Matrix-assisted laser desorption electrospray ionization (MALDESI) enables mass spectrometry imaging (MSI) capabilities to reveal the localization of a wide range of biomolecules across an organism. Three-dimensional (3D) MSI of biological tissues is typically accomplished by imaging two-dimensional sections followed by the creation of a 3D image informatically. In contrast to this sectioning-based approach, we employ an ablation-based 3D MSI technique to image , a medicinal herb that naturally produces the antimalarial drug artemisinin. We incorporated a novel high-energy burst-mode ultraviolet (UV) laser, and a chromatic confocal aberration (CA) probe with automatic -axis correction (AC) to measure the depth of ablation (i.e., -resolution). The combination of these techniques allowed the visualization of the artemisinin metabolic pathway along with other secondary metabolites beneath the tissue surface, as supported by the resulting data. This approach enabled detailed molecular mapping in 3D, providing a comprehensive view of the plant's molecular landscape layer by layer, offering new insights into its biosynthetic pathways in three dimensions.

Loss of hydrophobic peptides and proteins remains a significant challenge in bottom-up proteomics, resulting in under-representation of membrane and membrane-associated proteins that are critical for understanding cellular function and disease. This limitation is particularly acute for targeted applications such as S-palmitoylation analysis, where modifications occur preferentially on membrane-proximal cysteines. This study evaluated supplementation by -dodecyl-β-d-maltopyranoside (DDM), a mild detergent widely used in structural biology but not proteomics, during the postprecipitation resolubilization step to enhance hydrophobic protein recovery. Using immortalized bone marrow-derived macrophages (iBMDMs), we compared standard resolubilization (8 M urea in 50 mM ammonium bicarbonate) with DDM-supplemented conditions. In global proteomics, DDM supplementation improved peptide and protein identifications, with particularly pronounced benefits for membrane protein recovery. The 539 proteins uniquely identified with DDM were enriched for mitochondrial components, protein complexes, and membrane-bounded organelles. For acyl-biotin exchange (ABE) proteomics targeting palmitoylated proteins, DDM supplementation enhanced recovery of proteins, with 223 proteins consistently requiring DDM for identification. These DDM-dependent proteins showed enrichment for transport and localization functions characteristic of palmitoylated proteins. Comparison with the SwissPalm database revealed 336 previously unreported S-palmitoylation candidates, with DDM conditions contributing more novel identifications than urea alone. These findings demonstrate that DDM-assisted resolubilization addresses a key bottleneck in proteomics workflows, enabling more comprehensive characterization of hydrophobic and lipid-modified proteomes without requiring extensive protocol modifications.

Molecular networking is a computational mass spectrometry technique used to visualize and connect tandem mass spectra from putatively related molecules to reveal structural relationships. Despite their utility, existing tools for interpreting molecular networks are limited in the ability to easily organize fragmentation patterns within molecular families. We developed an interactive web-based tool, the Multiple Mass Spectral Alignment (MMSA) approach, that enhances the visualization of molecular networks by displaying detailed spectral alignment information among all the spectra in a network component in one visualization. MMSA identifies sets of consensus peaks that contribute to the alignment of multiple tandem mass spectra, offering insights into how structural moieties captured by specific fragments influence the construction of molecular networks. We demonstrate that MMSA facilitates insightful understanding of molecular networks and improves the interpretability of the tandem mass spectra, capturing the chemical modifications or core structures within a molecular family. We envision that the MMSA tool will significantly enhance the ability to interpret molecular networks, with implications for more rapid identification and prioritization of new metabolites for full characterization.

In order to transport ions from an intermediate-pressure collisional cooling device to a planar electrostatic ion trap (PEIT) mass analyzer in an ultrahigh-vacuum chamber, a segmented radio frequency (RF) quadrupole system was designed and investigated by using an ion optical simulation. Two gas-flow-limiting units were employed in the system, allowing the differential pumping to establish a pressure difference of 7 orders of magnitude. Flow-limiting units including a plain aperture lens and 3 small quadrupoles in different shapes were investigated, and the transmission efficiency and energy distribution of the transported ions were evaluated. The small quadrupoles with conical front-ends inserted between the multiple segments gave the best transmission efficiency, and the collisional cooled ions can maintain their low energy distribution after being transported to the ultrahigh-vacuum stage. With an RF voltage amplitude of 200 V, the maximum transmission efficiency reached above 90%. After ions are cooled in the high-pressure quadrupole segment, the extraction voltage difference in high-pressure region needs to be low enough to avoid reheating the ions during the transport. It was found that an extraction voltage difference below 1.2 V can restrict the half width of the energy spread within 0.5 eV, which is required by PEIT to get ultrahigh mass resolution.

Plant metabolomics faces major challenges in metabolite identification due to the structural diversity of plant metabolites and limited coverage in existing spectral libraries. To address this, we developed CIeaD (Collision-Induced and Electron-Activated Dissociation), an open-access plant metabolite spectral library containing complementary CID and EAD spectra. The library includes curated high-resolution spectra for 2,305 phytochemicals across major metabolite classes, acquired in both positive and negative modes with a dual fragmentation mechanism to capture a wide range of diagnostic ions. CIeaD library is provided in multiple formats and can be accessed at https://www.moleculardetective.org/Links.html.

Acrylamide is a carcinogen produced when foods containing sugar and asparagine are heated to 120 °C or higher during thermal cooking. As efforts are underway globally to reduce acrylamide intake, simple and practical methods to determine its levels in foods are needed. In this study, a highly sensitive and selective analytical method using liquid chromatography (LC) with tandem mass spectrometry (MS/MS) was developed for acrylamide detection. The -unsaturated carbonyl structure in acrylamide was derivatized with perfluoroalkyl thiol, and the obtained derivative was analyzed based on fluorous properties without interference from food product contaminants. The feasibility of this method was demonstrated by analyzing common food products, such as French fries and canola oil. Acrylamide in the food samples was directly derivatized by adding a perfluoroalkyl thiol reagent-containing solution to the samples without any extraction steps. After delipidation, the fluorous-derivatized acrylamide was separated and detected using a fluorous LC column and an MS/MS system, respectively. The detection sensitivity of acrylamide using this method was 185-fold higher than that of the underivatized form. Therefore, this method is applicable for analyzing trace amounts of acrylamide in food samples and monitoring the acrylamide formation and migration processes during the cooking of French fries.

In mass spectrometry (MS), the radio frequency (RF)-only multipole ion guide commonly used in a medium pressure range (0.1-10 Pa) is susceptible to a collision-damping-induced loss of instrumentation sensitivity, resulting in operation within a limited / bandwidth. In this study, a novel direct current (DC)-free gradient-threaded discrete twisted dipole ion guide (GTDIG) is proposed for high-efficiency ion propulsion and an expanded / bandwidth in a medium vacuum (0.1-10 Pa). An overview of the GTDIG working principles is provided with systematic optimization of the critical parameters. The GTDIG has been applied by using a series of printed circuit boards, utilizing an innovative discrete helical electrode design. The performance of the GTDIG prototype is evaluated using multiphysical numerical modeling and tested using a custom-built nanoelectrospray ionization (ESI)-MS platform, including a direct comparison with a quadrupole ion guide. The results indicate that the RF-only GTDIG offers a significantly larger / window while effectively maintaining an axial field, demonstrating that this concept can be utilized in the development of instruments with an enhanced performance.

Here, we describe ion confinement based on mobility in rotating electric fields under low E/N conditions. To do this, we constructed a device with a stack of eight segmented ring electrodes to which sinusoidal waveforms were applied with a 45° phase shift. Ion confinement was characterized by monitoring ion intensities measured using a quadrupole time-of-flight mass spectrometer. The All Pressure Ion Confinement (APIC) device was operated at a pressure range of 3.8-8.0 Torr. Two mixtures of phosphazene and tetraalkylammonium ions covering a broad mobility range were used to evaluate APIC transmission. The results showed that ion confinement depends on ion mobility in rotating electric fields. As pressure increases, the electric field strength required for maximum ion intensity also increases. Highly mobile ions need lower electric fields at a given pressure, while less mobile ions require stronger fields to reach maximum intensity. We also observed that ion confinement depends on the rotational speed of the electric field, highlighting the importance of balancing ion velocity and the rotating field speed. We define a dimensionless parameter α that scales with the ratio of ion velocity to the field's rotational speed. Varying electric field strength, ion mobility, and field rotation speed revealed a strong correlation between ion confinement and α, with optimal confinement observed when 0.1 < α < 1.5. These findings are useful for predicting mobility-dependent behaviors in low fields within rotating electric fields and can guide the design and operation of ion optics using such fields.

The analysis of complex biological mixtures remains a significant challenge in mass spectrometry (MS), particularly when using conventional direct infusion MS/MS approaches due to inherent limitations in resolving power and spectral complexity. Here, we demonstrate the integration of trapped ion mobility spectrometry (TIMS) with two-dimensional mass spectrometry (2DMS) to enable high-resolution TIMS-MS/2DMS experiments for detailed protein characterization within mixtures. TIMS provides separation based on the ion's size-to-charge ratio, effectively reducing the occurrence of chimeric tandem mass spectra containing fragments from more than one precursor ion. This coupling allows for an improved peak capacity and reduced ambiguity in tandem spectral interpretation. When applied to a model protein mixture, the TIMS-MS/2DMS method allows resolution of near / species, including isomeric and isonucleonic species, and it was possible to assign secondary fragmentation with greater confidence.

Despite structure-based mutagenesis being widely used for the rational evolution of engineering enzymes, the in-solution conformation dynamics of enzyme catalytic adaptability is still hard to profile and modulate. Herein, we utilize native mass spectrometry to probe the integrity of hemoprotein overall structure and 193 nm ultraviolet photodissociation to provide residue-level conformation dynamics of catalytic hotspots in peroxidation reaction. We demonstrate that the structure of hemoprotein is generally stable in 25% acetonitrile and methanol aqueous solutions, yet the hotspot conformation dynamics and peroxidase activity are significantly different. The hydrophobic heme-binding pocket becomes more flexible within 25% acetonitrile solution, releasing more space between heme and His64 to adapt hydrogen peroxide to form a peroxidation intermediate. In contrast, a His93-heme-His64 double coordination is formed in 25% methanol solution, preventing the formation of a peroxidation intermediate. These findings represent a paradigm shift in biocatalytic design, enabling the rational modulation of enzyme conformation in-solution to optimize the biocatalysis efficiency.

Matrix-assisted laser desorption ionization mass spectrometry imaging (MALDI-MSI) enables the direct visualization of metabolites from tissue sections with high spatial resolution. However, its application to untargeted spatial metabolomics is hindered by poor ionizing compounds and challenges in accurate metabolite annotation. On-tissue chemical derivatization (OTCD) is commonly employed to enhance the ionization of metabolites bearing specific functional groups, and platforms such as METASPACE facilitate high-throughput annotation of derivatized features. Nevertheless, distinguishing structural isomers for a large number of metabolites remains a major challenge, often resulting in incorrect annotations. To address this limitation, we developed an improved annotation workflow for OTCD-MALDI-MSI by integrating two filtering strategies. Functional group filtering leverages SMARTS-based substructure matching to retain only those metabolites that react with the applied OTCD reagent. In parallel, gas-phase hydrogen-deuterium exchange (HDX) in the MALDI source is used to determine the number of labile hydrogens for each feature, enabling the exclusion of annotations that are inconsistent with HDX behavior. We applied this workflow to MALDI-MSI of maize root sections using Girard's reagents T and P, along with the plant-specific COCONUT metabolite database. The combined filtering strategy reduced incorrect annotations by ∼67%, from ∼7.3 annotations per unique feature without filtering to ∼2.4 with filtering, substantially improving annotation accuracy and confidence. By coupling OTCD signal enhancement with structurally informed filtering, this workflow advances the utility of MALDI-MSI for untargeted spatial metabolomics, enabling more reliable and scalable metabolite profiling in complex biological tissues.

Desorption electrospray ionization mass spectrometry (DESI-MS) imaging is a well-established technique for molecular analysis of biological samples, although its spatial resolution has been limited when compared to other MS imaging techniques. Here, we describe the development and optimization of a low-flow DESI-MS method that allows for sub-10 μm spatial resolution tissue imaging using a commercial DESI sprayer. Key technical modifications that enabled low-flow high-resolution DESI-MS imaging include reduced solvent flow rates below 350 nL/min, increased solvent pump back-pressure for spray stability, and optimized sprayer design and geometry. We applied low-flow DESI to image porcine liver and rat brain tissue sections at a spatial resolution of 5-10 μm, and the resulting ion images showed high spatial fidelity and detailed tissue histologic features. Building on the nondestructive nature of DESI-MS, we demonstrate that a tissue section can be first imaged with low-flow DESI at lower resolution (100-200 μm pixel size), followed by high-resolution (5-10 μm pixel size) imaging of selected regions of interest in the same tissue section. Lastly, we applied low-flow DESI to image and classify human thyroid cancer tissue sections and fine-needle aspiration (FNA) biopsies at 10 μm spatial resolution, achieving accurate identification of cancer cells in the FNA sample. Altogether, these results demonstrate the robustness and applicability of low-flow DESI-MS for high spatial resolution imaging of tissue sections, which could in the future potentially be implemented across a variety of biomedical and clinical studies.

The Omnitrap-Orbitrap-Booster (OOB) mass spectrometry (MS) platform was developed to advance the top-down (TD) MS analysis of proteins. It integrates a multimodal tandem mass spectrometry (MS/MS) ion trap system (Omnitrap), a high-resolution Orbitrap Fourier transform mass spectrometer (FTMS), and a high-performance data acquisition system (FTMS Booster) to improve fragmentation efficiency and spectral quality by increasing the signal-to-noise (S/N) ratio of product ions. In this study, we evaluate the OOB platform for the electron capture dissociation (ECD)-based TD MS analysis of a P15 multiple myeloma antibody light chain. Single precursor charge state analysis of P15 23+ yielded relatively high sequence coverage of 68%, albeit indicating a limitation caused by the overlap of certain product ions with the charge reduced precursors. The corresponding method development, leveraging consecutive analysis of multiple precursor charge states (15+ to 19+) across triplicate LC-MS/MS runs on the OOB platform, enhanced P15 sequence coverage to 93%, demonstrating its capacity for comprehensive protein characterization. In addition, we demonstrate that the obtained ECD-based TD MS performance on the OOB platform for P15 light chain is comparable to the "gold-standard" electron transfer/higher-energy collision dissociation (EThcD)-based TD MS on an Orbitrap Eclipse. Serendipitously, ECD exhibits a lower spectral peak density (i.e., reduced spectral congestion) due to reduced redundancy of product ions. These results establish the OOB platform as a powerful and efficient tool for TD MS of proteins.

Intact amphibian skin peptides, apart from their intrinsic interest, are a challenging model system to demonstrate direct sequencing, avoiding any preliminary derivatization steps. They are relatively long (up to 46-mer), contain an intramolecular disulfide bridge, and include a number of isomeric Leu/Ile residues. Sixteen intact peptides from the skin secretion of the Rostov (Russia) population of were studied in the present research using EThcD, ExD, and ExciD fragmentation. Comparison of the efficiency of EThcD and ExD tandem mass spectrometry approaches demonstrated that both are appropriate for the direct sequencing of these peptides. Although the majority of the isomeric Leu/Ile residues could be differentiated using -ions, the usefulness of -ions, especially inside C-terminal disulfide rings, was also demonstrated. The -ions arise more often in ExD/ExciD than in the EThcD mode. EThcD and ExciD are complementary methods and together distinguished more isomeric residues than either of them alone. While both methods provided similar sequence information within intact C-terminal S-S loops, their combined use consistently yielded 100% sequence coverage. ExciD demonstrated superior results in determining peptide sequences due to the higher yield of all fragment ion types, establishing complete sequences for all peptides, including that of the longest (46-mer) esculentins, compared to six with EThcD alone. The increased number of characteristic ions ( and ) in ExciD further enhanced confidence in the sequence assignments. Ultimately, the complementary use of ExciD and EThcD resulted in reliable 100% sequence coverage for all 16 intact disulfide peptides analyzed in this study.

The identities and outgassing rates of contaminants associated with a material determine its suitability for space applications. Thermogravimetric analysis (TGA) is one test commonly used for evaluating these material properties. During TGA, contaminants deposited on quartz crystal microbalances (QCMs) are desorbed through heating while mass spectrometer (MS) data is collected. Three factors contribute to noise and artifacts in the MS data: (a) randomness in QCM outgassing flux, (b) MS measurement noise, and (c) constant chamber background contaminants. We present a two-step noise reduction approach that addresses these sources. First, we use QCM data to determine the number of outgassing species and kinetic parameters governing their desorption. Then, we apply these parameters to fit a linear statistical model to MS data, accounting for variance across the tested discretized mass spectrum. Once the variance is known for each mass bin, we use an adapted -sigma method to isolate signal from noise. Our approach effectively reduces all three types of noise, improving confidence and efficiency in species identification and enabling MS-based modeling for isothermal outgassing kinetics. Although our analysis relies on the relationship between QCM and MS data, it may be applicable to other test procedures taking MS data concurrently with a measured source of mass flux.

Mass spectrometry imaging is a powerful technique which maps the spatial distribution of thousands of biomolecules across tissue sections. The clear delineation of tissue is an important preceding analysis step typically requiring manual intervention. We present an end-to-end method for the automatic detection of tissue in mass spectrometry images (MSIs) using same-tissue-section pairs MSIs and histological images. First, the histological tissue masks were annotated using QuPath. Second, manually acquired landmarks were used to fit to affine transforms and map the tissue masks into the MSI space. Third, we proposed metabolite-independent representations of MSIs─based on total-ion-current, root-mean-square and Shannon-entropy images─to fit a tissue-detection model. Finally, a convolutional neural network was trained to detect tissue using cross-validation in a set of 68 images featuring a variety of tissue types, organisms and spatial resolutions. Our model achieved a cross-validation accuracy, precision, recall, and Sørensen-Dice coefficient of 0.953 ± 0.030, 0.939 ± 0.047, 0.923 ± 0.056, and 0.930 ± 0.041, respectively. Using unseen test data from two different studies, our model obtained an accuracy, precision, recall, and Sørensen-Dice coefficient of 0.945 ± 0.007, 0.965 ± 0.009, 0.915 ± 0.027, and 0.935 ± 0.011, respectively.

High-level programming languages such as Python and R are widely used in mass spectrometry data processing, where library searching is a standard step. Despite the availability of numerous library search algorithms, those developed by NIST and implemented in MS Search remain predominant, partly because commercial databases (e.g., NIST, Wiley) are distributed in proprietary formats inaccessible to custom code. MSPepSearch, another NIST tool, provides access to the same algorithms with greater flexibility for automation. However, its use requires calling a command-line interface with multiple flags and parsing output text files to retrieve results, which can be cumbersome. To address this, we developed mspepsearchr, an R package that streamlines the integration of library searches against NIST-format mass spectral databases into complex, multistep workflows. MSPepSearch is a single-threaded tool; therefore, parallelization was achieved externally by running multiple instances from within R. We describe the package, evaluate its performance, and illustrate its utility through the recognition of steroid-like compounds in untargeted gas chromatography-mass spectrometry analysis of biological samples.

This study explores unexplained fundamental principles of reflectron time-of-flight (R-TOF) mass spectrometry (MS). Conventional calculations focusing on the ion trajectory in reflectors concluded that multistage reflectors are necessary to achieve second-order velocity focusing at ion detectors. This study demonstrates that in an instrument equipped with a matrix-assisted laser desorption/ionization (MALDI) ion source a single-stage reflector can achieve second-order velocity focusing when the optimal experimental parameters predicted using the coupled space and velocity focusing (CSVF) principle are used. The optimization model indicates that the delayed extraction technique is more effective in compensating for the initial ion velocity spread than reflectors. The calculation shows that for ions with / of 10,000, the predicted maximum mass resolving power () can reach 750,000 using a single-stage R-TOF MS with an effective total length of about 2.4 m, or approximately 130,000 when accounting for the temporal response limit of ion detectors. The calculation model also reveals that in second-order focusing conditions, ions have two focusing points along the flight path, instead of just one at the detector as conventionally believed. The result indicates that the new model is critically important for the advancement of R-TOF MS.

Proteogenomics integrates genomics and mass spectrometry (MS) data to understand complex biological systems, disease mechanisms, and potential biomarkers. However, the high volume and noise in MS data present computational and interpretational challenges in proteogenomic studies where, despite best efforts, many spectra are often left unassigned. We developed , an easy-to-use tool for MS/MS quality assessment. We evaluated ten spectral features and, using the top features, developed a machine learning-based model to predict the quality of MS/MS spectra through a spectral quality score (SQS). SpecQuality can be used prior to database search or for postsearch validation of peptide spectrum matches (PSMs). This enables rapid prioritization of high-quality PSMs from proteomics and proteogenomics searches, thereby reducing false-positive identifications. We demonstrated its utility in proteogenomics applications by evaluating its performance on two data sets with ∼2.7 million spectra from Alzheimer's disease, where it successfully highlighted high quality spectra. The spectra that matched novel, variant, and modified peptides in the proteogenomics search were observed to be of high spectral quality. Additionally, a direct comparison with manually curated variant identifications demonstrated its capacity to mitigate false positives and enhance reliability. SpecQuality is an open-source, freely available, easy-to-use, simple, and versatile tool developed in both Python and Perl that can be leveraged in many proteomics pipelines. It can be easily used as a standalone tool or integrated as a part of a bioinformatics data analysis pipeline. SpecQuality provides a scalable and accessible approach to spectral prioritization, advancing data integrity in proteomics and proteogenomics. SpecQuality is available at https://github.com/alkayadav10/SpecQuality.

Trimethylamine N-oxide (TMAO) is an emerging biomarker of cardiovascular disease (CVD) risk, but current detection methods are limited by low throughput and lengthy workflows. To address this, we developed a high-throughput desorption electrospray ionization-mass spectrometry (DESI-MS) platform for rapid and accurate quantitation of TMAO in plasma. The method involves protein removal, spot deposition, and DESI-MS analysis using isotope-labeled internal standards for calibration. Validation showed strong linearity (R > 0.97), precision (CV < 20%), minimal matrix effects, and low carry-over (<5%). In a cohort of 197 patients from National Taiwan University Hospital, DESI-MS demonstrated high correlation with LC-MS/MS (R = 0.96), 92.9% concordance in risk classification, and a 10-fold reduction in processing time. Risk stratification revealed a 1.55-fold higher prevalence of coronary stenosis in the high-risk group. Capable of processing up to 2,000 samples per day, this DESI-MS platform shows strong potential for large-scale clinical screening and personalized cardiovascular risk assessment.