Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are a class of emerging contaminants that have been in use industrially since the 1940s. Their long-term and extensive commercial use has led to their ubiquitous presence in the environment. The ability to measure the bioconcentration and distribution of PFAS in the tissue of aquatic organisms helps elucidate the persistence of PFAS as well as environmental impacts. Traditional analysis by LC-MS/MS can measure total PFAS concentrations within an organism but cannot provide comprehensive spatial information regarding PFAS concentrations within the organism. In the current study, we used infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) to determine the limit of detection (LOD) of several PFAS utilizing a commercial standard mix spotted on mouse liver tissue. The traditional ice matrix and an alternative matrix, 1,8-bis (tetramethylguanidino)naphthalene (TMGN), were explored when determining the limits of detection for various PFAS by IR-MALDESI. The ice matrix alone resulted in a higher response than the combination of TMGN and ice. The resulting LOD for perfluorooctane sulfonic acid (PFOS) on a per voxel basis was 0.16 fmol/voxel. For comparison, zebrafish that were exposed to perfluorooctanoic acid (PFOA), PFOS, and perfluorohexanesulfonic acid (PFHxS) at different concentrations were homogenized, and PFAS were extracted by solid-liquid extraction, purified by solid phase extraction, and analyzed by LC-MS/MS to determine the level of bioaccumulation in the zebrafish. PFOS resulted in the highest level of bioaccumulation (731.9 μg/kg, or 234.2 fg/voxel). A zebrafish that had been exposed to a PFAS mixture of PFOA (250 ng/L), PFOS (250 ng/L), and PFHxS (125 ng/L) was cryosectioned and analyzed by IR-MALDESI. Images could not be generated as the accumulation of PFAS in the sectioned tissue was below detection limit of the technique.

Mass spectrometry (MS) is a versatile technique for elucidating the chemical composition of biological samples. Beyond analysis of crude extracts, MS can be further applied to spatially resolve compounds across the area of a sample with a technique called mass spectrometry imaging (MSI). The infrared matrix-assisted laser desorption ionization (IR-MALDESI) platform combines elements of matrix-assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) to enable MSI of mammalian tissue using endogenous water in the sample as a matrix. For laser-based techniques such as IR-MALDESI, changes in topography across the sample surface cause inconsistent ablation as the sample surface moves above and below the focal plane of the laser. The localization of chemical species in plants reveals crucial information about metabolic processes as reported by Nemes and Vertes (Anal. Chem. 79 (21), 8098-8106, 2007) and biosynthetic pathways by Zou et al. (Trends in Plant Science, 2024) and can even inform selective breeding of crops as discussed by Sakurai (Breed Sci 72 (1), 56-65, 2022); however, leaf topography raises a unique challenge. Features such as veins and trichomes exhibit unique topography, but flattening risks delocalization of analytes and activation of unwanted signaling pathways, and transferring metabolites to a membrane for indirect analysis may incur delocalization and limit metabolomic coverage. To overcome these challenges, a chromatic confocal sensor probe (CA probe) was incorporated for IR-MALDESI-MSI of sections of a collard (Brassica oleracea var. viridis) leaf. The CA probe measures the height at all points of the sample, and automatic z-axis corrections (AzC) are generated from height differences to continuously raise and lower the stage. These stage height corrections keep the sample surface in focus of the laser for the duration of analysis. This method has been applied to relatively homogenous samples, but has not yet been characterized on heterogeneous leaf tissue with considerable topography. Herein, data quality is compared between MSI analyses with and without AzC applied, focusing on the localization of analytes known to be concentrated in different layers of collard leaves.

Mass spectrometry imaging (MSI) of cells can elucidate metabolic changes with cellular and molecular specificity. Fibroblasts are mesenchymal cells that are important in tissue homeostasis and wound healing. During early wound healing, fibroblasts adhere to fibrinogen and migrate into fibrin clots, which are important interactions to stabilize early blood clots and promote subsequent tissue remodeling. It is understood that fibrinogen exists in distinct forms, fetal and adult, which have differing glycosylation and morphological effects on fibroblasts. Despite their importance to wound healing and the extracellular environment, fibroblasts are not commonly studied by MSI. While many MSI studies are conducted at the single-cell or subcellular level, there is still utility in accessing a broad view of the metabolic changes in a cell culture above single-cell spatial resolution. This enables imaging a wider area and larger number of cells directly from cell culture. In this work, dermal fibroblasts were imaged directly from cell culture chamber slides by infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI). This method enabled treating the chambers with adult or fetal fibrinogen prior to cell culture and reduced sample preparation prior to MSI. Many metabolic effects of serum and fibrinogen type were elucidated, with changes in many membrane lipids such as cholesterol and ceramides potentially contributing to the observed morphological effects of fibrinogen types on fibroblasts.

Quantitative mass spectrometry imaging (qMSI) provides the relative or absolute analyte quantities in a biological specimen in a spatially resolved manner. However, the chemical complexity and physical structure of biological specimens often require one to precisely account for matrix effects in qMSI platforms. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) completely ablates a volume of cryosectioned tissue. This enables the use of a normalization standard that is sprayed underneath the tissue for qMSI applications. Complete sampling has shown to be a significant advantage for qMSI by IR-MALDESI; however, the impact of high tissue heterogeneity has not been systematically studied or quantified. The bias introduced by tissue heterogeneity was investigated by uniformly spraying standards beneath and on top of a whole-body zebrafish section. The quantitative relationship between the signals of the two standards was investigated across this multi-organ model to serve future qMSI experiments by IR-MALDESI and other laser ablation-based sampling methods. The overall ratio between the standards sprayed on top of and beneath the tissue sections remained constant across the entire whole-body section despite significant tissue heterogeneity (e.g., gills, heart, and liver). Additionally, we noted that thinner and/or sucrose-embedded tissues improved these ratios, which will inform future qMSI investigations.

Quantitative mass spectrometry imaging (qMSI) provides information regarding the colocalization, relative abundance, and concentration of a target analyte in a tissue without homogenization. Ionization sources, including IR-MALDESI, commonly utilize an on-tissue spatial calibration curve approach; however, this approach has several limitations including tedious sample preparation, and this approach does not account for local matrix effects. To compensate for these two limitations, we developed voxel-by-voxel (V × V) quantification to provide an internal standard calibration point for every voxel which requires a simple sample preparation and accounts for local matrix effects. In this work, we evaluate the performance of V × V quantification against the spatial calibration curve to assess the quantitative capacity of this newly developed method. Quantification of glutathione (GSH) on a per-voxel basis involves homogenously spraying a known amount of stable isotope-labeled glutathione (SIL-GSH) on a microscope slide. Next, we mount liver sections on top of the coated slides and image them using IR-MALDESI MSI. Statistical analysis demonstrated high precision for V × V quantification over a wide concentration range; however, the method’s accuracy is currently limited due to the sprayer’s configuration. Results support the feasibility of V × V quantification as evidenced by concentration heatmaps. Additionally, V × V quantification allows for parallel reaction monitoring (PRM) imaging which provides high specificity. Combined with relativity, straightforward sample preparation, and promising initial statistics, the V × V method offers significant advantages over spatial calibration curves.

Plants release volatiles, specifically volatile organic compounds (VOCs), that play a key role in communication, defense mechanisms, and responses to environmental stress. One of those significant abiotic stresses is temperature, which has a variety of negative effects like reduced growth, impaired photosynthesis, and ultimately threatening plant survival. Understanding how these volatiles function under heat stress can provide insight into plant resilience mechanisms and pinpoint key signaling pathways that can be targeted to enhance stress tolerance. In this study, we employ a novel technique, temperature programming secondary electrospray ionization (TP-SESI) to investigate how temperature variations influence the signal of VOC emissions from Ocimum basilicum or basil leaves. With our sample stage's ability to systematically adjust temperature by applying a voltage, TP-SESI enables real-time, non-destructive monitoring of VOCs with enhanced sensitivity and thermal control. Our findings demonstrate that TP-SESI reliably detects temperature-dependent changes in VOC abundance and composition, confirming its utility as an orthogonal technique for investigating plant metabolic responses to heat.

We describe a method that allows high-resolution mass spectrometry (HRMS) imaging of metabolites in tissue sections from formaldehyde-fixed, paraffin-embedded (FFPE) biobanks. This top-down variant of MS imaging expands the molecular scope of mass spectrometry histochemistry (MSHC) from peptidomics to metabolomics. The method makes the vast archives of FFPE biobanks accessible for MSHC-based biomarker discovery research of not only small endogenous peptides but also (a subset of) metabolites. FFPE biobank tissues include well-documented clinical samples representing diseases with a high medical need and often presently not clinically diagnosable and/or curable.Our protocol starts with FFPE tissue sections prepared from samples procured from biobanks. We describe how to remove paraffin and coat the section with MALDI matrix while maximally reducing analyte delocalization or washout. We detail appropriate programming of the MSHC data acquisition and illustrate a way to process MSHC data (including conversion to the generic imzML format) and browse MSHC datasets. Finally, we show options to present the data in the form of annotated MSHC images.

Understanding the glycosylation patterns of mitochondrial proteins in microglia is critical for determining their role in neurodegenerative diseases. Here, we present a novel and high-throughput methodology for glycomic analysis of mitochondrial proteins isolated from cultured microglia. This method involves the isolation of mitochondria from microglial cultures, quality assessment of mitochondrial samples, followed by an optimized protein extraction to maximize glycan detection, and infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) high-resolution accurate mass (HRAM) mass spectrometry to provide detailed profiles of mitochondrial glycosylation. This protocol emphasizes the importance of maintaining mitochondrial integrity during isolation and employs stringent quality control to ensure reproducibility, including measuring mitochondrial purity after extraction. This approach allows for the comprehensive profiling of glycosylation changes in microglial mitochondria under various experimental conditions in vitro, which offers insight into mitochondrial changes associated with neurodegenerative diseases. This approach could be adapted to other in vitro treatments, other cultured cell types, or primary cells. Through this standardized approach, we aim to advance the understanding of microglial mitochondrial glycans, contributing to the broader field of neurodegenerative research.

RATIONALE:The field of mass spectrometry imaging is currently devoid of standardized protocols or commercially available products designed for system suitability testing of MSI platforms. Machine learning is an approach that can quickly and effectively identify complex patterns in data and use them to make informed classifications, but there is a technical barrier to implementing these algorithms. Here we package the machine learning algorithms into a user-friendly interface to make community-wide implementation of this protocol possible. METHODS:The software package is built entirely in the Python language using the PySimpleGUI library for the construction of the interface, Pandas and Numpy libraries for data formatting and manipulation, and the Scikit-Learn library for the implementation of machine learning algorithms. Training data is collected on an instrument under clean and compromised conditions that can then be used to evaluate model performance and to train models prior to interrogating unknown samples before, during, or after experiments. RESULTS:Detailed instructions are provided for the effective use of the SLICE-MSI software package to use machine learning to evaluate instrument condition of MSI platforms. File formatting and generalizable steps are clearly described to make the implementation of this package easy for multiple labs and different MSI platform configurations. CONCLUSIONS:In this protocol, we demonstrate SLICE-MSI, a machine learning graphical user interface for efficient and easy implementation of QC instrument classification of mass spectrometry imaging platforms.

RATIONALE:While quality control (QC) and system suitability testing (SST) methods are commonly employed in mass spectrometry, the field of mass spectrometry imaging (MSI) currently lacks any universally accepted QC/SST protocols. These methods can prevent the loss of precious samples due to suboptimal instrument conditions and/or data quality, but they are more challenging to implement on MSI platforms. Herein, a panel of analytes is conveniently analyzed in a setup that reflects a typical MSI imaging experiment, and guidance is provided for downstream QC/SST evaluation. METHODS:The analyte panel will be commercially available and consists of three pairs of unlabeled (NAT) analytes and their stable isotope-labeled (SIL) analogues; a deviation from the standard procedure is also included, which incorporates a polymer to expand m/z coverage. The NAT three-plex (or four-plex with the added polymer) is analyzed as a droplet on a slide, and the SIL three-plex is doped into the electrospray solvent, isolating the NAT and SIL compounds to different source components. Datasets are collected on clean and compromised instruments to inform QC/SST software and later evaluate instrument conditions or isolated metrics of data quality. RESULTS:A procedure was created for QC/SST analysis on MSI platforms, which can be optionally paired with the freely available software Supervised Learning for Instrument Classification and Evaluation for Mass Spectrometry Imaging (SLICE-MSI) to classify the condition of the instrument. The SIL data may be monitored separately during imaging experiments for continuous evaluation of electrospray stability. The protocol highlights areas that may be adapted for other ionization sources for widespread use. CONCLUSIONS:The protocol described herein uses a panel of NAT and SIL compounds to offer an objective and accurate determination of QC/SST on MSI platforms.

Mass calibration techniques are vital in achieving high mass measurement accuracy (MMA) of large biomolecules. Variable ion populations that shift the axial frequencies due to space charge effects have been a significant challenge in achieving sub-parts-per-million (sub-ppm) MMA of glycans on a high-resolution accurate mass (HRAM) orbitrap instrument without the activation of automatic gain control. As the role of glycans is critical to our understanding of diverse biological processes, accurate identification of glycans using sub-ppm MMA is critical for biological interpretations. Hence, this study aims to achieve sub-ppm MMA of glycans by exploring the impact of different ion accumulation times, data collection modes, in addition to custom calibration strategies and external mass correction to optimize accurate mass measurements. Using infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI), direct analysis was performed on N-linked glycans cleaved from bovine fetuin in negative polarity, where 17 N-linked glycans were detected and annotated. Our results indicate the significance of implementing a custom calibration external lock mass and other techniques, including the effect of external mass correction in achieving sub-ppm MMA of large biomolecules. Implementing such approaches in mass spectrometry imaging (MSI) of biological tissue will enhance the confidence of glycan annotation and enable more accurate biological conclusions.

Leveraging a depth profiling approach expands the chemical elucidation of mass spectrometry imaging techniques to another dimension. Three-dimensional MSI (3D MSI) reveals the distribution of analytes with greater anatomical detail to add another level of information in a biological study. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) has demonstrated utility for an ablation-based approach, enabling simplified sample preparation workflows and streamlined data processing pipelines compared to a serial-sectioning strategy. To improve 3D MSI on the IR-MALDESI platform, two technologies have been characterized in tandem for the intention of minimizing sampling bias: (1) a top-hat optical train and (2) a chromatic confocal probe (CA probe). While the modified optical train creates a square spot size to avoid a Gaussian ablation crater after the analysis of subsequent layers, the CA probe enables automatic z-axis correction (AzC) to maintain the laser’s focus on the surface of the sample. The work herein demonstrates the integration and optimization of these technologies on mouse skin, motivated by the clear biological skin layers that result in differential lipid expression and subsequent detection. Results support that a laser energy of 1.3 mJ/burst with the top-hat optical train and a 120 µm step size in the X and Y dimensions presented a comparable depth resolution to previous studies at under 7 µm. Further, the optimized parameters were utilized on two biological replicates to evaluate method reproducibility where lipid annotations and their abundance were considered.

Chondroitin sulfate (CS) is a type of glycosaminoglycan (GAG) that is abundant in cartilage and perineural networks (PNNs). Changes in the CS signature of PNNs have been implicated in several neurological diseases. Most CS-GAGs contain labile sulfate groups, which can be lost during ionization events that deposit large amounts of internal energy. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is a soft ionization technique used for mass spectrometry imaging. In this work, we determine the spatial distribution of CS-GAG disaccharides within rodent brain using IR-MALDESI MSI. Non-, mono-, and disulfated disaccharides were detected with various adducts. All disaccharides colocalized to the PNNs, which are most abundant in the cortex and hippocampus regions of the brain. This is the first MSI study to spatially resolve CS-GAG disaccharides within brain, paving the way for IR-MALDESI to measure GAGs in neurological diseases.

Matrix-assisted laser desorption electrospray ionization (MALDESI) conventionally utilizes a mid-infrared (IR) laser for the desorption of neutrals, allowing for detection of hundreds to thousands of analytes simultaneously. This platform enables mass spectrometry imaging (MSI) capabilities to not only detect specific molecules but also reveal the distribution and localization of a wide range of biomolecules across an organism. However, an IR laser comes with its disadvantages when imaging plants. At a mid-IR wavelength (2970 nm), the compartmentalized endogenous water within the leaf structure acts as an internal matrix, causing rapid heating, and, in turn, degrades the spatial resolution and signal quality. An ultraviolet (UV) laser operates at wavelengths that overlap with the absorption bands of secondary metabolites allowing them to serve as sacrificial matrix molecules. With the integration and optimization of a 355 nm UV laser into the MALDESI-MSI NextGen source for the analysis of plants, we were able to detect diverse molecular classes including flavonoids, fatty acid derivatives, galactolipids, and glucosinolates, at higher ion abundances when compared to the mid-IR laser. These results show that re-visiting UV-MALDESI-MSI, without the need for an exogenous matrix, provides a promising approach for the detection and imaging of important analytes in plants.

RationaleMass 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]).MethodsA 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.ResultsThe 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.ConclusionsThis 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.

Mass spectrometry imaging (MSI) platforms such as infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) are advantageous for a variety of applications, including elucidating the localization of neurotransmitters (NTs) and related molecules with respect to ion abundance across a sample without the need for derivatization or organic matrix application. While IR-MALDESI-MSI conventionally uses a thin exogenous ice matrix to improve signal abundance, it has been previously determined that sucrose embedding without the ice matrix improves detection of lipid species in striatal, coronal mouse brain sections. This work considers components of this workflow to determine the optimal sample preparation and matrix to enhance the detection of NTs and their related metabolites in coronal sections from the striatal region of the mouse brain. The discoveries herein will enable more comprehensive follow-on studies for the investigation of NTs to enrich biological pathways and interpretation related to neurodegenerative diseases and ischemic stroke.

Novel mass spectrometry (MS) based analytical platforms have enabled scientists to detect and quantify molecules within biological and environmental samples more accurately. Novel MS instrumentation starts as a prototype and, after years of development, can become a commercial product to be used by the larger MS community. Without the initial prototype, many MS-based instruments today would not be produced. Additionally, biotechnology companies are the main drivers for research, development, and production of novel instruments, but the tools for prototyping instrumentation have never been more accessible. Here, we present a tutorial on prototyping instrumentation through the case study of developing the Next Generation IR-MALDESI source to show that an engineering degree is not required to design and construct a prototype instrument with modern hardware and software. We discuss the prototyping process, the necessary skills required for efficient prototyping, and information about common hardware and software used within initial prototypes.

Native mass spectrometry (MS) is a powerful analytical technique to directly probe noncovalent protein-protein and protein-ligand interactions. However, not every MS platform can preserve proteins in their native conformation due to high energy deposition from the utilized ionization source. Most small molecules approved as drugs and in development interact with their targets through noncovalent interactions. Therefore, rapid methods to analyze noncovalent protein-ligand interactions are necessary for the early stages of the drug discovery pipeline. Herein, we describe a method for analyzing noncovalent protein-ligand complexes by IR-MALDESI-MS with analysis times of ∼13 s per sample. Carbonic anhydrase and the kinase domain of Bruton's tyrosine kinase are paired with known noncovalent binders to evaluate the effectiveness of native MS by IR-MALDESI.

Artemisinin is the leading medication for the treatment of malaria and is only produced naturally in Artemisia annua. The localization of artemisinin in both the glandular and non-glandular trichomes of the plant makes it an ideal candidate for mass spectrometry imaging (MSI) as a model system for method development. Infrared matrix-assisted laser desorption electrospray ionization MSI (IR-MALDESI-MSI) has the capability to detect hundreds to thousands of analytes simultaneously, providing abundance information in conjunction with species localization throughout a sample. The development of several new optical trains and their application to the IR-MALDESI-MSI platform has improved data quality in previous proof-of-concept experiments but has not yet been applied to analysis of native biological samples, especially the MSI analysis of plants. This study aimed to develop a workflow and optimize MSI parameters, specifically the laser optical train, for the analysis of Artemisia annua with the NextGen IR-MALDESI platform coupled to an Orbitrap Exploris 240 mass spectrometer. Two laser optics were compared to the conventional set up, of which include a Schwarzschild-like reflective objective and a diffractive optical element (DOE). These optics, respectively, enhance the spatial resolution of imaging experiments or create a square spot shape for top-hat imaging. Ultimately, we incorporated and characterized three different optical trains into our analysis of Artemisia annua to study metabolites in the artemisinin pathway. These improvements in our workflow, resulted in high spatial resolution and improved ion abundance from previous work, which will allow us to address many different questions in plant biology beyond this model system.

Spatial metabolomics using imaging mass spectrometry (MS) enables untargeted and label-free metabolite mapping in biological samples. Despite the range of available imaging MS protocols and technologies, our understanding of metabolite detection under specific conditions is limited due to sparse empirical data and predictive theories. Consequently, challenges persist in designing new experiments, and accurately annotating and interpreting data. In this study, we systematically measured the detectability of 172 biologically-relevant metabolites across common imaging MS protocols using custom reference samples. We evaluated 24 MALDI-imaging MS protocols for untargeted metabolomics, and demonstrated the applicability of our findings to complex biological samples through comparison with animal tissue data. We showcased the potential for extending our results to further analytes by predicting metabolite detectability based on molecular properties. Additionally, our interlaboratory comparison of 10 imaging MS technologies, including MALDI, DESI, and IR-MALDESI, showed extensive metabolite coverage and comparable results, underscoring the broad applicability of our findings within the imaging MS community. We share our results and data through a new interactive web application integrated with METASPACE. This resource offers an extensive catalogue of detectable metabolite ions, facilitating protocol selection, supporting data annotation, and benefiting future untargeted spatial metabolomics studies.### Competing Interest StatementT.A. holds imaging mass spectrometry patents and leads a startup on single-cell metabolomics at BioInnovation Institute. M.A.M. is an employee and B.S. is a consultant of TransMIT GmbH. J.O. is employed at Bruker Daltonics GmbH & Co. KG.