@article{sohn_kibbe_dioli_hector_bai_garrard_muddiman_2024, title={A statistical approach to system suitability testing for mass spectrometry imaging}, volume={38}, ISSN={["1097-0231"]}, DOI={10.1002/rcm.9725}, abstractNote={RATIONALE
Mass spectrometry imaging (MSI) elevates the power of conventional mass spectrometry (MS) to multidimensional space, elucidating both chemical composition and localization. However, the field lacks any robust quality control (QC) and/or system suitability testing (SST) protocols to monitor inconsistencies during data acquisition, both of which are integral to ensure the validity of experimental results. To satisfy this demand in the community, we propose an adaptable QC/SST approach with five analyte options amendable to various ionization MSI platforms (e.g., desorption electrospray ionization, matrix-assisted laser desorption/ionization [MALDI], MALDI-2, and infrared matrix-assisted laser desorption electrospray ionization [IR-MALDESI]).


METHODS
A novel QC mix was sprayed across glass slides to collect QC/SST regions-of-interest (ROIs). Data were collected under optimal conditions and on a compromised instrument to construct and refine the principal component analysis (PCA) model in R. Metrics, including mass measurement accuracy and spectral accuracy, were evaluated, yielding an individual suitability score for each compound. The average of these scores is utilized to inform if troubleshooting is necessary.


RESULTS
The PCA-based SST model was applied to data collected when the instrument was compromised. The resultant SST scores were used to determine a statistically significant threshold, which was defined as 0.93 for IR-MALDESI-MSI analyses. This minimizes the type-I error rate, where the QC/SST would report the platform to be in working condition when cleaning is actually necessary. Further, data scored after a partial cleaning demonstrate the importance of QC and frequent full instrument cleaning.


CONCLUSIONS
This study is the starting point for addressing an important issue and will undergo future development to improve the efficiency of the protocol. Ultimately, this work is the first of its kind and proposes this approach as a proof of concept to develop and implement universal QC/SST protocols for a variety of MSI platforms.}, number={9}, journal={RAPID COMMUNICATIONS IN MASS SPECTROMETRY}, author={Sohn, Alexandria L. and Kibbe, Russell R. and Dioli, Olivia E. and Hector, Emily C. and Bai, Hongxia and Garrard, Kenneth P. and Muddiman, David C.}, year={2024}, month={May} }
 @article{kibbe_muddiman_2024, title={Quantitative mass spectrometry imaging (qMSI): A tutorial}, volume={59}, ISSN={["1096-9888"]}, DOI={10.1002/jms.5009}, abstractNote={Mass spectrometry imaging (MSI) is an analytical technique that enables the simultaneous detection of hundreds to thousands of chemical species while retaining their spatial information; usually, MSI is applied to biological tissues. Combining these elements can create ion images, which allows for the identification and localization of multiple chemical species within the sample. Being able to produce molecular images of biological tissues has already impacted the study of health and disease; however, the next logical step is being able to combine MSI with quantitative mass spectrometry methods to both quantify and determine the localization of disease progression or drug action. In this tutorial, we will detail the main factors to consider when designing a qMSI experiment and highlight the methods that have been developed to overcome these added complexities, specifically for those newer to the field of MSI.}, number={4}, journal={JOURNAL OF MASS SPECTROMETRY}, author={Kibbe, Russell R. and Muddiman, David C.}, year={2024}, month={Apr} }
 @article{kibbe_muddiman_2023, title={Achieving Sub-Parts-per-Million Mass Measurement Accuracy on an Orbitrap Mass Spectrometry Imaging Platform without Automatic Gain Control}, volume={4}, ISSN={["1879-1123"]}, DOI={10.1021/jasms.3c00004}, abstractNote={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.}, journal={JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY}, author={Kibbe, Russell R. and Muddiman, David C.}, year={2023}, month={Apr} }
 @article{mellinger_kibbe_rabbani_meritet_muddiman_gamcsik_2022, title={Mapping glycine uptake and its metabolic conversion to glutathione in mouse mammary tumors using functional mass spectrometry imaging}, volume={193}, ISSN={["1873-4596"]}, DOI={10.1016/j.freeradbiomed.2022.11.010}, abstractNote={Although glutathione plays a key role in cancer cell viability and therapy response there is no clear trend in relating the level of this antioxidant to clinical stage, histological grade, or therapy response in patient tumors. The likely reason is that static levels of glutathione are not a good indicator of how a tissue deals with oxidative stress. A better indicator is the functional capacity of the tissue to maintain glutathione levels in response to this stress. However, there are few methods to assess glutathione metabolic function in tissue. We have developed a novel functional mass spectrometry imaging (fMSI) method that can map the variations in the conversion of glycine to glutathione metabolic activity across tumor tissue sections by tracking the fate of three glycine isotopologues administered in a timed sequence to tumor-bearing anesthetized mice. This fMSI method generates multiple time point kinetic data for substrate uptake and glutathione production from each spatial location in the tissue. As expected, the fMSI data shows glutathione metabolic activity varies across the murine 4T1 mammary tumor. Although glutathione levels are highest at the tumor periphery there are regions of high content but low metabolic activity. The timed infusion method also detects variations in delivery of the glycine isotopologues thereby providing a measure of tissue perfusion, including evidence of intermittent perfusion, that contributes to the observed differences in metabolic activity. We believe this new approach will be an asset to linking molecular content to tissue function.}, journal={FREE RADICAL BIOLOGY AND MEDICINE}, author={Mellinger, Allyson L. and Kibbe, Russell R. and Rabbani, Zahid N. and Meritet, Danielle and Muddiman, David C. and Gamcsik, Michael P.}, year={2022}, month={Nov}, pages={677–684} }
 @article{knizner_kibbe_garrard_nunez_anderton_muddiman_2022, title={On the importance of color in mass spectrometry imaging}, volume={57}, ISSN={["1096-9888"]}, DOI={10.1002/jms.4898}, abstractNote={Abstract 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.}, number={12}, journal={JOURNAL OF MASS SPECTROMETRY}, author={Knizner, Kevan T. and Kibbe, Russell R. and Garrard, Kenneth P. and Nunez, Jamie R. and Anderton, Christopher R. and Muddiman, David C.}, year={2022}, month={Dec} }