Journal of the American Society for Mass Spectrometry

Mass Spectrometry Imaging (MSI) is an emergent tool for analyzing spatial molecular distributions, yet data complexity often hinders effective analysis. MSI.EAGLE, an open-source R-Shiny application, makes analysis accessible for non-specialists by integrating advanced tools in a user-friendly interface. The workflow leverages tools from the Cardinal MSI package, with enhanced phenotyping, segmentation, statistical analysis and visualization. The application addresses a gap in spatial biology research by empowering a broader scientific community.

Technologies assessing the lipidomics, genomics, epigenomics, transcriptomics, and proteomics of tissue samples at single-cell resolution have deepened our understanding of physiology and pathophysiology at an unprecedented level of detail. However, the study of single-cell spatial metabolomics in undecalcified bones faces several significant challenges, such as the fragility of bone which often requires decalcification or fixation leading to the degradation or removal of lipids and other molecules and. As such, we describe a method for performing mass spectrometry imaging on undecalcified spine that is compatible with other spatial omics measurements. In brief, we use fresh-freeze rat spines and a system of carboxyl methylcellulose embedding, cryofilm, and polytetrafluoroethylene rollers to maintain tissue integrity, while avoiding signal loss from variations in laser focus and artifacts from traditional tissue processing. This reveals various tissue types and lipidomic profiles of spinal regions at 10 m spatial resolutions using matrix-assisted laser desorption/ionization mass spectrometry imaging. We expect this method to be adapted and applied to the analysis of spinal cord, shedding light on the mechanistic aspects of cellular heterogeneity, development, and disease pathogenesis underlying different bone-related conditions and diseases. This study furthers the methodology for high spatial metabolomics of spines, as well as adds to the collective efforts to achieve a holistic understanding of diseases via single-cell spatial multi-omics.

Interrelating compounds according to their aligned fragmentation spectra is central to tandem mass spectrometry-based metabolomics. However, current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences often fail to have their fragment ions aligned. Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE) is a new tool inspired by protein sequence alignment for aligning fragmentation spectra with statistical significance and allowance for multiple chemical differences. We found SIMILE yields 550% more pairs of structurally similar compounds than commonly used cosine-based scoring algorithms, and anticipate SIMILE will fill an important role by also providing p-values for fragmentation spectra alignments to explore structural relationships between compounds.

Multi-modal imaging analyses of dosed tissue samples can provide more comprehensive insight into the effects of a therapeutically active compound on a target tissue compared to single-modal imaging. For example, simultaneous spatial mapping of pharmaceutical compounds and endogenous macromolecule receptors is difficult to achieve in a single imaging experiment. Herein, we present a multi-modal workflow combining imaging mass spectrometry with immunohistochemistry (IHC) fluorescence imaging and brightfield microscopy imaging. Imaging mass spectrometry enables direct mapping of pharmaceutical compounds and metabolites, IHC fluorescence imaging can visualize large proteins, and brightfield microscopy imaging provides tissue morphology information. Single-cell resolution images are generally difficult to acquire using imaging mass spectrometry, but are readily acquired with IHC fluorescence and brightfield microscopy imaging. Spatial sharpening of mass spectrometry images would thus allow for higher fidelity co-registration with higher resolution microscopy images. Imaging mass spectrometry spatial resolution can be predicted to a finer value via a computational image fusion workflow, which models the relationship between the intensity values in the mass spectrometry image and the features of a high spatial resolution microscopy image. As a proof of concept, our multi-modal workflow was applied to brain tissue extracted from a Sprague Dawley rat dosed with a kratom alkaloid, corynantheidine. Four candidate mathematical models including linear regression, partial least squares regression (PLS), random forest regression, and two-dimensional convolutional neural network (2-D CNN), were tested. The random forest and 2-D CNN models most accurately predicted the intensity values at each pixel as well as the overall patterns of the mass spectrometry images, while also providing the best spatial resolution enhancements. Herein, image fusion enabled predicted mass spectrometry images of corynantheidine, GABA, and glutamine to approximately 2.5 m spatial resolutions, a significant improvement compared to the original images acquired at 25 m spatial resolution. The predicted mass spectrometry images were then co-registered with an H&E image and IHC fluorescence image of the - opioid receptor to assess co-localization of corynantheidine with brain cells. Our study also provides insight into the different evaluation parameters to consider when utilizing image fusion for biological applications.

AbstractMetabolites play critical roles in modulating gut-microbe interactions and are closely related to health and disease consequences. One critical aspect of the gut-microbe interactions is spatial heterogeneity, particularly at the interface space between gut tissue and luminal contents, where a unique microhabitat of diverse microbial functions encounters the host tissues. Exploring the spatial heterogeneity of this interface enables insights into these gut-microbe interactions. Previous studies commonly investigated metabolome through tissue homogenization and bulk analysis, but these methods result in the loss of spatial information. In this project, we use high-resolution matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) approaches to reveal the chemical spatial heterogeneity of colon tissue, luminal contents, and the tissue-lumen interface across multiple colonic regions. We applied a swiping technique to aid the preservation of luminal content integrity and used two MALDI matrices to cover a wide range of metabolites. The MALDI-MSI analyses revealed distinct patterns of metabolite spatial localization across the gut tissue-lumen interfaces, amongst which the interface-enriched features are of particular interest due to their possibly connection to the gut-microbe interactions. Overall, the rich spatial heterogeneity of metabolomic profiles across the gut tissue-lumen interfaces highlight the molecular participants in host-microbe interactions, providing new opportunities for examining host-microbiome-metabolome dynamics.

False-positives plague High Throughput Screening in general and are costly as they consume resource and time to resolve. Methods that can rapidly identify such compounds at the initial screen are therefore of great value. Advances in mass spectrometry have led to the ability to screen inhibitors in drug discovery applications by direct detection of an enzyme reaction product. The technique is free from some of the artefacts that trouble classical assays such as fluorescence interference. Its direct nature negates the need for coupling enzymes and hence is simpler with fewer opportunities for artefacts. Despite its myriad advantages, we report here a mechanism for false-positive hits which has not been reported in the literature. Further we have developed a pipeline for detecting these false-positive hits and suggest a method to mitigate against them. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=156 HEIGHT=200 SRC="FIGDIR/small/634670v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@a99654org.highwire.dtl.DTLVardef@1cc7ec8org.highwire.dtl.DTLVardef@978abaorg.highwire.dtl.DTLVardef@114c0d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

Nova is a software library for the reading, writing, and management of mass spectrometry data natively in the C# language. It complements similar software libraries ubiquitously used in application development for C++, Python, and Java. Few software libraries and resources for mass spectrometry data analysis have been developed for C# relative to other languages. C#, however, remains the dominant language of choice for development of real time mass spectrometry (RTMS) analysis and instrument control applications, illustrating the need for native C# libraries when developing RTMS software. Nova provides fast, easy-to-use structures and classes built upon interfaces that support open community standards, and are easily extensible for current or future mass spectrometry software development needs.

In todays fast-evolving landscape of omics research, tandem mass spectrometry has become a cornerstone for uncovering the complexities of biological systems. Yet, despite its essential role, the technology remains bound by the inherent limitations of quadrupole filters, which throw away up to 99% of the useful ion signal and caps the speed at which fragmentation data can be generated. These deficiencies often force compromises in data depth and accuracy, hindering breakthroughs that tie genomics, proteomics, and metabolomics together. A new technology is poised to break through these performance barriers, unlocking unprecedented capabilities in precision, sensitivity and throughput. This step-change in how mass spectrometry fragmentation analysis is performed will reshape the future of scientific discovery, pushing the boundaries of whats possible. The solution is high resolution ion mobility (HRIM), which offers a means to quickly and efficiently isolate ions prior to fragmentation and detection by a high resolution mass spectrometer (HRMS) while also resolving challenging isomeric and isobaric compounds that lead to chimeric MS/MS spectra. HRIM isolates ions in time as a result of a high speed separation rather than acting as a filter that discards ion signal like the pervasive quadrupole mass analyzer, allowing higher sensitivity analysis to be achieved. Also, since HRIM eliminates the need to hop or sweep electronics control parameters, as is the case with a quadrupole, fragmentation spectral generation can occur at a much faster rate, upwards of 500 Hz. This whitepaper describes an IM/MS methodology first contemplated over twenty years ago and today being positioned as the fastest method for high resolution fragmentation analysis. Revisiting this concept using the latest generation ion mobility technology based on structures for lossless ion manipulation (SLIM), which is the only HRIM technology that delivers similar resolution and range of analysis as a quadrupole, realizes the full potential of this approach to deliver benefits in both speed and sensitivity for high performance MS/MS measurements. This new way of achieving ion fragmentation in complex samples is set to revolutionize the mass spectrometry space, starting in the growing field of proteomics where all researchers are seeking faster methods to achieve more comprehensive proteomic coverage. Due to the advantages of HRIM physics, we predict this will set the bar for high throughput -omics within the coming years and will eventually be as ubiquitous as the quadrupole is today.

Mass spectrometry imaging (MSI) has gained increasing popularity for tissue-based diagnostics due to its ability to identify and visualize molecular characteristics unique to different phenotypes within heterogeneous samples. Data from MSI experiments are often visualized using single ion images and further analyzed using machine learning and multivariate statistics to identify m/z features of interest and create predictive models for phenotypic classification. However, often only a single molecule or m/z feature is visualized per ion image, and mainly categorical classifications are provided from the predictive models. As an alternative approach, we developed an aggregated molecular phenotype (AMP) scoring system. AMP scores are generated using an ensemble machine learning approach to first select features differentiating phenotypes, weight the features using logistic regression, and combine the weights and feature abundances. AMP scores are then scaled between 0 and 1, with lower values generally corresponding to class 1 phenotypes (typically control) and higher scores relating to class 2 phenotypes. AMP scores therefore allow the evaluation of multiple features simultaneously and showcase the degree to which these features correlate with various phenotypes, leading to high diagnostic accuracy and interpretability of predictive models. Here, AMP score performance was evaluated using metabolomic data collected from desorption electrospray ionization (DESI) MSI. Initial comparisons of cancerous human tissues to normal or benign counterparts illustrated that AMP scores distinguished phenotypes with high accuracy, sensitivity, and specificity. Furthermore, when combined with spatial coordinates, AMP scores allow visualization of tissue sections in one map with distinguished phenotypic borders, highlighting their diagnostic utility.

BackgroundCerebrospinal fluid (CSF) leak is typically diagnosed by detecting a protein marker {beta}2-transferrin ({beta}2-Tf) in secretion samples. {beta}2-Tf and {beta}1-transferrin ({beta}1-Tf) are glycoforms of human transferrin (Tf). A novel affinity capture technique for sample preparation, called microprobe-capture in-emitter elution (MPIE), was incorporated with high-resolution mass spectrometry (HR-MS) to analyze the Tf glycoforms and elucidate the structures of {beta}1-Tf and {beta}2-Tf. MethodsTo implement MPIE, an analyte is first captured on the surface of a microprobe, and subsequently eluted from the microprobe inside an electrospray emitter. The capture process is monitored in real-time via next-generation biolayer interferometry (BLI). When electrospray is established from the emitter to a mass spectrometer, the analyte is immediately ionized via electrospray ionization (ESI) for HR-MS analysis. Serum, CSF, and secretion samples were analyzed using MPIE-ESI-MS. ResultsBased on the MPIE-ESI-MS results, the structures of {beta}1-Tf and {beta}2-Tf were solved. As Tf glycoforms, {beta}1-Tf and {beta}2-Tf share the amino acid sequence but have varying N-glycans. {beta}1-Tf, the major serum-type Tf, has two G2S2 N-glycans on Asn413 and Asn611. {beta}2-Tf, the major brain-type Tf, has an M5 N-glycan on Asn413 and a G0FB N-glycan on Asn611. ConclusionsThe structures of {beta}1-Tf and {beta}2-Tf were successfully elucidated by MPIE-ESI-MS analysis. The resolving power of the novel MPIE-ESI-MS method was demonstrated in this study. On the other hand, knowing the N-glycan structures on {beta}2-Tf allows for the design of other novel test methods for {beta}2-Tf in the future.

Matrix-assisted laser desorption/ionization (MALDI) mass spectrometry imaging (MSI) is a powerful analytical tool that enables the visualization and comparison of relative abundances of metabolites across samples, shedding light on biological processes and disease mechanisms. Techniques such as scSpatMet enable the determination of cell boundaries and cell types through staining with 35 cell marker antibodies. Yet, distinguishing subpopulations of cells, such as astrocytes, oligodendrocytes, and neuronal clusters in the brain, remains challenging using antibodies. In this context, we introduce TransMetaSegmentation (TMS), an alternative segmentation and cell typing method that integrates MALDI MSI imagery with single-cell spatial transcriptomic analysis. This approach not only delineates cell boundaries and defines cell types based on a number of marker genes but also effectively allocates metabolites to specific cell types in a high-throughput manner. We anticipate that TMS will improve the granularity of MALDI MSI analyses, advance our understanding of metabolic alterations in diseases, and have an impact on various fields within biomedical sciences.

AO_SCPLOWBSTRACTC_SCPLOWHigh-resolution mass spectrometry is routinely used for the analysis of complex samples in pharmaceutical, environmental, and omics related studies. Such applications require instrumentation to be capable of combining sub-ppm mass accuracy, high resolving power, rapid full m/z range acquisition, and a wide dynamic range. Achieving these requirements simultaneously places constraints on analyzer design and performance. Multi-reflecting time-of-flight (MRT) based analyzers have been previously reported as a means of extending effective flight path length in compact TOF designs. Here, further instrument and functionality advances in a compact MRT mass spectrometer design are described and the impact of these enhancements is demonstrated for omics applications.

Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) is a powerful technique for studying lipid distribution in carotid plaques, key to understanding atherosclerosis. This study aimed to improve sample preparation for MALDI-MSI-based spatial lipidomics of carotid plaques by improving both matrix application and tissue handling. Human carotid plaques were collected from endarterectomy patients with ethical approval and sectioned at 10 {micro}m thickness for MALDI-MSI. We compared eight sample preparation methods, including hydroxypropyl methylcellulose-polyvinylpyrrolidone (HPMC-PVP) embedding media and Cryofilm-type IMS(R) to provide support and maintain tissue structural integrity during sectioning. Methods were assessed for signal intensity, lipid diffusion, lipid coverage, tissue morphology, and image co-registration which each criterion scored from 1-3. Cryofilm-based methods scored highest for preserving tissue morphology and minimising folding artifacts (2.9-3.0) but were limited in co-registration (2.0) due to reliance on adjacent sections. Sublimation methods generally produced greater lipid coverage with reduced lateral diffusion, while automated sprayer methods scored higher in signal intensity/sensitivity (3.0) but had increased lipid delocalisation, particularly for highly hydrophobic species such as triacylglycerols and sterols. The results highlight clear trade-offs between tissue structural preservation, lipid detection sensitivity, and spatial integrity in MALDI-MSI. Because spatial integrity cannot be compromised for imaging lipids in carotid atherosclerotic plaques, Cryofilm combined with sublimation offers a clear advantage. This work strengthens MALDI-MSI workflows enabling more precise spatial mapping and deeper biological interpretation of atherosclerotic lipid distributions.

Small molecule identification is central to research fields such as drug discovery, but in complex systems like Traditional Chinese Medicine (TCM), traditional mass spectrometry analysis methods remain constrained by bottlenecks including insufficient database coverage, fragmented analysis workflows, and poor result interpretability. To address these limitations, we developed MS4MS, a large language model-driven multi-agent system that enables an end-to-end automated pipeline from raw data to small molecule identification. Validation on a public benchmark demonstrates that MS4MS achieves state-of-the-art performance in molecular formula prediction. Furthermore, its innovative small molecule identification agent enables efficient and interpretable compound elucidation. Verification using herbal extracts indicates MS4MSs outstanding performance regarding analytical coverage and the discrimination of isomers. Consequently, MS4MS offers a novel, accurate, interpretable, and high-throughput end-to-end automated strategy for small molecule identification, overcoming the analytical bottlenecks of traditional mass spectrometry in natural products and complex TCM systems.

Mass spectrometry imaging (MSI) is a powerful tool for monitoring the spatial distributions of microbial metabolites directly from culture. MSI can identify secretion and retention patterns for microbial metabolites, allowing for the assessment of chemical communication within complex microbial communities. Microbial imaging via matrix-assisted laser desorption/ionization (MALDI) MSI remains challenging due to high sample complexity and heterogeneity associated with the required sample preparation, making annotation of molecules by MS1 alone challenging. The implementation of trapped ion mobility spectrometry (TIMS) has increased the dimensionality of MALDI-MSI experiments, allowing for the resolution of isomers and isobars, and can increase sensitivity of metabolite detection within complex samples. Parallel reaction monitoring - parallel accumulation serial fragmentation (prm-PASEF) leverages TIMS to enhance the targeted acquisition of MS2 data by increasing the number of precursors that can be fragmented in a single acquisition. Recently, imaging prm-PASEF (iprm-PASEF) has been developed to provide more accurate annotation from MALDI-TIMS-MSI datasets through the inclusion of MS2. Here, we showcase the use of MALDI iprm-PASEF to provide rapid and accurate annotation coproporphyrin III directly from a bacterial-fungal co-culture between Glutamicibacter arilaitensis (strain JB182) and Penicillium solitum (strain #12). Additionally, we present a workflow for untargeted iprm-PASEF precursor selection directly in SCiLS Lab, followed by direct export for iprm-PASEF acquisition. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=111 SRC="FIGDIR/small/653367v1_ufig1.gif" ALT="Figure 1"> View larger version (28K): org.highwire.dtl.DTLVardef@5bef36org.highwire.dtl.DTLVardef@1b5f199org.highwire.dtl.DTLVardef@87ff8org.highwire.dtl.DTLVardef@98857_HPS_FORMAT_FIGEXP M_FIG C_FIG

Ion mobility (IM) spectrometry provides semi-orthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics datasets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of {+/-}3%, an average 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insight on areas requiring further improvement.

Caenorhabditis elegans is a powerful model organism for studying development, neurobiology, aging, and toxicology, yet most investigations rely on genetic and phenotypic measurements that do not directly capture their biochemical landscape. Mass spectrometry (MS)-based metabolomics has begun to address this gap by enabling detection of small-molecule metabolites and lipids. However, conventional liquid or gas chromatography-MS approaches require homogenization of whole worms, eliminating spatial information. Mass spectrometry imaging (MSI) offers an avenue for visualizing metabolite distributions in situ, but its application to C. elegans is hindered by the nematodes small size and thick cuticle which make it challenging for structure preservation during cryo-sectioning and ion image interpretation. We develop an integrated sample preparation workflow to overcome these challenges and enable high-resolution MSI of C. elegans adult hermaphrodites. To facilitate anatomical and ion images interpretation, we employed a dual-fluorescent reporter strain expressing GFP in intestinal cells and RFP in neuronal nuclei. We optimized embedding media compositions and freezing strategies to preserve tissue architecture and genetically encoded reporter fluorescence during cryo-sectioning. Additionally, we implemented a simple "sandwiching" method that reproducibly orients worms, allowing consistent longitudinal sectioning. Using this optimized workflow, we achieved matrix-assisted laser desorption/ionization (MALDI) MSI of 10-m spatial resolution and demonstrated tissue-specific distributions of lipids and metabolites in adult hermaphrodites. With structural preservation, consistent orientation, and fluorescence-guided spatial annotation, our approach provides a foundation for investigating metabolic heterogeneity in C. elegans and can be adapted to other microscale biological systems for MSI applications.

Poor chemical annotation of high-resolution mass spectrometry data limit applications of untargeted metabolomics datasets. Our new software, the Integrated Data Science Laboratory for Metabolomics and Exposomics - Composite Spectra Analysis (IDSL.CSA) R package, generates composite mass spectra libraries from MS1-only data, enabling the chemical annotation of LC/HRMS peaks regardless of the availability of MS2 fragmentation spectra. We demonstrate comparable annotation rates for commonly detected endogenous metabolites in human blood samples using IDSL.CSA libraries versus MS/MS libraries in validation tests. IDSL.CSA can create and search composite spectra libraries from any untargeted metabolomics dataset generated using high-resolution mass spectrometry coupled to liquid or gas chromatography instruments. The cross-applicability of these libraries across independent studies may provide access to new biological insights that may be missed due to the lack of MS2 fragmentation data. The IDSL.CSA package is available in the R CRAN repository at https://cran.r-project.org/package=IDSL.CSA. Detailed documentation and tutorials are provided at https://github.com/idslme/IDSL.CSA. For Table of Contents Only O_FIG O_LINKSMALLFIG WIDTH=185 HEIGHT=200 SRC="FIGDIR/small/527886v2_ufig1.gif" ALT="Figure 1"> View larger version (86K): org.highwire.dtl.DTLVardef@1c1eb07org.highwire.dtl.DTLVardef@2d469aorg.highwire.dtl.DTLVardef@764e81org.highwire.dtl.DTLVardef@11c29a7_HPS_FORMAT_FIGEXP M_FIG C_FIG

Tandem mass tags (TMT) allow highly multiplexed and thus high-throughput, precisely quantitative proteomic analysis. Incorporation of additional deuterated reporter channels has near-doubled the multiplexation achieved with Thermo Scientific TMTpro reagents from 18 to 35-plex but requires extremely high [~]100k analyzer resolving power at m/z 128 to differentiate and quantify reporter ion channels, far beyond any single reflection time-of-flight analyzer, and exceeding the multi-reflection Thermo Scientific Astral analyzer in its standard operation. A multi-pass mode of Astral operation has been developed for the Thermo Scientific Orbitrap Astral Zoom mass spectrometer that triples the ion path to 90 m, more than doubling resolving power for a narrow m/z range. This "TMT HR mode" has been integrated into a new method of TMT proteomic analysis that splits regular MS2 analysis of labeled peptides into paired measurements comprising wide mass range scans for peptide identification, and TMT HR mode scans for reporter ion quantification. The method has been shown to accurately quantify 32-plex labeled HeLa protein lysate and provide far greater depth of analysis as state-of-the-art Orbitrap-only methods, while analysis of 11-plex labeled yeast showed no analytical depth sacrificed vs regular Orbitrap Astral TMT analysis. Further comparative measurements of a 2-cell line 35-plex sample demonstrated greater analytical depth, and similar quantitative precision, to "gold standard" Orbitrap MS3 methods.

We show a practical application of the Google Gemini large-language-model for simulating tandem mass spectra for compounds from the Blood Exposome Database. This approach bypasses the need for domain-specific model training, suggesting that the chemical fragmentation knowledge could be latently encoded within the Gemini model. General-purpose LLMs represent a useful and accessible tool for expanding in-silico spectral libraries and may accelerate the compound annotation in mass spectrometry-based metabolomics and exposomics.