Journal of the American Society for Mass Spectrometry最新文献

Direct analysis in real time-high-resolution mass spectrometry (DART-HRMS) enables the rapid chemical profiling of Cannabis samples. This approach reveals a prominent signal at nominal m/z 315, characteristic of protonated isomeric cannabinoids such as Δ⁹-tetrahydrocannabinol (Δ⁹-THC), cannabidiol (CBD), cannabichromene (CBC), Δ⁸-THC, and cannabicitran (CBT). Interestingly, the spectra of standards of these cannabinoids also display a peak at nominal m/z 629, corresponding to the protonated dimer species [2M + H]⁺. To elucidate the likely structures of these ions, density functional theory calculations were performed for protonated homo- and heterodimer combinations of the five cannabinoids. The calculations indicated that CBDH⁺••CBD, Δ⁹-THCH⁺••Δ⁹-THC, and Δ⁸-THCH⁺••CBC are the most stable. Population analysis calculations further revealed a temperature dependence, with Δ⁸-THCH⁺••CBC being the dominant species below 240 K, while CBDH⁺••CBD becomes the most abundant in the 240-800 K range. These findings imply that at a DART gas temperature of 623 K, the peak at m/z 629 detected in hemp, where CBD levels are high, is primarily attributable to CBDH⁺••CBD. Conversely, the population analysis studies showed that marijuana, which contains negligible CBD, would not be expected to exhibit a mass at m/z 629. Experimental DART-HRMS and field desorption (FD)-MS measurements of hemp and marijuana validated this prediction. The results support the conclusion that the peak at m/z 629 specifically observed in hemp samples is representative of the CBDH⁺••CBD adduct, as opposed to a natural product molecular marker of hemp, and it provides a rapid and reliable mass by which to distinguish hemp from marijuana by DART-HRMS.

Spatial proteomics visualizes protein localization within native cellular and tissue environments, providing critical insights into pathological processes, tissue heterogeneity, and diagnostic biomarkers. Mass spectrometry imaging (MSI) has emerged as a powerful tool for spatial proteomics, with matrix-assisted laser desorption/ionization (MALDI)-MSI being the most widely used platform. Recently, desorption electrospray ionization-mass spectrometry imaging (DESI-MSI) has been explored for mapping proteins and peptides in tissue sections, with distinct sets of proteins detected compared to MALDI MSI. However, current studies have focused on fresh-frozen tissues, and the application of DESI-MSI to formalin-fixed paraffin-embedded (FFPE) tissues, the predominant form of clinical specimens, remains unexplored. Here, we report the optimization and application of DESI-MSI for proteomic analysis of FFPE tissues. Sample pretreatment procedures and DESI parameters were systematically optimized using FFPE mouse brain tissue sections to enhance tryptic peptide detection. The optimized workflow was further applied to FFPE canine melanocytoma tissue to discover protein biomarkers. Protein identification was performed using nanoLC-MS/MS. Overall, we demonstrate the potential of DESI-MSI as a platform for spatial proteomic analysis of FFPE tissues, expanding its applicability to archived clinical specimens.

Understanding condition-specific molecular interactions in complex biological systems requires scalable and accurate network inference from high-dimensional omics data, often generated by mass spectrometry platforms. Accurately estimating condition-specific interaction networks is critical for highlighting biological mechanisms, yet it remains challenging due to high dimensionality, noise, and the difficulty of accurately comparing networks across experimental groups. We propose a scalable and flexible Bayesian framework, clustering-focused iterative (CFI) estimation, for joint inference of Gaussian graphical models in multicondition omics data. CFI leverages hierarchical clustering of pooled data to identify consistent subnetwork structures, parallel Bayesian estimation within clusters for each condition, and an iterative merging step to recover intercluster dependencies without relying on restrictive assumptions or bridge variables. Through extensive simulations, we demonstrate that CFI achieves substantial computational gains with up to 64% average reduction in runtime relative to running the same network methods without CFI, while maintaining or improving accuracy compared to traditional approaches. The approach is scalable, with demonstrated improvement for large networks with thousands of nodes. We applied CFI to a mass spectrometry-based proteomics data set comparing host responses for samples subjected to mock and SARS-CoV-2 infection. Out of 6721 proteins, CFI identified 576 edges in the mock condition and 589 in the SARS-CoV-2 condition, 159 altered connections involving 63 proteins, reflecting substantial differences in networks between conditions. Enrichment analysis of these differential subnetworks revealed key biological pathways implicated in viral response and host regulation. These results illustrate CFI's ability to scale to realistic omics data, uncover condition-specific network rewiring, and provide interpretable biological insights.

Neuropeptides modulate a diverse range of physiological functions, including those associated with feeding. Post-translational modifications (PTMs) contribute significantly to the dynamic nature of neuropeptide isoforms, influencing their functional diversity. Mass spectrometry is the gold-standard analytical technique for peptidomic analyses and is complemented by computational methods for peptide identification; however, the computational search space becomes increasingly difficult to manage as more potential modifications are considered. Using innovative approaches capable of addressing the vast combinations of possible modifications, such as the PEAKS PTM search algorithm, we globally profiled the neuropeptidome ofCancer borealis(Jonah crab) to investigate the role of PTMs in feeding- and appetite-related processes over time. Through an in-depth examination of several notable modifications, we proposed PTM-associated motifs for neuropeptides, which may enhance future identification capabilities. Furthermore, this work revealed neuropeptides that were characteristically modified depending on the crab's feeding status and time post-feeding, suggesting potential biological significance. This study represents the first large-scale investigation of the modified crustacean neuropeptidome, providing new insights into the regulatory implications of PTMs in biological systems.

Sample normalization is essential for lipid quantitation. While normalization methods involving pre- or postgravimetric measurements, counting, or protein prequantitation exist, a single, common sample normalization method is not widely accepted. Previously, we proposed and evaluated the sulfo-phospho-vanillin assay (SPVA), a total lipid quantitation reaction, for prequantifying total lipids for LC-MS/MS sample normalization purposes. This current investigation furthers our evaluation of the SPVA as a sample normalization method in untargeted lipidomic LC-MS/MS by comparing SPVA total lipid prequantitation to protein prequantitation and gravimetric measurements. This study was applied to a wide selection of matrices, including Escherichia coli, plasma, brain, heart, and kidney. Resulting relative lipid concentrations showed smaller bioreplica variation when a prequantitation method was applied, either measuring total lipid or total protein; however, several relative lipid abundances showed inverse concentration relationships when normalizing with total protein compared to total lipid (from SPVA measurements) or gravimetric sample normalization. Further investigation using lipid extracts linearly spiked with pure, non-native lipid showed that gravimetric and protein normalization nonlinearly overproduced significant lipids, while linear increases in significant lipid features were observed from SPVA normalization. Lipid extracts linearly spiked with pure, non-native protein yielded fewer significant lipid features using SPVA normalization, and this was unchanged as protein was added; however, both gravimetric and protein normalization continued to yield large numbers of significant lipid features. Together, these results suggest neither gravimetric nor protein sample normalization appropriately normalizes quantitative lipidomic experiments and greatly overgenerates statistically significant lipid features for biomarker investigation. SPVA normalization more accurately adjusts for lipid changes in bioreplicate samples, leading to fewer but more biologically relevant statistically significant lipid features.

Liquid chromatography coupled to mass spectrometry (LC-MS) is a powerful analytical technique for analyzing biological macromolecules. A long-standing challenge has been applying LC-MS at physiological pH under native conditions using volatile buffers. The predominant "buffer" used, ammonium acetate (AmAc, pK_a 4.75 for acetic acid and 9.25 for ammonium), does not offer sufficient buffering capacity in the physiological pH range of 7.0-7.4. To address this, we evaluated a set of fluorinated ethylamines, 2-fluoroethylamine (MFEA), 2,2-difluoroethylamine (DFEA), and 2,2,2-trifluoroethylamine (TFEA), producing conjugate acids with pK_a values of 8.9, 7.2, and 5.5, respectively, that together provide buffering across the 4.5-9.8 pH range. We show that protein separations on strong cation- and anion-exchange resins in these volatile mobile phases perform comparably to traditional nonvolatile buffers, with similar elution profiles and analyte elution ranking, albeit with slightly broader peaks. Using fully volatile gradients of pH or ionic strength, we chromatographically resolved charge variants of protein analytes such as mAbs and bovine serum albumin. For many of the eluting LC peaks, we obtained high-resolution mass spectra capable of resolving glycoforms of antibodies. Hydrophobic interaction chromatography (HIC) in volatile mobile phases preserved native separation order and further resolved drug-to-antibody ratio (DAR) species of the antibody-drug conjugate brentuximab-vedotin. For each chromatography modality we further compare innovator and biosimilar antibodies, demonstrating the reproducibility of results in the proposed volatile compounds. Together, our results establish fluorinated ethylamines, in combination with ammonium acetate, as a universal volatile buffer system for native LC-MS, broadly applicable across major chromatographic modalities.

Central carbon metabolism, comprising glycolysis, the tricarboxylic acid (TCA) cycle, and the pentose phosphate pathway (PPP), is essential for Escherichia coli survival and growth. While disruptions in these pathways are known to affect cellular physiology, the system-wide metabolite-level consequences of single-gene knockouts remain incompletely understood. Using untargeted LC-MS metabolomics, we systematically profiled E. coli knockouts of TCA core enzymes, isoforms, subunits, bypass routes, and TCA-associated pathways. Core TCA knockouts separated into two major metabolic clusters, with cluster 1 strains displaying strong divergence in amino acid metabolism and cluster 2 retaining partial similarity to the parent strain. Isoform-specific deletions revealed differential roles of aconitases (ΔacnA vs ΔacnB) and fumarases (ΔfumA vs ΔfumC), while subunit knockouts of 2-oxoglutarate dehydrogenase (ΔsucA, ΔsucB) and succinate dehydrogenase (ΔsdhA-D) produced localized but distinct metabolite shifts, particularly around glutamate- and 2-oxoglutarate-linked metabolism. Bypass enzyme deletions (ΔaceA, ΔaceB, ΔglcB, and ΔmaeB) disrupted carbohydrate- and redox-related metabolites, underscoring their role as metabolic safety nets. Importantly, knockouts also triggered off-target effects in glycolysis, PPP, and the electron transport chain, highlighting the interconnectivity of central carbon metabolism. Our systematic approach demonstrated the possibility of utilizing comprehensive and untargeted metabolomics to map gene-metabolite associations and decipher potential metabolic interlinks.

A miniature ion trap particle mass spectrometer with both optical and charge detection systems was constructed in this work. The instrument was initially constructed with an open architecture and mounted on a 600 × 450 mm optical bench. With further compact integration, the system can be accommodated within an aluminum enclosure with dimensions of 333 × 221 × 192 mm and a total mass of approximately 8 kg. In the optical detection mode, the stationary star pattern ion motion was observed by detecting the scattered light, and the m/Z value of the particle ion was calculated accurately. In the charge detection mode, the particle m/Z value, the charge number Z, and the particle mass were determined quickly. These two working modes can be switched freely. By using 3 μm polystyrene size standards and mice red blood cells as the sample, the feasibility of this instrument was demonstrated.

Fragmentation of protonated threonine isomers was investigated by multiple-stage tandem mass spectrometry (MSⁿ) with ion-trap collision-induced dissociation (CID) and density functional theory (DFT) calculations. Protonated molecules containing oxygen or nitrogen often produce heterocyclic fragments by CID. DFT calculations revealed that H₂O loss from threonine isomers produced cyclic amines, lactams, and lactones. The transition-state barriers and rate constant for the formation of these fragments are highly dependent on the ring size. Although 3-membered cyclic amines are observed in the product ion spectrum, lactams and lactones are formed only as rings larger than four and five members, respectively. H₂O loss from the protonated threonine side chain produced a 3-membered cyclic amine containing a carboxyl group, which undergoes loss of the combined elements of H₂O and CO upon CID. H₂O loss from protonated homoserine and β-homoserine produced 5-membered lactone and 4-membered lactam, respectively. Further dissociation of the corresponding lactone and lactam results in fragment ions formed via the loss of CO and CH₂CO, respectively. In contrast, H₂O loss from the protonated γ-amino-β-hydroxybutyric acid provides 5-membered lactams, which undergo only further H₂O loss upon CID. MS³ analysis of protonated threonine isomers through dehydrated precursor ions produced different fragment ions. The threonine isomers could be distinguished by characteristic fragment ions, and the molar ratio of the isomers in the mixture can be estimated from the relative abundances of their fragment ions. These results demonstrate MSⁿ with ion-trap CID to be a useful method for the identification and semiquantification of threonine isomers.