Mass-resolved ion microscope imaging over expanded mass ranges using double-field post-extraction differential acceleration,

Multiple-ion-ejection multi-reflection time-of-flight mass spectrometry for singlereference mass measurements with lapping ion species Review of Scientific Instruments 91, 023201 (2020);

A time-dependent postextraction differential acceleration (PEDA) potential was used to temporally focus increasingly heavy ions in a stigmatic imaging mass spectrometer, allowing them to be imaged with high mass and spatial resolutions over a broad mass-to-charge ( m / z ) range. By applying a linearly rising potential to the ion extraction electrode, sequential m / z ratios were subjected to a changing electric field, allowing their foci to coincide at the detector. Using this approach, at least 75% of the maximum mass resolution was obtained over a 300–600 Da range when the ion microscope was focused around 450 Da, representing more than a 10-fold increase over the conventional single-field PEDA method.

Mass Spectrometry Imaging (MSI) provides a useful tool to divide a tissue section into sub-regions with similar molecular profiles, namely tissue segmentation. However, owing to the lack of ground truth, there is no reliable evaluation approach to assess the validity of unsupervised segmentation outcomes of MSI. We propose a novel solution grounded on a presumption that a segmentation is reliable if it can be reproduced using distinct bio-information extracted from independent sources. Specifically, besides molecular information from MSI data, we also obtain morphological information over a tissue section from its Hematoxylin-Erosin (H&E) stained histopathological image. MSI has high molecular specificity but low spatial resolving power, the H&E image has no molecular specificity but it can capture microscopic details of the tissue with a spatial resolution two magnitudes higher than MSI. The whole H&E image is split into an array of small patches, which correspond to the spatial pixels of MSI. A spectrum of informative morphological features is computed iteratively for each patch and spatial segmentation can be generated by clustering the patches based on their morphological similarities. Adjusted Mutual Information (AMI) score measures the degree of agreement between MSI-based and H&E image-based segmentation outcomes, which is defined by us as an objective and quantitative evaluation metric of segmentation validity. We investigated various candidate morphological features: a combination of Deep Convolution Neural Network (DCNN) features and handcrafted Threshold Adjacency Statistics (TAS) features finally stood out. The most appropriate number of tissue segments was also determined according to AMI score. Moreover, we introduced Co-Clustering algorithm to MSI data to simultaneously group m/z variables and spatial pixels, so potential biomarkers associated to each sub-region were discovered without the need of further analysis. Eventually, by integrating the segmentation outcomes based on MSI and H&E image data, the confidence level of the segment assignment was displayed for each pixel, which offered a much more informative and compelling way to present the segmentation results.

Mass spectrometry imaging (MSI), which localizes molecules in a tag-free, spatially resolved manner, is a powerful tool for the understanding of underlying biochemical mechanisms of biological phenomena. When analyzing MSI data, it is essential to delineate regions of interest (ROIs) that correspond to tissue areas of different anatomical or pathological labels. Spatial segmentation, obtained by clustering MSI pixels according to their mass spectral similarities, is a popular approach to automate ROI definition. However, how to select the number of clusters (#Clusters), which determines the granularity of segmentation, remains to be resolved, and an inappropriate #Clusters may lead to ROIs not biologically real. Here we report a multimodal fusion strategy to enable an objective and trustworthy selection of #Clusters by utilizing additional information from corresponding histology images. A deep learning–based algorithm is proposed to extract “histomorphological feature spectra” across an entire hematoxylin and eosin image. Clustering is then similarly performed to produce histology segmentation. Since ROIs originating from instrumental noise or artifacts would not be reproduced cross-modally, the consistency between histology and MSI segmentation becomes an effective measure of the biological validity of the results. So, #Clusters that maximize the consistency is deemed as most probable. We validated our strategy on mouse kidney and renal tumor specimens by producing multimodally corroborated ROIs that agreed excellently with ground truths. Downstream analysis based on the said ROIs revealed lipid molecules highly specific to tissue anatomy or pathology. Our work will greatly facilitate MSI-mediated spatial lipidomics, metabolomics, and proteomics research by providing intelligent software to automatically and reliably generate ROIs.

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