Optimized C-Trap Timing of an Orbitrap 240 Mass Spectrometer for High-Throughput Screening and Native MS by IR-MALDESI

Comparison of Mid-Infrared and Ultraviolet Lasers Coupled to the MALDESI Source for the Detection of Secondary Metabolites and Structural Lipids in Arabidopsis thaliana

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.

Three-dimensional mass spectrometry imaging (3D MSI): incorporating top-hat IR-MALDESI and automatic z-axis correction

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.

Feasibility of IR-MALDESI Mass Spectrometry Imaging of PFAS

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.

Leveraging Supervised Machine Learning Algorithms for System Suitability Testing of Mass Spectrometry Imaging Platforms

Quality control and system suitability testing are vital protocols implemented to ensure the repeatability and reproducibility of data in mass spectrometry investigations. However, mass spectrometry imaging (MSI) analyses present added complexity since both chemical and spatial information are measured. Herein, we employ various machine learning algorithms and a novel quality control mixture to classify the working conditions of an MSI platform. Each algorithm was evaluated in terms of its performance on unseen data, validated with negative control data sets to rule out confounding variables or chance agreement, and utilized to determine the necessary sample size to achieve a high level of accurate classifications. In this work, a robust machine learning workflow was established where models could accurately classify the instrument condition as clean or compromised based on data metrics extracted from the analyzed quality control sample. This work highlights the power of machine learning to recognize complex patterns in MSI data and use those relationships to perform a system suitability test for MSI platforms.

Oversampling for Enhanced Spatial Resolution of Zebrafish by Top-Hat IR-MALDESI-MSI

Mass spectrometry imaging (MSI) has become a significant tool for measuring chemical species in biological tissues, where much of the impact of these platforms lies in their capability to report the spatial distribution of analytes for correlation to sample morphology. As a result, enhancement of spatial resolution has become a frontier of innovation in the field, and necessary developments are dependent on the ionization source. More particularly, laser-based imaging sources may require modifications to the optical train or alternative sampling techniques. These challenges are heightened for systems with infrared (IR) lasers, as their operating wavelength generates spot sizes that are inherently larger than their ultraviolet counterparts. Recently, the infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) source has shown the utility of a diffractive optical element (DOE) to produce square ablation patterns, termed top-hat IR-MALDESI. If the DOE optic is combined with oversampling methods, smaller ablation volumes can be sampled to render higher spatial resolution imaging experiments. Further, this approach enables reproducible spot sizes and ablation volumes for better comparison between scans. Herein, we investigate the utility of oversampling with top-hat IR-MALDESI to enhance the spatial resolution of measured lipids localized within the head of sectioned zebrafish tissue. Four different spatial resolutions were evaluated for data quality (e.g., mass measurement accuracy, spectral accuracy) and quantity of annotations. Other experimental parameters to consider for high spatial resolution imaging are also discussed. Ultimately, 20 μm spatial resolution was achieved in this work and supports feasibility for use in future IR-MALDESI studies.

Detection of Noncovalent Protein-Ligand Complexes by IR-MALDESI-MS

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.

Collaborative robotics to enable ultra-high-throughput IR-MALDESI

Over the last 5 years, IR-MALDESI-MS (Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry) has been demonstrated for use in a range of high-throughput biochemical and cellular assays with remarkable sample acquisition rates up to 22 Hz for a single 384-well assay plate. With such high single plate acquisition rates, the rate limiting step becomes how fast subsequent plates can be presented to the MS for analysis. To make this transfer as fast as possible while maintaining safe operation in a laboratory environment, we developed a collaborative robotic plate transfer system (CRPTS) that combines a 6-axis robot with dual plate grippers, a 7th axis conveyor stage, and a 420-plate capacity sample loading window. As a demonstration of the throughput and flexibility of CRPTS, we performed a biochemical assay that monitored the oxidation of tris(2-carboxyethyl)phosphine (TCEP) to screen for nuisance compounds. Using continuous and step motion scan profiles, we analyzed 158,799 compounds contained in 448 assay plates over the course of 12.5 h (Z-Factor=0.87) and 17.5 h (Z-factor=0.99), respectively. Extrapolating these results enables the screening of a million compounds within 6-7 working days.

Optimizing neurotransmitter pathway detection by IR-MALDESI-MSI in mouse brain

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.

Incorporation of Three Different Optical Trains into the IR-MALDESI Mass Spectrometry Imaging Platform to Characterize Artemisia annua

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.

A statistical approach to system suitability testing for mass spectrometry imaging

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.

Collision-Induced Unfolding, Tandem MS, Bottom-up Proteomics, and Interactomics for Identification of Protein Complexes in Native Surface Mass Spectrometry

Endogenously occurring salts and nonvolatile matrix components in untreated biological surfaces can suppress protein ionization and promote adduct formation, challenging protein identification. Characterization of labile proteins within biological specimens is particularly demanding because additional purification or sample treatment steps can be time-intensive and can disrupt noncovalent interactions. It is demonstrated that the combined use of collision-induced unfolding, tandem mass spectrometry, and bottom-up proteomics improves protein characterization in native surface mass spectrometry (NSMS). This multiprong analysis is achieved by acquiring NSMS, MS/MS, ion mobility (IM), and bottom-up proteomics data from a single surface extracted sample. The validity of this multiprong approach was confirmed by the successful characterization of nine surface-deposited proteins, with molecular weights ranging from 8 to 147 kDa, in two separate mixtures. Bottom-up proteomics provided a list of proteins to match against observed proteins in NSMS and their detected subunits in tandem MS. The method was applied to characterize endogenous proteins from untreated chicken liver samples. The subcapsular liver sampling for NSMS analysis allowed for the detection of endogenous proteins with molecular weights of up to ∼220 kDa. Moreover, using IM-MS, collision cross sections and collision-induced unfolding pathways of enzymatic proteins and protein complexes of up to 145 kDa were obtained.

Comparative analysis of sucrose-embedding for whole-body zebrafish MSI by IR-MALDESI

Infrared matrix-assisted laser desorption electrospray ionization mass spectrometry imaging (IR-MALDESI) conventionally utilizes fresh-frozen biological tissues with an ice matrix to improve the detection of analytes. Sucrose-embedding with paraformaldehyde fixation has demonstrated feasibility as an alternative matrix for analysis by IR-MALDESI by preserving tissue features and enhancing ionization of lipids. However, investigating multi-organ systems provides broader context for a biological study and can elucidate more information about a disease state as opposed to a single organ. Danio rerio, or zebrafish, are model organisms for various disease states and can be imaged as a multi-organ sample to analyze morphological and metabolomic preservation as a result of sample preparation. Herein, whole-body zebrafish were imaged to compare sucrose-embedding with paraformaldehyde fixation against conventional fresh-frozen sample preparation. Serial sections were analyzed with and without an ice matrix to evaluate if sucrose functions as an alternative energy-absorbing matrix for IR-MALDESI applications across whole-body tissues. The resulting four conditions were compared in terms of total putative lipid annotations and category diversity, coverage across the entire m/z range, and ion abundance. Ultimately, sucrose-embedded zebrafish had an increase in putative lipid annotations for the combination of putative annotations with and without the application of an ice matrix relative to fresh-frozen tissues which require the application of an ice matrix. Upon the use of an ice matrix, a greater number of high mass putative lipid annotations (e.g., glycerophospholipids, glycerolipids, and sphingolipids) were identified. Conversely, without an ice matrix, sucrose-embedded sections elucidated more putative annotations in lower molecular weight lipids, including fatty acyls and sterol lipids. Similar to the mouse brain model, sucrose-embedding increased putative lipid annotation and abundance for whole-body zebrafish.

Metabolite Annotation Confidence Score (MACS): A Novel MSI Identification Scoring Tool

Mass spectrometry imaging (MSI) is an analytical technique capable of measuring and visualizing the spatial distribution of thousands of ions across a sample. Measured ions can be putatively identified and annotated by comparing their mass-to-charge ratio (m/z) to a database of known compounds. For high-resolution, accurate mass (HRAM) imaging data sets, this is commonly performed by the annotation platform METASPACE. Annotations are reported with a metabolite-signal-match (MSM) score as a measure of the annotation's confidence level. However, the MSM scores reported by METASPACE often do not reflect a reasonable confidence level of an annotation and are not assigned consistently. The metabolite annotation confidence score (MACS) is an alternative scoring system based on fundamental mass spectrometry imaging metrics (mass measurement accuracy, spectral accuracy, and spatial distribution) to generate values that reflect the confidence of a specific annotation in HRAM-MSI data sets. Herein, the MACS system is characterized and compared to MSM scores from ions annotated by METASPACE.

Lipidomic Analysis of Mouse Brain to Evaluate the Efficacy and Preservation of Different Tissue Preparatory Techniques by IR-MALDESI-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.

Achieving Sub-Parts-per-Million Mass Measurement Accuracy on an Orbitrap Mass Spectrometry Imaging Platform without Automatic Gain Control

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.

Native Mass Spectrometry and Collision-Induced Unfolding of Laser-Ablated Proteins

Infrared laser ablation sample transfer (LAST) was used to collect samples from solid surfaces for mass spectrometry under native spray conditions. Native mass spectrometry was utilized to probe the charge states and collision-induced unfolding (CIU) characteristics of bovine serum albumin (BSA), bovine hemoglobin (BHb), and jack-bean concanavalin A (ConA) via direct injection electrospray, after liquid extraction surface sampling, and after LAST. Each protein was deposited from solution on solid surfaces and laser-ablated for off-line analysis or sampled for online analysis. It was found that the protein ion gas-phase charge-state distributions were comparable for direct infusion, liquid extraction, and laser ablation experiments. Moreover, calculated average collision cross section (CCS) values from direct injection, liquid extraction, and laser ablation experiments were consistent with previously reported literature values. Additionally, an equivalent number of mobility features and conformational turnovers were identified from unfolding pathways from all three methods for all charge states of each protein analyzed in this work. The presented work suggests that laser ablation yields intact proteins (BSA, BHb, and ConA), is compatible with native mass spectrometry, and could be suitable for spatially resolved interrogation of unfolding pathways of proteins.

Interrogating the Metabolomic Profile of Amyotrophic Lateral Sclerosis in the Post-Mortem Human Brain by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization (IR-MALDESI) Mass Spectrometry Imaging (MSI)

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.

Next-Generation Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Source for Mass Spectrometry Imaging and High-Throughput Screening

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) is a hybrid, ambient ionization source that combines the advantages of electrospray ionization and matrix-assisted laser desorption/ionization, making it a versatile tool for both high-throughput screening (HTS) and mass spectrometry imaging (MSI) studies. To expand the capabilities of the IR-MALDESI source, an entirely new architecture was designed to overcome the key limitations of the previous source. This next-generation (NextGen) IR-MALDESI source features a vertically mounted IR-laser, a planar translation stage with computerized sample height control, an aluminum enclosure, and a novel mass spectrometer interface plate. The NextGen IR-MALDESI source has improved user-friendliness, improved overall versatility, and can be coupled to numerous Orbitrap mass spectrometers to accommodate more research laboratories. In this work, we highlight the benefits of the NextGen IR-MALDESI source as an improved platform for MSI and direct analysis. We also optimize the NextGen MALDESI source component geometries to increase target ion abundances over a wide m/z range. Finally, documentation is provided for each NextGen IR-MALDESI part so that it can be replicated and incorporated into any lab space.

High-Throughput Intact Protein Analysis for Drug Discovery Using Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry

Mass spectrometry (MS) is the primary analytical tool used to characterize proteins within the biopharmaceutical industry. Electrospray ionization (ESI) coupled to liquid chromatography (LC) is the current gold standard for intact protein analysis. However, inherent speed limitations of LC/MS prevent analysis of large sample numbers (>1000) in a day. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI-MS), an ambient ionization MS technology, has recently been established as a platform for high-throughput small molecule analysis. Here, we report the applications of such a system for the analysis of intact proteins commonly performed within the drug discovery process. A wide molecular weight range of proteins 10-150 kDa was detected on the system with improved tolerance to salts and buffers compared to ESI. With high concentrations and model proteins, a sample rate of up to 22 Hz was obtained. For proteins at low concentrations and in buffers used in commonly employed assays, robust data at a sample rate of 1.5 Hz were achieved, which is ∼22× faster than current technologies used for high-throughput ESI-MS-based protein assays. In addition, two multiplexed plate-based high-throughput sample cleanup methods were coupled to IR-MALDESI-MS to enable analysis of samples containing excessive amounts of salts and buffers without fully compromising productivity. Example experiments, which leverage the speed of the IR-MALDESI-MS system to monitor NISTmAb reduction, protein autophosphorylation, and compound binding kinetics in near real time, are demonstrated.

Novel matrix strategies for improved ionization and spatial resolution using IR-MALDESI mass spectrometry imaging

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.

Normalization techniques for high-throughput screening by infrared matrix-assisted laser desorption electrospray ionization mass spectrometry

Mass spectrometry (MS) is an effective analytical tool for high-throughput screening (HTS) in the drug discovery field. Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) MS is a high-throughput platform that has achieved analysis times of sub-seconds-per-sample. Due to the high-throughput analysis speed, methods are needed to increase the analyte signal while decreasing the variability in IR-MALDESI-MS analyses to improve data quality and reduce false-positive hits. The Z-factor is used as a statistic of assay quality that can be improved by reducing the variation of target ion abundances or increasing signal. Herein we report optimal solvent compositions for increasing measured analyte abundances with direct analysis by IR-MALDESI-MS. We also evaluate normalization strategies, such as adding a normalization standard that is similar or dissimilar in structure to the model target drug, to reduce the variability of measured analyte abundances with direct analyses by IR-MALDESI-MS in both positive and negative ionization modes.

Ultra-High-Throughput Ambient MS: Direct Analysis at 22 Samples per Second by Infrared Matrix-Assisted Laser Desorption Electrospray Ionization Mass Spectrometry

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) mass spectrometry is an ambient-direct sampling method that is being developed for high-throughput, label-free, biochemical screening of large-scale compound libraries. Here, we report the development of an ultra-high-throughput continuous motion IR-MALDESI sampling approach capable of acquiring data at rates up to 22.7 samples per second in a 384-well microtiter plate. At top speed, less than 1% analyte carryover is observed from well-to-well, and signal intensity relative standard deviations (RSD) of 11.5% and 20.9% for 3 μM 1-hydroxymidazolam and 12 μM dextrorphan, respectively, are achieved. The ability to perform parallel kinetics studies on 384 samples with a ∼30 s time resolution using an isocitrate dehydrogenase 1 (IDH1) enzyme assay is shown. Finally, we demonstrate the repeatability and throughput of our approach by measuring 115200 samples from 300 microtiter plate reads consecutively over 5.54 h with RSDs under 8.14% for each freshly introduced plate. Taken together, these results demonstrate the use of IR-MALDESI at sample acquisition rates that surpass other currently reported direct sampling mass spectrometry approaches used for high-throughput compound screening.