Journal of the American Society for Mass Spectrometry最新文献

A direct headspace injection method is presented and optimized for the analysis of volatile organic compounds (VOCs) using dielectric barrier discharge ionization-mass spectrometry (DBDI-MS), incorporating an intermediate vial in which the sample headspace is injected. The setup is built of commonly available, cheap consumable parts and easily enables the incorporation of different gases for generating different ionization atmospheres. The method can be fully automated by using standard GC autosamplers, and its rapid analysis time is suitable for high-throughput applications. We show that this method is suitable for both profiling analysis of complex samples such as biofluids and quantitative measurements for real-time reaction monitoring. Our optimized method demonstrated improved reproducibility and sensitivity, with detection limits for compounds tested in the high nanomolar to the low micromolar range, depending on the compound. Key parameters for method optimization were identified such as sample vial volume, headspace-to-liquid ratio, incubation temperature, and equilibration time. These settings were systematically evaluated to maximize the signal intensity and improve repeatability between measurements. Two use cases are demonstrated: (i) quantitative measurement of ethanol production by a metal-organic framework from CO₂ and (ii) profiling of biofluids following the consumption of asparagus.

Amino acids are commonly used as nutritional fortification substances in functional foods, and their chiral configuration is an important determinant of food function. Rapid chiral screening methods are urgently needed in food analysis but are limited by the long-time chiral separation and matrix interference. In this study, we show a kinetic method coupled to thermal-assisted paper spray ionization mass spectrometry for direct determination of enantiomeric excess (ee) of multiple d/l-amino acids in complex food matrixes without sample pretreatment. 3-(2-Naphthyl)-l-alanine was selected as a new chiral reference ligand for the kinetic method to achieve efficient chiral differentiation (discrimination degree is 8.7 for d/l-phenylalanine and 10.2 for d/l-tyrosine). An additional thermal-auxiliary device was developed for paper spray ionization mass spectrometry to facilitate the enantiomeric purity determination. The developed method allowed a rapid simultaneous enantiomeric purity determination of multiple chiral amino acids (d/l-phenylalanine and d/l-tyrosine) within 30 s. Good linearities were achieved for the quantitation of ee (R² = 0.9996 for phenylalanine and 0.9995 for tyrosine) with unknown amino acid concentrations ranging from 10 μM to 600 μM. The developed method was successfully applied for the enantiomeric purity determination of multiple chiral amino acids in functional capsules and beverages and showed great potential for efficient enantiomer-related food safety screening and nutrition analysis.

Untargeted metabolomics often produce large datasets with missing values. These missing values are derived from biological or technical factors and can undermine statistical analyses and lead to biased biological interpretations. Imputation methods, such as k-Nearest Neighbors (kNN) and Random Forest (RF) regression, are commonly used, but their effects vary depending on the type of missing data, e.g., Missing Completely At Random (MCAR) and Missing Not At Random (MNAR). Here, we determined the impacts of degree and type of missing data on the accuracy of kNN and RF imputation using two datasets: a targeted metabolomic dataset with spiked-in standards and an untargeted metabolomic dataset. We also assessed the effect of compositional data approaches (CoDA), such as the centered log-ratio (CLR) transform, on data interpretation since these methods are increasingly being used in metabolomics. Overall, we found that kNN and RF performed more accurately when the proportion of missing data across samples for a metabolic feature was low. However, these imputations could not handle MNAR data and generated wildly inflated or imputed values where none should exist. Furthermore, we show that the proportion of missing values had a strong impact on the accuracy of imputation, which affected the interpretation of the results. Our results suggest imputation should be used with extreme caution even with modest levels of missing data and especially when the type of missingness is unknown.

A critical challenge in the structural characterization of metal complexes in apolar environments is distinguishing transient structural isomers within an ensemble of lower- and higher-order assemblies. These structural variations arise from subtle changes in ligand architecture and metal coordination chemistry, which are often difficult to deconvolute. Here, we utilize ion activation in both drift-tube and cyclic ion mobility spectrometry-mass spectrometry (IMS-MS) to resolve ligand conformational isomerism and metal coordination isomerism in metal sandwich complexes of cyclic depsipeptide ligands known for selective metal ion transport. Our approach reveals that isomerism driven by ligand structural rearrangements exhibits low energy barriers, allowing their interconversion to be captured on the IMS-MS time scale. In contrast, isomers involving distinct metal coordination states are characterized by higher energy barriers, precluding rapid interconversion. These findings establish a direct correlation between isomer distributions and selective metal binding and transport, providing mechanistic insights into the biological functions of cyclic depsipeptides. This work underscores the utility of IMS-MS for disentangling complex structural dynamics in biologically relevant metal-peptide ligand systems.

Infrared matrix-assisted laser desorption electrospray ionization (IR-MALDESI) was coupled with high-resolution accurate-mass-mass spectrometry (HRAM-MS) to image perfluoroalkyl and polyfluoroalkyl substances (PFAS) in stabilized soil cores. Previous field-scale research demonstrated a substantial decrease in the leachability of PFAS following the application of in situ stabilization and solidification (S/S) in an aqueous film-forming foam (AFFF) source zone. While this previous study empirically confirmed the effectiveness of S/S, there was no definitive identification of the operative retention mechanisms. Therefore, the objective of this follow-on study was to (1) develop a high-resolution mass spectrometry-based imaging technique for PFAS on stabilized and background control soil cores and (2) determine if chemical characteristics of the amendments were associated spatially with the PFAS distribution within the soil cores at a micrometer scale. Intact frozen soil cores were imaged in negative ion mode, targeted and suspect screening analyses were conducted, features were identified using suspect lists, and analytes were presented as raw abundances matched against several databases. IR-MALDESI imaging results confirmed the colocation of PFOS and PFHxS with non-PFAS chemical features (e.g., mono- and diglycerides) associated with treatments including amendments, which suggests chemical fixation as a mechanism of stabilization for PFAS in stabilized soil cores.

We compared matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) spatial N-glycomics data sets from Fourier-transform ion cyclotron resonance (FTICR) and orthogonal accelerated time-of-flight (timsTOF) mass spectrometers of FFPE preserved human kidney samples. We also tested different tissue section thicknesses. In these analyses, we assessed the impact of the mass analyzer and tissue section thickness on N-glycan coverage, sensitivity, and histological alignment. Our results indicate negligible differences in N-glycan coverage between the two mass analyzers, where N-glycan annotation numbers remained consistent and were highly reproducible. The timsTOF-MS analyses demonstrated significant advantages with higher duty cycles and better lateral resolution, allowing for finer spatial resolution without compromising signal integrity. Specifically, timsTOF was able to generate detailed MALDI-MS images at 20 μm step size, accurately identifying N-glycan Hex:5 HexNAc:5 dHex:1 as a tubular-specific marker without observable delocalization. Despite minor annotation discrepancies, where only three species detected by FTICR were not detected by using timsTOF, and a few false-positive annotations from the timsTOF analysis attributed to lower mass resolving power, the overall consistency between the instruments was high. Importantly, tissue section thickness did not affect analysis sensitivity in the timsTOF analyses, with the average glycan signal intensity remaining stable between 7 and 2 μm sections. These findings demonstrate that 2 μm thick tissue slices can be effectively used in spatial N-glycomics workflows, maintaining sensitivity while enhancing confidence in pathohistological evaluations.

In the rapidly evolving field of nanomedicine, understanding the interactions between nanoparticles (NPs) and biological systems is crucial. A pivotal aspect of these interactions is the formation of a protein corona when NPs are exposed to biological fluids (e.g., human plasma), which significantly influences their behavior and functionality. This study introduces an advanced capillary isoelectric focusing tandem mass spectrometry (cIEF-MS/MS) platform designed to enable high-throughput and reproducible top-down proteomic analysis of protein corona. Our cIEF-MS/MS technique completed each analysis within 30 min. It produced reproducible proteoform measurements of protein corona for at least 50 runs regarding the proteoforms' migration time [relative standard deviations (RSDs) <4%], the proteoforms' intensity (Pearson's correlation coefficients between any two runs >0.90), the number of proteoform identifications (71 ± 10), and the number of proteoform-spectrum matches (PrSMs) (196 ± 30). Of the 53 identified genes, 33 are potential biomarkers of various diseases (e.g., cancer, cardiovascular disease, and Alzheimer's disease). We identified 1-102 proteoforms per potential protein biomarker, containing various sequence variations or post-translational modifications. Delineating proteoforms in protein corona by our cIEF-MS/MS in a reproducible and high-throughput fashion will benefit our understanding of nanobiointeractions and advance both diagnostic and therapeutic nanomedicine technologies.

Additional multipole fields are unavoidable in real quadrupole linear ion traps (QLITs) and play a crucial role in influencing their performance. In this study, the impact of these multipole fields on ion ejection and dynamics in QLITs is exhaustively analyzed using a vectorized Runge-Kutta method and a comprehensive theoretical model of ion vibration involving all the common multipole fields. The comparison of nonlinear resonance under different added multipole fields reveals obvious ion ejection from hexapole and octopole resonances as well as multiple resonance points in most multipole fields. Ion ejection with dipole excitation under these fields demonstrates distinct variations at different excitation working values, influenced by the inherent power distribution of ion motion in a linear quadrupole and the energy dispersion caused by the added multipole fields at varying stability parameters. Furthermore, theoretical and numerical analyses of ion dynamics mutually validate each other, offering the first comprehensive demonstration of ion excitation responses under various multipole fields across a wide stability range. The results show that for positive even-order multipole fields, forward scans lead to lower and more stable maximum amplitude responses compared to reverse scans, while the opposite is true for negative fields. In hexapole fields, the forward scan responses are lower than the reverse scan responses, and both increase sharply near nonlinear resonance points, regardless of field polarity. This work provides a thorough theoretical foundation for optimizing multipole field applications in QLITs.

Collision induced dissociation (CID) and collision induced unfolding (CIU) experiments are important tools for determining the structures of and differences between biomolecular complexes with mass spectrometry. However, quantitative comparison of CID/CIU data acquired on different platforms or even using different regions of the same instrument can be very challenging due to differences in gas identity and pressure, electric fields, and other experimental parameters. In principle, these can be reconciled by a detailed understanding of how ions heat, cool, and dissociate or unfold in time as a function of these parameters. Fundamental information needed to model these processes for different ion types and masses is their heat capacity as a function of the internal (i.e., vibrational) temperature. Here, we use quantum computational theory to predict average heat capacities as a function of temperature for a variety of model biomolecule types from 100 to 3000 K. On a degree-of-freedom basis, these values are remarkably invariant within each biomolecule type and can be used to estimate heat capacities of much larger biomolecular ions. We also explore effects of ion heating, cooling, and internal energy distribution as a function of time using a home-built program (IonSPA). We observe that these internal energy distributions can be nearly Boltzmann for larger ions (greater than a few kDa) through most of the CID/CIU kinetic window after a brief (few-μs) induction period. These results should be useful in reconciling CID/CIU results across different instrument platforms and under different experimental conditions, as well as in designing instrumentation and experiments to control CID/CIU behavior.

Nanobubbles (NBs) are tiny gas cavities with diameters around 200 nm that remain stable in solution due to their unique properties, including low buoyancy and negative surface charges. Ammonium bicarbonate (ABC) is an alternative buffer to commonly used ammonium acetate during protein analysis by electrospray ionization (ESI) mass spectrometry. The addition of ABC under high voltage and temperature conditions can lead to protein unfolding, a phenomenon termed electrothermal supercharging (ETS). The role of CO₂ bubbles in ETS has been hypothesized and disputed. The solution stability of NBs allows for the direct observation of their effects on protein charge states and unfolding, providing insights into the potential role of CO₂ bubbles during ETS. A novel method based on flow regime switching using a Tesla valve is employed to generate stable nanobubbles in solution. NBs were also created by sonication and pressure cycling. Nitrogen and carbon dioxide nanobubbles, when produced by flow regime switching and by pressure cycling, unfold proteins such as cytochrome c and ubiquitin but not to the same extent as with ABC addition to the ESI working solution. Complete unfolding of these proteins by NBs only occurs when the ammonium ion is also present in solution. Myoglobin, known to be less structurally stable, does unfold completely under NB influence. Further, amino acids, previously shown to provide stability to proteins under ETS conditions, also prevent unfolding when NBs are present, providing additional support for the role of gas bubbles during ETS.