Desorption electrospray ionization (DESI) is an ambient mass spectrometry (MS) technique that can be operated in an imaging mode. It is known to provide valuable information on disease state and grade based on lipid profiles in tissue sections. Comprehensive exploration of the spatial and chemical information contained in 2D MS images requires further development of methods for data treatment and interpretation in conjunction with multivariate analysis. In this study, we employ an interactive approach based on principal component analysis (PCA) to interpret the chemical and spatial information obtained from MS imaging of human bladder, kidney, germ cell and prostate cancer and adjacent normal tissues. Normal and tumor human tissue sections 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 range m/z 150-1000. For each DESI-MS image, the information was coded using the corresponding data hypercube. 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. Multivariate analyses were carried through in-house Matlab routines. This multivariate strategy facilitated characterization between tumor and normal tissue by comparing the lipid information with pathological evaluation of the same samples. Some common lipid ions, such as those of m/z 885.5 and m/z 788.5, nominally PI (18:0/20:4) and PS (18:0/18:1), as well as ions of free fatty acids and their dimers, appeared to be highly characterizing for different types of human cancers, while other ions, such as those of m/z 465.5 (cholesterol sulfate) for prostate cancer tissue and m/z 795.5 (seminolipid 16:0/16:0) for germ tissue, appeared to be extremely selective for the type of tissue analyzed. These data confirm that lipid profiles can reflect not only the disease/health state of tissue but also are characteristic of tissue type. The interactive strategy presented here – based on a brushing approach – is particularly useful to visualize the information contained in hyperspectral MS images by automatically connecting regions of PCA score space to pixels of the 2D physical object. The procedures developed in this study consider all the spectral variables and their inter-correlations, and guide subsequent investigations of the mass spectra and single ion images, allowing one to perform thorough characterizations of different regions of any DESI-MS image.
Interactive hyperspectral approach for exploring and interpreting DESI-MS images of cancerous and normal tissue sections
PIRRO, VALENTINA;
2012-01-01
Abstract
Desorption electrospray ionization (DESI) is an ambient mass spectrometry (MS) technique that can be operated in an imaging mode. It is known to provide valuable information on disease state and grade based on lipid profiles in tissue sections. Comprehensive exploration of the spatial and chemical information contained in 2D MS images requires further development of methods for data treatment and interpretation in conjunction with multivariate analysis. In this study, we employ an interactive approach based on principal component analysis (PCA) to interpret the chemical and spatial information obtained from MS imaging of human bladder, kidney, germ cell and prostate cancer and adjacent normal tissues. Normal and tumor human tissue sections 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 range m/z 150-1000. For each DESI-MS image, the information was coded using the corresponding data hypercube. 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. Multivariate analyses were carried through in-house Matlab routines. This multivariate strategy facilitated characterization between tumor and normal tissue by comparing the lipid information with pathological evaluation of the same samples. Some common lipid ions, such as those of m/z 885.5 and m/z 788.5, nominally PI (18:0/20:4) and PS (18:0/18:1), as well as ions of free fatty acids and their dimers, appeared to be highly characterizing for different types of human cancers, while other ions, such as those of m/z 465.5 (cholesterol sulfate) for prostate cancer tissue and m/z 795.5 (seminolipid 16:0/16:0) for germ tissue, appeared to be extremely selective for the type of tissue analyzed. These data confirm that lipid profiles can reflect not only the disease/health state of tissue but also are characteristic of tissue type. The interactive strategy presented here – based on a brushing approach – is particularly useful to visualize the information contained in hyperspectral MS images by automatically connecting regions of PCA score space to pixels of the 2D physical object. The procedures developed in this study consider all the spectral variables and their inter-correlations, and guide subsequent investigations of the mass spectra and single ion images, allowing one to perform thorough characterizations of different regions of any DESI-MS image.
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