psi nebulizing gas (N2) pressure. The spray solvent was acetonitrile:water (50:50 v/v). Mass spectra were acquired over the m/z range 50-1000. Reduction of the spectral variables (1:4) by a consecutive-window averaging algorithm, normalization with respect to the total ion current (TIC) and column-centering were performed. For each organ, representative training images for normal and tumor tissues were built and efficiency of class models in recognizing tumor tissue was evaluated on new DESI-MS imaged samples, which were independently characterized as tumor or normal tissue by pathological examination. In more detail, the class-modelling method unequal dispersed classes (UNEQ) was applied on the PC-reduced data. Preliminary results show the suitability of such a supervised strategy to identify tumor tissue in biopsied human tissues, which can be used as a valuable tool in cancer diagnosis, complementary to traditional histopathological tissue examination.
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
