Changes of Mass Spectra Patterns on a Brain Tissue Section Revealed by Deep Learning with Imaging Mass Spectrometry Data.

The characteristic patterns of mass spectra in imaging mass spectrometry (IMS) strongly reflect the tissue environment. However, the boundaries formed where different tissue environments collide have not been visually assessed. In this study, IMS and convolutional neural network (CNN), one of the deep learning methods, were applied to the extraction of characteristic mass spectra patterns from training brain regions on rodents' brain sections. CNN produced classification models with high accuracy and low loss rate in any test data sets of mouse coronal sections measured by desorption electrospray ionization (DESI)-IMS and of mouse and rat sagittal sections by matrix-assisted laser desorption (MALDI)-IMS. On the basis of the extracted mass spectra pattern features, the histologically plausible segmentation and classification score imaging of the brain sections were obtained. The boundary imaging generated from classification scores showed the extreme changes of mass spectra patterns between the tissue environments, with no significant buffer zones for the intermediate state. The CNN-based analysis of IMS data is a useful tool for visually assessing the changes of mass spectra patterns on a tissue section, and it will contribute to a comprehensive view of the tissue environment.

Unsupervised machine learning using an imaging mass spectrometry dataset automatically reassembles grey and white matter.

Current histological and anatomical analysis techniques, including fluorescence in situ hybridisation, immunohistochemistry, immunofluorescence, immunoelectron microscopy and fluorescent fusion protein, have revealed great distribution diversity of mRNA and proteins in the brain. However, the distributional pattern of small biomolecules, such as lipids, remains unclear. To this end, we have developed and optimised imaging mass spectrometry (IMS), a combined technique incorporating mass spectrometry and microscopy, which is capable of comprehensively visualising biomolecule distribution. We demonstrated the differential distribution of phospholipids throughout the cell body and axon of neuronal cells using IMS analysis. In this study, we used solarix XR, a high mass resolution and highly sensitive MALDI-FT-ICR-MS capable of detecting higher number of molecules than conventional MALDI-TOF-MS instruments, to create a molecular distribution dataset. We examined the diversity of biomolecule distribution in rat brains using IMS and hypothesised that unsupervised machine learning reconstructs brain structures such as the grey and white matters. We have demonstrated that principal component analysis (PCA) can reassemble the grey and white matters without assigning brain anatomical regions. Hierarchical clustering allowed us to classify the 10 groups of observed molecules according to their distributions. Furthermore, the group of molecules specifically localised in the cerebellar cortex was estimated to be composed of phospholipids.