Brain Region-Specific Dynamics of On-Tissue Protein Digestion Using MALDI Mass Spectrometry Imaging

In mass spectrometry imaging (MSI), on-tissue proteolytic digestion is performed to access larger protein species and to assign protein identities through matching the detected peaks with those obtained by LC–MS/MS analyses of tissue extracts. The on-tissue proteolytic digestion also allows the analysis of proteins from formalin-fixed, paraffin-embedded tissues. For these reasons, on-tissue digestion-based MSI is frequently used in clinical investigations, for example, to determine changes in protein content and distribution associated with a disease. In this work, we sought to investigate the completeness and uniformity of the digestion in on-tissue digestion MSI. On the basis of an extensive experiment investigating three groups with varying incubation times: (i) 1.5 h, (ii) 3 h, and (iii) 18 h, we have found that longer incubation times improve the repeatability of the analyses. Furthermore, we discovered morphology-associated differences in the completeness of the proteolysis for short incubation times. These results support the notion that a more complete proteolysis allows better quantitation

Comprehensive Analysis of the Mouse Brain Proteome Sampled in Mass Spectrometry Imaging

On-tissue enzymatic digestion is performed in mass spectrometry imaging (MSI) experiments to access larger proteins and to assign protein identities. Most on-tissue digestion MSI studies have focused on method development rather than identifying the molecular features observed. Herein, we report a comprehensive study of the mouse brain proteome sampled by MSI. Using complementary proteases, we were able to identify 5337 peptides in the matrix-assisted laser desorption/ionization (MALDI) matrix, corresponding to 1198 proteins. 630 of these peptides, corresponding to 280 proteins, could be assigned to peaks in MSI data sets. Gene ontology and pathway analyses revealed that many of the proteins are involved in neurodegenerative disorders, such as Alzheimer’s, Parkinson’s, and Huntington’s disease

Automatic Registration of Mass Spectrometry Imaging Data Sets to the Allen Brain Atlas

Mass spectrometry imaging holds great potential for understanding the molecular basis of neurological disease. Several key studies have demonstrated its ability to uncover disease-related biomolecular changes in rodent models of disease, even if highly localized or invisible to established histological methods. The high analytical reproducibility necessary for the biomedical application of mass spectrometry imaging means it is widely developed in mass spectrometry laboratories. However, many lack the expertise to correctly annotate the complex anatomy of brain tissue, or have the capacity to analyze the number of animals required in preclinical studies, especially considering the significant variability in sizes of brain regions. To address this issue, we have developed a pipeline to automatically map mass spectrometry imaging data sets of mouse brains to the Allen Brain Reference Atlas, which contains publically available data combining gene expression with brain anatomical locations. Our pipeline enables facile and rapid interanimal comparisons by first testing if each animal’s tissue section was sampled at a similar location and enabling the extraction of the biomolecular signatures from specific brain regions

Automatic Generic Registration of Mass Spectrometry Imaging Data to Histology Using Nonlinear Stochastic Embedding

The combination of mass spectrometry imaging and histology has proven a powerful approach for obtaining molecular signatures from specific cells/tissues of interest, whether to identify biomolecular changes associated with specific histopathological entities or to determine the amount of a drug in specific organs/compartments. Currently there is no software that is able to explicitly register mass spectrometry imaging data spanning different ionization techniques or mass analyzers. Accordingly, the full capabilities of mass spectrometry imaging are at present underexploited. Here we present a fully automated generic approach for registering mass spectrometry imaging data to histology and demonstrate its capabilities for multiple mass analyzers, multiple ionization sources, and multiple tissue types