Using a single, high mass resolution mass spectrometry platform to investigate ion suppression effects observed during tissue imaging.

RATIONALE: The signal intensity of a given molecule across a tissue section when measured using mass spectrometry imaging (MSI) is prone to changes caused by the molecular heterogeneity across the surface of the tissue. Here we propose a strategy to investigate these effects using electrospray ionization (ESI) and matrix-assisted laser desorption/ionization (MALDI) on a single high-resolution mass spectrometry (HRMS) platform. METHODS: A rat was administered with a single inhaled dose of a compound and sacrificed 1 h after dosing. Sections were prepared from the excised frozen lung and analysed using MALDI, liquid extraction surface analysis (LESA) nano-ESI-MS and nano-ESI liquid chromatography (LC)/MS. The ESI and MALDI ion sources were mounted either side of the ion transfer system of the same Fourier transform ion cyclotron resonance (FT-ICR) mass spectrometer. RESULTS: MALDI MSI clearly demonstrated widespread distribution of the dosed molecule throughout the lung, with the exception of a non-lung section of tissue on the same sample surface. Comparison of the lipid signals across the sample indicated a change in signal between the lung and the adipose tissue present on the same section. Use of ESI and MALDI, with and without an internal standard, supported the evaluation of changes in the signal of the dosed molecule across the tissue section. CONCLUSIONS: The results demonstrate the successful application of a dual ion source HRMS system to the systematic evaluation of data from MALDI MSI, used to determine the distribution of an inhaled drug in the lung. The system discussed is of great utility in investigating the effects of ion suppression and evaluating the quantitative and qualitative nature of the MSI data.

On-tissue dataset-dependent MALDI-TIMS-MS2 bioimaging.

Trapped ion mobility spectrometry (TIMS) adds an additional separation dimension to mass spectrometry (MS) imaging, however, the lack of fragmentation spectra (MS2) impedes confident compound annotation in spatial metabolomics. Here, we describe spatial ion mobility-scheduled exhaustive fragmentation (SIMSEF), a dataset-dependent acquisition strategy that augments TIMS-MS imaging datasets with MS2 spectra. The fragmentation experiments are systematically distributed across the sample and scheduled for multiple collision energies per precursor ion. Extendable data processing and evaluation workflows are implemented into the open source software MZmine. The workflow and annotation capabilities are demonstrated on rat brain tissue thin sections, measured by matrix-assisted laser desorption/ionisation (MALDI)-TIMS-MS, where SIMSEF enables on-tissue compound annotation through spectral library matching and rule-based lipid annotation within MZmine and maps the (un)known chemical space by molecular networking. The SIMSEF algorithm and data analysis pipelines are open source and modular to provide a community resource.

MALDI imaging combined with hierarchical clustering as a new tool for the interpretation of complex human cancers.

Proteomics analyses have been exploited for the discovery of novel biomarkers for the early recognition and prognostic stratification of cancer patients. These analyses have now been extended to whole tissue sections by using a new tool, that is, MALDI imaging. This allows the spatial resolution of protein and peptides and their allocation to histoanatomical structures. Each MALDI imaging data set contains a large number of proteins and peptides, and their analysis can be quite tedious. We report here a new approach for the analysis of MALDI imaging results. Mass spectra are classified by hierarchical clustering by similarity and the resulting tissue classes are compared with the histology. The same approach is used to compare data sets of different patients. Tissue sections of gastric cancer and non-neoplastic mucosa obtained from 10 patients were forwarded to MALDI-Imaging. The in situ proteome expression was analyzed by hierarchical clustering and by principal component analysis (PCA). The reconstruction of images based on principal component scores allowed an unsupervised feature extraction of the data set. Generally, these images were in good agreement with the histology of the samples. The hierarchical clustering allowed a quick and intuitive access to the multidimensional information in the data set. It allowed a quick selection of spectra classes representative for different tissue features. The use of PCA for the comparison of MALDI spectra from different patients showed that the tumor and non-neoplastic mucosa are separated in the first three principal components. MALDI imaging in combination with hierarchical clustering allows the comprehensive analysis of the in situ cancer proteome in complex human cancers. On the basis of this cluster analysis, classification of complex human tissues is possible and opens the way for specific and cancer-related in situ biomarker analysis and identification.