Ratiometric Mass Spectrometry Imaging for Stain-Free Delineation of Ischemic Tissue and Spatial Profiling of Ischemia-Related Molecular Signatures

Recent developments and applications of ambient mass spectrometry imaging in pharmaceutical research: an overview

The application of ambient mass spectrometry imaging "MSI" is expanding in the areas of fundamental research on drug delivery and multiple phases of the process of identifying and developing drugs. Precise monitoring of a drug's pharmacological workflows, such as intake, distribution, metabolism, and discharge, is made easier by MSI's ability to determine the concentrations of the initiating drug and its metabolites across dosed samples without losing spatial data. Lipids, glycans, and proteins are just a few of the many phenotypes that MSI may be used to concurrently examine. Each of these substances has a particular distribution pattern and biological function throughout the body. MSI offers the perfect analytical tool for examining a drug's pharmacological features, especially in vitro and in vivo effectiveness, security, probable toxic effects, and putative molecular pathways, because of its high responsiveness in chemical and physical environments. The utilization of MSI in the field of pharmacy has further extended from the traditional tissue examination to the early stages of drug discovery and development, including examining the structure-function connection, high-throughput capabilities in vitro examination, and ex vivo research on individual cells or tumor spheroids. Additionally, an enormous array of endogenous substances that may function as tissue diagnostics can be scanned simultaneously, giving the specimen a highly thorough characterization. Ambient MSI techniques are soft enough to allow for easy examination of the native sample to gather data on exterior chemical compositions. This paper provides a scientific and methodological overview of ambient MSI utilization in research on pharmaceuticals.

Ion-to-Image, i2i, a Mass Spectrometry Imaging Data Analysis Platform for Continuous Ionization Techniques

Mass spectrometry imaging (MSI) techniques generate data that reveal spatial distributions of molecules on a surface with high sensitivity and selectivity. However, processing large volumes of mass spectrometry data into useful ion images is not trivial. Furthermore, data from MSI techniques using continuous ionization sources where data are acquired in line scans require different data handling strategies compared to data collected from pulsed ionization sources where data are acquired in grids. In addition, for continuous ionization sources, the pixel dimensions are influenced by the mass spectrometer duty cycle, which, in turn, can be controlled by the automatic gain control (AGC) for each spectrum (pixel). Currently, there is a lack of data-handling software for MSI data generated with continuous ionization sources and AGC. Here, we present ion-to-image (i2i), which is a MATLAB-based application for MSI data acquired with continuous ionization sources, AGC, high resolution, and one or several scan filters. The source code and a compiled installer are available at https://github.com/LanekoffLab/i2i. The application includes both quantitative, targeted, and nontargeted data processing strategies and enables complex data sets to be processed in minutes. The i2i application has high flexibility for generating, processing, and exporting MSI data both from simple full scans and more complex scan functions interlacing MSn and SIM scan data sets, and we anticipate that it will become a valuable addition to the existing MSI software toolbox.

Data-Driven Deciphering of Latent Lesions in Heterogeneous Tissue Using Function-Directed t-SNE of Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI), which quantifies the underlying chemistry with molecular spatial information in tissue, represents an emerging tool for the functional exploration of pathological progression. Unsupervised machine learning of MSI datasets usually gives an overall interpretation of the metabolic features derived from the abundant ions. However, the features related to the latent lesions are always concealed by the abundant ion features, which hinders precise delineation of the lesions. Herein, we report a data-driven MSI data segmentation approach for recognizing the hidden lesions in the heterogeneous tissue without prior knowledge, which utilizes one-step prediction for feature selection to generate function-specific segmentation maps of the tissue. The performance and robustness of this approach are demonstrated on the MSI datasets of the ischemic rat brain tissues and the human glioma tissue, both possessing different structural complexity and metabolic heterogeneity. Application of the approach to the MSI datasets of the ischemic rat brain tissues reveals the location of the ischemic penumbra, a hidden zone between the ischemic core and the healthy tissue, and instantly discovers the metabolic signatures related to the penumbra. In view of the precise demarcation of latent lesions and the screening of lesion-specific metabolic signatures in tissues, this approach has great potential for in-depth exploration of the metabolic organization of complex tissue.