Analytical and Bioanalytical Chemistry

Background and AimsDigoxin, a cardiotonic steroid (CTS), is commonly prescribed for patients with atrial fibrillation and heart failure. Endogenous CTS have been implicated in cardiovascular disease pathogenesis and can interact with digoxin. We developed an ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) assay for the quantification of eleven CTS. Materials and MethodsIsotopically labelled internal standards were added to samples, followed by protein precipitation and solid-phase extraction. Steroids were separated using an Acquity uPLC chromatography system with a Waters CORTECS T3 column (1.6 m 2.1x50 mm) and quantification performed on a Waters TQ-XS mass spectrometer using electrospray ionisation in positive ion mode. Separation used a methanol/ water elution system containing 0.1% formic acid and post-column infusion of lithium chloride. ResultsRun time was 13.5 minutes. Lower limits of quantification ranged from 0.025 to 0.1 ng/ml. Serum recovery ranged from 40.0-98.6% with matrix effects from -22.9% to 7.6%. Plasma recovery ranged from 27.4-83.9% and matrix effects were -27.6-39.1%. Accuracy and precision at three concentrations were within ideal range (<15%) for seven CTS and <20% for the others. ConclusionThis validated UHPLC-MS/MS method provides a comprehensive assessment profiling 11 cardiotonic steroids, offering a sensitive and specific tool for clinical and pre-clinical investigations. HighlightsO_LIMass spectrometry method simultaneously quantifies multiple cardiotonic steroids C_LIO_LIAccurate and specific measurement of clinically relevant digoxin concentration C_LIO_LIMethod validated for measurement of cardiotonic steroids in serum and plasma C_LIO_LIPost-column infusion of lithium chloride substantially improves sensitivity C_LI Research fundingThis work was funded by the British Heart Foundation (PG/17/55/33087, FS/PhD/22/29309, FS/19/12/34204, RG/17/15/33106 to DP, Accelerator Award AA/18/2/34218 to Institute of Cardiovascular Sciences), Wellcome Trust (Seed Award Grant 109604/Z/15/Z to DP) and Department of Clinical Laboratory Sciences, Faculty of Applied medical Sciences, University of Hail. PK was partially supported by European Union AFFECT-AF (grant agreement 847770), and MAESTRIA (grant agreement 965286), British Heart Foundation (PG/17/30/32961; PG/20/22/35093; AA/18/2/34218), German Centre for Cardiovascular Research supported by the German Ministry of Education and Research (DZHK), Deutsche Forschungsgemeinschaft (Ki 509167694), and Leducq Foundation. The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication. Financial disclosuresProf. Kotecha reports grants from the National Institute for Health Research (NIHR CDF-2015-08-074 RATE-AF; NIHR130280 DaRe2THINK; NIHR132974 D2T-NeuroVascular; NIHR203326 Biomedical Research Centre), the British Heart Foundation (PG/17/55/33087, AA/18/2/34218 and FS/CDRF/21/21032), the EU/EFPIA Innovative Medicines Initiative (BigData@Heart 116074), EU Horizon (HYPERMARKER 101095480), UK National Health Service -Data for R&D-Subnational Secure Data Environment programme, UK Dept. for Business, Energy & Industrial Strategy Regulators Pioneer Fund, the Cook & Wolstenholme Charitable Trust, and the European Society of Cardiology supported by educational grants from Boehringer Ingelheim/BMS-Pfizer Alliance/Bayer/Daiichi Sankyo/Boston Scientific, the NIHR/University of Oxford Biomedical Research Centre and British Heart Foundation/University of Birmingham Accelerator Award (STEEER-AF). In addition, he has received research grants and advisory board fees from Bayer, Amomed and Protherics Medicines Development; all outside the submitted work. PK received research support for basic, translational, and clinical research projects from European Union, British Heart Foundation, Leducq Foundation, Medical Research Council (UK), the Deutsche Forschungsgemeinschaft (DFG) and German Centre for Cardiovascular Research, from several drug and device companies active in atrial fibrillation, and has received honoraria from several such companies in the past, but not in the last three years. PK is listed as inventor on two issued patents held by University of Hamburg (Atrial Fibrillation Therapy WO 2015140571, Markers for Atrial Fibrillation WO 2016012783). Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/561354v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@180bd42org.highwire.dtl.DTLVardef@109441corg.highwire.dtl.DTLVardef@1568cd9org.highwire.dtl.DTLVardef@1390caf_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundThe clinical translation of molecularly targeted therapeutics and imaging agents represents a cornerstone of precision oncology, with the global theranostics market projected to exceed $25 billion by 2030. However, the development of theragnostic agents or diagnostic companions remains constrained by analytical bottlenecks in quality control, such as target-binding specificity, which are increasingly required by regulatory agencies as product release criteria during the translation process. Current methods, including enzyme-linked immunosorbent assay (ELISA), which require specialized resources or external CROs, or bead-based assays for radiolabeled compounds, which involve complex multi-step protocols; these limitations and others hamper their practical implementation in clinical manufacturing environments. Assay delays can postpone clinical trial initiation, increase development costs, and delay patient access to these agents. ResultsWe have developed and validated a rapid, size-exclusion high-performance liquid chromatography (SE-HPLC) method for the determination of target-binding fractions of labeled biologics. The method separates the unbound biologic from the larger antigen-bound complex, allowing for rapid quantification. We validated the method using a panel of fluorescently labeled antibodies (panitumumab-IRDye800CW, nivolumab-IRDye800CW) and radiolabeled biologics ([18F]GEH200521, [18F]NOTA-ABY-030), assessing linearity, specificity, and concentration independence. The SE-HPLC method achieved excellent separation of bound and unbound species with a resolution (Rs) of 3.2. A strong linear relationship (R2 = 0.999) was observed between the antigen-to-antibody ratio and the measured binding fraction. The method demonstrated high specificity, with no binding detected with non-target antigens. The total assay and analysis time was less than 35 minutes, a significant improvement over traditional methods. ConclusionsSE-HPLC provides a rapid, specific, and cost-effective alternative to traditional binding fraction assessment methods, reducing quality control timelines from weeks/hours to minutes. The methods compatibility with both fluorescent and radiolabeled biologics and integration with existing HPLC infrastructure represents a significant advancement in development workflows.

There is an increased interest in using high-resolution mass spectrometry (HRMS) in the full scan mode (FS) for simultaneous qualitative and quantitative analysis with accurate mass based extracted ion chromatograms (HRMS-AM) for metabolomic profiling workflows. Herein, the capabilities and features of MQ, a common platform developed to support quantitative analysis from HRMS data are described. Module for qualitative annotation utilizes peak features that include mass accuracy, peak width, and relative abundance of isotopic distribution that offer reliable metabolic confirmation. Additionally, MQ provides multivariate clustering with Principal Component Analysis and a module for high-throughput database query based on user configurable annotated metabolite lists. MQ is capable of handling system-agnostic data formats from both direct HRMS and LC-HRMS analysis. Data processed using MQ for LC-HRMS-AM quantitation of amino acids benchmarked using a commercial tool is presented that validates the quantitative capabilities of the algorithm developed. Datasets representative of three different HRMS-AM based analysis scenarios have been showcased to demonstrate the utility of MQ. Availability and implementationFreely available on web at, http://www.ldims.com/services/software. MQ is implemented in Java and supported on Linux and Microsoft Windows system having Java runtime environment (JRE) pre-installed.

Human saliva contains a plethora of proteins whose presence and concentration can be monitored for diagnosis and progression of disease. Saliva has been extensively probed for the diagnosis of several systemic and infectious diseases because of the ease with which it can be collected. However, amylase, the most abundant protein found in saliva can obscure the detection of low-abundance proteins by MALDI-ToF MS (matrix-assisted laser desorption/ionization-time of flight mass spectrometry) and diminish the diagnostic utility of this specimen type. In the present study, we used a device to deplete salivary amylase from water-gargle samples through affinity adsorption. After depletion, profiling of the saliva proteome was performed by MALDI-ToF MS on gargle samples from subjects whose COVID-19 (coronavirus disease 2019) status was confirmed by NP (nasopharyngeal) swab RT-qPCR (reverse transcription polymerase chain reaction). Amylase depletion led to the enhancement of signal intensities of various peaks as well as the detection of previously unobserved peaks in the MALDI-ToF spectra. The overall specificity and sensitivity after amylase depletion was 100% and 85.17% respectively for detecting COVID-19. Our simple, rapid and inexpensive technique to deplete salivary amylase can be used to unmask spectral diversity in saliva by MALDI-ToF MS, reveal low-abundant proteins and aid in the establishment of novel biomarkers for diseases.

Machine learning (ML) approaches applied to Matrix-Assisted Laser Desorption Ionization-Time of Flight Mass Spectrometry (MALDI-TOF MS) spectra have shown promise for the typing of Clostridioides difficile, yet their deployment in routine clinical settings remains challenging due to strong sensitivity to acquisition variability. Differences in culture media, incubation time, protein extraction protocols, and instrumentation across hospitals often lead to substantial performance degradation when models are evaluated under heterogeneous or previously unseen conditions. In this work, we systematically analyze the impact of methodological and technical variability on ML-based C. difficile typing and investigate whether data augmentation (DA) strategies can mitigate these effects. Using a dedicated dataset of 60 isolates acquired under diverse conditions, we show that DA substantially improves robustness to variability when training on spectra from selective C. difficile agar media. Importantly, models trained with DA achieve performance levels approaching those obtained using enriched Schaedler agar media, while relying exclusively on standard 24-hour incubation. Evaluation on an independent cohort of 28 newly acquired isolates confirms that DA significantly reduces performance degradation under real-world domain shift. To facilitate adoption and reproducibility, we release MAL-DIDA, an open-source Python library for DA of MALDI-TOF MS spectra.

Purpose of the researchMultiple myeloma and plasmacytomas belong to a group of disorders, namely plasma cell dyscrasias and are identified by the presence of a monoclonal protein (M-protein). MALDI-TOF-mass spectrometry (MS) has demonstrated superior analytical sensitivity for the detection of M-protein and is now used for screening of M-protein at some centres. We present the results of an alternative methodology for M-protein analysis by MALDI-TOF MS. MethodsSerum samples from patients with newly diagnosed multiple myeloma or plasmacytoma with positive M-protein detected by serum protein electrophoresis, immunofixation electrophoresis and serum free light chain analysis, underwent direct reagent-based extraction process using Acetonitrile (ACN) precipitation. Serum{kappa} and{lambda} light chains were validated using immunoenrichment by anti-{kappa} and anti-{lambda} biotin-labelled antibodies immobilised on streptavidin magnetic beads. MALDI-TOF MS measurements were obtained for intact proteins using alpha-cyano-4-hydroxycinnamic acid as matrix. The images obtained were overlaid on apparently healthy donor serum samples to confirm the presence of M-protein. Principle resultsCharacteristic M-protein peaks were observed in the ACN precipitates of serum in the predicted mass ranges for{kappa} and{lambda} . The{kappa} and{lambda} peaks were confirmed by immunoenrichment analysis. Twenty-seven patient samples with either newly diagnosed multiple myeloma or plasmacytoma with monoclonal gammopathy detected by the standard methods were chosen for Acetonitrile precipitation and analysed by MALDI-TOF MS. All 27 patient samples demonstrated a peak suggestive of M-protein with mass/charge (m/z) falling within the{kappa} and{lambda} range. The concordance rate with serum immunofixation electrophoresis and free light chain analysis was above 90%. Major conclusionsWe report the results of a low-cost reagent-based extraction process using Acetonitrile precipitation to enrich for{kappa} and{lambda} light chains, which can be used for the screening and qualitative analysis of M-protein.

Background/ObjectivesDevelopability assessment is a critical step in advancing antibody-based molecules toward clinical application. This evaluation typically begins during clinical candidate selection and continues throughout all modifications of the molecule during development. It is guided by the target product profile, which includes the intended administration route and regimen, formulation parameters, and process conditions encountered during manufacturing, storage, and delivery. While developability testing is well established for conventional therapeutic antibodies, strategies for assessing single-domain antibodies (sdAbs) and their conjugates remain underexplored. Here we present a strategy to test the developability of sdAbs as a case study for two clinical candidates intended as precursors for the production of diagnostic tracers for clinical imaging. MethodsAssays were developed to evaluate chemical and thermodynamic stability, target binding affinity and capacity, and chelation efficiency ("chelatability"). Accelerated stability studies were conducted for both unconjugated sdAbs and their chelator conjugated forms following incubation at two pH conditions, at multiple time points, and after twelve freeze-thaw cycles to simulate process conditions and long-term storage. Analytical assays were applied stepwise in a hierarchical approach to minimized experimental effort and material consumption. Candidates exhibiting critical developability features were selectively addressed by assays with increasing precision. ResultsA tailored panel of analytical assays optimized for low molecular weight proteins was established and applied to the two clinical candidates, identifying instability hotspots as well as potential mitigation strategies. Successful engineering of a candidate with an initially critical developability profile was achieved. ConclusionThis study demonstrates the implementation of a structured developability assessment strategy for sdAb conjugates. The approach integrates physicochemical and functional stability evaluations, supporting robust candidate selection, formulation development, and method optimization for this class of molecules.

D-2-Hydroxyglutarate and L-2-Hydroxyglutarate (D-2HG/L-2HG) are typically metabolites of non-specific enzymatic reactions that are kept in check by the housekeeping enzymes, D-2HG /L-2HG dehydrogenase (D-2HGDH/L-2HGDH). In certain disease states, such as D-2HG or L-2HG aciduria and cancers, accumulation of these biomarkers interferes with oxoglutarate-dependent enzymes that regulate bioenergetic metabolism, histone methylation, post-translational modification, protein expression and others. D-2HG has a complex role in tumorigenesis that drives metabolomics investigations. Meanwhile, L-2HG is produced by non-specific action of malate dehydrogenase and lactate dehydrogenase under acidic or hypoxic environments. Characterization of divergent effects of D-2HG/L-2HG on the activity of specific enzymes in diseased metabolism depends on their accurate quantification via mass spectrometry. Despite advancements in high-resolution quadrupole time-of-flight mass spectrometry (HR-QTOF-MS), challenges are typically encountered when attempting to resolve of isobaric and isomeric metabolites such as D-2HG/L-2HG for quantitative analysis. Herein, available D-2HG/L-2HG derivatization and liquid chromatography (LC) MS quantification methods were examined. The outcome led to the development of a robust, high-throughput HR-QTOF-LC/MS approach that permits concomitant quantification of the D-2HG and L-2HG enantiomers with the benefit to quantify the dysregulation of other intermediates within interconnecting pathways. Calibration curve was obtained over the linear range of 0.8-104 nmol/mL with r2 [&ge;] 0.995 for each enantiomer. The LC/MS-based assay had an overall precision with intra-day CV % [&le;] 8.0 and inter-day CV % [&le;] 6.3 across the quality control level for commercial standard and pooled biological samples; relative error % [&le;] 2.7 for accuracy; and resolution, Rs= 1.6 between 2HG enantiomers (m/z 147.030), D-2HG and L-2HG (at retention time of 5.82 min and 4.75 min, respectively) following chiral derivatization with diacetyl-L-tartaric anhydride (DATAN). Our methodology was applied to disease relevant samples to illustrate the implications of proper enantioselective quantification of both D-2HG and L-2HG. The stability of the method allows scaling to large cohorts of clinical samples in the future.

ObjectiveA catheter-free, non-radiolabeled method that permits in vivo measurement of tissue-specific glucose uptake does not exist. To address this gap, we sought to develop and validate a new, higher throughput mass spectrometry (MS)-based method that combines an injection of insulin with a non-radiolabeled glucose tracer, 2-fluoro-2-deoxyglucose (2FDG), to determine insulin-stimulated tissue-specific glucose clearance in conscious, unrestrained mice. MethodsInjections of saline or insulin with 2FDG were coupled with LC-Q Exactive Hybrid Quadrupole-Orbitrap (LC) MS-based measures of plasma 2FDG and tissue (2-fluoro-2-deoxyglucose-6-phosphate) 2FDGP to determine glucose clearance in mice under several different conditions. ResultsThe newly developed method was first applied to a dose response experiment in mice. Next, the ability of this method to quantify changes in glucose clearance in response to an insulin stimulus was assessed, and glucose clearance was compared between chow and high fat fed mice. Results from these studies showed that insulin-stimulated skeletal muscle and heart glucose clearance can be estimated following a bolus injection of tracer, and these fluxes are blunted in diet-induced obese mice. The broad applicability of this approach was then demonstrated by assessing glucose clearance in a mouse model with anticipated changes in insulin-stimulated skeletal muscle glucose metabolism. ConclusionsThe results validated a new LC-MS method to quantify insulin-stimulated tissue-specific glucose clearance in vivo without the use of catheters or radiolabeled tracers. The method offers great potential because it is designed for application to pre-clinical studies seeking high throughput tests and/or assays that can be coupled with discovery technologies such as genomics, proteomics and metabolomics. HIGHLIGHTSO_LIIn vivo glucose clearance can be estimated by a new non-radiolabeled method. C_LIO_LIThe plasma tracer to tracee ratio is required to determine tissue tracer phosphorylation. C_LIO_LIMeasures of plasma glucose and tracer kinetics are critical for data interpretation. C_LIO_LIThe new method can be combined with omics technologies such as metabolomics. C_LI

BackgroundSeveral small molecule biomarkers have been reported in the literature for prediction and diagnosis of (pre)diabetes, its co-morbidities and complications. Here, we report the development and validation of a novel, quantitative, analytical method for use in the diabetes clinic. This method enables the determination of a selected panel of 36 metabolite biomarkers from human plasma.\n\nMethodsBased on a review of the literature and our own data, we selected a panel of metabolites indicative of various clinically-relevant pathogenic stages of diabetes. We combined these candidate biomarkers into a single ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method and optimized it, prioritizing simplicity of sample preparation and time needed for analysis, enabling high-throughput analysis in clinical laboratory settings.\n\nResultsWe validated the method in terms of limit of (a) detection (LOD), (b) limit of quantitation (LOQ), (c) linearity (R2), (d) linear range, and (e) intra- and inter-day repeatability of each metabolite. The methods performance was demonstrated in the analysis of selected samples from a diabetes cohort study. Metabolite levels were associated with clinical measurements and kidney complications in type 1 diabetes (T1D) patients. Specifically, both amino acids and amino acid-related analytes were associated with macro-albuminuria. Additionally, specific bile acids were associated with kidney function, anti-hypertensive medication, statin medication and clinical lipid measurements.\n\nConclusionsThe developed analytical method is suitable for robust determination of selected plasma metabolites in the diabetes clinic.

A comprehensive understanding of drug metabolism is crucial for advancements in drug development. Automation has improved various stages of this process, from compound procurement to data analysis, supporting small molecules, peptides, and oligonucleotides. However, challenges remain, particularly in the time-consuming analysis of samples for metabolite identification. This article introduces new algorithms for automated Liquid Chromatography-High-Resolution Mass Spectrometry (LC-HRMS) data applicable to both small and macromolecules. While methodologies for small molecules are well established, adapting them for macromolecules presents challenges, including computational demands, peak detection complexities, and visualization issues. A data analysis employing diverse algorithms in the data preprocessing step was conducted across six datasets, ranging from small/medium linear or macrocyclic peptides to oligonucleotides with natural and unnatural monomers. Two peak detection approaches were evaluated: using the monoisotopic mass versus the most abundant isotope for mass calculation. Additionally, an exploration of two distinct structure visualization options was conducted for one of the datasets. Furthermore, data obtained through two different acquisition modes was processed. The computational time required for data processing was recorded throughout, ranging from 5 minutes to 2 hours per experiment. The results have been compared against prior studies, revealing substantial reductions in processing time, consistent identification of degradation products, and improved visualization techniques, thereby enhancing result interpretation. A comprehensive identification of 970 metabolites was achieved under varied incubation conditions across the six datasets, showcasing the workflows efficiency in managing experimental data within a molecular range from 700 to 7630 Daltons (Da). Particularly in larger molecules, the most abundant mass algorithm demonstrated higher scores and a greater number of matches, instilling greater confidence in the accurate prediction of metabolite structures. It has been illustrated how the visualization algorithm for macromolecules allows the combination of monomer and atom/bond notation, facilitating a clear depiction of metabolic changes in the molecular structure.

Ceramides and dihydroceramides are sphingolipids that present in abundance at the cellular membrane of eukaryotes. Although their metabolic dysregulation has been implicated in many diseases, our knowledge about circulating ceramide changes during the pregnancy remains limited. In this study, we present the development and validation of a high-throughput liquid chromatography-tandem mass spectrometric (LC/MS/MS) method for simultaneous quantification of 16 ceramides and 10 dihydroceramides in human serum within 5 mins by using stable isotope-labeled ceramides as internal standards (ISs). This method employs a protein precipitation method for high throughput sample preparation, reverse phase isocratic elusion for chromatographic separation, and Multiple Reaction Monitoring (MRM) for mass spectrometric detection. To qualify for clinical applications, our assay was validated against the FDA guidelines: the Lower Limit of Quantitation (LLOQ as low as 1 nM), linearity (R2>0.99), precision (Coefficient of Variation<15%), accuracy (Percent Error<15%), extraction recovery (>90%), stability (>85%), and carryover (<0.1%). With enhanced sensitivity and specificity from this method, we have, for the first time, determined the serological levels of ceramides and dihydroceramides to reveal unique temporal gestational patterns. Our approach could have value in providing insights into disorders of pregnancy.

The use of Multi-attribute method (MAM) for identity and purity testing of biopharmaceuticals offers the ability to complement and replace multiple conventional analytical technologies with a single mass spectrometry (MS) method. Method qualification and phase-appropriate validation is one major consideration for the implementation of MAM in a current Good Manufacturing Practice (cGMP) environment. We developed an improved MAM workflow with optimized sample preparation using Lys-C digestion for therapeutic monoclonal antibodies. In this study, we qualified the enhanced MAM workflow for mAb-1 identity, product quality attributes (PQAs) monitoring and new peak detection (NPD). The qualification results demonstrated the full potential of the MAM for its intended use in mAb-1 characterization and quality control in regulated labs. To the best of our knowledge, this is the first report of MAM qualification for mAb identity, PQA monitoring, and new peak detection (NPD) in a single assay, featuring 1) the first full qualification of MAM using Lys-C digestion without desalting using a high-resolution MS, 2) a new approach for mAb identity testing using MAM, and 3) the first qualification of NPD for MAM. The developed MAM workflow and the approaches for MAM qualification may serve as a reference for other labs in the industry.

Carbapenemases are major drivers of carbapenem resistance in Gram-negative bacteria and pose a critical threat to last-line antibiotic therapy. Rapid identification of carbapenemase classes is essential for appropriate treatment and epidemiological surveillance; however, current functional methods lack class-level resolution and may yield false-negative results for OXA-48-like enzymes. In this study, we developed and validated an assay based on liquid chromatography-mass spectrometry with trapped ion mobility spectrometry-time-of-flight [LC-MS (timsTOF)] for simultaneous detection and class-level differentiation of five clinically relevant carbapenemases (KPC, NDM, VIM, IMP, and OXA-48-like). The method employs three carbapenem substrates (meropenem, imipenem, and ertapenem). A total of 55 clinical isolates were analyzed using a standardized 2-hour incubation protocol, with a total analysis time of 7 min per sample. Ion mobility enabled unambiguous identification of the OXA-48-specific meropenem-derived {beta}-lactone based on its distinct collisional cross-section (185 [A]{superscript 2} vs. 191 [A]{superscript 2} for intact meropenem), despite identical mass and nearly identical retention time. This marker was detected in all OXA-48-like producers and was absent in all other groups. In contrast, imipenem and ertapenem did not provide comparable discrimination, highlighting the central role of meropenem. Distinct hydrolysis profiles enabled class-level differentiation supported by multivariate analysis. LC-MS (timsTOF) thus enables rapid, sensitive, and specific functional detection of carbapenemases within a single workflow. The ion mobility dimension is critical for accurate identification of OXA-48-like enzymes and supports the potential implementation of this approach in routine clinical microbiology laboratories. ImportanceThis study introduces an ion mobility-enabled LC-MS (timsTOF) approach for functional detection and class-level differentiation of clinically relevant carbapenemases within a single analytical workflow. By leveraging collisional cross-section measurements, the method enables reliable identification of OXA-48-like carbapenemase through detection of a meropenem-derived {beta}-lactone that is indistinguishable by mass alone. This directly addresses a major diagnostic limitation of conventional activity-based assays, which may yield false-negative results for OXA-48-like enzymes. The approach further demonstrates the potential of integrating ion mobility into routine clinical mass spectrometry to enhance specificity beyond traditional mass and retention time measurements. These findings support the development of next-generation diagnostic strategies capable of detecting both known and emerging resistance mechanisms without reliance on predefined targets.

N-acylethanolamines (NAEs) and primary fatty amides (PFAMs) are of a great interest due to the range of physiological effects they exhibit, potentially serving as neuromodulators. However, they are present at nano and picomolar concentrations in human cerebrospinal fluid (CSF) samples, posing challenges for detection and measurement using conventional Ultra-high performance liquid chromatography systems coupled to tandem mass spectrometry (UHPLC-MS). UHPLC-MS was used in dynamic multiple reaction monitoring (dMRM) mode. Seven deuterated NAEs internal standards were used to develop the method. Six solvent combinations were tested for extraction efficiency, accuracy, precision, matrix effect, linearity, limits of detection. Lastly the method was applied to CSF from healthy individuals (n=33) to estimate their natural range of concentrations. Extraction with acetonitrile/acetone showed the highest efficiency and recovery. The presented method was able to measure the following 17 NAEs and PFAMs in human CSF: linoleoyl ethanolamide, heptadecanoyl ethanolamide, stearoyl ethanolamide, palmitoyl ethanolamide, dihomolinolenoyl ethanolamide, eicosatrienoic acid ethanolamide, behenamide, octadecanamide, lauramide, tetradecanamide, erucamide, linoleamide, palmitamide, myristic monoethanolamide, pentadecanoyl ethanolamide, oleamide and palmitoleoyl ethanolamide. In healthy individuals the concentrations ranged three-fold from pg/mL to mg/mL. Further studies could apply this method to clinical CSF samples.

Identification of peptides and their linked amino acids from chemically cross-linked protein complexes with bifunctional N-hydroxysuccinimidyl (NHS) esters can reveal interacting proteins and their spatial arrangements. With NHS esters both amide- and ester cross-links can be formed. Retention time prediction for strong cation exchange chromatography (SCXC) of cross-linked peptides at pH 3 can distinguish between ester and amide cross-links based on their charge differences. By this approach we show that about 98 % of cross-links are formed by two amide bonds. However MS/MS analysis revealed the presence of an ester linkage in more than 5% of peptide pairs predicted by SCXC to be linked by amide bonds. This discrepancy can be explained by intra-peptide amide-ester rearrangement in the gas phase during MS/MS analysis. So, SCXC retention time prediction can be used to distinguish amide-amide, amide-ester and ester-ester linkages actually formed in the cross-linking reaction and to detect scrambling of cross-linked sites. This information is important for studies aimed to understand the spatial arrangement of protein complexes by cross-linking at the highest possible resolution.

Ambient Ionisation Mass Spectrometry techniques: Desorption Electrospray Ionisation (DESI) and Laser Desorption - Rapid Evaporative Ionisation Mass Spectrometry (LD-REIMS) were used to detect the SARS-CoV-2 in dry nasal swabs. 45 patients were studied from samples collected between April - June 2020 in a clinical feasibility study. Diagnostic accuracy was calculated as 86.7% and 84% for DESI and LD-REIMS respectively. Results can be acquired in seconds providing robust and quick analysis of COVID-19 status which could be carried out without the need for a centralised laboratory. This technology has the potential to provide an alternative to population testing and enable the track and trace objectives set by governments and curtail the effects of a second surge in COVID-19 positive cases. In contrast to current PCR testing, using this technique there is no requirement of specific reagents which can cause devastating delays upon breakdowns of supply chains, thus providing a promising alternative testing method.

False-positives plague High Throughput Screening in general and are costly as they consume resource and time to resolve. Methods that can rapidly identify such compounds at the initial screen are therefore of great value. Advances in mass spectrometry have led to the ability to screen inhibitors in drug discovery applications by direct detection of an enzyme reaction product. The technique is free from some of the artefacts that trouble classical assays such as fluorescence interference. Its direct nature negates the need for coupling enzymes and hence is simpler with fewer opportunities for artefacts. Despite its myriad advantages, we report here a mechanism for false-positive hits which has not been reported in the literature. Further we have developed a pipeline for detecting these false-positive hits and suggest a method to mitigate against them. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=156 HEIGHT=200 SRC="FIGDIR/small/634670v1_ufig1.gif" ALT="Figure 1"> View larger version (49K): org.highwire.dtl.DTLVardef@a99654org.highwire.dtl.DTLVardef@1cc7ec8org.highwire.dtl.DTLVardef@978abaorg.highwire.dtl.DTLVardef@114c0d1_HPS_FORMAT_FIGEXP M_FIG C_FIG

A machine learning approach to molecular formula assignment is crucial for unlocking the full potential of ultra-high resolution mass spectrometry (UHRMS) when analyzing complex mixtures. By combining data-driven models with rigorous benchmarking, the accuracy, consistency, and speed in identifying plausible molecular formulas from vast spectral datasets can be improved. Compared with traditional de novo methods that rely heavily on rule-based heuristics, and manual parameter tuning, machine learning approaches can capture complex patterns in data and adapt more readily to diverse sample types. In this paper, we describe the application of a machine learning methods using the k-nearest neighbors (KNN) algorithm trained on curated chemical formula datasets of UHRMS analysis of dissolved organic matter (DOM) covering the saline river continuum and tropical wet/dry season variability. The influence of the mass accuracy (training set with 0.15-1ppm) was evaluated on a blind test set of DOMs of different geographical origins. A Decision Tree Regressor (DTR) and Random Forest Regressor (RFR) based on mass accuracy (<1ppm) was used. Results from our ML models exhibit 43% more formulas annotated than traditional methods (5796 vs 4047), Model-Synthetic achieved 99.9% assignment rate and annotated/assigned 2x more formulas (8,268 vs 4047). DTR and RFR achieved formula-level accuracies (FA) of 86.5% and 60.4%, respectively. Overall, results show an increase in formula assignment when compared with traditional methods. This ultimately enables more reliable characterization of complex natural and engineered systems, supporting advances in fields such as environmental science, metabolomics, and petroleomics. Furthermore, the novel data set produced for this study is made publicly available, establishing an initial benchmark for molecular formula assignment in UHRMS using machine learning. The dataset and code are publicly available at: https://github.com/pcdslab/dom-formula-assignment-using-ml CCS CONCEPTSComputing methodologies [-&gt;] Machine Learning [-&gt;] Learning paradigms [-&gt;] Supervised Learning

Detection of food fraud and geographical traceability of ingredients is a continually sought goal for government institutions, producers, and consumers. Herein we explore the use of non-target high-resolution mass spectrometry approaches and demonstrate its utility through a particularly challenging case study - to distinguish wheat and spelt cultivars. By employing a data-independent acquisition (DIA) approach for sample measurement, the spectra are of considerable size and complexity. We utilize artificial intelligence (AI) algorithms (artificial neural networks) to evaluate the extensive proteomic footprint of several wheat and spelt cultivars. The AI model thus obtained is used to classify newer varieties of spelt, processed flour, and bread samples. Additionally, we discuss the validation of such a method coupling DIA and AI approaches. The novel framework for method validation enables calculation of precision parameters for facile comparison of the discriminatory power of the method and in the development of a reliable decision rule.