Journal of the American Society for Mass Spectrometry最新文献

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 m/z of 10,000, the predicted maximum mass resolving power (R_m) 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.

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.

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.

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 N-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.

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.

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 m/z species, including isomeric and isonucleonic species, and it was possible to assign secondary fragmentation with greater confidence.

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.

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 α,β-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.

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.