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.

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).

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.

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.

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).