Analytical and Bioanalytical Chemistry

Plasma proteomics is used for a variety of applications including biomarker discovery, disease monitoring, and drug development. Data-independent acquisition (DIA) has vastly improved the breadth of proteins that are identified from samples; however, given challenges in reproducibility and translation, it is critical that the quantitative performance of these methods is reliable. Analysis of global proteomics data typically incorporates information from all detected peptides. However, some peptides do not reflect their parent protein amount, due to irreproducible digestion, modification, analytical interferences or instability. We hypothesise that including these peptides impacts protein relative quantification, and thus, a refined spectral library containing only quantitatively representative peptides provides superior protein quantification. By analysing a defined multi-species spike-in model, we show that refining a plasma spectral library by removing precursors that fail to meet quality control metrics (25.4% of all identified precursors) reduces noise and variability, improving precision, accuracy and differential abundance analysis by up to [~]11%, with minimal identification losses and substantial reduction in computational demand. This demonstrates proof-of-concept that refining spectral libraries produces results that prioritize quantification quality over quantity. This approach could enable development of universal tissue-specific refined spectral libraries able to improve quantification quality with easy implementation and minimal processing time. Significance of the StudyAs DIA mass spectrometry proteome depth increases, the quality of the associated protein quantifications must be considered alongside identification breadth, particularly in complex matrices such as plasma, which presents additional technical challenges. The spectral library used for protein identification and quantification is a critical determinant of DIA performance, and its composition requires considerable consideration. This work illustrates an initial step toward improving protein quantification starting at the spectral library level by filtering precursors which are poor quantitative representatives of their parent proteins. In doing so, the resulting data is more reliable for downstream and biological interpretation, with fewer false differential abundance assignments and reduced quantitative noise. As such, this work represents a broader shift away from the habitual focus of MS workflows on maximising the number of protein and differential abundance identifications and instead prioritises the quality of quantification over quantity. These initial findings lay the groundwork for further development of spectral library refinement strategies, with the potential to continue improving the accuracy and precision of protein quantification in DIA-based proteomics.

Comprehensive detection of steroids, beyond the limited panels typically analyzed in clinical chemistry laboratories, has become increasingly important given their pivotal roles in diverse biological processes. However, steroid quantification poses several analytical challenges, including differences in ionization efficiency and structural similarities across the entire steroid metabolic network. To address these challenges, we developed a targeted ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) assay to analyze 21 steroids using reverse-phase chromatography combined with rapid polarity switching. Mass spectrometry (MS) analysis was performed in scheduled multiple reaction monitoring (sMRM) mode. Depending on the steroid and matrix, the validated lower limits of quantitation (LLOQ) ranged from 12.0 pM to 1216 pM in plasma and 41.1 pM to 384 pM in fecal sample homogenates. In adipose tissue, it was from 0.01 pmol/g to 9 pmol/g. Measured steroid concentrations obtained from the commercial control samples (MassTrak Steroid Serum QC Set 1 and the MassCheck Steroid Panel 1 Serum Control) showed close agreement with the reference values. As a proof of concept, the method was successfully applied to 469 plasma samples in several projects, 15 adipose tissue samples, and 332 fecal samples, demonstrating its applicability to large-scale studies. In conclusion, the method enables sensitive, derivatization-free quantification of an expanded steroid panel in plasma and complex biological matrices, including adipose tissue and fecal samples, representing a significant advancement in comprehensive steroid profiling.

Capillary electrophoresis-mass spectrometry (CE-MS) offers unique analytical advantages for polar metabolite profiling but has remained underutilized in metabolomics relative to liquid chromatography-MS (LC-MS), in part due to challenges in managing migration time drift during data analysis. Here we introduce the use of indexed migration time (iMT) for easily managing this aspect of CE-MS data for metabolomics. Migration time indexing using a panel of stable isotope-labeled (SIL) amino acid reference standards, stored as an iRT database in Skyline, outperformed both uncorrected migration time and relative migration time (RMT) correction across three independent analytical batches spanning 90 samples from four biological matrices. The indexed migration time approach achieved sub-1% relative standard deviation (RSD) in migration index across batches, compared to up to [~]15% RSD for uncorrected migration times. Additionally, we evaluate the use of single-point external calibration in Skyline for the purposes of metabolite quantification from complex matrices in order to ease the burden of translational metabolite quantification from metabolomics using high-resolution mass spectrometry (HRMS). Single-point external calibration using a biological matrix-based calibrator was benchmarked against a 13-point linear calibration curve across a panel of amino acids; above 1 M, greater than 95% of back-calculated concentrations fell within {+/-}20% of multi-point calibration. Application of the complete workflow to plasma, serum, urine, and NIST Standard Reference Material (SRM)-1950 demonstrated low inter-batch variability by principal components analysis, broad metabolite coverage across 126 quantifiable analytes, and strong quantitative concordance (Deming slope = 0.862, pseudo-R2 = 0.994, n = 64 analytes) with an independent comprehensive reference dataset for NIST SRM-1950. Together, these results establish a practical mCE-HRMS metabolomics workflow that bridges targeted and discovery metabolomics paradigms and lays the groundwork for single-point external calibration as a powerful tool for translational metabolomics.

Class imbalance remains a challenge in metabolomics research, where biological and technical variability can affect statistical inference and machine learning (ML) performance. Class-balancing algorithms address this issue by either increasing minority-class observations or reducing the number of majority-class samples. This study evaluated the impact of oversampling and undersampling algorithms on targeted and untargeted metabolomics datasets derived from LC-MS and GC-MS analyses of plasma samples from patients with glioblastoma, meningioma, and controls. Synthetic Minority Oversampling Technique (SMOTE) and Random Undersampling (RUS) were applied to balance the datasets, and their effects on data distribution, inter-feature correlations, and machine learning model performance were compared. RUS preserved the original feature distributions but reduced representativeness by removing the majority-class samples. In contrast, SMOTE introduced synthetic samples that altered covariance structures, increasing the risk of overfitting, particularly in small datasets (n=10). These effects diminished with larger groups (n=30), partially restoring correlations between metabolites. Model performance varied across the class-balancing algorithms. Random Forest classifiers benefited from both balancing methods, with undersampling often yielding higher F1 scores, whereas Support Vector Machine models showed reduced classification performance. These findings highlight the importance of selecting class-balancing strategies based on dataset size, analytical platform, and ML algorithm in metabolomics studies.

N-glycolylneuraminic acid (Neu5Gc) has been repeatedly associated with human cancer, but reliable detection has remained elusive, generating controversy regarding its presence in human samples. To address this, matrix-matched calibration curves, which have been pioneered in proteomics and metabolomics for assessing changes in complex mixtures, were measured of released N-glycans at four orders of magnitude dynamic range in defined mixtures, systematically benchmarking Neu5Gc-containing N-glycan detection across multiple LC-MS platforms and sites. Orthogonally, the gold-standard analytical method, consisting of fluorescence detection of labelled monosaccharides separated by LC, was applied to the same samples, yielding absolute concentrations of Neu5Gc. LC-MS demonstrated an extended detection range of three or more orders of magnitude while retaining intact N-glycan measurement, improving assay specificity and enabling detection of the variety of Neu5Gc-bearing N-glycans. By combining orthogonal dimensions of evidence, including chromatographic separation, isotopic distribution matching, and composition-confirming MS/MS, LC-MS confidently resolved Neu5Gc signals from noise, even at low abundance. In comparison, DMB-LC-FLR was limited to two orders of magnitude dynamic range, insufficient for detection of Neu5Gc in commercially available pooled human sera. These findings strongly support that DMB-LC-FLR assay specificity and sensitivity are insufficient for Neu5Gc detection in human samples due to noise overwhelming the Neu5Gc signal. By establishing a reusable benchmarking framework for future glycomic studies, we aim to use LC-MS to improve the measurement of Neu5Gc in clinical samples.

Cystatin-C is an important renal function biomarker, and conventional quantification requires centralized laboratory analyzers, which limits timely testing in primary care and resource-limited settings. To address this need, we developed and validated a simple, rapid, and quantitative smartphone-based (SP) lateral flow immunoassay (LFIA) for measuring serum Cystatin-C. The SP-LFIA platform consists of a colorimetric LFIA strip and a custom SP reader with uniform LED illumination and macro lens for image capture. Quantitative image analysis of the colorimetric signal is performed by a dedicated application using a pre-defined third order polynomial calibration model. Following systematic optimization, the assay demonstrated a wide quantitative range of 0.32-8.00 mg/L, with a limit of detection of 0.15 mg/L. Analytical validation conducted according to CLSI guidelines showed excellent precision, with intra- and inter-assay coefficients of variation below 10%, and no significant interference from bilirubin, triglycerides, hemoglobin, or rheumatoid factor. Accelerated stability testing confirmed robust strip performance after storage at 50 {degrees}C for 28 days. Method comparison using 100 clinical serum samples showed high agreement with a commercial PETIA reference method (R{superscript 2} = 0.993) and minimal bias. These results indicate that the developed smartphone-based LFIA provides a reliable, cost-effective, and practical tool for point-of-care Cystatin-C monitoring.

Human hair is a common contaminant in GMP-controlled manufacturing environments, and its identification is important for contamination source investigation and corrective action. Because human hair can originate from multiple body sites, it is often necessary to determine not only the species of origin but also the anatomical source of the hair. Conventional forensic approaches distinguish scalp hair from body hair by microscopic examination of cuticle patterns, medullary structure, cross-sectional morphology, and pigment distribution. However, these methods depend on examiner expertise, are difficult to apply to damaged specimens, and provide limited quantitative information. In this study, we developed a proteomics-based approach for distinguishing scalp hair from pubic hair using identical sample preparation and analytical workflows. Comparative proteomic analysis identified keratin-associated proteins KAP 4-3 and KAP 9-6 as enriched in scalp hair, whereas cuticular keratins Ha7 and Ha8 were strongly enriched in pubic hair. Amino acid composition analysis further revealed that scalp hair-enriched proteins were highly cysteine-rich, consistent with sulfur-rich cross-linking matrix proteins, whereas pubic hair-enriched proteins exhibited characteristics of structural keratin filaments. These results demonstrate that proteomic signatures can provide a quantitative and objective means of determining the anatomical origin of human hair and may contribute to contamination source tracing in GMP manufacturing and forensic investigations.

BACKGROUND In line with International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) recommendations for high-sensitivity cardiac troponin assays, analytical validation and reference limit assessments are required to confirm that an assay meets performance criteria. This study evaluated the analytical performance and established the 99th percentile upper reference limit (URL) for the SPINCHIP High-Sensitivity Cardiac Troponin I (SPINCHIP hs-cTnI) point-of-care assay. METHODS Analytical performance characteristics, including the limit of blank (LoB), limit of detection (LoD), and limit of quantification (LoQ), were assessed. Additionally, 1,053 plasma samples and 1,055 whole-blood samples were used to determine the URL. Imprecision around the 99th percentile URL was evaluated as part of the analytical validation. High-sensitivity criteria were assessed by confirming measurable cTnI in [&ge;]50% of healthy individuals (n=432 plasma; n=431 whole blood) and achieving imprecision <10% at the 99th percentile (plasma, n=960; whole blood, n=480). RESULTS SPINCHIP hs-cTnI demonstrated a LoB of 0.3 ng/L; LoDs of 0.8 ng/L (plasma) and 0.9 ng/L (whole blood); and LoQs of 1.1 ng/L (plasma) and 1.4 ng/L (whole blood). The analytical measuring range was 1.1-9,000 ng/L. Imprecision at the common 99th percentile URL (14 ng/L) was 5.8%; for men (URL=16 ng/L) 5.6% and for women (URL=10 ng/L) 6.3%. Greater than 85.2% (94.0% and 76.1% in men and women, respectively) of healthy individuals showed measurable cTnI above the LoD. CONCLUSIONS The SPINCHIP hs-cTnI assay meets the IFCC high-sensitivity requirements, demonstrating <10% imprecision at the 99th percentile, reliable low-concentration precision and cTnI detection in more than half of healthy individuals.

Members of the transforming growth factor-{beta} (TGF-{beta}) superfamily, including inhibins and activins, are structurally related glycoprotein dimers that regulate reproductive and endocrine signaling. Their high degree of molecular similarity presents challenges for label-free analytical discrimination. To evaluate the ability of Raman spectroscopy to distinguish closely related TGF-{beta} superfamily proteins based on intrinsic vibrational fingerprints. Raman spectra of recombinant human Inhibin -subunit, Inhibin B ({beta}B homodimer), and Activin A ({beta}A--{beta}A) were acquired using confocal Raman microscopy with 532 nm excitation. Spectra were baseline-corrected, area-normalized, and analysed using principal component analysis (PCA). Distinct spectral signatures were observed across the 500--1800 cm-1 region. Differences within the S--S stretching region (500--550 cm-1) were consistent with variations in disulfide-bond environments, with the Inhibin -subunit exhibiting the highest relative intensity in this region. Variations in the amide I band (1600--1700 cm-1) suggested differences in protein secondary structure, while aromatic amino acid vibrations provided additional discriminatory features. PCA revealed clear clustering and separation of all three protein classes based on their Raman fingerprints. Raman spectroscopy enables label-free differentiation of structurally related endocrine glycoproteins and demonstrates potential for the structural characterization and classification of inhibin and activin proteins within the TGF-{beta} superfamily.

Peptide spectrum annotation is critical for the assignment of peptides and the localisation of modifications. While many existing tools provide spectrum annotation capacities, they often lack the flexibility required to allow bespoke spectral annotation of peptides containing multiple labile modifications or the accurate assignment of peptides in which fragmentation deviates from canonical patterns. In these cases, user-guided annotation is widely used to improve assignment completeness, however it typically does not integrate peptide scoring, making it challenging to assess the empirical improvement of the associated annotation and its impact on downstream false-discovery rate estimations. Here, we introduce an interactive annotation environment, the 'MassSpectrum Analyzer', which aims to streamline the exploration and analysis of modified peptides by enabling user-defined customisation with peptide scoring. Using (2-Aminoethyl)trimethylammonium carboxyl-derivatised peptides and glycopeptides as case studies we demonstrate the capacity of the MassSpectrum Analyzer to rapidly explore and allow the assessment of modified peptide datasets. By enabling direct assessment of the impact of user-guided choices on peptide scoring, we show how the detection of highly modified peptides can be improved through post-search integration of modification fragmentation information in a statistically robust manner. Similarly, by permitting comparisons of peptide ion intensities across spectra, we show that global fragmentation patterns can be quantified allowing the interrogation of trends that only become clear when spectra are assessed en masse. Combined, the MassSpectrum Analyzer streamlines the generation of publication-ready spectra and provides a means to assess how the inclusion of annotated features influences assignment scores.

Luminescence is a powerful method for detecting trace analytes and monitoring biological processes. However, most bioluminescence reagents, including luciferase and its substrates, are sensitive to temperature, limiting their useable shelf lives, and resulting in inconsistent performance. Enhancing the stability of these reagents could improve data quality, simplify workflows, and address cold chain storage issues. In this study, we demonstrate the application of the platform stabilization technology, capillary-assisted vitrification (CAV), as a tool to stabilize different luciferases and their substrates, and the application of the stabilized reagents in both in vitro and in vivo bioluminescent assays. We demonstrate that CAV-stabilized reagents can be stored and shipped ambiently, maintain consistent performance over time, and are suitable for use in cell viability quantification, tumor monitoring, in vivo imaging, microbial detection, and immunoassays. Additionally, different reagents can be co-formulated to make ready-to-use assay kits that can also be shipped and stored ambiently. Our results demonstrate that CAV stabilization is a viable alternative to traditional storage methods, with broad potential to improve bioluminescence workflows.

The chorioallantoic membrane (CAM) model represents a promising three-dimensional in vivo platform for preclinical drug testing in human tissues. In this study, we investigated whether the tissue penetration and distribution of benzbromarone, a known inhibitor of the Ca2+ activated chloride channel TMEM16A and potential therapeutic agent for autosomal dominant polycystic kidney disease (ADPKD), can be successfully visualized in human renal cyst tissue cultured on the CAM. To this end, desorption electrospray ionization mass spectrometry imaging (DESI-MSI) combined with an ultrahigh-resolution time-of-flight mass spectrometer was employed. We achieved spatially resolved molecular mapping of endogenous metabolites and lipids as well as the applied compound. MSI enabled clear differentiation between CAM and cystic tissue based on their distinct lipid profiles. Benzbromarone was reproducibly detected in the cyst specimens and exhibited selective accumulation along the cyst epithelium, which is considered the principal site of action. These observations were complemented by multivariate analyses including Uniform Manifold Approximation and Projection (UMAP), and sparse multinomial logistic zero-sum classification. The data-driven approach confirmed molecular differences between tissue types and allowed accurate classification of drug-treated and untreated regions. This study demonstrates that topically applied benzbromarone penetrates human renal cyst tissue in the CAM model and localizes to pharmacologically relevant tissue regions, notably the location of the Ca2+ activated chloride channel TMEM16A in the epithelial lining. The integration of high-resolution DESI-MSI with advanced statistical analysis provides a robust and label-free method to study drug distribution in human tissue grafts. Our findings contribute to the advancement of translational research in analytical chemistry and pharmacology.

BACKGROUND: Erythrophagocytosis constitutes a major pathogenic mechanism of metabolic dysfunction associated fatty liver disease (MAFLD). Our previous research established a quantitative thin-layer chromatography (TLC) technique for sphingomyelin, revealing reduced levels in the red blood cells (erythrocytes) of patients with metabolic dysfunction associated fatty liver disease (MAFLD). This reduction was accompanied by erythrocyte sphingosine accumulation, a driver of pro-inflammatory erythrophagocytosis, though sphingosine 1-phosphate release remained stable. To better understand erythrocyte sphingosine metabolism, we adapted our quantitative TLC method to analyze sphingosine within the erythrocyte-conditioned media (ECM) of MAFLD patients. Methodology Separation was performed on 10X10cm Silica gel 60 F254 plates using a mobile phase of chloroform, methanol, acetic acid, and water (60:50:1:4 v/v/v/v). The dynamic range, linearity, and range of linearity were assessed by analysing sphingosine levels from 0.1 to 10microg/spot. We validated the system precision and sensitivity by performing triplicate analyses of sphingosine standards (1.25, 2.5, and microg). The limits of detection and quantification were derived from the calibration curve slope and standard deviation (3.3 XSD/slope for LOD; 10 XSD/slope for LOQ). Accuracy was assessed via recovery tests at 100%, 200%, and 300% of a 2.5microg load. We confirmed specificity by evaluating the retention factors against other lipid species. This protocol was applied to Folch-extracted lipids from the ECM (5 X 107 cells/ml) of four MAFLD patients and four healthy controls, spiked with 5microg of sphingosine. Findings The calibration model, based on combined Green and Blue color intensities, followed the linear equation y = -11.171x + 353.25(R2 = 0.94). Interday precision values were 0.21%, 1.65%, and 0.44%, while recovery rates (accuracy) ranged from 94.5% to 98.7%. The measured LOD and LOQ were 0.75microg and 1.21microg, respectively. The sensitivity was calculated at 90ng. Statistical analysis showed no significant variance in sphingosine concentrations in erythrocyte-conditioned media between the MAFLD group and the control group. Summary The described thin layer chromatography is accurate, precise, sensitive, with good limits of detection and quantification, and most importantly is low-cost and time-efficient. Using this method, we show that while erythrocytes of MAFLD patients exhibit sphingosine accumulation, the utilisation of exogenous sphingosine from their erythrocytes is not affected. This suggests that the metabolic shift may be driven by increased sphingosine supply from the plasma.

Micro and nano plastics (MNPs) have become ubiquitous contaminants in the environment with their occurrence being detected in air, water and food. They can cross biological barriers and slowly build up in different organs, including the placenta, raising concerns about possible impacts on maternal and fetal health. Placenta, a highly metabolically active organ composed primarily of trophoblast cells, requires substantial energy for proper development and function. However, the effects of MNPs exposure on trophoblast biology and mitochondrial health remain poorly understood. This study investigated the in vivo systemic accumulation of MNP in different organs of pregnant mice and their localization within various organelles in vitro. These effects influenced trophoblast energy metabolism and led to reduced migration. Mice received fluorescent polystyrene MNPs via their drinking water. Biodistribution was evaluated in vivo using IVIS whole-body imaging, while ex vivo fluorescence imaging confirmed accumulation of these particles in multiple organs and cells. In parallel, human HTR-8/SVneo trophoblast cells were exposed to MNPs, demonstrating rapid cellular uptake and mitochondrial and nuclear localization via fluorescence microscopy. TEM analysis uncovered mitochondrial structural alterations and the localization of MNPs. Seahorse analysis revealed impaired mitochondrial respiration and oxygen consumption rates, indicating compromised cellular bioenergetics in MNPs-treated cells, which led to inflammation, altered mtDNA copy number, and impaired trophoblast migration. Overall, these findings indicate that pregnant mice exposed to MNPs undergo systemic transfer, with trophoblast uptake marked by mitochondrial dysfunction, inflammation, and reduced migration. Our study identifies mitochondrial dysfunction as a central mechanism underlying MNP-mediated placental toxicity and underscores the potential role of environmental microplastic exposure in adverse pregnancy outcomes.

Background chemical ions are a pervasive but often underappreciated limitation in LC-MS metabolomics, where they can suppress analyte signal, obscure endogenous metabolites, increase spectral complexity, and consume MS/MS acquisition events. Tributylamine (TBA) ion-pairing reversed-phase LC-MS provides stable retention and broad coverage of polar anionic metabolites, including central carbon intermediates, nucleotides, cofactors, and bile acids, but the back-ground burden introduced by the ion-pairing reagent itself has not been systematically addressed. Here, we identify commercial TBA as a major source of nonbiological contaminant ions and develop a practical strategy to reduce background burden while preserving metabolite coverage. Serial solid-phase extraction of TBA using orthogonal reversed-phase, strong anion-exchange, and strong cation-exchange sorbents removed chemically diverse contaminants, including isobaric background ions that interfered with endogenous hydroxybutyrate isomers. We further optimized the workflow by reducing medronic acid concentration, restricting medronic acid to the organic mobile phase, replacing phosphoric-acid column conditioning with metal-passivated column hardware, and adding EDTA to the sample reconstitution solvent to improve citrate detection. In mouse liver extracts, the optimized method increased signal intensity for most annotated metabolites and improved the fraction of full-scan ion current attributable to target analytes. Method optimization also altered compound-specific retention behavior, resolving some co-elution-based interferences while introducing new suppression relationships for selected analytes. Across mouse liver, human B lymphocytes, and NIST SRM 1950 plasma, the optimized workflow increased total feature detection by 45%, 72%, and 42%, respectively, and improved the number of low-variance features, precursors with data-dependent MS/MS spectra, and MS/MS library matches. These findings establish background-ion mitigation as a central design principle for LC-MS method development. More broadly, this work provides a generalizable framework for identifying, reducing, and validating reagent- and additive-derived background to improve targeted and untargeted LC-MS data quality.

Botanical dietary supplements (like wheat, barley, teff, oats, white lupin, pumpkin seed, and chickpeas) may contain trace amounts of toxicants in addition to important micronutrients. Developing and validating a reliable protocol for the simultaneous quantification of Cu, Fe, Zn, Mo, Se, Mn, Pb, Al, Ni, and Cr using a PerkinElmer (NexIONTM2000 model) quadrupole ICP MS (including a He collision and reaction cell when needed) with closed vessel microwave digestion using (HNO3 + H2O2) was the aim of this study.The method was subsequently utilized in a sample survey, and the outcomes were evaluated against WHO/JECFA standards. From five study regions, twenty-seven farm-collected botanical powder samples representing seven species were acquired. To create one composite per species, field subsamples were cleaned, air dried, ground, and blended (nine subsamples per botanical: three grabs from each of three farms). HNO3/H2O2 was used to digest aliquots (0.250-0.500gm) in closed microwave containers. Internal standards, multi-point external calibration, procedural blanks, verified reference materials, matrix spikes, and duplicates were all used in ICP MSs multi-element quantitation. Method LODs/LOQs, accuracy (CRM recoveries), and precision (RSD) were calculated.The technique produced low LODs that were suitable for dietary evaluation (typical LOD ranges: Cu, Fe, Zn, Mn, Ni, Cr (0.001-0.01) mg/kg; Mo, Se, Pb, Al (0.002-0.05) mg/kg. For the majority of analytes, within-run RSDs were less than 5%, while CRM recoveries ranged from 88.9 to 110%. The concentrations of essential elements varied greatly (average mg/kg: Fe (280.7{+/-}25.6); Zn (6.0{+/-}0.541); Cu (2.8{+/-}0.269); Mn (398.3{+/-}23.8); {micro}gm/kg: Se (0.061{+/-}0.006); Mo (1.0 {+/-}0.022). Although some composites approached or exceeded conservative intake thresholds for Pb and Al under high consumption scenarios, toxic elements were generally low (mean mg/kg: Pb (0.062{+/-}0.007); Al(185.2{+/-}18.5); Ni(1.6{+/-}0.163); Cr(1.8{+/-}0.171).For the simultaneous nutritional and contaminant profiling of supplements derived from cereals and those not, the validated ICP- MS workflow with microwave HNO3 and H2O2 digestion is suitable. Accurate labeling and consumer safety can be supported by routine screening and supply chain controls.

Background: The N-terminal ectodomain (NTE) of the SARS-CoV-2 membrane (M) glycoprotein is a short, flexible region that remains exposed on the virion surface and exhibits immunogenic potential across multiple coronaviruses. Despite its small size and conformational plasticity, this region contains conserved linear epitopes that may serve as practical surrogates for full-length proteins in serological diagnostics. Objective: To develop and evaluate a synthetic peptide-based diagnostic assay targeting the NTE of the SARS-CoV-2 M protein. Methods: Epitope prediction, peptide synthesis, and antibody affinity assays were performed to design homomultivalent peptide analogs that exploit avidity effects through disulfide polymerization. The resulting peptide antigens were tested in an enzyme-linked immunosorbent assay (ELISA) using clinical samples from RT-PCR-confirmed COVID-19 patients and biobanked controls. Results: The selected peptide analogs (M1, M1i, M1s) corresponded to a conserved surface-exposed motif of the SARS-CoV-2 M protein. Polymeric M1 exhibited a twofold gain in apparent affinity (Kdapp = 4.33 nM) compared with the monomeric form (Kdapp = 8.00 nM). Clinical validation using 1,222 patient samples yielded a sensitivity of 95.26% and specificity of 52.27%, with an overall diagnostic accuracy of 88.70%. Conclusion: The M peptide analogs demonstrate that synthetic peptide antigens can serve as stable, high-sensitivity surrogates for whole-protein assays. This design principle may be applied to other emerging pathogens where rapid assay development and scalability are critical. Keywords: Peptides, Antibodies, COVID-19, Enzyme-Linked Immunosorbent Assay, Protein Binding

Proteins are dynamically regulated by a myriad of post-translational modifications (PTMs) that control their stability, conformation, activity, subcellular localization, and local interactions. Capturing the precise composition of these various modification states, or proteoforms, is a principal objective of top-down proteomics (TDP). By ionizing intact proteoforms and combining measurements of precursor ion and fragment ion masses, the position, stoichiometry, and combination of PTMs can be determined. Despite the highly valuable measurements that TDP can provide, it is typically less sensitive than corresponding peptide-level analysis with many reports utilizing input material in the microgram to milligram range. Contributing to this lack of sensitivity is the risk of sample loss due to non-specific binding to surfaces during sample preparation. The most widely employed sample preparation approaches for TDP either require high sample input (e.g. precipitation and ultra-filtration) or fail to effectively remove surfactants (e.g. solid-phase extraction). These limitations have hindered advancement of targeted TDP applications involving immunoprecipitation and other enrichment strategies. Bead-assisted protein aggregation, also referred to as single-pot, solid-phase-enhanced sample preparation (SP3), has emerged as a popular sample preparation strategy for bottom-up proteomic workflows, but has only been used in TDP with secondary ion exchange chromatography cleanup. We envisioned a magnetic bead based protein cleanup approach that proceeds directly to MS analysis with judicious choice of bead surface chemistry and elution conditions. Here we report a sample preparation method using hydroxyl-functionalized magnetic beads for top-down proteomics applications.

Dried blood spots (DBS) are well-established microsamples used in clinical testing and newborn screening. However, their use in deep proteomics is hindered by highly abundant blood proteins and inefficient protein recovery from filter paper matrices. The non-targeted analysis of non-specifically DBS-absorbed proteins (NANDA) workflow partially overcomes the impact of abundant blood proteins and has enabled the identification of over 5,000 proteins from DBS samples. Nonetheless, residual abundant proteins, including hemoglobin and fibrinogen, constrain deep proteomic analysis. Therefore, this study aimed to evaluate the effects of the metal chelator ethylenediaminetetraacetic acid (EDTA) on the depth of DBS proteomic analysis. An optimized EDTA-enhanced NANDA protocol that incorporated a 100 mM EDTA wash step was compatible with standard DBS collection procedures and required no modification of current clinical workflows, markedly enhancing the depletion of abundant proteins and facilitating its potential use in clinical and translational settings. When combined with Orbitrap Astral data-independent acquisition mass spectrometry, this approach enabled the single-shot identification of more than 7,000 proteins from DBS samples; to the best of our knowledge, this represents the deepest proteome coverage reported to date, and the workflow further supported high-throughput and highly reproducible analyses. Additionally, its application to mouse disease models revealed disease-specific systemic immune signatures from minimal blood volumes. Collectively, these results establish EDTA-enhanced NANDA as a practical and scalable workflow that overcomes longstanding limitations of DBS proteomics, thereby enabling deep, high-throughput, minimally invasive proteomic profiling across diverse biological and experimental contexts.

Ribonucleic acid (RNA) modifications, with over 170 identified types, play diverse roles in cellular processes. The past decade has witnessed surging demand for accurate identification and localization of RNA modifications in both endogenous and synthetic therapeutic RNAs. With accurate spectral annotation for RNA, tandem mass spectrometry (MS/MS) can meet this demand. Here we present RNabel, a user-friendly software tool for in-depth annotation of MS/MS spectra of RNA oligonucleotides. RNabel considers a full set of backbone-cleavage ions (a, b, c, d, a-B, w, x, y, z) in which the ribonucleotide unit could be A, U, C, G, Y (pseudouridine), or I (Inosine). Additionally, RNabel considers 196 modifications on the base, the phosphoribose linkage, the 5' or the 3' terminus, or detachment of a sub-nucleotide fragment as a neutral or charged group. Users can create new components if needed, including ribonucleotides, modifications, neutral or charged groups that could detach from a ribonucleotide. RNabel efficiently processes large datasets in four acceptable formats including .mgf, .raw, .txt from msConvert, and RNabel batch files. Multiple statistical metrics are provided for quality assessment of spectral annotation. To accelerate RNA modification analysis, RNabel is made freely available for Mac and Windows users at https://github.com/songge1111/RNabel/releases. Graphic Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=116 SRC="FIGDIR/small/733900v1_ufig1.gif" ALT="Figure 1"> View larger version (30K): org.highwire.dtl.DTLVardef@8ccae5org.highwire.dtl.DTLVardef@15c8cfaorg.highwire.dtl.DTLVardef@12b93a2org.highwire.dtl.DTLVardef@1e9aab9_HPS_FORMAT_FIGEXP M_FIG C_FIG