Journal of the American Society for Mass Spectrometry最新文献

Recent developments in charge detection mass spectrometry have allowed for the broad characterization of individual proteoforms and mixtures of proteoforms from individual ions collected in Orbitrap mass spectrometers. In this study, we describe Theropod, a set of software tools to perform Selective Temporal Overview of Resonant Ions (STORI) analysis from transients collected on an external digital oscilloscope. Using a relatively wide charge correction, we are able to calculate the uncharged mass of ubiquitin, myoglobin, carbonic anhydrase, enolase, and the Pierce Intact Protein Standard protein mixture. We can also identify post-translational modifications from the protein mixture and the individual protein samples. Theropod is available at https://github.com/clelandtp/Theropod

Microbiome communities are found across diverse environments and play critical roles in both ecosystem function and human health. Mass-spectrometry-based metaproteomics provides a powerful means for directly identifying and quantifying microbial proteins. However, its application is hindered by the shared peptide problem, where peptides map to multiple proteins across taxa, complicating taxon–protein quantification. To address this challenge, we extend a previously published modified expectation–maximization algorithm that incorporates taxonomic biomass constraints into the Microorganism Classification and Identification (MiCId) workflow. This enhanced expectation–maximization algorithm is used to quantify taxon–protein pairs derived from clusters of identified taxon–protein pairs, thereby enabling more accurate quantification and representation of taxonomic-level proteomes. The performance of the approach is evaluated using synthetic datasets consisting of simple mixtures with known relative species abundances, a more complex 24-species synthetic dataset, and a clinical human stool microbiome dataset. It is shown that, in simple synthetic datasets, fold changes computed for species–protein pairs closely match the expected values and are consistent with those obtained from MaxQuant. Using the 24-species synthetic dataset, we show that the algorithm accurately redistributes peptide extracted ion count among taxon–protein pairs that share peptides. Finally, analyzing the clinical stool microbiome dataset, we demonstrate that MiCId’s results are accurate and consistent with previously reported findings. These results demonstrate the robustness of MiCId’s algorithm for quantifying taxon–protein pairs in complex microbial communities. By resolving the shared peptide problem, the method enables accurate representation of taxonomic-level proteomes, thereby advancing the application of metaproteomics in microbiome research.

A variety of non-proximate mass spectrometry techniques have been implemented to sample organic analytes from distant surfaces, but they frequently risk signal carryover and loss by relying on unheated plastic tubing to transfer analytes back to the instrumentation. Non-proximate desorption photoionization (NPDPI) has used heated and passivated stainless steel transfer tubing since its inception, allowing clean but painstaking analysis of large objects. A unique new design based on gooseneck tubing confers flexibility comparable to that of plastic but resists organic molecule loss to the transfer tube wall. An integrated desorption probe based on a heated gas jet is constructed around the non-proximate terminal for easy manual operation in conjunction with a hand-held controller. Real-time probe analysis is demonstrated on typically inaccessible surface geometries, using a bottom surface contact sensor and side-contact piezo sensors to aid the user in optimally positioning the probe when it cannot be seen. Neutrally desorbed, the gas-phase analyte is efficiently transferred up to 4 m to the doped photoionization source. Involatile and volatile analytes are observed from commonplace objects that have narrow dimensions, blind concave surfaces, and low weights that cannot be forcefully pressed by the probe contact.

This Account is a personal perspective on the development of the current atmospheric pressure ionization (API) LC/MS techniques. These include atmospheric pressure chemical ionization (APCI), which appeared first, followed by electrospray ionization. Early commercial LC/MS interfaces began to appear in the late 1970s coupled with either electron ionization (EI) or chemical ionization (CI) ion sources, which operated under vacuum. There was an understandable challenge in successfully coupling the HPLC effluent liquid introduction into a vacuum system. The analytical success for the early LC/MS interfaces was slow until John Fenn published his results on the nano flow infusion electrospray analysis with an atmospheric pressure ionization (API) mass spectrometer. Our laboratory was excited about Fenn’s report, but we were concerned that the current HPLC techniques operated at mobile phase flow rates much too high for Fenn’s electrospray. We commenced studies to increase the ability of electrospray to operate at higher liquid flows by implementing a high flow of nitrogen gas coaxially with the electrospray emitter. This became pneumatically assisted electrospray, which we later called Ion Spray. This technique eventually became commercially available from each mass spectrometer vendor and today is called “electrospray” even though it is actually “Ion Spray”.

Per- and polyfluoroalkyl substances (PFAS) are highly water-, grease-, and heat-resistant compounds known as “forever chemicals” due to their resistance to degradation. Aqueous film-forming foams (AFFFs) are PFAS-containing firefighting foams and a known source of environmental PFAS contamination. Recent research characterizing PFAS contamination at historical AFFF usage sites has advanced our understanding of environmental fate and transport of water-soluble PFAS. However, emissions of volatile or semivolatile PFAS from AFFFs during their storage and application and from AFFF-contaminated sites remain largely uncharacterized, in part due to analytical method limitations. This work quantified fluorotelomer alcohols (FTOHs) in the headspace above three AFFF formulations using two emerging analytical techniques: thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) and real-time iodide chemical ionization mass spectrometry (I-CIMS). Thermal desorption tube samples were collected for offline analysis via TD-GC/MS, while I-CIMS simultaneously sampled the headspace. Then, the FTOH headspace concentrations quantified by the two techniques were compared. In all three formulations, 6:2 and 8:2 FTOHs were the most and second-most abundant, respectively. The FTOH concentrations quantified by TD-GC/MS ranged from 0.06 to 2.0 μg/m³, while those quantified by I-CIMS ranged from 0.13 to 4.4 μg/m³. The percent error between the two methods ranged from 10 to 102%. These results underscore the need for additional research exploring the factors that impact the quantitative accuracy of both methods.

Ion mobility spectrometry (IMS) is increasingly recognized as a powerful technique in analytical chemistry, enabling the separation of gas-phase ions based on their size-to-charge ratio. Among the most advanced implementations of IMS are the Structures for Lossless Ion Manipulations (SLIM) platform, and the cyclic IMS system, which use traveling electric waveforms across precisely patterned electrodes to transport and separate ions. Most SLIM and cyclic IMS systems employ square voltage waves to transmit the ions; however, due to fringing effects, the ions usually observe pseudosinusoidal electric fields as they travel. This fringing effect has not been addressed despite a myriad of studies of different waveforms. In this work, we derive and solve the axisymmetric two-dimensional Nernst–Planck equation under several linearly varying electric field conditions to analyze waveform performance. By comparing field profiles, we show that a linearly decreasing field offers a unique advantage: it suppresses longitudinal diffusion. This condition, in which the standard deviation of the ion distribution remains constant or even shrinks during transport, sets a theoretical benchmark for field-based separations. Practically, however, a decreasing field must always be paired with an increasing field to complete the traveling-wave cycle, which partially neglects the effect. We discuss the implications of this solution, offering guidance for next-generation SLIM device design, while theoretically showing that resolving powers in the thousands are attainable through waveform optimization.

Protein glycosylation plays essential roles in various biological processes, and thus determining the glycan structure present on the protein is essential to comprehensively understand these events. However, distinguishing saccharide stereoisomers is challenging, especially when their structures are very similar and their molecular weight and potential glycosylation sites are identical. One representative example is O-linked β-N-acetylglucosamine (O-GlcNAc) and O-linked α-N-acetylgalactosamine (Tn antigen). Traditional biochemistry approaches used in separating O-GlcNAc- and Tn antigen-modified peptides mainly include chemical derivatizations, lectins, and antibodies. However, subsequent mass spectrometry (MS) analysis is still required if one aims to determine the exact glycosylation site. Herein, a straightforward approach using the ratio of relative abundance (RA) of two fragment ions (RA_126.055/RA_138.055) in higher-energy collisional dissociation (HCD) MS without relying on the traditional biochemistry technique is reported to discriminate between O-GlcNAc and Tn antigen. This ratio was verified by synthetic glycopeptides and proteomic analysis in HeLa cells, where 10 proteins were found to be O-GlcNAcylation and 4 proteins were found to be Tn antigen-modified. Overall, this method can be extensively employed in liquid chromatography–mass spectrometry (LC-MS)-based proteomic studies and thus is of importance in biological and biomedical research.

Accurate prediction of Collision Cross-Section (CCS) values is essential for identifying molecular structures in complex environmental mixtures. This study integrates supervised machine learning and deep learning to predict CCS values for a diverse array of dissolved organic molecules, including carbohydrates, hydrocarbons, lignins, lipids, proteins, tannins, and unassigned molecules. We evaluated eight regression models─Gradient Boosted Regression, K-Nearest Neighbors, LASSO, Linear Regression, Partial Least Squares, Random Forest, Support Vector Regression, and a Voting Regressor─alongside a Graph Neural Network (GNN) trained on molecular fingerprints (SMILES) and structural descriptors (m/z, O/C, H/C, AImod, DBE). Model performance varied by molecular class and the characteristics of the data set. The best-performing models were as follows: Voting Regressor for carbohydrates and unknowns, Random Forest for hydrocarbons and proteins, SVR for lignins and lipids, and LASSO for tannins. The GNN consistently delivered competitive accuracy across all classes. Validation using High-Resolution Mass Spectrometry (HRMS) data from the Arctic Ocean confirmed the predictive power of these models, enabling more precise selection of correct molecular structures from candidate lists generated by conventional workflows. This work presents a robust, data-driven framework for CCS prediction that enhances molecular classification and improves contaminant detection in environmental samples.

The characterization of small-molecule therapeutics containing basic moieties, such as pyrrolidine groups common in KRAS G12C inhibitors, presents challenges for structure elucidation via tandem mass spectrometry (MS/MS). During fragmentation, the pyrrolidine preferentially sequesters the proton, leading to a dominant, uninformative fragment ion and a corresponding loss of structural detail. This hinders the efficient identification of related impurities and metabolites in complex mixtures. To circumvent this limitation, we developed an easily transferable analytical workflow that intentionally utilizes In-Source Fragmentation (ISF). Optimizing source parameters promotes the selective neutral loss of the pyrrolidine moiety prior to MS/MS, yielding core fragment ions (e.g., m/z 525 for GDC-6036). Tandem mass spectrometry on this ISF-generated precursor provides extensive fragmentation and structural coverage, outperforming traditional higher-energy collisional dissociation. We demonstrate the successful application of this optimized workflow to characterize GDC-6036, its synthetic intermediate, and structurally distinct KRAS inhibitors (Adagrasib and MRTX1133) across a chromatographic time scale. This approach offers a universal tool for enhancing the structure elucidation of challenging basic compounds, critical for supporting pharmaceutical process development.