In mass spectrometry imaging (MSI) applications of infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI), an exogenous ice layer is the gold standard for an energy-absorbing matrix. However, the formation of the ice matrix requires additional time and instrument hardware, so glycerol was investigated herein as an alternative to the ice matrix to potentially improve spatial resolution and ionization, while decreasing experiment time. Glycerol solutions of varying concentrations were sprayed over top of rat liver tissue sections for analysis by IR-MALDESI and compared to the typical ice matrix condition. Additionally, we tested if combining the ice matrix and glycerol matrix would further improve analyses. Matrix conditions were evaluated by comparing ion abundance of six lipid species, the laser ablation spot diameter, and number of METASPACE annotations. The ion abundances were also normalized to the volume of tissue ablated to correct for lower abundance values due to less ablated tissue. It was observed that utilizing a 50% glycerol matrix without ice provides improved spatial resolution with lipid abundances and annotations comparable to the ice matrix standard, while decreasing the time required to complete an IR-MALDESI tissue imaging experiment.

The collection of profile data is standard practice within the field of mass spectrometry (MS). However, profile data collection often results in large data files that require extensive processing times, especially in mass spectrometry imaging (MSI) studies where thousands of high-resolution scans are recorded. Natively collecting centroid MS data is an alternative that effectively reduces both the resulting file size and the data processing time. Herein, high-resolution accurate mass (HRAM) Orbitrap MSI data on mouse liver tissue sections without automatic gain control (AGC) were natively collected in both profile and centroid modes and compared based on the file size and processing time. Additionally, centroid data were evaluated against the profile data with regard to the spectra integrity, mass measurement accuracy (MMA), and the number of lipid annotations to ensure that centroid data did not compromise the data quality. For both native and postacquisition centroided data, the variation in mass measurement accuracy decreased relative to the profile data collection. Furthermore, centroid data collection increased the number of METASPACE database annotations indicating higher sensitivity and greater accuracy for lipid annotation compared to native profile data collection. Profile MSI data was shown to have a higher likelihood of false positive identifications due to an increased number of data points on either side of the peaks, whereas the same trend was not observed in data collected in native centroid data collection. This publication explores and explains the importance in properly centroiding MSI data, either natively or by adequate centroiding methods, to obtain the most accurate information and come to the best conclusions. These data support that natively collecting centroid data significantly improves MMA to sub-ppm levels without AGC and reduces false positive annotations.

Mass spectrometry imaging (MSI) data visualization relies on heatmaps to show the spatial distribution and measured abundances of molecules within a sample. Nonuniform color gradients such as jet are still commonly used to visualize MSI data, increasing the probability of data misinterpretation and false conclusions. Also, the use of nonuniform color gradients and the combination of hues used in common colormaps make it challenging for people with color vision deficiencies (CVDs) to visualize and accurately interpret data. Here we present best practices for choosing a colormap to accurately display MSI data, improve readability, and accommodate all CVDs. We also provide other resources on the misuse of color in the scientific field and resources on scientifically derived colormaps presented herein.

No abstract