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

1Sex steroid hormones are not exclusively localised in the circulation and can be found in numerous extragonadal tissues, in concentrations unrelated to the circulating fraction. Existing methodology to measure intramuscular steroid hormone concentrations includes both immune-based assays and liquid chromatography-mass spectrometry (LC-MS), the gold standard for hormone measurements. To date, no LC-MS based methods validation has been published on the measurement of intramuscular sex steroid hormones, despite clear biological relevance. Here, we describe the development and validation of a simple, high-throughput LC-MS Orbitrap method for the measurement of 10 intramuscular sex steroid hormones, including pregnenolone, progesterone, dehydroepiandrosterone, androstenedione, testosterone, epitestosterone, dihydrotestosterone, oestrone, oestradiol, and oestriol. In brief, isotope labelled standards were added to 5-6 milligrams of lyophilised muscle tissue, homogenised and extracted with ethyl acetate. The extracts were dried down and sequentially derivatised with 1-methylimidazole-2-sulfonyl chloride and hydroxylamine hydrochloride to target both the phenolic hydroxyl groups and ketone groups. The limit of detection was 1.0 {+/-} 1.0 pg/mg (range 0.36 - 3.26 pg/mg), with a R2 > 0.99 for all analytes. Matrix effects were 90-110% for all analytes except for dihydrotestosterone (143.6%), and precision was <10 CV% for all analytes in the presence of a muscle matrix. Our method allows for 20-40 samples to be prepared in [~]4 h, with a sample data acquisition time of 13 minutes. Moreover, our method provides the opportunity for specific analysis of steroid hormone concentrations in skeletal muscle, allowing target tissue specificity instead of relying on proxy measures from the circulation.

Authentication of acai products is increasingly important due to the risk of species substitution among morphologically similar Euterpe taxa, with implications for food quality, labeling accuracy, and consumer trust. Despite advances in molecular methods, rapid and cost-effective tools for discriminating closely related Euterpe species in processed commercial matrices remain limited. This study evaluated High-Resolution Melting (HRM) analysis targeting two complementary chloroplast markers -- psbK-I and ycf1b -- as a practical approach for species-level authentication of acai (Euterpe oleracea and E. precatoria) and jucara (E. edulis) products. In silico specificity analysis confirmed that the ycf1b primer pair shows amplification restricted to the Arecaceae family, supporting the analytical robustness of the method. The combined markers enabled reliable differentiation of all target species, including closely related taxa, with a detection limit of approximately 10% in admixed samples. When applied to 50 commercial products, HRM successfully authenticated 46 samples, substantially outperforming DNA sequencing, which was limited by amplification failure and mixed chromatograms. Mislabeling was detected in one acai sorbet and three frozen acai pulps marketed as acai but molecularly identified as E. edulis, constituting a violation of Brazilian food labeling regulations. These findings demonstrate that HRM analysis provides a robust, rapid, and scalable strategy for routine species authentication in processed plant-based matrices, with potential for integration into food quality control workflows and large-scale commercial monitoring programs.

Human milk contains structurally diverse glycans with key roles in shaping infant development, yet analytical constraints limit characterisation from low-volume samples. Glycosaminoglycans (GAGs), including chondroitin sulphate (CS), are understudied due to existing protocols requiring sample volumes of at least 5 mL and lengthy extraction steps prior to instrumental analysis. This study establishes a workflow for quantifying CS disaccharides from 25 {micro}L of human milk, enabling analysis of samples previously inaccessible to GAG profiling, such as those collected as salvage samples from neonatal intensive care units. For CS quantification, the CS is first enzymatically depolymerised using chondroitinase ABC to release repeating disaccharide units. Matrix complexity is reduced via two rounds of acetonitrile-based protein and lipid precipitation. Disaccharides are separated by hydrophilic interaction liquid chromatography and detected using a Triple Quadrupole Mass Spectrometer, providing robust sensitivity for all CS disaccharides. Method development and validation were performed using pooled mature human milk from term infants. This workflow facilitates detection of all CS disaccharides, with low but reproducible recoveries for total CS. Low- and high-level spike recoveries were 41.3% (RSDr 7.5%, RSDiR 15.9%) and 43.7% (RSDr 24.4%, RSDiR 27.9%), respectively. Despite modest absolute accuracy, precision remained sufficient to make relative comparison of CS concentrations between samples. This method expands the analytical toolkit for human milk glycomics, enabling same day preparation and CS profiling from sample volumes that are 200 times smaller than prior work, supporting future investigations into GAG-mediated functions in early life. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/723732v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@176dffborg.highwire.dtl.DTLVardef@16ae4ccorg.highwire.dtl.DTLVardef@d333c2org.highwire.dtl.DTLVardef@1eb3216_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Schematic of sample preparation protocol 25 L of human milk is combined with lyase enzymes and TRIS buffer containing the internal standard prior to incubation. Samples then undergo multiple rounds of centrifugation and refrigeration before analysis via LC-MS/MS. Made using BioRender.com. Glycan nomenclature following Varki et al., 2015. C_FIG

Lipid metabolism is increasingly recognized as a hallmark of cancer, yet translating lipidomic discoveries into clinically actionable biomarkers remains constrained by analytical variability and limited standardized validation frameworks. This challenge is further compounded by a chicken-or-egg problem, where expensive standards and labelled internal standards are required to identify and quantitate target lipids, but the diagnostic importance of these targets is uncertain until they can be reliably measured. Previous work had indicated the potential of 48 lipid biomarker species for the prediction of breast cancer from plasma samples using high resolution liquid chromatography mass spectrometry. This study aimed to identify each of these 48 species and develop a quantitative method to determine the absolute concentrations of these lipids in plasma to provide the basis for the development of a clinical assay for use in breast cancer detection. In doing so, we present a pragmatic workflow that bridges lipid discovery with lipid identification and robust quantitative analysis. A curated library of 48 lipid species was established using authentic standards to verify plasma lipids through retention-time matching and high-resolution spectral comparison. In plasma, 41 lipids were confidently identified based on co-elution with standards and diagnostic fragment ions. Method qualification, including assessment of accuracy, precision, recovery, and linearity, was performed across all 48 lipids in parallel with identification, and 46 lipids ultimately met all predefined qualification criteria. Notably, practical constraints, including time, cost, and availability of authentic standards, necessitated performing identification and targeted method development in parallel, highlighting challenges inherent to translating lipidomics into commercial or clinical assays. This workflow provides a reproducible framework for harmonizing lipid identification and quantification, enabling the reliable integration of lipidomic data into biomarker discovery and clinical applications.

Biological metal chelators are of great interest for investigation due to their capacity to retain or mobilize metals from the environment. While some biological and bioinspired chelators find use in medical applications, others are promising platforms for the mining or recycling of technologically important metal ions. In particular, the siderophores, which are primarily iron chelators, have been studied. Four siderophores of relevance are schizokinen and its derivatives, which have been isolated from bacterial and algae cultures, in addition to soil. These siderophores have shown metal chelating activity with different metals such as iron, copper, and aluminum. In the time of metabolomics, it is required to unambiguously determine the identity of the produced siderophores as quickly as possible. Thus, Liquid Chromatography coupled to High Resolution Mass Spectrometry and mass-tandem fragmentation (LC-HRMS-MS) provides a quick and applicable alternative for identification of schizokinen and its derivatives. Here, we report an analytical method for the identification and potential quantification of the schizokinen siderophore series. We developed a working method through LC-HRMS-MS, which provides the unequivocal identification of the four schizokinen derivatives, which has not been reported to date. Additionally, we constructed the molecular network for the four molecules to enable their identification using the Global Natural Products Social Molecular Networking (GNPS) platform. Most importantly, this contribution can help speed up the characterization of schizokinen producers and facilitate the dereplication process of siderophores.

Native mass spectrometry (nMS) is well established for measuring protein masses and stoichiometries using nano-electrospray ionization (nESI), yet salt adduction and source activation energies can limit routine measurements. In this study, we benchmark submicron quartz nanopipette nESI emitters (<50 nm internal diameter) across three mass spectrometry platforms (quadrupole-time-of-flight, quadrupole-Orbitrap, and tribrid-Orbitrap platforms) and a wide protein mass range (17-800 kDa). We analysed holo-myoglobin (17 kDa) over a range of concentrations (10 M-10 nM) and capillary voltages to determine limits of detection and define a gentle operating regime. We additionally observe reduced Na+ adduction and preservation of the Zn2+-bound metalloproteoform of carbonic anhydrase II (29 kDa). Proteins and protein complexes spanning the mid-to-high mass range including ovalbumin ([~]44 kDa), malate dehydrogenase ([~]70 kDa), glutamate dehydrogenase ([~]350 kDa), {beta}-galactosidase ([~]465 kDa), and GroEL ([~]800 kDa), were readily detected using nanopipette emitters. Compared with conventional 1-2 m internal diameter borosilicate emitters, quartz nanopipettes provided higher signal-to-noise ratios and fewer adducts. Finally, direct analysis of clarified bacterial lysate expressing -synuclein yielded a clear monomeric charge-state distribution, demonstrating compatibility with complex biological matrices. Collectively, these results establish quartz nanopipette nESI as an instrument-portable, salt-tolerant approach suitable for routine nMS analysis across a broad range of protein molecular weights and sample complexities.

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.

Native mass spectrometry (nMS) is a powerful tool for analyzing biomolecules and their complexes under near native conditions. The preservation of the native state depends strongly on the ionization methods used to transfer intact molecules from solution to gas phase. In this work, capillary vibrating sharp-edge spray ionization (cVSSI)- based nMS and in-droplet hydrogen deuterium exchange mass spectrometry (HDX-MS) were used to evaluate calcium-dependent interactions between calmodulin and calmidazolium (CDZ). We found that cVSSI produced a narrow charge-state-distribution (CSD) with low average charge states indicating that this method preserved the native-like state. cVSSI was also able to resolve stepwise Ca2+-binding containing one to four Ca2+-bound species of the protein. In absence of Ca2+, no detectable CDZ-binding was observed. However, CDZ-binding was observed when calmodulin was fully loaded with Ca2+. CDZ-binding to the protein caused marked redistribution of the CSD toward lower charge states, consistent with ligand-induced stabilization of the protein into a more compact conformation. The apparent dissociation constant (Kd) of the interaction was determined to be 261 {+/-} 29 nM and 126 {+/-} 17 nM from Langmuir and quadratic binding models, respectively. Complementary in-droplet HDX-MS showed an approximately 23% reduction in deuterium uptake upon ligand binding indicating reduced solvent accessibility and increased structural stabilization supporting nMS findings. Together, these results demonstrate that cVSSI-based nMS coupled with in-droplet HDX-MS provides an integrated platform for simultaneously resolving metal loading, ligand binding, binding affinity, and ligand-induced conformational changes. This approach complements traditional structural methods by enabling direct interrogation of dynamic, metal-dependent protein-ligand interactions in their native states.

Aroma perception during food consumption results from the combined effects of food composition, oral processing (such as chewing and saliva action), the release and transport of volatile compounds toward the olfactory epithelium, followed by cognitive integration in the brain. Recent advances in real-time analytical techniques, particularly Proton Transfer Reaction-Time-of-Flight Mass Spectrometry (PTR-ToF-MS), enable in vivo monitoring of aroma release with high temporal resolution and have become widely used for analyzing the composition of exhaled air. However, the interpretation of aroma release kinetics remains challenging due to substantial intra- and inter-individual variability caused by differences in physiology, anatomy, oral behavior, and respiratory patterns. In this context, the present study was designed to quantify aroma release associated with different food oral processing (FOP) mechanisms, such as chewing and swallowing, using simple model matrices containing a single aroma compound, and to document inter- and intra-individual variability among subjects. Real-time PTR-MS measurements were combined with self-reported oral events and simultaneous respiratory monitoring to analyze aroma release from aqueous solutions and gummy discs flavored with isoamyl acetate. The results showed that inter-individual variability was higher than intra-individual variability and allowed its quantification in aroma release. Significant differences in aroma release kinetics were observed depending on FOP protocols. The importance of considering swallowing events when analyzing aroma release data was also highlighted.

The protein sample preparation methods for shotgun proteomics are nowadays well-established unlike the ones for whole protein analysis. The goal of my work has been to create a simple methodology which provides a single uncomplicated sample preparation tool for these two fields. Nowadays the bulk of proteomics work is done using detergents for protein solubilization. The presented concept, which is based on unspecific adsorption of protein molecules on wide pore materials, allows for protein capture and clean-up from solutions of the most commonly used sodium dodecyl sulfate detergent. It could also be applied to proteins in detergent-free solutions. After the capture and clean-up, proteins could be either cleaved for the downstream peptide analysis or eluted for the whole protein analysis. If required, the eluted whole proteins could be recaptured and cleaved into peptides. Depending on the experimental goals, the sample preparation device could be fitted with embedded proteolytic enzymes to simplify routine sample processing and/or reversed phase media for the downstream peptide or protein separation.

Phytohormones are key players in the regulation of plant development and metabolism. The different phytohormone classes comprise numerous chemically very diverse compounds, which are often present at very low concentrations. The chemical properties of phytohormones range from acidic to basic and from polar to non-polar. Furthermore, concentration varies strongly among different phytohormones, between plant species, tissues and developmental stages. Challenges often arise when only small amounts of plant material are available and when plant species are investigated in which the phytohormone profile has not yet been characterized. To establish a method for comprehensive phytohormone analysis we addressed these challenges by choosing and optimizing a suitable extraction method followed by optimized HPLC separation. We compared the most widely-used mass spectrometric detection methods, multiple reaction monitoring (MRM) on a triple quad instrument with high-resolution mass spectrometry (HRMS) on a Q-TOF instrument, and discuss the advantages of both methods and their limitations. O_LIWe compared various methods described in literature for the extraction of six phytohormone classes by liquid-liquid extraction and solid phase extraction purification and describe our optimizations to the selected method. C_LIO_LIWe optimized HPLC separation for 50 different phytohormones. C_LIO_LIWe evaluated the application of MRM and HRMS detection strategies. C_LI

Subtyping of ketoacidosis, a metabolic state characterized by blood acidification due to various causes, remains challenging in forensic casework. Postmortem omics samples paired with machine learning offers an independent tool to address this challenge. However, such data, especially related to real forensic cases, are rare. In Sweden, high-resolution mass spectrometry data routinely collected in forensic toxicology, can be leveraged for metabolomic analysis. Here, we integrate postmortem metabolomics and machine learning models to detect and subtype ketoacidosis-related deaths using real forensic cases in Sweden. From femoral blood samples of 109 alcoholic ketoacidosis cases, 220 diabetic ketoacidosis cases, 140 hypothermia cases, and 1,229 controls (hanging cases), we developed and tested three machine learning models, which achieved over 90% accuracy in ketoacidosis detection and over 80% in subtyping. Validation with independent cohorts (21 starvation cases, 29 alcoholic controls, and 40 diabetic controls) confirmed robustness with over 80% of starvation cases classified as ketoacidosis-related. Feature clustering highlighted metabolites such as cortisol to be important for subtyping. In summary, our findings demonstrate that combining machine learning with postmortem metabolomics enables accurate detection and subtyping of ketoacidosis-related deaths, which is useful for forensic casework.

Carbon monoxide (CO) poisoning is responsible for around 50,000 emergency department visits per year in the U.S. alone. With the present standard of care, persistent neurological sequelae occur in [~]30-40% of severe CO poisoning cases. Currently, there is no available targeted molecular antidote for CO poisoning. In previous work, we have developed an antidotal therapy for CO poisoning based on an engineered hemeprotein, human neuroglobin (Ngb-H64Q-CCC). Intravenous infusion of Ngb-H64Q-CCC removes CO from the circulating red blood cells and improves survival in a lethal CO-poisoning mouse model. However, the infusion of heme-containing proteins has inherent heme toxicity risks that may limit the dose that can be used safely without liver or kidney toxicity. In order to overcome these problems, we have investigated the development of immobilized Ngb in a solid matrix. This approach allows for the development of a CO removal system using an extracorporeal blood circulating system coupled with a stationary matrix with immobilized Ngb-H64Q-CCC. Such system avoids drug infusion and possible organ injury, allows for antidote recycling, and provides advantages for storage and handling of the antidote. By assessing the efficacy of Ngb-H64Q-CCC immobilized through different linkage strategies, we have identified N-hydroxysuccinimide agarose resin as a viable stationary phase. The immobilized protein shows preserved heme redox activity, can be chemically reduced/oxidized for activation/CO release purposes, and retains its CO removal capacity after successive regeneration cycles. We expect that this novel approach will advance the development of new scavenger-based therapies for CO poisoning.

In conventional native mass spectrometry (MS), one faces severe limitations when challenged with heterogenous, high mass samples, commonly failing to resolve clear peak distributions and thus mass determination. Charge detection MS (CDMS) has emerged as a premier method to analyze these samples by determining mass-to-charge ratio (m/z) and charge (z) simultaneously. Here, the two currently available commercialized CDMS systems, the Orbitrap-based Direct Mass Technology (DMT) and the electrostatic linear ion trap (ELIT)-based Xevo CDMS are applied to human norovirus capsids from two different strains, GI.1 Norwalk and GII.17 Kawasaki. The norovirus capsid is highly heterogenous due to N-terminal processing on the repeating subunits that it is built from and commonly forms T = 3 and sometimes T = 4 particles. Both CDMS approaches were able to determine similar masses in both strains. GII.17 Kawasaki exhibits both T = 3 and T = 4 particles, though the Xevo CDMS measurements were closer to the theoretical mass than the DMT instrument. Interestingly, GII.17 Kawasaki also displayed non-classical mass distributions with high abundance in-between T = 3 and T = 4 which was then confirmed by cryogenic electron microscopy (cryo-EM), demonstrating an oval capsid shape. GI.1 Norwalk displays a wide mass distribution in both instruments that exceeds the theoretical T = 3 mass by 8-10 %. Proteomics and native MS experiments suggest possible interactions with a protein from the expression system. This study demonstrates the capabilities of two distinct CDMS methodologies on two viral capsids and presents the first non-classical capsid assembly in a GII.17 noroviral capsid.

The extracellular matrix (ECM) and cell surface glycocalyx are key components of biology and play crucial roles in development and tissue function, as well as disease. Proteoglycans, and their glycosaminoglycan (GAG) side chains, are critical components of the ECM and the glycocalyx. GAGs can bind to many different proteins, such as chemokines, and form hydrated barriers around cells. Existing and new methods are helping us to uncover more about the roles of GAGs in biology. Here, we expand on existing technologies and provide streamlined, standardised and well-documented methods that can be easily adopted in standard analytical facilities. We provide extensive detailed step-by-step guides describing sample disruption, GAG disaccharide preparation from biological tissues and their analysis by HILIC-MS/MS. In addition, we demonstrate utility of this method when using a range of different samples as biological sources. This method will sit alongside existing and new techniques to help improve access to GAG analysis, and thereby further the field of understanding GAG function in complex biological contexts.

Accurate quantification of fungi is important for a myriad of applications but remains challenging. Previously, we demonstrated that an approach called the adenine-HPLC method can quantify bacteria, including those with aggregating properties that are difficult to quantify using conventional methods, by measuring cellular adenine derived from DNA and converting the adenine amount to genome copy number, without being influenced by cell morphology. However, in this study, when this adenine-HPLC method was applied to the quantification of budding yeast as a model fungus, accurate measurement proved impossible. This limitation was attributed to adenine release from other adenine-containing biomolecules, such as RNA and ATP, and we therefore developed a method that suppresses adenine release from these molecules. This method involves reducing the temperature of the acid treatment and prewashing the cells before acid treatment. In addition, we incorporated a process that corrects for the naturally occurring free adenine level as background during total adenine measurement. The improved adenine-HPLC method based on these modifications enables accurate quantification of budding yeast using genomic DNA content in whole cells as the quantification unit.

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.