The identification of tumor subpopulations that adversely affect patient outcomes is essential for a more targeted investigation into how tumors develop detrimental phenotypes, as well as for personalized therapy. Mass spectrometry imaging has demonstrated the ability to uncover molecular intratumor heterogeneity. The challenge has been to conduct an objective analysis of the resulting data to identify those tumor subpopulations that affect patient outcome. Here we introduce spatially mapped t-distributed stochastic neighbor embedding (t-SNE), a nonlinear visualization of the data that is able to better resolve the biomolecular intratumor heterogeneity. In an unbiased manner, t-SNE can uncover tumor subpopulations that are statistically linked to patient survival in gastric cancer and metastasis status in primary tumors of breast cancer.intratumor heterogeneity | mass spectrometry imaging | t-SNE | biomarker | cancer M ass spectrometry imaging (MSI) is a technology that simultaneously provides the spatial distribution of hundreds of biomolecules directly from tissue (1, 2). The two most common techniques, matrix-assisted laser desorption and desorption electrospray ionization, lead to minimal loss of histological information. Accordingly, the same tissue section can be histologically assessed and registered to the MSI dataset. In this manner, the mass spectral signatures of specific cell types or histopathological entities (e.g., tumor cells) can be extracted from the often highly heterogeneous tissues encountered in patient tumors (3). This high cellular specificity is behind the increasing popularity of MSI in cancer research and its proven ability to identify diagnostic and prognostic biomarkers (4).There is growing awareness that MSI also can be used to annotate tissues based on the local mass spectrometry profiles and thereby differentiate tissues/regions that are not histologically distinct. Deininger et al. (5) were the first to report that MSI may reveal the biomolecular intratumor heterogeneity associated with a tumor's clonal development. A hierarchical cluster analysis of the MSI data revealed a patchwork of molecularly distinct regions, which were postulated to reflect the tumor's clonal evolution. It was recently demonstrated that such an approach, using multivariate analysis of the MSI data to identify regions with distinct mass spectral signatures and then linking these molecularly distinct regions to patient outcome, enables the identification of tumor subpopulations that are statistically associated with poor survival and tumor metastasis (6).All methods used to date for revealing intratumor heterogeneity have been linear dimensionality-reduction techniques, but this linearity constraint focuses the results on the global characteristics of the data space at the expense of finer details (7). Accordingly, linear methods might not be sensitive to the subtle changes expected to demarcate the clonal progression of tumors, in which the molecular differences between nearly sequential subpopulations may...

Previous MRI studies reported cortical iron accumulation in early-onset (EOAD) compared to late-onset (LOAD) Alzheimer disease patients. However, the pattern and origin of iron accumulation is poorly understood. This study investigated the histopathological correlates of MRI contrast in both EOAD and LOAD. T2*-weighted MRI was performed on postmortem frontal cortex of controls, EOAD, and LOAD. Images were ordinally scored using predefined criteria followed by histology. Nonlinear histology-MRI registration was used to calculate pixel-wise spatial correlations based on the signal intensity. EOAD and LOAD were distinguishable based on 7T MRI from controls and from each other. Histology-MRI correlation analysis of the pixel intensities showed that the MRI contrast is best explained by increased iron accumulation and changes in cortical myelin, whereas amyloid and tau showed less spatial correspondence with T2*-weighted MRI. Neuropathologically, subtypes of Alzheimer's disease showed different patterns of iron accumulation and cortical myelin changes independent of amyloid and tau that may be detected by high-field susceptibility-based MRI.

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.

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.

Matrix assisted laser desorption ionization mass spectrometry imaging (MALDI MSI) is an emerging analytical technique, which generates spatially resolved proteomic and metabolomic images from tissue specimens. Conventional MALDI MSI processing and data acquisition can take over 30 min, limiting its clinical utility for intraoperative diagnostics. We present a rapid MALDI MSI method, completed under 5 min, including sample preparation and analysis, providing a workflow compatible with the clinical frozen section procedure.