Rationale The level of visual detail of a mass spectrometry image is dependent on the spatial resolution with which it is acquired, which is largely determined by the focal diameter in infrared laser ablation‐based techniques. While the use of mid‐IR light for mass spectrometry imaging (MSI) has advantages, it results in a relatively large focal diameter and spatial resolution. The continual advancement of infrared matrix‐assisted electrospray ionization (IR‐MALDESI) for MSI warranted novel methods to decrease laser ablation areas and thus improve spatial resolution. Methods In this work, a Schwarzschild‐like reflective objective was incorporated into the novel NextGen IR‐MALDESI source and characterized on both burn paper and mammalian tissue using an ice matrix. Ablation areas, mass spectra, and annotations obtained using the objective were compared against the current optical train on the NextGen system without modification. Results The effective resolution was determined to be 55 μm by decreasing the step size until oversampling was observed. Use of the objective improved the spatial resolution by a factor of three as compared against the focus lens. Conclusions A Schwarzschild‐like reflective objective was successfully incorporated into the NextGen source and characterized on mammalian tissue using an ice matrix. The corresponding improvement in spatial resolution facilitates the future expansion of IR‐MALDESI applications to include those that require fine structural detail.

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.

Biospecimens with nearly flat surfaces on a flat stage are typically required for laserbased mass spectrometry imaging (MSI) techniques. However, sampling stages are rarely perfectly level, and accounting for this and the need to accommodate non-flat samples requires a deeper understanding of the laser beam depth of focus. In ablation-based MSI methods, a laser is focused on top of the sample surface, ensuring that the sample is at the focal point or remains within depth of focus. In general, the depth of focus of a given laser is related to the beam quality (M 2 ) and the wavelength (λ). However, because laser is applied on a biological sample, other variables can also impact the depth of focus, which could affect the robustness of the MSI techniques for diverse sample types. When the height of a sample ranges outside of the depth of focus, ablated area and the corresponding ion abundances may vary as well, increasing the variability of results. In this tutorial, we examine the parameters and equations that describe the depth of focus of a Gaussian laser beam. Additionally, we describe several approaches that account for surface roughness exceeding the depth of focus of the laser.

Rationale The development and characterization of the novel NextGen infrared matrix‐assisted laser desorption electrospray ionization (IR‐MALDESI) source catalyzed new advancements in IR‐MALDESI instrumentation, including the development of a new analysis geometry. Methods A vertically oriented transmission mode (tm)‐IR‐MALDESI setup was developed and optimized on thawed mouse tissue. In addition, glycerol was introduced as an alternative energy‐absorbing matrix for tm‐IR‐MALDESI because the new geometry does not currently allow for the formation of an ice matrix. The tm geom was evaluated against the optimized standard geometry for the NextGen source in reflection mode (rm). Results It was found that tm‐IR‐MALDESI produces comparable results to rm‐IR‐MALDESI after optimization. The attempt to incorporate glycerol as an alternative matrix provided little improvement to tm‐IR‐MALDESI ion abundances. Conclusions This work has successfully demonstrated the adaptation of the NextGen IR‐MALDESI source through the feasibility of tm‐IR‐MALDESI mass spectrometry imaging on mammalian tissue, expanding future biological applications of the method.

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.

Increasing the spatial resolution of a mass spectrometry imaging (MSI) method results in a more defined heatmap of the spatial distribution of molecules across a sample, but it is also associated with the disadvantage of increased acquisition time. Decreasing the area of the region of interest to achieve shorter durations results in the loss of potentially valuable information in larger specimens. This work presents a novel MSI method to reduce the time of MSI data acquisition with variable step size imaging: nested regions of interest (nROIs). Using nROIs, a small ROI may be imaged at a higher spatial resolution while nested inside a lower-spatial-resolution peripheral ROI. This conserves the maximal spatial and chemical information generated from target regions while also decreasing the necessary acquisition time. In this work, the nROI method was characterized on mouse liver and applied to top-hat MSI of zebrafish using a novel optical train, which resulted in a significant improvement in both acquisition time and spatial detail of the zebrafish. The nROI method can be employed with any step size pairing and adapted to any method in which the acquisition time of larger high-resolution ROIs poses a practical challenge.

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.