沃特世集团公司

The aim of metabolic phenotyping (metabotyping) is to discover and identify metabolites (including lipids) that can be used to characterize biological samples and differentiate between different physiological states. The identification of the metabolites responsible for this differentiation is essential if mechanistic understanding is to be obtained. Confident metabolite identification arguably represents the most important outcome of untargeted metabolomics studies but currently the standards used for metabolite identification reported in many publications do not strictly follow the various published guidelines and thus these identifications lack sufficient proof. In this perspective we define problems that currently plague the field of metabolite identification using MS-based techniques, particularly LC-MS, in untargeted metabolic phenotyping. Despite considerable efforts by the community (researchers, instrument manufacturers, software, and database developers) this continues to be a contentious and error-prone step in the metabolomics workflow. The majority of publications provide only sparse data on the evidence for metabolic markers “identified” and we have observed an alarming increase in the frequency of erroneous metabolite identifications. Here, we describe the problem and provide several illustrative case studies. Our goal is to raise awareness and highlight the issue of poor metabolite identification, since it is also increasingly apparent that these errors are not always recognised during the reviewing process, such that papers with potentially erroneous metabolite identities reach publication. Poor metabolite identification potentially represents an existential threat to the credibility of untargeted “discovery” metabolomics and can pollute the literature. Here we describe the aetiology of the problem and explain how and why this issue affects the field. We argue that coordinated action is required by researchers, database managers, scientific societies and the reviewers, editors and publishers of scientific journals to both acknowledge and address this important problem.

Amyloid-beta (Aβ) oligomers are key contributors to the pathology and progression of Alzheimer’s disease (AD), making their characterization essential for understanding aggregation processes and developing potential therapeutic strategies. This study provides a systematic framework for analyzing Aβ(1–42) oligomers in vitro using cyclic ion mobility-mass spectrometry (cIMS). Compared to previous generations of traveling wave ion mobility (TWIM) devices, the cIMS platform offers superior resolution through its scalable ion mobility path length. However, the multistage character of the cIMS platform requires thorough investigation of parameters affecting oligomer transmission and activation to ensure reliable analysis of labile and dynamic systems such as Aβ oligomers. Our findings highlight the critical influence of cone voltage (CV) on in-source ion activation, subsequent structural changes, and oligomer detection. By balancing CV, we achieved detection of a broad range of oligomeric species while limiting their activation and maximizing signal intensity. Moreover, we present the first comprehensive set of optimized ion optics and ion mobility parameters that enable effective transmission and separation of oligomer Aβ(1–42) ions. Using the optimized method, we successfully detected a spectrum of Aβ(1–42) oligomers ranging from dimers to dodecamers. Additionally, the method was applied in collision-induced unfolding experiments, revealing size-dependent conformational transitions proving its applicability. This optimized cIMS methodology establishes a foundation for future studies on Aβ(1–42) aggregation mechanisms, AD pathogenesis, and therapeutic applications. Furthermore, our results offer valuable insights into cIMS instrument tuning, with potential applications in the analysis of other complex biological systems.

Hydrophilic interaction chromatography (HILIC) has recently gained attention as a powerful tool for the analysis of nucleic acid-based therapeutics, offering high resolving power and excellent compatibility with mass spectrometry. However, the successful development of HILIC methods requires a clear understanding of the underlying retention mechanisms. Despite numerous studies, the respective contributions of hydrophilic partitioning, polar interactions, and ionic interactions to oligonucleotide retention remain poorly understood. In this work, the role of hydrogen bonding, dipole-dipole interactions, solvophobic effects, and ionic interactions in governing oligonucleotide retention have been systematically investigated. A series of targeted design-of-experiments studies, performed on a representative panel of samples to investigate the impact of oligonucleotide structure on retention, provided a refined framework for interpreting HILIC separations of oligonucleotides. All experiments were performed on an amide-bonded stationary phase, and the proposed descriptor-based retention model applies specifically to this surface chemistry. Our results demonstrate that solvophobic effects and hydrogen bonding are the dominant drivers of retention, whereas hydrophilic partitioning contributes negligibly under practical conditions. This mechanistic insight has important consequences for method development: introducing protic solvents into the mobile phase, conditions that would disrupt the water-rich layer, substantially increases selectivity and enables the resolution of closely related oligonucleotide species. Altogether, these findings shift the conceptual basis of HILIC for nucleic acids from a hydrophilic partitioning/interaction model toward a retention mechanism best described as a solvophobic-interaction‑dominated HILIC regime, in which protic solvents assist selectivity through H‑bond competition. This new perspective provides a more accurate foundation for designing, optimizing, and interpreting HILIC methods for oligonucleotide analysis.

Mass spectrometry is an indispensable tool for the rapid and in-depth analysis of complex mixtures across diverse biologically important fields including metabolomics, lipidomics, and proteomics. These applications demand high speed instruments with subppm mass measurement accuracy over a wide dynamic range of sample concentrations. Here, we introduce an liquid chromatography-mass spectrometry/MS (LC-MS/MS) quadrupole time-of-flight mass spectrometer featuring a novel collision cell, a high dynamic range detector, and a compact multireflecting orthogonal time-of-flight analyzer. This innovative instrument achieves high analytical performance, acquiring full mass range spectra at 100,000 Full Width Half Maximum (FWHM) resolution up to 100 spectra/s acquisition speed. The instrument achieves excellent linearity within a dynamic range of 105, with a correlation coefficient R 2 = 0.984. The speed, resolution and dynamic range are in excellent balance as demonstrated by the analysis of isotopically labeled lipids in human blood plasma.

Background: Cyclic ion mobility spectrometry (cIMS) has emerged as a powerful tool for enhancing the resolution of isomeric and epimeric species that remain unresolved by traditional LC-MS or single-pass ion mobility techniques. In this study, we integrated multipass cIMS into a UPLC-HRMS workflow to address the long-standing challenge of separating epimeric pyrrolizidine alkaloids (PAs), a class of plant-derived toxins of regulatory concern. Four representative PA epimer pairs-lycopsamine/indicine, rinderine/echinatine, rinderine-N-oxide/ echinatine-N-oxide, and integerrimine-N-oxide/senecivernine-N-oxide-were subjected to offline high resolution cyclic ion mobility separation. Results: Baseline or near-baseline separations were achieved for all PA epimer pairs, with multi pass mode, either in their protonated or sodium adduct forms, and no significant loss of signal intensity was observed at the number of passes applied. Single-pass CCS values were measured and compared with previously reported values obtained using linear IMS, revealing limited resolution for several epimers. In contrast, multipass CCS values were then calculated for each epimer, enabling differentiation in cases where single-pass values were identical. Finally, the optimized cyclic sequences were integrated into a LC-cIMS-HRMS method, targeting specific retention time windows and m/z to introduce an additional dimension of separation. This approach was successfully applied to the analysis of a green tea extract, demonstrating the method's suitability for real food matrices. Significance: This work highlights, for the first time, the integration of multiple targeted multipass cIMS separations within an untargeted LC-HRMS workflow, underscoring its potential to expand separation power and analytical confidence for structurally related small molecules. The proposed workflow is especially valuable for the analysis of complex mixtures where epimeric compounds co-occur, as commonly found in naturally contaminated foods.