Identification of hypoxia-regulated proteins using MALDI-mass spectrometry imaging combined with quantitative proteomics.

Hypoxia is present in most solid tumors and is clinically correlated with increased metastasis and poor patient survival. While studies have demonstrated the role of hypoxia and hypoxia-regulated proteins in cancer progression, no attempts have been made to identify hypoxia-regulated proteins using quantitative proteomics combined with MALDI-mass spectrometry imaging (MALDI-MSI). Here we present a comprehensive hypoxic proteome study and are the first to investigate changes in situ using tumor samples. In vitro quantitative mass spectrometry analysis of the hypoxic proteome was performed on breast cancer cells using stable isotope labeling with amino acids in cell culture (SILAC). MS analyses were performed on laser-capture microdissected samples isolated from normoxic and hypoxic regions from tumors derived from the same cells used in vitro. MALDI-MSI was used in combination to investigate hypoxia-regulated protein localization within tumor sections. Here we identified more than 100 proteins, both novel and previously reported, that were associated with hypoxia. Several proteins were localized in hypoxic regions, as identified by MALDI-MSI. Visualization and data extrapolation methods for the in vitro SILAC data were also developed, and computational mapping of MALDI-MSI data to IHC results was applied for data validation. The results and limitations of the methodologies described are discussed.

Longitudinal evaluation of clinically early relapsing-remitting multiple sclerosis with diffusion tensor imaging.

Diffusion tensor imaging (DTI) parameters such as mean diffusivity (MD) and fractional anisotropy (FA) assess aspects of structural integrity within tissue. In relapsing-remitting (RR) multiple sclerosis (MS), abnormalities in normal appearing brain tissue (NABT) have been shown cross-sectionally. The evolution of these abnormalities over time is unclear. We present a longitudinal study investigating early RR MS subjects. The aims were to determine DTI changes over two years and assess the potential of DTI as a longitudinal quantitative marker at this stage of MS. Fifteen controls and 28 patients with RR MS (median disease duration 1.9 years; median EDSS 1.5) had DTI yearly for two years. NABT and whole brain tissue (NABT plus lesions) FA and MD histograms analysed. At baseline, differences in FA were noted between patients and controls (mean [p = 0.042] and peak height [p = 0.008]), while at two years differences in MD were observed (mean [p = 0.008] and peak location [p = 0.024]). However there were no significant DTI differences in longitudinal rates of change between patients and cohorts. In conclusion, although subtle NABT abnormalities were detected in early RR MS, the absence of longitudinal change suggests a limited role for global DTI assessment of NABT in following the early disease course.

Unbiased segmentation of diffusion-weighted magnetic resonance images of the brain using iterative clustering.

Segmentation of diffusion-weighted echo-planar imaging (DW-EPI) is challenging because of concerns regarding spatial resolution and distortion. Methods commonly used require manual input and often need thresholding measures to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). This may introduce operator bias and misclassification error. When comparing patients with a diffuse disease process-such as multiple sclerosis (MS)--with healthy controls, although information from all images may be biased due to disease effect, this is more so if the data set employed to perform segmentation is also used as a measured outcome for the study, for example, fractional anisotropy maps. Presented in this work is an unbiased method for segmenting DW-EPI data sets using the b=0 and single-shot inversion recovery EPI into WM, GM and CSF. The method employs an iterative clustering technique to account for partial volume effects and signal variation caused by radiofrequency inhomogeneity. The technique is evaluated with both real and synthetic brain data and results compared with statistical parametric mapping (SPM02). With synthetic brain data, where a gold standard of segmentation exists, the presented method showed less misclassification compared to SPM02. The unbiased method proposed may provide a more accurate methodology of segmentation in the analysis of DWI-EPI images in conditions such as MS.

Bayesian model averaging for ligand discovery.

High-throughput screening (HTS) is now a standard approach used in the pharmaceutical industry to identify potential drug-like lead molecules. The analysis linking biological data with molecular properties is a major goal in both academic and pharmaceutical research. This paper presents a Bayesian analysis of high-dimensional descriptor data using Markov chain Monte Carlo (MCMC) simulations for learning classification trees as a novel method for pharmacophore and ligand discovery. We use experimentally determined binding affinity data with the protein pyruvate kinase to train and assess our model averaging algorithm and then apply it to a large database of over 3.7 million molecules. We compare the results of a number of variations on the central Bayesian theme to that of two Neural Network (NN) architectures and that of Support Vector Machines (SVM). The main Bayesian algorithm, in addition to achieving high specificity and sensitivity, also lends itself naturally to classifying test sets with missing data and providing a ranking for the classified compounds. The approach has been used to select and rank potential biologically active compounds and could provide a powerful tool in compound testing.