The Analyst

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.

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

Glycosaminoglycans (GAGs) are linear polysaccharides with essential roles in a myriad of biological processes. Despite their biological importance, methods to determine both spatial and compositional information is limited. Time-of-flight secondary ion mass spectrometry (ToF-SIMS) provides spatially resolved compositional information of biological molecules without enzymatic digestion or label incorporation, enabling unbiased analysis independent of enzyme or label selectivity, overcoming many current limitations in GAG analysis. Here, we present the identification and validation of GAG discriminatory ions from biological samples by comparison of spectra from purified GAGs and cells with genetically modified GAG biosynthetic pathways. Ions discriminatory of specific GAG sub-families are identified and related to GAG structural components. The analysis is applied to human induced pluripotent stem cells engineered to lack heparan sulphate (HS), where compensatory changes in GAG display that link to function were observed. Furthermore, the broad applicability and spatial resolution of the technique is highlighted through detection of a disease-induced reduction in HS within the individual glomeruli of diabetic mice.

Understanding proteoforms, i.e., the various molecular forms in which proteins can exist, is important for deciphering biological processes and diseases. While capillary zone electrophoresis (CZE) proved advantageous for proteoform separation, limited sample loading capabilities restrict its application. Here, we present a novel comprehensive two-dimensional nanoLCxCZE-MS platform for deep top-down proteomics (TDP). The 2D platform is highly automated, enabling robust performance and the possibility to perform proteoform quantitation as demonstrated by isobaric labeling experiments. The high orthogonality of reversed-phase LC and CZE leads to a peak capacity of 2200, leading to an increase in the number of identified proteoforms in a human Caucasian colon adenocarcinoma cell lysate sample by a factor of 3 compared to nanoLC-MS. Furthermore, CZE mobilities enable the attribution of many more proteoforms to a certain proteoform family on the MS1-level. Overall, the flexible platform enables highly efficient separation of intact proteoforms combined with sensitive MS-based TDP workflows, both for untargeted and targeted analysis of complex biological samples. Graphical AbstractWe report a robust and automated comprehensive nanoLCxCZE-MS platform for top-down proteomics. In addition to large volume sample injection and separation by hydrophobicity in the nanoLC, the orthogonal separation by CZE in the second dimension leads to a strong increase in peak capacity and, thus, in the number of identified proteoforms. CZE mobilities also enable the attribution of many more proteoforms to a proteoform family on the MS1-level. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=46 SRC="FIGDIR/small/725123v1_ufig1.gif" ALT="Figure 1"> View larger version (11K): org.highwire.dtl.DTLVardef@df07b6org.highwire.dtl.DTLVardef@736d5corg.highwire.dtl.DTLVardef@10cef1org.highwire.dtl.DTLVardef@1825b55_HPS_FORMAT_FIGEXP M_FIG C_FIG

Data-driven materials development requires large, well-characterized libraries of precisely defined formulations. While microfluidic platforms excel at generating highly controlled materials, their throughput is often limited by the challenge of efficiently interfacing device outputs with standard well plates. This bottleneck frequently necessitates manual transfer or non-microfluidic workflows, constraining both throughput and reproducibility. Here, we present LMNOP-bot (Libraries of Micro- and Nano-materials, OPen-source bot), an open-source robotic platform for the automated generation and collection of micro- and nanomaterial libraries from serial microfluidic outputs. Using synchronized, pressure-driven flow, LMNOP-bot enables continuous formulation and direct deposition into standard well plates. The system is low-cost (<$700, excluding pressure regulators), constructed from readily available or easily fabricated components, and designed for broad accessibility. LMNOP-bot collects [&ge;]30 {micro}L per formulation at a rate of one sample every four seconds, representing an approximately 50x increase in throughput over existing serial microfluidic workflows, and operates robustly for over 10,000 runs without maintenance. We demonstrate compatibility with both PDMS/glass and commercial polycarbonate devices, with seamless interfacing to 96- and 384-well plates. Repeated sampling confirms high precision and reproducibility. By removing a key bottleneck in microfluidic library generation, LMNOP-bot enables rapid, scalable, and accessible exploration of material design spaces.

Manual colony counting remains the rate-limiting, operator-dependent step in culture-based food microbiology quality control (QC). Automated colony analysis using machine learning (ML) offers the potential to standardise, accelerate, and improve the traceability of this process. However, systematic multi-method validation data for AI-based platforms against recognised international standards remain scarce. We conducted a prospective, multi-study validation of the Reshape Smart Incubator which is an automated imaging and ML-based colony analysis system, across eight ISO microbiological reference methods. In total, 887 plates were analysed, spanning qualitative (presence/absence) detection of Listeria spp. (ISO 11290-1) and Salmonella spp. (ISO 6579), and quantitative enumeration of total viable count (ISO 4833), Bacillus cereus (ISO 7932), Enterobacteriaceae (ISO 21528), coagulase-positive Staphylococci (ISO 6888), yeasts and moulds (ISO 21527), and lactic acid bacteria (ISO 15214). Automated results were benchmarked against the consensus of three or more trained technicians. The platform achieved 100% agreement with manual assessment for all both qualitative detection methods (ISO 11290-1, ISO 6579) with zero false positives and zero false negatives. For quantitative enumeration, agreement ranged from 92.97% (ISO 15214, n=122, using ISO-aligned {+/-}10%/>30 CFU thresholds) to 98.46% (ISO 21528, n=130). Where discrepancies occurred, they largely coincided with plates showing high inter-technician variability. Precision testing demonstrated a coefficient of variation of 5.88% and a mean standard deviation of 0.44 CFU for low-count plates. This study presents a comprehensive multi-ISO validation of an AI-based colony analysis system to date. The AI models demonstrated performance comparable to or exceeding that of trained human technicians across a broad range of microbiological targets, agar types, and colony morphologies, thereby supporting their use as a validated and traceable alternative to manual plate reading in accredited food microbiology quality control laboratories.

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.

In todays world, point-of-care nucleic acid detection still remains extensively constrained and limited by the heavy dependence on centralized urban instrumentation facilities and complex assay workflows. Here, we elucidate a glucometer-based analytical platform that enables label-free detection of nucleic acids and the nucleic acid amplification products through a simple redox-mediated mechanism. The approach leverages the potassium ferricyanide (K3[Fe(CN)6])/ potassium ferrocyanide (K4[Fe(CN)6]), redox system, which is intrinsic to commercial glucometers, complementing with interactions between methylene blue (MB) and nucleic acids. These interactions transduce concentration differences in nucleic acids into quantifiable electrochemical signal readouts. Distinct varied signal outputs are observed between single-stranded and double-stranded DNA, enabling the direct detection as well as integration with nucleic acid amplification tests (NAATs), including polymerase chain reaction, rolling circle amplification, and loop-mediated isothermal amplification. Optimization of reaction parameters and conditions leads to enhancement of the overall signal discrimination and sensitivity across various assay formats. This innovation repurposes widely available off-the-shelf glucometers as a low-cost, portable nucleic acid detectors, thus eliminating the need for any specialized instrumentation. Our results enumerate and establish a generalized and scalable strategy for nucleic acid sensing. The platform thus supports sustainable and environmentally responsible point-of-care testing, thereby enabling improved accessibility and public health monitoring at resource-limited and remote settings.

Droplet-based microfluidics enable researchers to observe phenotypic heterogeneity within complex biological mixtures through parallel encapsulation of individual samples followed by imaging. Observing or quantifying dynamic heterogeneity remains challenging due to complexities associated with trapping and tracking many individual droplets. Current approaches for time-lapse imaging require specialized devices with droplet traps that limit accessibility and throughput. Here, using readily available materials and software, we demonstrate a simple method for stabilizing and monitoring many, individual droplets for up to 12 hours. We leveraged our method to track bacterial growth within droplets in a high-throughput manner. Our method allows tracking the changes and variation in growth rate within and across droplets, revealing heterogeneity in growth patterns hidden in batch assays. Improving the affordability and throughput of time-dependent phenotyping assays helps to advance biological discovery and biotechnology innovation.

The cardiac autonomic nervous system is a key driver of various cardiac disorders and arrhythmias. However, investigating neuronal regulation of the human heart has proven difficult due to immitted and reliable experimental models. Here, we present a novel microphysiological system utilizing a compartmentalized microfluidic device (MFD) to integrate co-cultured human induced pluripotent stem cell (hiPSC)-derived cardiomyocytes (hiPSC-CMs) and sympathetic neurons (hiPSC-SNs). MFD is composed of two wide-open chambers separated by microfluidic microchannels. hiPSC-SNs were characterized by confocal imaging and RT-qPCR for the expression of peripherin, tyrosine hydroxylase, and {beta}-tubulin III, as well as high levels of dopamine {beta}-hydroxylase and nicotinic acetylcholine receptors. Furthermore, patch-clamp techniques confirmed their functional maturity, showing spontaneous action potentials and positive responses to nicotine (1{micro}M). Co-culturing hiPSC-CMs and hiPSC-SNs within the MFD facilitated axonal projection into the cardiomyocyte chamber, establishing a physical connection between the two cell types. After 10 days of co-culture, functional integration was confirmed by a significant increase in the action potential frequency and beating rate of hiPSC-CMs, as recorded by patch-clamp and video motion tracking, respectively. Notably, nicotine application in the neuronal chamber accelerated these rates in hiPSC-CMs chamber, whereas the administration of the {beta}-blocker, propranolol (5{micro}M), effectively decreased the beating rates. Collectively, these data demonstrate the feasibility of differentiating hiPSCs into functional sympathetic neurons and establishing a robust neuro-cardiac interface. This microphysiological system represents a powerful platform for investigating disorders characterized by impaired neuro-cardiac interactions, offering a valuable tool for both disease modeling and pharmacological screening.

Metabolomics offers a sophisticated analytical framework for characterising the molecular phenotype of biological organisms and complex living systems at a high resolution. As the functional endpoint of the omics cascade, the metabolome serves as a close reflection of cellular activity. It integrates genetic, transcriptomic and proteomic variations with external environmental influences. However, the inherent complexity of metabolomic datasets, characterised by high-dimensional chemical diversity, wide dynamic ranges, and significant matrix effects, necessitates a rigorous suite of chemometric and bioinformatic workflows. For researchers uninitiated in computational biology, the multi-stage requirement for raw data pre-processing, signal deconvolution, and multivariate statistical modelling (such as PCA or PLS-DA) presents a substantial barrier to entry. Navigating these convoluted data architectures remains a primary challenge in deriving biological meaning from the global metabolic profile. Here, we present a workflow to use Python Dash Apps to create a user-friendly interface for simplifying data processing and statistical calculations. Users can select their desired samples to initiate calculations for various statistical tests, generating interactive and publication-quality figures to explore their results. These apps were deployed on an Apache server via cPanel, allowing individuals to share their findings with collaborators and for research facilities to share metabolomics results with their users.

Deep characterization of intact proteoforms remains an analytical challenge in functional proteomics, particularly for heterogenous multi-site post-translational modifications at distinct amino acid residues. Histones are among the most dynamically and diversely post-translationally modified proteins in eukaryote cells, carrying multiple, co-occurring and reversible modifications that can give rise to isomeric proteoform species. Tandem mass spectrometry with multimodal fragmentation capabilities is a promising approach for deep characterization of intact proteoforms, such as modified histones. We applied the novel timsOmni mass spectrometer, which incorporates the Omnitrap platform enabling multimodal MS workflows, for residue-level mapping of histone modifications, including acetylation and methylation. Recombinant histones H3.1 and H4 were in vitro acetylated by enzymes GCN5, PCAF and p300 to generate mono- and multi-acetylated proteoforms. Complementary MS2 electron- and collision-based dissociation (ECD, EID, RCID and ECciD), together with MS3 strategies, produced complete or near-complete backbone fragmentation of intact protein ions (>92% amino acid sequence coverage). For monoacetylated species generated by the more site-selective lysine acetyltransferases, the dominant proteoform matched the known catalytic preferences of the enzymes (H3.1K14ac for GCN5 and PCAF, and H4K8ac for PCAF), while minor positional isomers were also identified and their relative abundance estimated. In contrast, the broader substrate specificity of p300 produced a wide distribution of H4 proteoforms bearing up to seven acetylated lysine residues. Species carrying six and seven acetylations were characterized by multimodal MS2/MS3 experiments, enabling localization of individual acetylation sites and discrimination of positional isomers. Finally, endogenous histone proteoforms from liver extracts were analyzed, yielding sequence coverages of 92-93% for the most abundant species and enabling confident localization of multiple PTMs (acetylation and methylation). These results illustrate that multimodal MSn fragmentation of intact proteins supports residue-level assignment of combinatorial histone marks and coexisting positional isomers. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=165 HEIGHT=200 SRC="FIGDIR/small/722147v1_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@387ab5org.highwire.dtl.DTLVardef@2410org.highwire.dtl.DTLVardef@13fc392org.highwire.dtl.DTLVardef@140e054_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LIMultimodal MS{superscript 2}/MS3 maps histone PTMs on intact proteins. C_LIO_LIECD, EID, RCID, and ECciD provide complete or near-complete sequence coverage. C_LIO_LIMS3 localizes acetylation sites, distinguishes positional isomers. C_LIO_LIEndogenous H4 proteoforms are assigned with site-specific PTM mapping. C_LI

Biomarkers in sweat and saliva offer a promising avenue for non-invasive health monitoring. Electrochemical sensors have the potential to measure such biomarkers simultaneously. However, they are limited in discriminating individual biomarkers in mixtures, as redox potentials often overlap, resulting in current signatures that cannot be deconvoluted. This study focuses on differentiating biomarkers using orthogonal sensing materials combined with machine learning. We introduce a flexible electrochemical sensor array comprising carbon flower electrodes modified with poly(vinylidene fluoride) (PVDF) or poly(4-vinylpyridine) (P4VP) for the detection of estradiol (E2), ascorbic acid (AA), serotonin (5-HT), and melatonin (Mel). The two polymers act by altering the redox potential and current response of each biomarker, thereby enhancing signal diversity and enabling peak separation. Using multi-output regression models on 450 single and mixture measurements, the array accurately predicts concentrations (R2 = 0.95) over a wide dynamic range spanning nanomolar to micromolar levels. Polymer-resolved analysis reveals that PVDF-modifications enhance E2 and Mel detection, while P4VP-modifications improve AA and 5-HT quantification, highlighting the benefit of complementary orthogonal sensing electrodes. This finding is further supported by feature attribution analysis, which shows that the machine learning model relies on polymer-specific electrochemical signatures, directly linking improved performance to distinct polymer-analyte interactions. Overall, these results demonstrate that combining polymer-modified orthogonal electrodes with machine learning enables accurate, multiplexed sensing in complex mixtures, advancing selective detection strategies for future sensor platforms.

Species-level Candida identification can inform antifungal management, but reliable identification platforms remain inaccessible in many clinical microbiology laboratories, whereas phase contrast microscopy -- a common feature of routine laboratory microscopes -- is widely available. We asked whether this ubiquitous optical tool, combined with a consumer smartphone and deep transfer learning, could provide a feasible low-cost approach for preliminary Candida species discrimination. Fifteen clinical isolates of four species (C. albicans, C. glabrata, C. tropicalis, C. krusei) were collected from a single clinical microbiology laboratory and imaged using a consumer-grade smartphone coupled to a standard phase contrast microscope. Suspensions in human serum were imaged immediately after preparation (T0) and after 2-hour incubation at 37{degrees}C (T2). Pretrained vision backbone architectures were evaluated as fixed feature extractors under strict Leave-One-Strain-Out cross-validation. The best-performing model -- EfficientNet-B0 embeddings with a Linear Support Vector Machine applied to T2 images -- achieved an apparent internally cross-validated strain-level balanced accuracy of 0.833 and an overall strain accuracy of 86.7% (13/15 strains correctly classified). C. albicans, C. glabrata, and C. tropicalis were each identified with 100% recall. Both misclassified strains belonged to C. krusei -- the species with the smallest panel representation (n=3 strains) -- with misclassification attributable to limited strain diversity and suboptimal image quality. These findings demonstrate promising feasibility for preliminary image-based Candida species discrimination from smartphone-acquired phase contrast microscopy images, and support further evaluation in larger, externally validated strain collections.

High-resolution microscopy techniques are used across research and industry to analyse biological systems, from biomolecules to subcellular organelles, multicellular models and tissues. As multimodal imaging workflows and quantitative analysis of bioimaging data become increasingly widespread, there is a growing need for materials and methods to calibrate imaging systems and evaluate the fidelity of generated image data. Here, we present three-dimensional microscopy phantoms fabricated using two-photon photolithography from transparent resins that exhibit both broadband visible autofluorescence and Raman scattering across the fingerprint and C-H stretching regions. Suitable for analysis using optical profilometry, the phantoms were dimensionally calibrated with SI traceability using a metrological confocal microscope. Immersible in air and common aqueous imaging media, the phantoms are compatible with a wide variety of optical microscopy techniques, including one and two-photon excited fluorescence and coherent Raman scattering microscopy. We employed a forked wedge design to validate image deconvolution results and a stacked lattice phantom to recover image distortion matrices under realistic biological imaging conditions. We demonstrate the impact of correcting chromatic offsets and axial scaling errors for a representative application: analysis of a cell seeded scaffold using confocal laser scanning fluorescence microscopy. These phantoms provide a versatile platform for calibration, quality control and validation of multimodal imaging pipelines and improved quantitative optical microscopy.

Paper-based diagnostics such as lateral flow assays (LFAs) and microfluidic paper-based analytical devices ({micro}PADs) have attracted considerable attention because of their low cost, portability, and ease of use. Currently, to enable fabrication of {micro}PADs and improve LFA performance, hydrophobic blocks are patterned on paper substrates. However, fabrication of high-resolution hydrophobic barriers remains a major challenge. In this work, we developed a novel silicone extrudable ink for the fabrication of hydrophobic features on paper substrates. The ink was formulated using a vinyl-terminated polydimethylsiloxane (vPDMS) and polymethylhydrosiloxane (PMHS) system crosslinked through platinum-catalyzed hydrosilylation, and its rheological properties were tailored by incorporating silica fillers, obtaining a shear-thinning gel suitable for extrusion. The resulting formulation provided tunable properties, controlled deposition, and stable feature formation, enabling simple, low-cost, rapid, and robust fabrication of high-resolution hydrophobic barriers. Using this approach, we demonstrated improved fluid confinement and pattern fidelity on paper substrates, fabricated high-resolution paper microfluidic devices down to 150 {micro}m channel width, and enhanced the sensitivity of an LFA for a malaria diagnostic test. These results highlight the potential of this silicone ink platform as a practical and scalable strategy for advancing high-performance paper-based diagnostic technologies.

Coenzyme A is an essential cofactor synthesized from pantothenate, cysteine, and ATP, and is involved in numerous processes of cellular metabolism through its ability to carry activated acyl groups. Coenzyme A participates in catabolism of carbohydrate, fat and amino acids; biosynthesis of fatty acids, cholesterol and heme; and protein modification including acetylation and 4-phosphopantetheinylation. Despite CoAs critical functions, the regulation of CoA levels and the rate of CoA synthesis in different cell types and disease states are not well understood. One reason for this gap is that many acyl-CoA species are analytically challenging to measure due to factors including instability, poor ionization, and the wide range of biochemical properties conferred by different acyl chain lengths. In addition, most current methods do not support analysis of CoA isotopic labeling, which is required to quantify CoA synthesis rate or to measure absolute concentration using isotope-labeled internal standards. Here, we describe a method to quantify the concentration and isotopic labeling of total CoA, defined as the sum of CoASH plus all acyl-CoA species. Acyl-CoA species are hydrolyzed using sodium hydroxide to remove acyl chains, then CoA is derivatized on the thiol with N-ethylmaleimide (NEM). Following protein precipitation and solid phase extraction, samples are analyzed by liquid chromatography-mass spectrometry. This method is linear in a wide range that captures mouse tissue CoA levels, with accuracy within 15% error and precision below 15% relative standard deviation for both pure standards and tissue samples. We applied this method to measure total CoA concentration in five tissues from male and female mice, and total CoA synthesis rate in mouse liver via infusion of 13C-15N-pantothenate. Overall, this method offers a tractable approach to measure total CoA concentration and isotopic labeling to enable study of total CoA synthesis rates and concentrations in health and disease.

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.

Prodigiosin is a red pigment produced by various bacteria, including Serratia marcescens. Despite its wide and promising range of biological activities, the large-scale production of prodigiosin is currently limited by its high cost and low yields. Here we propose and optimize an innovative, low-cost, peanut-based solid culture medium that enhances the yield of prodigiosin produced by Serratia marcescens. Colorimetric assays revealed that peanut significantly stimulates prodigiosin synthesis. Further HPLC-MS analysis allowed us to unambiguously identify prodigiosin and shows that our medium specifically improves the yield of prodigiosin. Overall, our innovative culture medium could help lower prodigiosin production costs and, ultimately, open new industrial applications.

Peptide mapping is a critical tool for characterizing biotherapeutic proteins and is essential for the development of monoclonal antibody drugs. Here we describe a new direct infusion technology that streamlines peptide mapping data collection and analysis, accelerating the method by up to 100-fold. This method, which we term RaPiD-mAb-MS, combines high-throughput plate-based sample preparation with direct infusion mass spectrometry analysis. RaPiD-mAb-MS allows analysis of 96 samples within [~] 1.5 to 2 hours, routinely achieves >95% sequence coverage, and has been successfully applied to 28 unique antibodies and over 2,000 samples. Here we demonstrate that RaPiD-mAb-MS detects and quantifies oxidation, deamidation, isomerization, glycosylation, and sequence variants with results comparable to conventional LC-MS based methods in a fraction of the time. Further, by eliminating chromatography, data analysis is greatly streamlined and simplified. By allowing for the collection of [~] 1,000 peptide maps per day, RaPiD-mAb-MS is positioned to accelerate all phases of antibody-based drug discovery & development and sets the stage for collection of massive datasets that would allow artificial intelligent prediction of optimal antibody variants and formulations.