Mass spectrometry imaging (MSI) is an important analytical technique that simultaneously reports the spatial location and abundance of detected ions in biological, chemical, clinical, and pharmaceutical studies. As MSI grows in popularity, it has become evident that data reporting varies among different research groups and between techniques. The lack of consistency in data reporting inherently creates additional challenges in comparing intra‐ and inter‐laboratory MSI data. In this tutorial, we propose a unified data reporting system, SMART, based on the common features shared between techniques. While there are limitations to any reporting system, SMART was decided upon after significant discussion to more easily understand and benchmark MSI data. SMART is not intended to be comprehensive but rather capture essential baseline information for a given MSI study; this could be within a study (e.g., effect of spot size on the measured ion signals) or between two studies (e.g., different MSI platform technologies applied to the same tissue type). This tutorial does not attempt to address the confidence with which annotations are made nor does it deny the importance of other parameters that are not included in the current SMART format. Ultimately, the goal of this tutorial is to discuss the necessity of establishing a uniform reporting system to communicate MSI data in publications and presentations in a simple format to readily interpret the parameters and baseline outcomes of the data.

The field of mass spectrometry imaging (MSI) is constantly evolving to analyze a diverse array of biological systems. A common goal is the need to resolve cellular and subcellular heterogeneity with high spatial resolution. As the field continues to progress towards high spatial resolution, other parameters must be considered when developing a practical method. Here, we discuss the impacts of high spatial resolution on the time of acquisition and the associated implications they have on an MSI analysis (e.g., area of the region of interest). This work presents a brief tutorial serving to evaluate high spatial resolution MSI relative to time of acquisition and data file size.

Amyotrophic lateral sclerosis (ALS) is an idiopathic, fatal neurodegenerative disease characterized by progressive loss of motor function with an average survival time of 2–5 years after diagnosis. Due to the lack of signature biomarkers and heterogenous disease phenotypes, a definitive diagnosis of ALS can be challenging. Comprehensive investigation of this disease is imperative to discovering unique features to expedite the diagnostic process and improve diagnostic accuracy. Here, we present untargeted metabolomics by mass spectrometry imaging (MSI) for comparing sporadic ALS (sALS) and C9orf72 positive (C9Pos) post-mortem frontal cortex human brain tissues against a control cohort. The spatial distribution and relative abundance of metabolites were measured by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MSI for association to biological pathways. Proteomic studies on the same patients were completed via LC-MS/MS in a previous study, and results were integrated with imaging metabolomics results to enhance the breadth of molecular coverage. Utilizing METASPACE annotation platform and MSiPeakfinder, nearly 300 metabolites were identified across the sixteen samples, where 25 were identified as dysregulated between disease cohorts. The dysregulated metabolites were further examined for their relevance to alanine, aspartate, and glutamate metabolism, glutathione metabolism, and arginine and proline metabolism. The dysregulated pathways discussed are consistent with reports from other ALS studies. To our knowledge, this work is the first of its kind, reporting on the investigation of ALS post-mortem human brain tissue analyzed by multiomic MSI.

Numerous preparatory methods have been developed to preserve the cellular and structural integrity of various biological tissues for different -omics studies. Herein, two preparatory methods for mass spectrometry imaging (MSI) were evaluated, fresh-frozen and sucrose-embedded, paraformaldehyde (PFA) fixed, in terms of ion abundance, putative lipid identifications, and preservation of analyte spatial distributions. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI)-MSI was utilized to compare the preparatory methods of interest with and without the use of the conventional ice matrix. There were 2.5-fold and 1.6-fold more lipid species putatively identified in positive-and negative-ion modes, respectively, for sucrose-embedded, PFA-fixed tissues without an ice matrix relative to the current IR-MALDESI-MSI gold-standard, fresh-frozen tissue preparation with an exogenous ice matrix. Furthermore, sucrose-embedded tissues demonstrated improved spatial distribution of ions resulting from the cryo-protective property of sucrose and paraformaldehyde fixation. Evidence from these investigations supports sucrose-embedding without ice matrix as an alternative preparatory technique for IR-MALDESI-MSI.

This month's tutorial is a result of a collaborative effort that defines a unified data reporting standard for mass spectrometry imaging (MSI). SMART covers the central components of a mass spectrometry image with the purpose of improving communication of results in publications and presentations. They suggest that step size, spot size, scan total (S), molecular identification confidence (M), number of annotations (A), mass resolution or resolving power (R), and time of acquisition (T) be reported in a compact label alongside MSI data. Welcoming feedback and suggestions, this tutorial serves as a way to nucleate discussion in the MSI field about responsible and transparent data reporting.

This month's tutorial relates time of acquisition and high spatial resolution in mass spectrometry imaging (MSI) applications. The tutorial addresses many of the challenges presented by high spatial resolution MSI and resulting data file sizes. Herein, varying methodologies are discussed in order to achieve high resolution MSI while maintaining feasible experimental time of acquisition and downstream data processing times. This tutorial welcomes suggestions and functions as a guide to discern when high spatial resolutions are applicable to MSI, and the implications of the resulting times of acquisition on data as a whole.