Concise representation of mass spectrometry images by probabilistic latent semantic analysis.

Imaging mass spectrometry (IMS) is a promising technology which allows for detailed analysis of spatial distributions of (bio)molecules in organic samples. In many current applications, IMS relies heavily on (semi)automated exploratory data analysis procedures to decompose the data into characteristic component spectra and corresponding abundance maps, visualizing spectral and spatial structure. The most commonly used techniques are principal component analysis (PCA) and independent component analysis (ICA). Both methods operate in an unsupervised manner. However, their decomposition estimates usually feature negative counts and are not amenable to direct physical interpretation. We propose probabilistic latent semantic analysis (pLSA) for non-negative decomposition and the elucidation of interpretable component spectra and abundance maps. We compare this algorithm to PCA, ICA, and non-negative PARAFAC (parallel factors analysis) and show on simulated and real-world data that pLSA and non-negative PARAFAC are superior to PCA or ICA in terms of complementarity of the resulting components and reconstruction accuracy. We further combine pLSA decomposition with a statistical complexity estimation scheme based on the Akaike information criterion (AIC) to automatically estimate the number of components present in a tissue sample data set and show that this results in sensible complexity estimates.

Mass spectrometric approaches for elucidation of antigenantibody recognition structures in molecular immunology.

Mass spectrometric approaches have recently gained increasing access to molecular immunology and several methods have been developed that enable detailed chemical structure identification of antigen-antibody interactions. Selective proteolytic digestion and MS-peptide mapping (epitope excision) has been successfully employed for epitope identification of protein antigens. In addition, "affinity proteomics" using partial epitope excision has been developed as an approach with unprecedented selectivity for direct protein identification from biological material. The potential of these methods is illustrated by the elucidation of a beta-amyloid plaque-specific epitope recognized by therapeutic antibodies from transgenic mouse models of Alzheimer's disease. Using an immobilized antigen and antibody-proteolytic digestion and analysis by high resolution Fourier transform ion cyclotron resonance mass spectrometry has lead to a new approach for the identification of antibody paratope structures (paratope-excision; "parex-prot"). In this method, high resolution MS-peptide data at the low ppm level are required for direct identification of paratopes using protein databases. Mass spectrometric epitope mapping and determination of "molecular antibody-recognition signatures" offer high potential, especially for the development of new molecular diagnostics and the evaluation of new vaccine lead structures.

Tools and strategies for visualization of large image data sets in high-resolution imaging mass spectrometry.

Mass spectrometry based proteomics is one of the scientific domains in which experiments produce a large amount of data that need special environments to interpret the results. Without the use of suitable tools and strategies, the transformation of the large data sets into information is not easily achievable. Therefore, in the context of the virtual laboratory of enhanced science, software tools are developed to handle mass spectrometry data sets. Using different data processing strategies for visualization, it enables fast mass spectrometric imaging of large surfaces at high-spatial resolution and thus aids in the understanding of various diseases and disorders. This article describes how to optimize the handling and processing of the data sets, including the selection of the most optimal data formats and the use of parallel processing. It also describes the tools and solutions and their application in mass spectrometric imaging strategies, including new measurement principles, image enhancement, and image artifact suppression.

Toward digital staining using imaging mass spectrometry and random forests.

We show on imaging mass spectrometry (IMS) data that the Random Forest classifier can be used for automated tissue classification and that it results in predictions with high sensitivities and positive predictive values, even when intersample variability is present in the data. We further demonstrate how Markov Random Fields and vector-valued median filtering can be applied to reduce noise effects to further improve the classification results in a posthoc smoothing step. Our study gives clear evidence that digital staining by means of IMS constitutes a promising complement to chemical staining techniques.

The molecular scanner in microscope mode.

The combination of microscope mode matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) with protein identification methodology: the molecular scanner, was explored. The molecular scanner approach provides improvement of sensitivity of detection and identification of high-mass proteins in microscope mode IMS. The methodology was tested on protein distributions obtained after separation by sodium dodecyl sulfate/polyacrylamide gel electrophoresis (SDS-PAGE). High-quality, high-spatial-resolution ion images were recorded on a TRIFT-II ion microscope after gold coating of the MALDI sample preparation on the poly(vinylidenedifluoride) capture membranes. The sensitivity of the combined method is estimated to be 5 pmol. The minimum amount of sample consumed, needed for identification, was estimated to be better than 100 fmol. Software tools were developed to analyze the spectral data and to generate broad mass range and single molecular component microscope mode ion images and single mass-to-charge ratio microprobe mode images.