Analytical and Bioanalytical Chemistry

Oxylipins are potent signaling lipids that affect inflammation, vascular tone, and metabolism, making them relevant in many diseases. Oxylipins are measured with liquid chromatography-mass spectrometry (LC-MS), but challenges in quantification arise due to low abundance and rapid degradation. In this study, we optimize LC-MS methods to improve the quantification of oxylipins in human plasma given growing interest in oxylipins and their impact on clinical research. Plasma samples were obtained from healthy participants and extracted by solid-phase extraction to concentrate the oxylipins. We then utilized a reversed phase targeted LC-MS/MS method using an Agilent 6495D triple quadrupole with transitions for 248 oxylipin species. Ion funnel voltages were set at 50 or 100 volts. Given the rapid degradation of oxylipins with bio-reactive surfaces, we compared both standard and Altura (bio-inert) columns, as well as standard and bio- inert LC setups. We observed that ion funnel parameters significantly alter detectable levels of oxylipins within LC-MS/MS analysis. By decreasing voltages applied to ions inside the ion funnel, signal was increased for most oxylipin species while peak quality was maintained. We also demonstrated that fully bio-inert setups quantify more compounds and show increased levels of some compounds, but fewer epoxyoctadecadienoic acid (EpODE) species. To explore this further, we injected analytical grade alpha-linolenic acid (ALA), the direct precursor of EpODEs, and observed formation of EpODEs within the instrumentation when using stainless steel columns. Our data shows that oxylipins benefit from fully bio-inert systems and optimized pre-mass analyzer parameters. The stainless-steel components of the column may also be contributing to epoxidation reactions of polyunsaturated fatty acids (PUFAs), generating oxylipin species during analysis. Finally, we utilized this method to perform oxylipin analysis in other human tissues including granulocytes, mononuclear cells, erythrocytes, skeletal muscle, and THP-1 cells, a human derived monocyte cell line.

Multimodal imaging workflows increasingly require multiple analytical techniques to be applied to the same tissue section to improve spatial co-registration and molecular interpretation. Polyethylene naphthalate (PEN) membrane substrates are attractive for this purpose because of their low thickness and compatibility with downstream modalities, but their use for matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI MSI) remains challenging due to low conductivity, reduced ion yield and susceptibility to laser-induced damage. In this study, MALDI MSI conditions for small-molecule analysis on PEN substrates were systematically optimised using bovine brain homogenate and mouse brain tissue using a TimsTOF flex MALDI-2 platform. Matrix selection, laser energy, pixel size (5 and 20 m), ionisation mode and PEN type were evaluated to identify acquisition conditions that balanced signal intensity with spectral quality and preservation of sample and membrane integrity. Among the matrices tested, 1,5-diaminonaphthalene (DAN) resulted in the best overall performance, providing higher ion intensities, less damage to the PEN substrates and a broader usable laser energy range than the other matrices tested. Different PEN substrate formats were also compared, showing distinct trade-offs between analytical performance and practical suitability for multimodal analysis. Under the tested conditions, glass-supported PEN (gPEN) provided the most practical overall compromise, while frame-supported PEN was not suitable for routine analysis on the instrument configuration used. Application of the optimised method to mouse brain tissue confirmed that spatially resolved metabolite imaging on PEN substrates is feasible and can support same-section multimodal workflows.

Analytical technologies that can provide quick, precise, and continuous information regarding process performance are necessary for the development of biopharmaceutical manufacturing. Conventional bioprocess monitoring is largely dependent on laboratory-based data and offline sampling, which can restrict process management and cause delays in decision-making. This study develops a machine learning-enabled Raman spectroscopy framework for Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) applications in bioprocess manufacturing. Five predictive modeling techniques--Partial Least Squares (PLS) regression, Support Vector Regression (SVR), Random Forest, Extreme Gradient Boosting (XGBoost), and Neural Networks--were used to analyze Raman spectral data from an Escherichia coli fermentation dataset. The models were assessed using the coefficient of determination (R{superscript 2}), root mean square error (RMSE), and mean absolute error (MAE) to predict two crucial fermentation parameters: the concentrations of glucose and acetate. The superior performance of PLS regression for glucose prediction and the improved prediction accuracy of XGBoost for acetate concentration demonstrated the importance of selecting modeling techniques based on biological complexity. Explainable artificial intelligence using SHAP analysis was incorporated to improve model transparency by identifying Raman spectral regions contributing to predictions. The suggested architecture shows how Raman spectroscopy and machine learning can be combined to assist automated process monitoring, enhance process comprehension, and hasten the implementation of real-time quality judgments in next-generation biomanufacturing. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=114 SRC="FIGDIR/small/740653v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@6a460aorg.highwire.dtl.DTLVardef@11c68e5org.highwire.dtl.DTLVardef@2acf7aorg.highwire.dtl.DTLVardef@9b890a_HPS_FORMAT_FIGEXP M_FIG C_FIG Overall workflow of the Raman spectroscopy-based machine learning framework for PAT and RTRT implementation. Raman spectra collected from E. coli fermentation were preprocessed and analyzed using multiple machine learning algorithms for the prediction of glucose and acetate concentrations. Model performance evaluation and SHAP-based explainable AI analysis enabled the identification of important spectral features for real-time bioprocess monitoring. HighlightsO_LIDeveloped a Raman spectroscopy-based machine learning framework for real-time monitoring of critical bioprocess parameters. C_LIO_LICompared traditional chemometric modeling (PLS regression) with advanced machine learning approaches, including SVR, Random Forest, XGBoost, and neural networks. C_LIO_LIShowed that the biochemical target affects the models performance, with XGBoost improving acetate prediction and PLS offering better glucose prediction. C_LIO_LIIntegrated explainable artificial intelligence to identify Raman spectral regions contributing to bioprocess predictions. C_LIO_LIEstablished a pathway toward interpretable Raman-based Process Analytical Technology (PAT) and Real-Time Release Testing (RTRT) implementation. C_LI

Simultaneous quantification of monoglycosphingolipid stereoisomers is required to monitor changes in defective enzymatic pathways linked to diseases such as Gaucher Disease, Parkinson's Disease, and Krabbe Disease. Resolution of beta-glucosyl and beta-galactosyl epimers cannot be achieved by standard liquid chromatography, electrospray ionization, tandem mass spectrometry (LC-ESI-MS/MS). Separation becomes possible when field asymmetric ion mobility spectrometry (FAIMS), also known as differential mobility mass spectrometry (DMS), is added as an orthogonal separation technique to LC. FAIMS/DMS separates epimeric ion clusters in a high versus low electric field (separation voltage, SV) then redirects the target epimeric ions to the mass spectrometer through the application of a direct current (compensation voltage, CoV). Resolving SVs and CoVs must be manually determined for each lipid. Manual derivation is a labour-intensive process that requires pure synthetic standards, limiting the number of stereoisomers a user can include in an assay. To address this problem, we introduce here intelligent DMS (iDMS). iDMS is an in silico supervised neural network model that learns the ion mobility relationships between SV and CoV and the monoglycosphingolipid structural features of sugar headgroup, N-acyl chain length, and N-acyl degree of unsaturation. iDMS predicts the SV and CoV combinations capable of resolving any stereoisomer pair from a training dataset of composed of measured signal intensities across a range of SVs and CoVs of 12 lipids. This machine learning alternative to manual DMS optimization promises to accelerate the deployment of multiple-reaction-monitoring mode (MRM) RPLC-ESI-DMS-MS/MS assays for the routine and rapid quantification of biologically relevant monoglycosphingolipid stereoisomers.

High dietary fructose consumption is a major contributor to the development of obesity and related cardiometabolic diseases, highlighting the need for accurate assessment of fructose metabolism in humans. Stable isotope tracer approaches, such as 13C6-fructose, require highly sensitive and specific analytical methods to quantify both concentrations and isotopic enrichments. In this study, we developed and validated a robust gas chromatography-triple quadrupole mass spectrometry (GC-QQQ)-based method for the simultaneous measurement of unlabeled and 13C6-fructose in human plasma. The method employs oximation and per-acetate derivatization, and demonstrates high specificity and accuracy. Intra- and inter-assay precision were below 10%, with no detectable carry-over, and a lower limit of quantification (LLOQ) of 0.1 nmol/mL for concentration and 0.02 molar percent excess (MPE%) for enrichment and no interference from glucose. We further compared data acquisition using multiple reaction monitoring (MRM) and selected ion monitoring (SIM). MRM showed superior performance at the low concentrations and enrichment levels characteristic of clinical plasma samples, resulting in improved sensitivity and lower LLOQs compared to SIM. Overall, this validated method provides a sensitive and reliable approach for fructose tracer studies in humans. Its application will facilitate robust investigations into fructose metabolism, and its role in metabolic dysregulation and obesity-related disease.

Hydrogen-deuterium exchange mass spectrometry (HDX-MS) requires extraction of broad, time-dependent peptide isotope envelopes from noise and overlapping signals. The ExMS/ExMS2 lineage established a retention-time-aware framework for raw-data extraction and integrated HDX-MS analysis. Building on this foundation, msengine is an open-source Python engine that adapts mass-selection windows and retention-time boundaries to the observed data. Spectra are reconstructed as nonnegative combinations of the theoretical undeuterated (all-H) isotope distribution shifted across allowed deuteration states, without prescribing a population shape. The fitted weights, termed deuteration coefficients, describe each state's contribution and guide refinement of mass-selection windows. Spectral matches establish an initial retention interval, which is then expanded using chromatographic intensity to include weaker portions of the elution profile. Interference screening and final quality checks assess the recovered interval. In a 1:1 mixture of undeuterated and deuterated material, interval expansion changed the relative population intensities within the extracted envelope resulting in closer agreement to the 1:1 target, illustrating the importance of boundary selection. Across a four-timepoint experiment with the SET1/SWD2 complex collected in duplicate, msengine accepted an average of 901 peptide-charge targets per measurement versus 636 for ExMS2, a 41.7% increase, while improving replicate-centroid disagreement and retention-window widths between replicates. In a same-workstation benchmark, total wall time decreased from 71.9 to 7.3 min, with higher peak memory use. Together, these results demonstrate adaptive signal extraction across mass and retention time as a means to retain multimodal envelopes while increasing accepted-target yield, persistence, accuracy, and throughput.

Molecular specificity remains a major challenge in mass spectrometry imaging (MSI), particularly when low-abundance species coexist with structurally related isomers that cannot be distinguished by accurate mass and exhibit similar fragmentation behavior. Bis(monoacylglycero)phosphates (BMPs), lysosomal lipids increasingly implicated in lipid homeostasis and disease, represent a particularly demanding example because they are structural isomers of phosphatidylglycerols (PGs) and display highly similar negative-ion fragmentation. Here, we developed an ion mobility-guided targeted MALDI-MS/MS imaging workflow for direct on-tissue discrimination of endogenous BMP/PG isomeric pairs. Orthogonal HILIC-DDA-PASEF analysis provided accurate-mass, retention-time, fragmentation, and ion-mobility information used to define mobility-constrained precursor coordinates for scheduled MALDI-iPRM-PASEF acquisition. Ion-mobility measurements showed high agreement across ESI-TIMS, MALDI-TIMS, and tissue-based MALDI-TIMS-MSI, while optimization of laser sampling minimized ion-load-dependent mobility shifts. Narrow mobility windows reduced reciprocal PG/BMP cross-talk to below 4% while preserving selective detection under strongly unbalanced abundance conditions. The workflow enabled distinct precursor- and product-ion imaging of endogenous PG 34:1 and BMP 34:1 in sagittal mouse brain, supporting their acyl-chain-level assignment as PG 16:0_18:1 and BMP 16:0_18:1. Application to a CLN3-knockout mouse model revealed BMP-specific reductions across brain, kidney, and lung that were not mirrored by the corresponding PG isomers, providing an orthogonal biological validation of the analytical discrimination. Mobility-constrained targeted MS/MS additionally resolved type-II isotopic interference that remained ambiguous at the MS1 level. Overall, this work provides a strategy for reciprocal spatial discrimination and structural confirmation of endogenous BMP and PG isomers directly in tissue and highlights the value of combining ion mobility with targeted product-ion imaging to increase molecular specificity in spatial lipidomics.

Per-and polyfluoroalkyl substances (PFAS) constitute a diverse class of persistent synthetic chemicals utilized across industrial, medical, and consumer sectors that are pervasive global pollutants. Exposure to PFAS is linked to adverse impacts on both innate and adaptive immune systems. Human lactoferrin (hLF) is a key antimicrobial component of the developing innate immune system present in colostrum and breast milk. We hypothesized that hLF is a potential PFAS binding protein related to PFAS immunotoxicity. The results of thermal stability experiments indicated that all 11 tested PFAS bind and destabilize the structure of hLF. Notably PFBA, PFOS, HFPO-DA, and 6:2 FTSA decreased apo-hLF melting temperatures from 64oC to [≤] 37oC, suggesting that PFAS exposures destabilize the native hLF protein under physiological conditions. Relative binding affinities (Kd) ranged from 0.2-11 mM across tested PFAS. Molecular docking was used to confirm experimental binding affinities and identify molecular interactions involved with PFAS binding. Calculated Gibbs Free Energies of binding ranged from -4.4 to -8.8 kcal/mol. Together, these results demonstrate that PFAS bind hLF at affinities comparable to human serum albumin and other PFAS binding proteins, and that some PFAS can destabilize hLF protein structure at physiologically relevant temperatures and conditions.

Abstract Chemical cross-linking mass spectrometry (XL-MS) is a powerful technique for elucidating protein structures and interactions, with NHS ester-based cross-linkers being the most widely used. Traditionally, NHS esters are considered highly specific for primary amines; although their reactivity toward cysteine thiols has been reported, it has not been systematically characterized in XL-MS due to the extreme lability of the resulting thioester bonds. Here, we demonstrate that NHS esters efficiently label cysteine residues and form stable lysine-cysteine (K-C) cross-links--species previously deemed labile in XL-MS. To preserve these thioester-based cross-linked sites , we optimized the standard XL-MS workflow and applied it to four model proteins. K-C cross-links constitute 23% to 59% of total detected cross-links, with C-C distances predominantly ranging from 12 to 40 [A], and 60% satisfying theoretical linker constraints -- confirming that K-C cross-links are as reliable as canonical lysine-lysine (K-K) cross-link sites. Cross-software validation and benchmarking against the heterobifunctional K-C cross-linker GMBS further demonstrated that our optimized workflow captures ~ 40% of GMBS-identified K-C cross-linked sites. Collectively, these findings expand the practical utility of NHS esters and enhance protein structural characterization by enabling simultaneous acquisition of K-K and K-C distance constraints.

Affinity purification-mass spectrometry (AP-MS) enables the characterization of protein-protein interactions (PPIs), and the ease and sensitivity of such experiments has progressively increased. Beyond steady-state interactions of target proteins, a strong interest has emerged in monitoring how PPIs change upon significant biological perturbations, such as in disease contexts or small molecule modulation of the target protein. These perturbations likely not only induce PPI changes but can also lead to altered expression of proteins not of direct interest. Changes in protein abundance may alter which proteins adsorb to the affinity purification matrix, and due to the sensitivity of modern mass spectrometers, these differential ''background binders'' can masquerade as differential interactors. Contemporary approaches often do not account for differences in the background proteome, potentially inflating the number of false positives and negatives reported. Here, we provide technical considerations for the reliable annotation of dynamic PPIs, using the O-GlcNAc transferase (OGT) as a case study. We describe the installation of affinity epitope tags on endogenous OGT in mouse embryonic stem cells (mESCs), which we then apply for OGT interactor identification via AP-MS. We show that accurate representation of the bead background, which depends on the affinity matrix in use, is critical for elimination of false positive and false negative PPIs. This became even more pertinent as OGT PPI dynamics were measured under OGT catalytic inhibition via OSMI-4, which is known to perturb gene expression. The proteomes of OSMI-4-treated and control-treated mESCs differed, leading to distinct bead backgrounds in which the differential background proteins appeared as interaction gains or losses. These false positives were resolved by incorporating straightforward experimental controls through a practical statistical framework, allowing for a direct and confident comparison between treatment conditions. Incorporating these considerations into workflows investigating PPI dynamics will improve data fidelity and reproducibility.

Polyethylene glycol (PEG) is a synthetic polymer ubiquitous in pharmaceuticals, personal care products, food additives, and industrial manufacturing. Despite its widespread use and potential importance as an exposure chemical, the prevalence of PEG exposure and its excretion in human populations remain largely uncharacterized. Moreover, PEG detected in human biofluids is frequently assumed to arise from analytical contamination during sample preparation, potentially obscuring its contribution to the human exposome and confounding metabolic phenotyping studies. Here, we show that PEG is as component of the human xenobiotic exposome and is further metabolized into PEG hydoxy acid and diacid metabolites in humans. We further find that PEG exposure is associated with alterations in microbiome composition and short-chain fatty acid metabolism, suggesting its biological impact of its exposure. We identify PEG exposure in approximately 2.3% of publicly available metabolomics data files and provide a reusable 85,484 candidate PEG and PEGylated MS/MS spectral library for future use for the metabolomic community. Together, these findings establish PEG signal in human biofluids can reflect genuine exposure, and that PEG exposure is neither metabolically inert nor biologically silent.

Proteolysis can be completed in a single-step, rapid (5-30 minutes) reaction using hyperthermoacidic archaeal (HTA) proteases. Protocol simplicity and stability of HTA proteases in ambient storage conditions removes many logistical barriers to widespread deployment of proteomic analysis. HTA protease cleavage selectivity is complicated but quantitatively reproducible and generates peptides that negligibly overlap with trypsin. Tryptic and HTA protease peptides emphasize different proteins and provide complementary coverage of amino acid sequence and post-translational modification (PTM) sites. Digestion of a sample's separate aliquot with HTA protease is a minor addition to trypsin-based workflows, but LC-MS analysis of both sets of peptides is not practical for large cohorts. This manuscript evaluates the identifications and reproducibility of LC-MS analysis of tryptic and HTA protease peptide mixtures. Adding HTA protease peptides to 20% increased total protein identifications by 24% on average in neat plasma and identified 216 additional non-artifact modifications compared to conventional trypsin-only bottom-up proteomic analysis. This approach is reproducible and does not increase sample preparation or analysis time. Peptides from each protease present independent opportunities to quantify overlapping proteins and a quantification model was developed in which the protease with the most complete data is selected for each protein while the second protease is used to infer missing values. This cross-protease inference is more accurate than the alternative minimum, mean, or nearest neighbor imputations. Finally, biomarkers discovered through HTA protease peptides are amenable to rapid HTA protease only proteomic workflows thus streamlining translation from discovery to fieldable applications.

The binding affinities of a chimeric analog of a riboflavin derivative linked to biotin, (6- (7,8-dimethyl-2,4-dioxo-3,4-dihydrobenzo[g]pteridin-10(2H)-yl)hexyl 5-((3aS,4S,6aR)-2- oxohexahydro-1H-thieno[3,4-d]imidazol-4-yl)pentanoate), referred to as C6-Rf-biotin-tag, to the riboflavin binding retain or streptavidin are in the M range, 1.29 {+/-} 0.277 and 3.00 {+/-} 0.459, respectively. These values suggest that C6-Rf-biotin-tag has potential applications in diagnostic assay and labelling target flavin binding proteins. The C6-Rf-biotin-tag which was characterized with respect to physical and biochemical properties retains UV/Vis spectroscopic and fluorescence behavior similar to riboflavin.

In the enzymatic toolbox of depollution, hydrolytic dehalogenases are of particular interest for the bioremediation of organohalogenated compounds. To better identify and characterize these enzymes, it is important to develop appropriate sensing methods. Fluorescent probes for dehalogenase detection have been developed in the past years, however, to this date none were available for application using the common FITC parameters (i.e. excitation laser at 488 nm). To address this limitation, we herein report a new fluorescent probe, COU4-Cl, excitable in the violet (405 nm) and blue (488 nm) wavelengths adapted for the detection and characterization of haloalkane dehalogenases. Its applicability is discussed and evaluated in comparison to the previously described COU2-Cl probe only excitable in the violet (405 nm). This study shows the advantages and complementarity of the two probes, and further demonstrate their application for the discrimination of dehalogenase activity in whole cell assays.

Dried blood spot (DBS) sampling is becoming a popular alternative to traditional blood sampling approaches, offering advantages such as convenience of collection, transportation, and storage, as well as lower biohazard risk. N-glycosylation, a major post-translational modification of proteins associated with numerous biological and pathological functions, is one area of interest for DBS analysis. In this study, we utilize a protocol for N-glycosylation profiling of DBS by ultra-high-performance liquid chromatography based on hydrophilic interactions and fluorescence detection (HILIC-UHPLC-FLR). The protocol includes DBS cutting, protein extraction and enzymatic digestion, labeling with 2-aminobenzamide, followed by cleanup and HILIC-UHPLC-FLR measurement. We compare DBS with plasma and demonstrate the stability of DBS N-glycosylation profile when DBS are prepared from fresh blood, frozen whole blood, or a combination of separated frozen blood cells and corresponding frozen plasma. Additionally, we compared DBS N-glycans from pre- and diabetic subjects. Fucosylation, bisection, and galactosylation showed a statistically non-significant increasing trend in diabetes, whereas sialylation showed a statistically non-significant decreasing trend in diabetes. The main advantage of this method is the ability to repurpose samples, which were initially not intended for biomarker N-glycan analysis, such as frozen whole blood. Additionally, DBS N-glycan profiling is the easier, cheapest and the least invasive approach to conventional plasma in pre-diabetes and diabetes patients' diagnostics and monitoring.

Variable-temperature electrospray ionization native mass spectrometry (vT-ESI nMS) provides a means to determine how temperature-dependent changes in protein and solution dynamics influence individual ligand-binding reactions within multiligand protein complexes. Here, the GroEL single-ring mutant (SR1), which binds up to seven nucleotides, serves as a model system due to its sensitivity to solution conditions. Native MS resolves individual ligand-bound populations, while vT-ESI extends these measurements across temperature (3-43 {degrees}C), enabling determination of binding affinities and the associated Gibbs free energy, enthalpy, entropy, heat capacity changes ({Delta}Cp), and enthalpy-entropy compensation (EEC) for each binding reaction. Temperature-dependent changes in van,t Hoff curvature produce distinct {Delta}Cp profiles, indicating changes in the dynamics governing ligand binding that may reflect contributions from protein conformational and protonation microstates. Temperature-dependent shifts in average charge state, consistent with changes in solvent-accessible surface area, indicate corresponding changes in protein conformation. Binding is predominantly enthalpy-driven below ~23 {degrees}C, with increasing entropic contributions at higher temperatures, while the seventh ADP binding reaction exhibits a distinct EEC profile. Comparison of the seventh ADP binding reaction in H2O and D2O reveals pronounced differences in van,t Hoff curvature,{Delta}Cp, and EEC, demonstrating that changes in the hydration environment alter the thermodynamic response. Collectively, these measurements show that temperature-dependent changes in protein and solution dynamics alter the distributions and thermodynamics of individual ligand-bound states and demonstrate the utility of vT-ESI nMS for resolving these effects in complex multiligand systems.

Multicenter studies are essential for benchmarking analytical workflows, yet their interpretation is often confounded by the combined effects of experimental protocols and instrumentation. To address this challenge, we introduce a simple normalization-based analytical framework, the recovery metric ({rho}), designed to decouple protocol driven effects from instrument dependent variability. We applied this framework to the 13th Proteomics Multicentric Experiment (PME13), a large multicentric proteomics dataset generated across 27 laboratories using high sensitivity workflows and varying sample preparation protocols. By leveraging a common digested reference sample, {rho} enables direct cross-comparison of all datasets on a unified scale, effectively minimizing instrument-related biases. Using this approach, we demonstrate that apparent instrument dependent trends are largely removed when evaluated through {rho}, revealing consistent protocol driven effects across laboratories. Statistical modeling identified key variables influencing {rho}, including sample input amount, reduction and alkylation, and the use of n-dodecyl-{beta}-D-maltoside (DDM). While DDM was associated with improved {rho}, reduction and alkylation and additional handling steps led to reduced performance, particularly at low input levels. We further highlight practical considerations for the application of ratio based normalization, including the occurrence of values exceeding theoretical bounds, which reflect deviations from underlying assumptions and require appropriate filtering. Overall, this work establishes a generalizable analytical strategy for disentangling confounding factors in multicentric datasets and provides practical guidelines for optimizing high sensitivity proteomics (HSP) workflows. The proposed framework is broadly applicable to other analytical fields where cross laboratory comparability is required.

Nile Red is widely used for the detection of microplastics because its fluorescence emission is sensitive to local polarity and can distinguish hydrophobic plastics from hydrophilic ones. The fluorescence of the molecular rotor, 9-(dicyanovinyl)-julolidine (DCVJ) is less sensitive to polarity but more to viscosity. DCVJ is less widely used for microplastic analysis, although it has been used to detect polystyrene nanobeads. Here, we compared these dyes with standard samples from the Hawaii Pacific University Polymer Kit 1.0 and confirmed that Nile Red, in general, was better for the detection and identification of microplastics. Fluorescence emission was analyzed using photography, as well as spectroscopy. The color and peak emission wavelength of some stained environmental microplastics were affected by additives. Raman spectroscopy was used to confirm the chemical identity of such samples. Although DCVJ emits green fluorescence on binding to some microplastics, a peak at 620 nm has been reported with polystyrene nanobeads, attributed to dimer/excimer formation. We confirmed this property and directly observed diffraction-limited spots using fluorescence microscopy, attributed to single or just a few nanobeads. Nile Red also stains polystyrene nanobeads and gave stronger signals than with DCVJ, but Nile Red was prone to false positives due to dye aggregation in aqueous solutions.

ShortThe SwissProt database contains a stable 20,418 human protein-coding genes and 42,541 human protein sequences. Ribo-Seq suggests about 7,000 additional, non-canonical Open Reading Frames (ORFs) are present in humans, though only a few of them are confirmed by Mass Spectrometry (MS). Detecting these proteins requires extensive database searches, increasing computational load and inflating False Discovery Rates (FDR). Using the ionbot search engine with the OpenProt database allows for reliable detection of non-canonical proteins while controlling FDR. Ionbot surpasses the Trans-Proteomics Pipeline (TPP) in reproducibility, identifying more peptides and proteins supported by multiple spectra. In addition, open modification searches yield better PSMs compared to closed searches. This work highlights the importance of employing cutting-edge search engines in non-canonical protein research, as well as the value of open modification search in correcting errors in non-canonical protein detection. LongO_ST_ABSBackgroundC_ST_ABSThe SwissProt database reports a quite stable 20,418 human protein-coding genes and 42,541 human protein sequences, figures that have remained stable. New techniques like Ribo-Seq indicate that approximately 7,000 additional, non-canonical Open Reading Frames (ORFs) are translated in humans, few of which have been confirmed by Mass Spectrometry (MS). Detecting these non-canonical proteins requires comprehensive database searches, which increase computational load and False Discovery Rate (FDR). Here, we use the open search engine ionbot in combination with the OpenProt proteogenomics database to reproducibly detect non-canonical proteins while maintaining a well-controlled FDR. ResultsCompared to the current gold standard, the Trans-Proteomics Pipeline (TPP), ionbot shows higher reproducibility, with a higher number of peptides and proteins supported by multiple spectra, and across multiple samples. We observe that PSMs from the open modification search against OpenProt have higher fragment ion intensity correlation compared to PSMs obtained from the closed search, or by only searching canonical proteins. ConclusionsIn this work, we show the potential for open modification searching to correct potential mistakes in non-canonical proteins detection by preventing modified canonical peptides or variants from being incorrectly identified as non-canonical peptides. We also highlight the importance of assessing the FDR of non-canonical identifications separately from canonical ones, as global FDR calculations are biased by the scarcity of non-canonical identifications in each dataset.

Same-tissue section multimodal imaging is a powerful strategy that spatially profiles tissue histology, cell populations and molecular composition while maximizing tissue economy, preserving spatial molecular relationships, and increasing co-registration capacity. However, performing multiple modalities on the same tissue section can destroy or chemically alter the tissue, compromising downstream data. Here, we systematically assess integration of picrosirius red staining, hematoxylin and eosin staining, and multiplexed immunofluorescence into N-glycan and extracellular matrix peptide matrix-assisted laser/desorption ionization imaging mass spectrometry (IMS) workflows. We evaluate alterations in tissue morphology, stain efficiency, IMS feature intensity as well as IMS feature localization after upstream modality integration. We propose an optimized multimodal sequence that maximizes data quality and follows a very specific order of: autofluorescence microscopy, multiplexed immunofluorescence, picrosirius red staining, N-glycan IMS, hematoxylin and eosin staining, and extracellular matrix peptide IMS. Overall, this work develops an optimized multimodal workflow that comprehensively images tissue morphology, collagen fibers, and cell populations at single-cell resolution as well as multiplexed N-glycan composition and multiplexed extracellular matrix peptides with post-translational modification status from a single tissue section.