Analytical and Bioanalytical Chemistry

IntroductionRetinoids are bioactive vitamin A derivatives that regulate cellular differentiation and gene expression, yet their reliable quantification remains challenging due to low abundance, structural isomerism, and sensitivity to ionization conditions while handling. ObjectivesIn this study, we performed a systematic optimization of liquid chromatography-mass spectrometry (LC-MS)-based detection of retinoids across tissues and biofluids. MethodsChromatographic separation, adduct formation, ionization parameters, fragmentation behavior, and extraction procedures were evaluated in an integrated workflow. ResultsChromatographic conditions influenced not only retention time but also the ionic species detected, affecting precursor selection for MS{superscript 2} analysis. Retinoids exhibited compound-dependent responses to electrospray ionization and collision energy, requiring tailored acquisition parameters. Extraction experiments demonstrated differential recovery among retinoid classes and revealed matrix-dependent behavior, indicating that protocols used for tissues cannot be directly transferred to low-abundance biofluids. Using optimized conditions, retinoids were detected in mouse cerebrospinal fluid (CSF) at concentrations approaching the analytical detection limit, where MS{superscript 2} confirmation was necessary for reliable identification. ConclusionTogether, our results provide a framework for reproducible retinoid profiling across biological matrices and enables comparative studies of retinoid biology in low-volume and low-abundance biofluids.

The development of chemiluminescent immunoassays (CLIAs) is a complex and iterative process that relies on costly laboratory infrastructure, limiting its accessibility and application across healthcare settings and disease areas. Here, we detail the CLIA Mobile Development Kit (CLIAMDK) a modular, mobile, and inexpensive platform to assess image sensors, smartphones and data processing workflows for CLIA development. For its demonstration, we developed two CLIAs targeting renin and aldosterone, key biomarkers for diagnosing primary aldosteronism. The results from our performance study, including 50 patient samples, demonstrate the potential of our platform in a real-world scenario. We found that the performance of our mobile reader platform is comparable to that of a state-of-the-art plate reader, with a Lower Limit-of-Detection (LLoD) approaching 41 femtomolar. We envision that our platform will help accelerate CLIA development, make it more accessible, and lay the foundations for novel, distributed, yet highly sensitive diagnostic tests.

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

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.

BackgroundHigh-content assays (HCAs) have problems distinguishing biologically significant effects from the incidental effects of non-repeatable technical factors. Non-repeatable results are attributed to variations in the cell culture environment and the numerous, heterogeneous descriptors evaluated. The aim here was to determine whether preprocessing operations impacted the reproducibility of class assignments of experimental data. MethodsBatch effects that could affect reproducibility, i.e., signal/noise ratio, instrumental conditions, and segmentation, were controlled variables. The remaining batch effects, variations in materials, personnel, and culture environment could not be controlled. Descriptors values were measured directly from images. Exploratory factor analysis was used to solve the identifiable and interpretable feature, factor 4. In each of five trials, one sample was treated with the same chemical mixture (EXP) and another with the solvent vehicle alone (CON). ResultsRepeated CON and EXP samples showed significant differences among factor 4 means in data regularized within each trial. The mean of Trial 3 CON differed significantly from all other CON samples. These differences disappeared upon regularization to comprehensive databases. Among repeated EXPs, the Trial 2 mean differed from three other EXPs, but regularization to comprehensive databases had little effect. However, classification patterns were unchanged after regularization to any comprehensive database derived by the same protocol. After regularization to datasets derived by two different protocols, the classification pattern differed but only reflected elevation of differences that had been marginal to statistical significance. Outlier removal was deleterious. Even with the most sparing definition of outliers, over 3% of a single samples contents were removed from most trials. Elimination based on the overall within-trial distributions caused type I and type II errors. ConclusionsNon-repeatable factor 4 means in repeated trials had negligible influence on classification outcomes, so repeatability may not be a good indicator of assay quality. Irreducible batch effects, combined with small sample sizes and skewed distributions of descriptors values, may account for non-repeatability. As the current results are based on real-world data, they suggest that non-repeatability is an uncorrectable feature of these assays. Classification patterns are not affected by several irreducible technical factors, namely materials, personnel, and non-repeatable environmental variables.

Background: Mucopolysaccharidosis type IIIA (MPS IIIA; Sanfilippo syndrome) is a fatal neurodegenerative lysosomal storage disorder caused by impaired degradation of heparan sulfate (HS). Despite rapid advances in gene and enzyme therapies, there remains a critical need for an analytically validated, quantitative biomarker that accurately reflects central nervous system (CNS) substrate burden. Such biomarker would be a valuable tool in assessing disease progression and monitoring therapeutic efficacy. Objective: This study describes the method development, fit for purpose validation, and preliminary clinical application of a quantitative liquid chromatography-mass spectrometry (LC-MS/MS) assay for the HS-derived disaccharide N-sulfoglucosamine-glucuronic acid (GlcNS-GlcUA) in human cerebrospinal fluid (CSF), a critical biomarker for diagnosis, disease monitoring, and regulatory evaluation of emerging MPS IIIA therapies. Methods: A structurally defined GlcNS-GlcUA reference standard and its [13C6]-labeled internal standard were used in a derivatization and detection workflow employing 1-phenyl-3-methyl-5-pyrazolone labeling, and LC-MS/MS. Results: The method exhibited acceptable linearity across 0.005-0.500 nmol/mL (r[≥]0.9976), with intra- and inter-assay imprecision [≤]3.5%CV and accuracy within 95%-110% of nominal concentrations. No matrix or hemolysis interference or carryover was observed, and the analyte remained stable during freeze-thaw storage conditions. Application of the method to 12 CSF samples from patients with MPS IIIA demonstrated quantifiable GlcNS-GlcUA levels ranging from 0.0054 to 0.106 nmol/mL, confirming suitability for clinical and regulatory use. Comparison of the MPS IIIA sample results between the development laboratory and the contract research organization laboratory support robust inter-lab assay transfer. Conclusions: This validated LC-MS/MS method establishes a regulatory-grade quantitative assay for measurement of CSF HS in MPS IIIA. Its high analytical sensitivity and reproducibility enable reliable assessment of CNS substrate reduction and pharmacodynamic response, supporting biomarker-driven therapeutic development and accelerated approval pathways for neuronopathic mucopolysaccharidoses.

Peptide-level analyses are becoming increasingly popular in mass spectrometry-based proteomics and are being applied, for example, in immunopeptidomics, structural proteomics, and analyses of post-translational modifications. In such analyses, peptides that are not biologically meaningful but instead arise as artifacts prior to mass spectrometry analysis pose the risk of data misinterpretation. Here, we describe an approach based on retention time analysis and precise chromatographic peak matching to identify peptides generated by in-source fragmentation (ISF), which occurs between chromatographic separation of peptide mixtures and the first mass filter of a tandem mass spectrometer (MS). To understand the prevalence and properties of ISF, we generated 13 proteomics datasets and analyzed them along with additional 25 previously published datasets spanning a broad range of sample types, MS, and proteomics approaches including classical bottom-up proteomics, immunopeptidomics, structural proteomics, and phosphoproteomics. We found that, in typical trypsin-digested samples on average 1 % of fully-tryptic peptides and 22 % of semi-tryptic peptides originated from ISF. However, we observed large variations between datasets, and in-source fragments exceeded, in some cases, a third of the total peptide identifications. The extent of ISF was dependent on the peptide sequence, the instrument, method parameters, and sample complexity. Although ISF did not impair relative quantification across samples, it generated peptides that could be misinterpreted qualitatively, inflated peptide identifications, and comprised up to 37 percent of peptides shorter than 9 amino acids in immunopeptidomics datasets. We propose that, for peptide-centric applications, our open-source ISF detection approach be used to re-annotate peptides generated by ISF and remove them to avoid misinterpretation of data. ISF is an increasing concern with improving mass spectrometers, as they enable detection of an ever-increasing number of m/z features, including low abundance features like ISF products. Our work thus addresses a growing issue in proteomics and presents solutions to mitigate the impact of in-source fragment peptides. In the future, improved feature detection algorithms may enable elucidation of new ISF patterns affecting side chains that have been missed so far, which could contribute to explaining the vast space of as-yet unannotated proteomics data.

Cultivated meat production requires robust and validated analytical methods for comprehensive characterization. While transcriptomics-based approaches establish the foundational profile of molecular analysis, proteomics provides additional resolution that further enhances scientific certainty in both product development and safety characterization. However, the industry adoption of proteomics is currently hindered by technical complexity and a critical lack of analytical standardization, which leads to significant workflow-dependent variations in proteome coverage. To address this gap, we investigated the influence of key workflow steps (digestion, cleanup, LC-MS conditions) on the proteome profile of cultivated duck biomass. We compared five bottom-up sample preparation protocols - two traditional in-solution options (urea and SDC-based protocols), two device-based approaches (PreOmics iST and EasyPep kits), and an innovative protocol (SPEED), and demonstrated that device-based protocols offered the highest peptide yield and proteome coverage. However, optimization allowed cost-effective in-solution methods to achieve comparable performance. Specifically, an optimal digestion time of 3 hours at 37{degrees}C and the use of polymer-based desalting columns significantly enhanced protein identification ([~]4500 - 5000 IDs). Moreover, data independent acquisition (DIA) provided deeper proteome coverage than data dependent acquisition (DDA) with higher precision ([~]6500 vs 5000 IDs). The validated Standard Operating Procedures presented here establish a standardized framework for bulk bottom-up proteomics in cultivated meat, facilitating the generation of reliable and comparable data required for robust multi-omics characterization. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/713501v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@5b61b8org.highwire.dtl.DTLVardef@16c7e65org.highwire.dtl.DTLVardef@1de21d2org.highwire.dtl.DTLVardef@7e984a_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIComplexity and non-standardization limit MS-proteomics use in cultivated meat (CM). C_LIO_LICM protein profile varies with sample prep, LC-MS, and data processing pipeline. C_LIO_LIDevice-based and optimized cost-effective protocols offer a high proteome coverage. C_LIO_LIProteomics can complement transcriptomics for a comprehensive CM characterization. C_LIO_LIProposed standardized methods ensure reliable data for future regulatory submissions. C_LI

MicroRNAs (miRNAs) are ultra-short RNA molecules characterized by high sequence homology, frequent post-transcriptional modifications, and typically low abundance, particularly in circulating biofluids. These inherent biological features present substantial technical challenges for RT-qPCR- based quantification. Consequently, the development of miRNA RT-qPCR assays has required architectural adaptations at the reverse transcription (RT) stage to generate extended cDNA templates, thereby enabling effective downstream quantitative PCR amplification. One widely adopted approach involves the enzymatic addition of a poly(A) tail to the 3' end of miRNAs, followed by poly(T)-primed universal reverse transcription, which has gained broad acceptance due to its perceived sensitivity and simplified workflow. However, independent experimental evidence indicates that this architecture does not consistently provide the level of specificity required for reliable single-nucleotide (SN) discrimination, particularly when quantifying low-abundance circulating miRNA targets, as demonstrated in our previous study. An alternative strategy relies on miRNA-specific reverse transcription using stem-loop priming has been equally well accepted. When generically generated, this approach offers certain improved specificity, but its performance in resolving single-nucleotide differences remains limited. In this article, we employed precision engineering to maximize specificity for both reverse transcription and qPCR steps. By tailoring both primer design and reaction architecture to the specific sequence features of each miRNA, we enable robust single nucleotide discrimination among these ultra-short targets. Prototype of ten different miRNova assays quantifying miRNAs whose sequences are differed in various configurations were tested on synthetic miRNA targets. For miRNova assay validation, saliva samples were elite rugby players submitted to small RNA extraction, then RT-qPCR. Spike-in of synthetic targets was applied for each quantification point to characterized the sensitivity, specificity and accuracy of the assays. Comparative analysis was performed between miRNova and two commercially available kits on the same sample set. The obtained results show a superior performance of miRNova assays allowing for sensitive and accurate quantification of miRNAs in saliva samples. Altogether, this results in modular, reproducible assays optimized for low-abundance miRNA detection in challenging biofluids, including saliva, positioning the platform beyond existing sensitivity-focused solutions toward true diagnostic-grade specificity.

Recent advances in high-resolution imaging and spatial transcriptomics have enabled reconstruction of complex 3D tissue maps, providing unprecedented insights into cellular connectivity, organization, and tissue architecture. However, standardization challenges hinder integration, sharing, and analysis of these datasets across research communities. We developed NetTracer3D to simplify three-dimensional image analysis across diverse datasets. NetTracer3D is an integrated tool for defining, processing, and sharing 3D tissue maps with standardized data formats and interactive exploration capabilities. It provides three broadly applicable network analysis modalities: Connectivity networks for analyzing functional tissue units or cells connected via secondary structures such as nerves or vasculature; Branch Adjacency and Branchpoint networks for converting branched anatomical structures into analyzable representations; and Proximity networks for grouping structures by spatial relationships to identify cellular organization patterns. We demonstrate several use cases applying NetTracer3D to analyze multidimensional data from CODEX and label free Raman spectroscopy, multiscalar data encompassing subcellular and anatomical scales and a range of modalities. NetTracer3D was able to characterize neural relationships between functional tissue units in human and mouse kidneys and mouse bronchi. Branchpoint networks were used to identify vascular defects in human brain angiogram and define the innervation structure of a lymph node. Finally, we demonstrate how proximity networks characterize the tumor microenvironment in 3D light sheet cancer images and auto-detect cellular neighborhoods in multiplexed 2D CODEX datasets. Beyond network creation, NetTracer3D enables analysis, spatial statistics, and visual analytics tailored for volumetric tissue data. By establishing interoperable formats and analysis workflows, this work provides accessible and reproducible analytical tools for 3D spatial biology, enabling new discoveries of relationships between structure and physiology.

Sirtuins (SIRTs), which remove protein lysine acyl modifications, play crucial roles in diverse cellular processes, including metabolism, gene transcription, DNA damage repair, cell survival, and stress response. Several sirtuins are considered non-oncogene addiction of cancer cells and promising targets for anticancer drug development. High-throughput screening (HTS) methods for sirtuins are critical for the development of potent and isoform-selective sirtuin inhibitors, which are needed to validate the therapeutic potential. Herein, we designed and synthesized a fluorescent polarization (FP) tracer, KP-SC-1. Using this high-affinity tracer, we developed a robust, high-throughput FP competition assay for screening SIRT1-3 inhibitors. The assay was validated by testing known SIRT1-3 inhibitors. The assay can detect NAD+-independent SIRT1-3 inhibitors, as well as NAD+-dependent inhibitors, such as Ex-527 and TM. Finally, our assay showed satisfactory stability and outstanding performance in a pilot library screening. Compared to previous assays, the FP assay uses much less SIRT1-3 enzymes, a feature important for high-throughput library screening. We believe that the FP assay developed here will accelerate the discovery and development of SIRT1-3 inhibitors.

Mass spectrometry imaging (MSI) characterizes the spatial heterogeneity of molecular abundances in biological samples. Experiments with complex designs, involving multiple conditions and multiple samples, provide particularly useful insight into differential abundance of analytes. However, analyses of these experiments require attention to details such as signal processing, selection of regions of interest, and statistical methodology. This manuscript contributes a statistical analysis workflow for detecting differentially abundant analytes in MSI experiments with complex designs. Using a case study of histologic samples of human tibial plateaus from knees of osteoarthritis patients and cadaveric controls, as well as simulated datasets, we illustrate the impact of the analysis decisions. We illustrate the importance of signal processing and feature aggregation for preserving biological relevance and alleviating the stringency of multiple testing. We further demonstrate the importance of selecting regions of interest in ways that are compatible with differential analysis. Finally, we contrast several common statistical models for differential analysis, showcase the appropriate use of replication, and demonstrate model-based calculation of sample size for followup investigations. The discussion is accompanied by detailed recommendations and an open-source R-based implementation that can be followed by other investigations.

Tumour biomarkers such as CA125, CA15-3, CA19-9, AFP and CEA are routinely used in the oncology clinic to diagnose cancer, monitor response to therapy, and detect relapse. However, their quantification depends on immunoassay-based methods that are time-consuming, reagent-dependent, and poorly suited to resource-limited settings. Here, we present a machine learning-assisted ATR-FTIR spectroscopy approach for label-free tumour biomarker analysis to enable simple and rapid quantification at the bedside. Using principal component analysis (PCA), we first demonstrate that these five clinically relevant biomarkers are spectrally separable, with the protein-associated region (1200-1700 cm-1) providing the greatest discriminative information. We then develop partial least squares regression (PLSR) models to quantify CA125 in phosphate-buffered saline (R2 = 0.95) and in human serum across a clinically relevant concentration range, achieving reliable predictions at and above the clinical decision threshold of 35 U/mL. A semi-quantitative classification model further demonstrated robust identification of elevated CA125, with a macro-average sensitivity of 0.86 and specificity of 0.92. These results support ATR-FTIR spectroscopy as a rapid, reagent-free platform for cancer biomarker monitoring, with potential utility in resource-limited settings. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/714840v1_ufig1.gif" ALT="Figure 1"> View larger version (27K): org.highwire.dtl.DTLVardef@1be9c03org.highwire.dtl.DTLVardef@f49e5eorg.highwire.dtl.DTLVardef@1c93e39org.highwire.dtl.DTLVardef@1141e6f_HPS_FORMAT_FIGEXP M_FIG C_FIG

Raman spectroscopy offers a uniquely rich window into molecular structure and composition, making it a powerful tool across fields ranging from materials science to biology. However, the reproducibility of Raman data analysis remains a fundamental bottleneck. In practice, transforming raw spectra into meaningful results is far from standardized: workflows are often complex, fragmented, and implemented through highly customized, case-specific code. This challenge is compounded by the lack of unified open-source pipelines and the diversity of acquisition systems, each introducing its own file formats, calibration schemes, and correction requirements. Consequently, researchers must frequently rely on manual, ad hoc reconciliation of processing steps. To address this gap, we introduce TRaP (Toolbox for Reproducible Raman Processing), an open-source, GUI-based Python toolkit designed to bring reproducibility, transparency, and portability to Raman spectral analysis. TRaP unifies the entire preprocessing-to-analysis pipeline within a single, coherent framework that operates consistently across heterogeneous instrument platforms (e.g., Cart, Portable, Renishaw, and MANTIS). Central to its design is the concept of fully shareable, declarative workflows: users can encode complete processing pipelines into a single configuration file (e.g., JSON), enabling others to reproduce results instantly without reimplementing code or reverse-engineering undocumented steps. Beyond convenience, TRaP integrates configuration management, X-axis calibration, spectral response correction, interactive processing, and batch execution into a workflow-driven architecture that enforces deterministic, repeatable operations. Every transformation is explicitly recorded, making the full processing history transparent, inspectable, and reproducible. This eliminates ambiguity in how results are generated and ensures that identical protocols can be applied consistently across datasets and experimental contexts. Through representative use cases, we show that TRaP enables seamless, reproducible preprocessing of Raman spectra acquired from diverse platforms within a unified environment. We hope TRaP can empower Raman data processing as a reproducible, shareable, and systematized scientific practice, aligning it with modern standards for computational research. TRaP is released as an open-source software at https://github.com/hrlblab/TRaP

Isoprenoid quinones are ubiquitous redox lipids that mediate electron transfer in various cellular processes across all domains of life. These molecules also serve as taxonomic and metabolic markers, facilitating the characterisation of microbial communities. However, their structural diversity and extreme hydrophobicity pose challenges for comprehensive detection and quantification in complex biological matrices. In this study, we present a semi-quantitative HPLC-MS/MS method that enables the sensitive analysis of the widest range of quinones reported to date. Using a 16-quinone standard mixture, we optimized separation within a 14-minute HPLC gradient and achieved femtomole-level sensitivity in targeted analyses. When applied to sewage sludges sampled weekly over three weeks, our method detected 57 distinct quinones, revealing stage-specific quinone profiles that reflect shifts in bacterial communities during wastewater treatment. This rapid and sensitive workflow provides a robust tool for accurate quinone profiling in complex samples, opening avenues for the discovery of novel quinones through untargeted approaches. By pushing the boundaries of quinone profiling, our method holds significant promise for advancing microbial ecology, environmental monitoring, and biotechnological applications. HighlightsO_LIuHPLC-Orbitrap method for the semi-quantitative profiling of isoprenoid quinones C_LIO_LIAnalysis of the widest range of isoprenoid quinones to date C_LIO_LIFemtomole-level sensitivity in just 14 minutes of chromatographic separation C_LIO_LIDetection of 57 quinones in complex wastewater sludge matrices C_LIO_LIMost comprehensive set of quinone standards including microbially-purified quinones C_LI

Background Cardiac MRI (CMR) is often utilized for patients with suspected cardiac amyloidosis (CA). However, data are lacking for use in patients with advanced renal dysfunction (ARD) (GFR<30 mL/min/1.73 m2, dialysis dependent, or renal transplant). This study evaluates the utility of CMR for diagnosis of CA in this population. Methods Patients with ARD who underwent CMR in a 3T field for suspicion of CA between 2010 and 2024 at our institution were included. A diagnosis of CA was made if any of the following were present a)?PYP scintigraphy grade ? 2, b) positive endomyocardial biopsy, or c) positive extracardiac biopsy with clinical features of CA. Two CMR-trained physicians independently assessed T1 relaxation time, ECV, Ti scout, LGE, and overall likelihood of CA. Results Out of the 65 patients included 14 (22%) had a diagnosis of CA. Although T1 time [1352 (1276-1428) ms] and ECV (40.3% +/- 9.1%) were elevated across the cohort, they were significantly higher in patients with CA (p<0.001 for both). Both ECV and T1 time reliably predicted CA (AUC of 0.87 and 0.88 respectively). ECV of ?45% had 75% sensitivity and 80% specificity for CA. A T1 time ? 1390 ms had 75% sensitivity and 85% specificity for CA. LGE was prevalent and was seen in 86% and 84% patients with and without CA respectively. Of the 31 patients deemed to be unlikely CA by a CMR reader, 6% had CA. However, of the 34 patients read as possible/likely CA, only 35% had confirmed CA. Conclusions In this understudied population of ARD, CMR parametric mapping exhibits high negative predictive value (NPV) for CA and improved positive predictive value (PPV) when higher cutoffs are used for T1 time and ECV. CMR reader overall impression exhibits high NPV but low PPV for CA.

Polymeric 3D printing of microfluidic devices for biosensing is an appealing fabrication alternative for rapid manufacturing of biosensing devices with complex geometry in a streamlined, repeatable and cost-effective manner without the need for expensive instrumentation such as those employed in photochemical etching and soft lithography. Hybrid 3D printed paper-based microfluidics is an emerging area which harnesses the unique properties of both, merging the construction of microfluidic structures and the inherent capillary-driven flow within paper substrates. In this work, we have fabricated hydrophobic barriers by 3D printing a single layer of machinable wax, thermoplastic polyurethane, polylactic acid and polypropylene directly on chromatography paper to create open microchannels and determine the most suitable material. Characterization of each open microchannel using the four materials revealed polypropylene as the most reliable material with high hydrophobic barrier integrity and resolution. Polypropylene achieved functional microchannels with a resolution of 621 {+/-} 33{micro}m, hydrophobic barrier integrity of (93.75 {+/-} 9.16%), wicking speed of 0.38mm/s and optimal hydrophilicity of channels (51.4 {+/-} 8.36 {degrees}) with minimal embedding during thermal curing. To demonstrate proof of principle, a fluorescence assay demonstrating the formation of a dimeric g-quadruplex structure from a g-rich sequence which significantly enhances fluorescence of thioflavin T was implemented.

Background: External Quality Assurance (EQA) is an essential component of modern laboratory medicine. Current scientific evidence on EQA focuses primarily on the analyses carried out by EQA providers while relatively little research has been conducted in individual clinical laboratories. Methods: In this retrospective single-center observational study in a clinical laboratory, EQA results were analyzed over a period of four years (2021-2024). The evaluation was based on EQA action reports documented in the institutes internal quality management system. Deviations were classified according to department, type of discrepancy, root cause category (analytical, preanalytical, systemic, unidentifiable), and measures taken. Results: A total of 7226 EQA participations were evaluated during the observation period. The overall error rate remained consistently low, ranging between 0.8% and 1.6%, with no significant change over time (p = 0.87). Most deviations occurred in the departments of clinical chemistry and immuno/autoimmune diagnostics (p < 0.001). These were predominantly quantitative discrepancies (false low/false negative or false high/false positive). Root cause analysis showed a clear dominance of analytical causes (p < 0.001), while preanalytical and systemic causes were identified less frequently. In most cases, corrective measures, such as re-analyses, recalibrations, process adjustments, or staff training, were implemented promptly. Hard structural measures, such as changing methods or discontinuing tests, were rarely necessary. Conclusion: In a clinical laboratory, EQA is an important tool for structured error analysis and continuous quality improvement. Consistent processing of deviating EQA results goes hand in hand with stable analytical performance and a low error rate.

Extracellular vesicles (EVs) are lipid spheres released from cells. Research utilizing EVs has met several hurdles owing to the small size of the majority of EVs and other nanoparticles (<150 nm) and the lack of detection technologies capable of providing high-throughput single particle measurements at this scale. The use of high-throughput single particle measurements is critical for the assessment of EV heterogeneity and abundance which are features often used to assess the development of isolation protocols or particle characterization. The Coulter principle, known in the field as resistive pulse sensing (RPS), has been used for several decades to size and count cells. More recently, this technology has evolved to accommodate nanoparticle analysis. In the last decade a platform utilizing microfluidic resistive pulse sensing (MRPS) has been demonstrated for nanoparticles, offering ergonomic characterization of nanoparticles along with utilizing open format data. To date, assessment of MRPS accuracy and reporting standards have not been assessed. With the aim of increasing data accuracy, ergonomics, and reporting transparency, we developed a microfluidic resistive pulse sensing post-acquisition analysis software (RPSPASS) application for automated cohort calibration, population gating, statistical output, QC plot generation, alternative data file outputs, and standardized reporting templates.

Open modification search (OMS) strategies have gained popularity in mass spectrometry-based proteomics for identification of peptides carrying unknown or unexpected post-translational modifications. However, most OMS search engines report only the overall mass difference between the precursor and the matched peptide and do not explicitly identify or score combinations of multiple modifications at the peptide-spectrum match (PSM) level, leaving the interpretation of mass shifts up to the end user and to using downstream analysis tools. Here, we introduce MS Andrea, a novel OMS search engine developed to directly identify and score combinations of up to four variable modifications per peptide without having to predefine them. MS Andrea uses a sequence tag-based strategy to efficiently filter candidate peptides prior to scoring. Remaining candidates are evaluated using the MS Amanda scoring function, first considering fixed modifications only, followed by a second scoring stage in which combinations of modifications from the Unimod database are considered based on the observed mass difference and matched to the spectrum. We evaluated MS Andrea using phosphopeptide datasets from HeLa cells and Arabidopsis thaliana and compared its performance with the widely used OMS engines MSFragger and Sage. Across datasets, MS Andrea identified the highest number of PSMs at 1% false discovery rate while achieving comparable peptide-level identifications. Importantly, MS Andrea directly reports modification identities and sites at the PSM level and enables the identification of peptides having up to four variable modifications. Together, these results demonstrate that MS Andrea facilitates more detailed and interpretable characterization of peptide modifications while maintaining competitive identification performance in OMS-based proteomic analyses. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=132 SRC="FIGDIR/small/714851v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@52f65forg.highwire.dtl.DTLVardef@acf4e3org.highwire.dtl.DTLVardef@10171caorg.highwire.dtl.DTLVardef@1d594ad_HPS_FORMAT_FIGEXP M_FIG C_FIG