Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation

Multi-modal mass spectrometry imaging of a single tissue section

Mass spectrometry imaging (MSI) was developed to visualize spatial chemical information within tissues, thereby facilitating spatial multi-omic analysis. However, due to the limited spatial information provided by individual modal MSI, correlating various chemical data within tissues remains a significant challenge. In recent years, multimodal MSI has garnered considerable attention due to its ability to visualize the spatial distributions of multiple biomolecules within tissues. Among the strategies employed in this field, multimodal imaging on a single tissue section circumvents multiple issues introduced by integration of images of consecutive tissue sections. In this minireview, we provide an overview of multimodal MSI on a single tissue section, with a particular focus on the use of Matrix-Assisted Laser Desorption/Ionization-MSI for spatial multi-omic investigations that offer a comprehensive and in-depth elucidation of the biological state and activities, aiming to inspire the development of new approaches in this field.

[Research progress of deep learning applications in mass spectrometry imaging data analysis]

Mass spectrometry imaging (MSI) is a promising method for characterizing the spatial distribution of compounds. Given the diversified development of acquisition methods and continuous improvements in the sensitivity of this technology, both the total amount of generated data and complexity of analysis have exponentially increased, rendering increasing challenges of data postprocessing, such as large amounts of noise, background signal interferences, as well as image registration deviations caused by sample position changes and scan deviations, and etc. Deep learning (DL) is a powerful tool widely used in data analysis and image reconstruction. This tool enables the automatic feature extraction of data by building and training a neural network model, and achieves comprehensive and in-depth analysis of target data through transfer learning, which has great potential for MSI data analysis. This paper reviews the current research status, application progress and challenges of DL in MSI data analysis, focusing on four core stages: data preprocessing, image reconstruction, cluster analysis, and multimodal fusion. The application of a combination of DL and mass spectrometry imaging in the study of tumor diagnosis and subtype classification is also illustrated. This review also discusses trends of development in the future, aiming to promote a better combination of artificial intelligence and mass spectrometry technology.

Untargeted Pixel-by-Pixel Imaging of Metabolite Ratio Pairs as a Novel Tool for Biomedical Discovery in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful technology used to define the spatial distribution and relative abundance of structurally identified and yet-undefined metabolites across tissue cryosections. While numerous software packages enable pixel-by-pixel imaging of individual metabolites, the research community lacks a discovery tool that images all metabolite abundance ratio pairs. Importantly, recognition of correlated metabolite pairs informs discovery of unanticipated molecules contributing to shared metabolic pathways, uncovers hidden metabolic heterogeneity across cells and tissue subregions, and indicates single-timepoint flux through pathways of interest. Here, we describe the development and implementation of an untargeted R package workflow for pixel-by-pixel ratio imaging of all metabolites detected in an MSI experiment. Considering untargeted MSI studies of murine brain and embryogenesis, we demonstrate that ratio imaging minimizes systematic data variation introduced by sample handling and instrument drift, markedly enhances spatial image resolution, and reveals previously unrecognized metabotype-distinct tissue regions. Furthermore, ratio imaging facilitates identification of novel regional biomarkers and provides anatomical information regarding spatial distribution of metabolite-linked biochemical pathways. The algorithm described herein is applicable to any MSI dataset containing spatial information for metabolites, peptides or proteins, offering a potent tool to enhance knowledge obtained from current spatial metabolite profiling technologies.

aws-s3-integrity-check: an open-source bash tool to verify the integrity of a dataset stored on Amazon S3

Amazon Simple Storage Service (Amazon S3) is a widely used platform for storing large biomedical datasets. Unintended data alterations can occur during data writing and transmission, altering the original content and generating unexpected results. However, no open-source and easy-to-use tool exists to verify end-to-end data integrity. Here, we present aws-s3-integrity-check, a user-friendly, lightweight, and reliable bash tool to verify the integrity of a dataset stored in an Amazon S3 bucket. Using this tool, we only needed ∼114 min to verify the integrity of 1,045 records ranging between 5 bytes and 10 gigabytes and occupying ∼935 gigabytes of the Amazon S3 cloud. Our aws-s3-integrity-check tool also provides file-by-file on-screen and log-file-based information about the status of each integrity check. To our knowledge, this tool is the only open-source one that allows verifying the integrity of a dataset uploaded to the Amazon S3 Storage quickly, reliably, and efficiently. The tool is freely available for download and use at https://github.com/SoniaRuiz/aws-s3-integrity-check and https://hub.docker.com/r/soniaruiz/aws-s3-integrity-check.

Delineating regions of interest for mass spectrometry imaging by multimodally corroborated spatial segmentation

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.