The Analyst

Mass spectrometry based immunopeptidomics is limited by peptide identification. HLA class I ligands are non-tryptic, frequently carry low charge states and often fragment incompletely, yet nearly all workflows rely on a single activation method, higher-energy collisional dissociation (HCD). Unlike proteomics, where a protein is inferred from several peptides, in immunopeptidomics the peptide is itself the analyte and the candidate therapeutic target, so a ligand that is not identified is a target that is not found. Here we evaluate whether two alternative activation chemistries, ion trap collision-induced dissociation (CID) and infrared multiphoton dissociation (IRMPD), recover ligands that HCD does not. To our knowledge this is the first application of either method to HLA class I immunopeptidomics, made practical by the integrated infrared laser of the Orbitrap Tribrid Apex MultiOmics. Four HLA-typed cell lines were analysed in technical triplicate against the canonical human proteome and a Ribo-seq-derived cryptic ORF database. Because fragment-intensity predictors are trained almost exclusively on HCD spectra, AlphaPeptDeep was fine-tuned separately for each method; fine-tuning and Percolator rescoring increased unique peptide identifications in every cell line and method combination (median +17.1% canonical, +54.5% cryptic), and fine-tuned models transferred across cell lines with a median loss of 0.4%, placing the three methods on a common analytical footing. On that footing the three methods yielded immunopeptidomes of similar size and composition, but each recovered peptides the others missed. HCD produced the most complete fragment-ion ladders and recovered more peptides from the basic-anchor alleles A*11:01 and A*33:03, both members of the A3 supertype. CID and IRMPD recovered a larger share of alleles without a basic C-terminal anchor and, for peptides containing no lysine, arginine or histidine, produced higher search scores and more matched b/y ion current in three of four cell lines. Each method therefore contributed exclusive peptides, and at a fixed budget of three LC-MS/MS runs, one run per method yielded on average more unique peptides than triplicate runs of a single method in all four cell lines (+0.4% to +10.9%). Of 239 novel canonical predicted HLA binders in the two cancer cell lines (HCT116 and MDA-MB-231) absent from the Immune Epitope Database, 55% were detected by only one method; nine derive from cancer-associated antigens. CID and IRMPD can therefore provide sequences that may not be identified by HCD alone, or with better spectral quality for a chemically defined subset of ligands. Their value lies alongside HCD rather than in its place, and realising it will require fragment-intensity models and acquisition schemes built for these chemistries rather than adapted from HCD.

Central carbon metabolism coordinates energy production and carbon skeleton allocation across photosynthetic and heterotrophic organisms. Here, we established an optimized targeted metabolomics workflow for Chlamydomonas reinhardtii and Arabidopsis thaliana using a single-run HILIC-MS/MS assay with alternating positive and negative ionization. Our method provides robust multi-class coverage across 21 amino acids, 11 carboxylic acids, 4 sugars, 3 adenine nucleotides, 2 nucleotide sugars, 18 phosphorylated compounds in CCM, and Acetyl-CoA in a 20-minute run using both ESI modes. An optimal concentration of medronic acid with a BEH Amide column at high pH prevented metal adsorption, and low quantification limits (10 to 50 nM) for labile intermediates such as PEP, ATP and RUBP were achieved. Retention time variations remained below 1.3%, and over 90% of analytes showed high linearity (R2 > 0.990) across dynamic ranges spanning two to three orders of magnitude (typically 0.01-0.5 uM up to 15-20 uM). For sample preparation, glass-fiber filters (GF/C) outperformed nitrocellulose for harvesting C. reinhardtii. For A. thaliana, it was proved that no further grinding with micro pestle and extended cooling were necessary. A two-phase chloroform/methanol/water extraction was essential, as a single-phase extraction co-extracted green chlorophyll, which heavily suppressed phosphorylated signals. In Arabidopsis, glutamate, fumaric acid and citric acid were the dominant primary pools, indicating the importance of dilution and column washing for preventing carryover, and sensitive intermediates were reliably quantified at physiological levels. Together, this unified analytical and extraction strategy enables robust, artifact-free, high-throughput profiling of central metabolism in photosynthetic organisms.

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

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

Paper-based colorimetric sensors offer a low-cost and accessible platform for point-of-care (POC) analysis, but enzyme activity loss during coating and drying can weaken analytical signals and require high enzyme loadings or complex immobilization procedures. Although surfactants are widely used to improve wettability in paper-based assays, their potential contribution to colorimetric performance beyond these effects remains unclear. Here, we investigated surfactant-assisted colorimetric signal enhancement in a glucose assay implemented on a 96-puddle paper plate (96-PPP) and identified Tween 20 as the most effective surfactant. Its effect on detection performance became more pronounced as glucose oxidase (GOx) loading decreased; at 0.1 mg/mL GOx, Tween 20 lowered the limit of detection (LoD) from 0.113 to 0.034 mg/mL (approximately 3.3-fold) over a working range of 0-5 mg/mL, despite no statistically significant change in the measured contact angle at this loading. Tween 20 had no appreciable effect on the reaction in solution but preserved 95% of the apparent reaction rate constant after drying, compared with 11% without it, and atomic force microscopy (AFM) revealed a more dispersed dried enzyme morphology on mica. Tween 20-containing sensors also showed slower signal decay during repeated wetting-drying cycles and thermal stress, retained 77% (vs 26%) of the response at 400 mM NaCl, and exhibited within-PPP and between-batch coefficients of variation (CVs) below 10% (vs 12.3-19.5%), while maintaining glucose selectivity over potentially interfering molecules. These results indicate that Tween 20 enhances paper-based glucose sensing beyond wettability, in part by retaining enzyme cascade activity during drying, although the contributions of the individual enzymes and the underlying mechanism remain to be established.

The acquisition of sufficient nitrogen for growth, as well as the disposal of highly toxic nitrogen-containing degradation products, represent serious metabolic challenges in all multicellular organisms and microbial ecosystems. Understanding these processes requires a broadly applicable system to accurately quantify the concentration of a wide spectrum of biological amines and inorganic nitrogen-containing compounds. To date, this has been challenging due to limited specificity, selectivity, breadth and/or sensitivity of assays. Here we present a simple, optimized method based on liquid chromatography and high-resolution mass spectrometry, with an accompanying analysis pipeline, for the accurate quantification of more than 30 of these compounds, including ammonia. We demonstrate the effectiveness of this method in quantifying relevant nitrogen-containing compounds in mouse and human gut content, bacterial cultures, algal symbiosis in a cnidarian and freshwater ecosystem samples. As a proof-of-concept, we applied the method to study the influence of the gut microbiome on cecal nitrogen distribution in mice with different gnotobiotic microbiomes. This revealed a profound influence of gut microbiota composition on amino acid, ammonia, and amine concentrations in the upper large intestine. Overall, we provide a broadly applicable approach that can be used across microbial ecology fields to generate novel insights into the biology of nitrogen assimilation, exchange, and disposal.

Hormone dynamics are central to a multitude of conditions in women's health; however, limitations in the cost, convenience, and multiplexing capacity of current hormone assays preclude routine longitudinal hormone testing. This limitation severely restricts our understanding in this historically neglected field, presenting a need for improved bioanalytical tools to support hormone-informed care. Silicon photonic resonator sensors have excellent multiplexing capacity and scalable fabrication, and offer strong potential to meet this important need. Here we demonstrate, for the first time, multiplexed silicon photonic detection of two urinary hormone biomarkers, follicle-stimulating hormone (FSH) and pregnanediol-3-glucuronide (PdG), using sub-wavelength grating ring resonators and a polydopamine-based, regenerable surface chemistry. We combine a sandwich immunoassay for FSH and an antigen-down competitive immunoassay for PdG on the same chip, both amplified using a commercially available quantum-dot-streptavidin conjugate and with on-chip regeneration over multiple measurement cycles. In artificial urine, this proof-of-principle multiplexed assay achieves limits of detection of 0.594 ng/mL for FSH and 19.1 ng/mL for PdG. This architecture is inherently scalable to tens of hormone targets on a millimetre-scale chip (a >10x multiplexing improvement over typical lateral-flow immunoassays) enabling future low-cost, quantitative at-home hormone panels that could transform research, diagnostics, and treatment in women's health.

Detecting low-abundance bacterial peptides in samples dominated by human proteins using liquid chromatography tandem mass spectrometry (LC MS/MS) is challenging. Bacterial peptides are typically far less abundant than host-derived peptides, and sequence similarity between bacterial and human peptides can complicate confident attribution to a bacterial origin. To address this problem, we developed and benchmarked an in silico spectral library protein identification workflow for detecting Staphylococcus aureus USA300 peptides against a background of A375 cells. Peptide and protein identifications obtained at a 1% peptide-level false discovery rate were compared between the in silico spectral library search and a conventional sequence database search across a defined bacterial dilution series containing 0, 1%, 2%, 10%, 25%, 50%, and 100% bacterial protein. A human sequence similarity filter was applied to identifications from both search strategies to reduce the likelihood of retaining human derived or human like peptide sequences. Both search strategies showed concentration dependent bacterial detection and shared a practical detection limit of approximately 1 to 2% bacterial protein but exhibited distinct performance profiles. The in silico spectral library search produced fewer bacterial-like background identifications in the human-only control, whereas MS2Rescore substantially increased spectral library identifications at higher bacterial concentrations, exceeding database search at 100% bacterial protein. The partially overlapping peptide sets identified by the two strategies indicate that they provide complementary coverage of the bacterial proteome.

Studying algae requires specialized equipment to characterize culture growth and physiological performance and non-invasive monitoring of these cultures typically relies on optical density (OD) measurements. Currently, OD is often measured using standard spectrometers that suffer from detector saturation at high cell densities, which requires time consuming sample dilutions and lacks compatibility with flat-walled cell culture flasks like Roux bottles. Here we describe a 3D-printable device to measure optically dense algal cultures directly within these cultivation vessels. This instrument combines light attenuation and scattering, resulting in a dual-sensor system that utilizes near-infrared light (950 nm) to precisely quantify biomass without interference from changing photosynthetic pigments. Cultures that reach high cell densities can be assumed to be accurately quantified in situ using the scattered signal, preserving the integrity of the culture and eliminating the need for subsampling. This method is adaptable to the most common geometries of Roux bottles (T-25, T-75, and T-175) and could be used to monitor small-scale batch cultures without contamination risks. A cyanobacteria algal species (Limnospira platensis) and three flask sizes were analysed on light attenuation linearity and scattering detection limits at different cell concentration, demonstrating that the light attenuation module can reliably measure biomass up to an OD of 3.1 (equivalent to >1.5 g/L) without requiring dilution.

The rapid expansion of mRNA-based medicines has driven demand for robust, high-resolution analytical methods capable of characterising critical quality attributes including sequence identity, 5[prime] capping efficiency, and poly(A) tail length and heterogeneity. Here, we present two complementary liquid chromatography-mass spectrometry based workflows for mRNA characterisation, both based on site-specific digestion with the endoribonuclease MazF from Escherichia coli. A middle-up approach, optimised via specific 5[prime]-ACA cleavage enables rapid characterisation via oligoribonucleotide mass mapping providing complete sequence coverage and simultaneous assessment of 5[prime]-capping efficiency and 3[prime]-poly(A) tail length and heterogeneity. This workflow was successfully applied to three different mRNA constructs, NLuc, eGFP and FLuc mRNAs, achieving sequence coverages of 100%, 87%, and 74%, respectively. A complementary bottom-up approach, employing less specific 5[prime]-ACX cleavage enables tandem mass spectrometry-based sequencing of shorter oligoribonucleotides and detailed characterisation via mRNA sequence mapping. Applied to SARS-CoV-2 Spike Protein mRNA, this method yielded 50% sequence coverage based on unique oligoribonucleotides with MazF alone, extended to 90% by combining MazF, partial RNase T1, and partial RNase U2 digests. Together, these workflows provide a flexible, orthogonal platform for both high-throughput quality assessment and in-depth primary sequence characterisation of mRNA vaccines and therapeutics.

In bottom-up proteomics, protease specificity determines peptide length and LC/MS/MS detectability; in N-terminomics, this is critical because each protein N-terminal peptide is unique and cannot be substituted by other peptides. CHromatographic AMplification of Protein N-terminal peptides (CHAMP-N) combines LysargiNase digestion with StageTip-based strong cation exchange chromatography (SCX), removing internal peptides by exploiting their newly generated N-terminal Lys/Arg residues and thereby selectively isolating protein N-terminal peptides. However, LysargiNase cleavage near the protein N-terminus can generate excessively short peptides. Here, we developed two complementary CHAMP-N approaches that modurate N-terminal peptide length while retaining the charge-based SCX separation principle. LysN digestion avoided cleavage at N-terminal-proximal Arg residues, whereas D3-acetylation blocked Lys cleavage by LysargiNase to produce ArgN-like specificity. Both approaches extended otherwise short N-terminal peptides while preserving N-terminal basic residues on internal peptides. Conventional LysargiNase, LysN, and ArgN-like CHAMP-N generated complementary sets of N-terminal peptides. Integration of the three workflows identified 2,552 protein N-termini, approximately twice the number identified by conventional CHAMP-N alone. The median relative standard deviations of N-terminal peptide intensities were approximately 15% for all three workflows, indicating comparable technical reproducibility. These results demonstrate that coordinating protease cleavage specificity with SCX selectivity reduces sequence-dependent losses and expands N-terminome coverage while preserving the simplicity and reproducibility of CHAMP-N.

In breast cancer, collagen fiber alignment and the second harmonic generation forward-to-backward ratio (F/B), an optical measurement sensitive to fibril-scale organization within collagen fibers, have each been associated with metastatic outcome. Although both are measured at the tumor-stromal interface, in vitro studies have largely examined them separately, because experimental systems have not allowed the two to be varied independently. To address this technical gap, we used a microfluidic biofabrication approach to generate paired aligned and unaligned regions within 3D collagen hydrogels via extensional strain and varied measured F/B across gels by changing the pre-gel pH. The resulting library covered alignment values reported in breast tumors and F/B values associated with poorer metastatic outcome in invasive ductal carcinoma at matched matrix stiffness, pore diameter, and fiber fraction. In MDA-MB-231 cells, alignment biased migration along the fiber axis without changing migration speed, as expected, but the magnitude of the contact guidance response varied with measured F/B. In contrast, MCF-7 cells showed little overall contact guidance, yet migrated ~30% faster in high F/B than low F/B gels in both aligned and unaligned regions. Together, these results show that cells migrated differently in matrices with similar fiber alignment. Thus, including measured F/B may provide additional information to help anticipate how cells migrate in collagen environments that appear similar based on alignment alone.

Raman spectroscopy (RS) has been shown to differentiate various disease states in tissue samples, yet the impact of sample substrate on spectral quality and downstream machine-learning (ML) performance is under-explored. We collected 960 spectra from 36 formalin-fixed, paraffin-embedded colonic specimens mounted in parallel on calcium fluoride (CaF2) and mirrored stainless-steel (SS) slides. Raw SS spectra were more intense than CaF2, and routine dewaxing left prominent paraffin peaks on CaF2 that were largely absent on SS. Extending CaF2 xylene immersion to 48 h reduced key paraffin peaks by 11 to 23%, aligning its biochemical profile with SS. Nested cross-validated classifiers were built to distinguish true disease (T1) from epithelial misplacement (EM). The highest accuracies were observed on SS (0.65 - 0.66), while CaF2 improved from 0.41 - 0.57 to 0.65 - 0.69 after extended dewaxing, chiefly via gains in recall. Although optimal dewaxing renders CaF2 diagnostically non-inferior, SS still offers superior accuracy, faster preparation, at approximately 4% of the cost, supporting its integration into scalable, AI-enabled RS pathology workflows.

Significance: Fluorescence imaging remains largely qualitative and device-specific, limiting reproducibility and intersystem comparisons. SI-traceable radiometric characterization requires calibrated sources suitable for transferring known radiance to imaging systems. Aim: Establish and evaluate a compact, calibrated solid-state radiometric emitter target (RET) for SI-traceable radiance transfer, and assess methods for evaluating radiance invariance, imager responsivity, and extension of radiance transfer to digitally defined regions of interest (ROIs). Approach: The radiance invariance of an LED-based RET was evaluated by measuring its radiance across varying source-to-aperture distances and collection apertures. A previously described radiance-transfer formalism based on calibrated source radiance and collection geometry was implemented to determine imager responsivity (R{lambda}), which was evaluated across varying working distances and lens apertures. Grubbs and MAD outlier tests and one-way ANOVA were used to assess radiance and responsivity invariance. Finally, responsivity obtained using digitally defined ROIs was compared with matched physical apertures to assess ROI-based radiance transfer. Results: The measured RET radiance remained invariant across the tested source-to-aperture distances and collection apertures, with no significant differences observed across distance (p = 0.88) or aperture (p = 0.15). Imager responsivity remained stable across working distances for both tested lens configurations (p = 0.48 and p > 0.99). Responsivity was also invariant across lens apertures, except for deviations at f/11 that were consistent with practical aperture tolerances. Digitally defined ROIs reproduced responsivity values obtained using matched physical apertures, demonstrating that the radiance-transfer approach can be extended to image-defined regions. Conclusions: The presented methods provide an approach for evaluating compact LED-based sources for invariant-radiance emission and suitability for SI-traceable radiance transfer. The characterized RET supported stable imager responsivity across changes in imaging geometry and extension to digitally defined ROIs. These methods can be adapted to other LED-based sources, with further angular characterization and uncertainty analysis needed to establish broader applicability.

Fermentation is a process through which microorganisms are used to transform basic molecules into high value substances in various applications such as biopharma, food production, and biofuel generation. In such processes, monitoring the health of microbial cells is essential, as it is a key factor in the final product yield. Traditional methods of assessing the health of fermenter populations have involved manually extracting samples from the fermenter for benchtop testing, which lacked the accuracy and efficacy of real-time continuous measurements. This has opened the door for inline sensors to be integrated into fermenters for continuous measurement; including microfluidic based Electrochemical Impedance Spectroscopy (EIS). EIS utilizes a range of frequencies to gain insight into the characteristics of microbial populations, such as cell size and membrane capacitance. EIS cytometry, in particular, has emerged as a relevant tool, as it enables single-cell level detection in real-time. However, current EIS cytometry devices cannot be deployed directly within fermenters due to the high cell density conditions. This has limited EIS analysis in fermenters to bulk measurements averaged across a population. Here, we present a microfluidic chip that uses passive cell-flux-partitioning geometry to direct cells from high-density culture through a single-cell-constrained flow path containing integrated impedance-sensing electrodes. The proposed chip combines geometric channel design and embedded electrodes to enable continuous impedance measurements from cell-containing flow while operating at input cell densities on the scale of 10^9 CFU/mL. This work demonstrates a first step toward integrating single-cell-constrained microfluidic handling with impedance sensing for inline monitoring in industrial biomanufacturing.

Unlike nucleic acids, proteins cannot be directly amplified. Ultrasensitive detection must therefore couple efficient analyte-to-signal conversion with strong suppression of target-independent background. Consistently detecting ~100 molecules (below which Poisson sampling noise alone causes CV to exceed 10%) across protein targets in a format as scalable and accessible as PCR remains unsolved. Separation-based ultrasensitive immunoassays require target binding stable enough to survive stringent washing and are therefore constrained by wash-induced signal loss and, more fundamentally, the relative scarcity of exceptionally tight binders. Performance consequently varies across targets; detection limits below 1,000 molecules remain rare. Conventional separation-free proximity assays avoid washing but incur background from random probe collisions, with reported detection limits frequently exceeding 100,000 molecules. Here we introduce Successive Proximity Extension Amplification Reaction (SPEAR), a separation-free architecture in which autonomous engineered DNA nanodevices emulate biological kinetic proofreading. Two successive irreversible proximity-extension steps compound discrimination between analyte-dependent and background reactions, suppressing spurious signals while preserving efficient analyte-to-signal conversion. This design reduces reliance on high-affinity binders and enables sub-fM detection from 1 uL samples through simple 3-step mixing followed by standard qPCR readout. Across 11 biomarkers tested, SPEAR consistently achieved sub-100-molecule detection limits. Where compared, it outperformed ultrasensitive separation-based platforms, analytically and clinically, including for low-abundance Alzheimer's disease biomarkers. SPEAR also precisely quantified SARS-CoV-2 neutralizing antibody titers from finger-prick dried blood spots, concordant with the cell-based reference assay on venous blood. It detected a distinct mixed phosphorylation state of Tau, and targeting this state resolved previously observed capillary-venous discordance and improved amyloid-status discrimination. These results establish engineered autonomous successive molecular discrimination as a practical strategy for near-single-molecule protein detection, extending the sensitivity, scalability and accessibility of PCR-based testing to non-amplifiable analytes.

Cardiac amyloidosis is caused by deposits of misfolded proteins in the heart and can lead to heart failure. Histological confirmation typically involves staining tissue sections with Congo red and examining them under polarized light for apple-green birefringence; however, achieving an interpretable, label-free, and observer-independent assessment remains challenging. Here we show that Pearson kurtosis of channel intensities within the amide I window provides an interpretable, fitting-free contrast for cardiac amyloid in unstained human cardiac tissue sections from ten participants imaged by ultrabroadband multiplex coherent anti-Stokes Raman scattering (CARS). In a discovery participant, high-kurtosis regions corresponded with Congo red-positive tissue in two CARS-histology pairs of adjacent sections (descriptive spatial AUCs, 0.79 and 0.73). Across 15 fields from nine additional participants, field-level kurtosis values did not overlap between severe-deposition and Congo red-negative tissue fields. Among the descriptors examined, kurtosis showed the largest standardized participant-level separation. Exploratory multivariate curve resolution yielded a deposit-associated component with aromatic-residue features shared with reagent transthyretin. In one field from the discovery participant, the second harmonic generation (SHG) signal, acquired simultaneously with CARS, showed a broadly inverse spatial pattern relative to the amide I kurtosis map, revealing distinct tissue-architectural contrast. Together, these findings support a potential label-free approach to detecting cardiac amyloid deposits that combines amide I kurtosis derived from broadband CARS with SHG-derived architectural contrast.

Surface plasmon resonance (SPR) and biolayer interferometry (BLI) are well-established label-free analytical technologies used to determine biomolecular interaction rates (ka, kd) and equilibrium constants (KD). While assays historically rely on multi-cycle kinetics (MCK), this approach requires intermediate surface regeneration, risking sensor degradation and increasing reagent consumption. Single-cycle kinetics (SCK) avoids regeneration by sequentially injecting increasing analyte concentrations. However, implementing SCK workflows on biosensor platforms like the OctetRR8 BLI instrument (Sartorius) remains a challenge due to the lack of built-in or online data analysis tools. To address this, we introduce SCKAnalyzer, a free, user-friendly web application designed for processing and fitting SCK datasets. SCKAnalyzer accepts column-based text files and native raw BLI data (.frd format) from Sartorius instruments, performing data pre-processing and non-linear fitting to a 1:1 interaction model. Additionally, it offers advanced simulation capabilities that allow variable association and dissociation durations across sequential injection steps, a feature absent in existing platforms such as BiacoreTMSimul8. The performance of SCKAnalyzer was experimentally validated using a 3-step SCK method (SCK3) for DNA/RNA duplex formation on a Biacore T200, as well as 4-step (SCK4) and 5-step (SCK5) methods for DNA/DNA duplex formation on an OctetRR8 BLI instrument. Binding parameters (ka, kd, KD) derived from SCKAnalyzer showed excellent agreement with values from established proprietary softwares such as Biacore T200 Evaluation Software v3.2.1 and BIAevaluation v4.1.1. SCKAnalyzer provides a practical alternative to MCK, reducing probe consumption and extending SCK utility across biosensor platforms. SCKAnalyzer is available at sck.iecb.u-bordeaux.fr (source code: github.com/synthaze/sckanalyzer).

GeneCapture is developing a novel, point-of-care, rapid AST technique that, when fully automated, can accomplish AST within 2 hours directly from a raw sample, representing a significant advancement in diagnostic capabilities. For this study, a panel of uropathogen isolates was collected from clinical specimens positive for urinary tract infection from various laboratories in the southern US. A selection of these isolates were then spiked into fresh clean-catch urine samples to produce mock samples covering a variety of antibiotic responses. Testing was performed on 20 strains of Escherichia coli, the cause of over 80% of UTIs, and 6 strains each of Klebsiella pneumoniae and Proteus mirabilis, the next most common causes. Each strain was tested against the susceptible, intermediate and resistant (S-I-R) breakpoints of up to 5 appropriate antibiotics including Amoxicillin-Clavulanate, Cefpodoxime, Ciprofloxacin, Nitrofurantoin and Trimethoprim-Sulfamethoxazole. The new method uses a phenotypic assessment of bacterial growth, comparing responses from bacteria treated with antibiotics at S-I-R breakpoint concentrations to untreated controls as the metric for determining susceptibility. Exposure to the drugs for only 90 minutes gave a Categorical Agreement of 98% to gold standard culture results across the resulting 247 bacteria/antibiotic pairs. Testing on separate days of nine of these strains in triplicate gave a reproducibility of 97%. The sequential steps of this novel AST - sample processing, growth, labeling, and counting - can readily be performed on an automated cartridge that is currently under development. The presented work, which took place in a laboratory setting, demonstrates the efficacy of the assay.

Antibiotic resistance is an increasingly serious problem worldwide. Identifying the bacterial species can help guide antibiotic selection and improve patient outcomes. In sepsis, where every minute counts, rapid identification is particularly important, and new approaches are needed. In this study, deep learning models were trained to identify bacterial species from phase-contrast time-lapse images of cells growing in microfluidic traps, without fluorescent labels. We demonstrated this approach in two previous studies using laboratory strains. Here, the previous work is extended to clinical bacterial isolates, evaluating performance on isolates not seen during training. Furthermore, it was tested whether autofluorescence could aid species identification. Two validation sets were acquired in separate experiments from the training data, the first containing clinical-isolate traps with five species (Escherichia coli, Enterococcus faecalis, Klebsiella pneumoniae, Staphylococcus aureus and Enterobacter cloacae), imaged every two minutes, and the second containing clinical isolates of two species (E. coli and K. pneumoniae) recovered from spiked donor blood, imaged every minute. Training with the best previous CNN method achieved unsatisfactory results. Therefore, vision transformers were explored, and a self-supervised adaptation procedure was developed for pretrained DINOv3 using a frozen teacher, followed by supervised classification using time-lapse montages. Mean macro-F1 was 93.6 {+/-} 1.3% for isolates precultured in Mueller-Hinton medium using 60 frames (118 minutes) and 87.2 {+/-} 2.7% for isolates precultured in blood using 55 frames (54 minutes) (mean {+/-} sample standard deviation across training seeds). Performance was evaluated over time by increasing the number of frames available to the model during testing, and Fourier-domain filtering approximated reduced numerical aperture. The results are promising and suggest that the aperture can be decreased while retaining good results. However, further evaluation on independent isolates, experiments and patient samples is needed to establish generalization. Together with previous findings, these results bring us closer to rapid image-based diagnostics that could transform the initial treatment of acute bacterial infections by enabling better-informed antibiotic selection.