Analytical Chemistry

Mass spectrometry (MS) is a powerful technique for characterizing modified RNA as it directly sequences and quantifies all mass-altering modifications simultaneously. However, the physicochemical properties of RNA result in poor ionization efficiencies during electrospray ionization, presenting a major barrier to sensitive MS measurements necessary for low abundance RNA samples and RNAs with low modification stoichiometries. Here, we report a ligation-based approach to increase ionization efficiencies of RNA oligonucleotides. We show that short ([~]5 nt), chemically modified DNA oligonucleotides can be enzymatically ligated to RNA to serve as MS signal enhancers. Among a series of signal enhancers appended with various alkyl and alkylimidazolium functional groups, we found that decyl-functionalized derivatives improved MS sensitivity by [~]15-fold compared to unlabeled oligonucleotide. When ligated to RNA standards, the decyl-modified signal enhancer increased MS signals 2-4-fold with the additional benefit of improved retention during liquid chromatography (LC) separations without ion pairing agents. To apply the ligation-based approach to RNase T1 digests of longer RNAs, a multi-step enzymatic approach was optimized to maximize ligation efficiencies. We then ligated signal enhancers to a yeast transfer RNA (tRNA) digest and observed increased MS signals for numerous sequence-informative digestion products. Importantly, the sequences of RNA oligonucleotides ligated to signal enhancers were readily determined by tandem mass spectrometry with collision-induced dissociation. This ligation-based strategy for enhancing LC-MS/MS characterization of RNA creates opportunities to measure low abundance RNA samples and their modifications.

In this study, we demonstrate that urea enables reliable melting temperature (Tm) determination of hyperthermostable proteins by nano differential scanning fluorimetry (nanoDSF) Under native conditions, Pfu DNA polymerase and its Sso7d-fusion variant showed no detectable unfolding transitions, despite their Tm values falling within the instruments operational range, reflecting their extreme kinetic stability. In the presence of up to 7 M urea, intrinsic tyrosine and tryptophan fluorescence revealed clear unfolding transitions, yielding extrapolated Tm values of 104.8 {+/-} 0.09 {degrees}C for Pfu and 106.8 {+/-} 0.33 {degrees}C for its Sso7d-fusion variant. These results demonstrate that urea-gradient nanoDSF overcomes both instrumental and kinetic limitations, providing a simple and robust method for assessing the thermal stability of (hyper)thermostable proteins. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=59 SRC="FIGDIR/small/717478v1_ufig1.gif" ALT="Figure 1"> View larger version (16K): org.highwire.dtl.DTLVardef@1b8c5e3org.highwire.dtl.DTLVardef@1c7d395org.highwire.dtl.DTLVardef@14093forg.highwire.dtl.DTLVardef@16b2f25_HPS_FORMAT_FIGEXP M_FIG C_FIG

BackgroundConstructing digital twins in healthcare requires biological data sources that are simultaneously informative, dynamic, and practical for routine collection. Dried blood spot (DBS) sampling combined with untargeted metabolomics is well suited to meet these requirements: DBS can be self-collected at home and mailed at ambient temperature, while untargeted LC-MS/MS captures thousands of metabolites reflecting individual physiology, lifestyle, and exposures. We previously demonstrated proof-of-concept individual identification from DBS-derived metabolomic profiles in 277 volunteers (80-92% accuracy). Here, we report a large-scale validation on a substantially expanded cohort. MethodsWe collected 18,288 DBS samples from 1,257 individuals across 134 analytical batches over 15 months. Samples were self-collected at home, mailed via standard postal service, and analyzed by untargeted LC-MS/MS on a high-resolution Orbitrap platform in positive ESI mode. Our classification pipeline comprises batch-aware normalization, supervised feature selection, biological signal filtering, dimensionality reduction, and user-level majority voting across all available samples. This voting reflects the real-world use case: participants contribute multiple self-collected DBS cards over time, taken at different times of day and under varying conditions. We employed GroupKFold cross-validation with group=batch to ensure zero batch leakage between training and testing sets. ResultsIn 10-fold GroupKFold cross-validation (group=batch, zero batch leakage), our pipeline achieved 94.1% user-level identification accuracy (85.5% sample-level). In a fully held-out validation on 17 future batches -- with all feature selection, normalization, and model fitting performed exclusively on training data -- performance was even stronger: 96.1% user-level and 92.6% sample-level across 1,134 classes (chance level: 0.088%). Feature selection stability was confirmed via bootstrap analysis. We identified batch leakage as a critical methodological pitfall for the field: naive random splitting inflated accuracy by sharing 92.8% of test samples (user, batch) pairs with the training set. The top discriminative metabolites span biologically relevant pathways including amino acid metabolism, fatty acid transport, and sphingolipid biosynthesis. ConclusionsUntargeted metabolomics from dried blood spots supports batch-aware, closed-set individual identification in a single-laboratory setting, with potential relevance for longitudinal sample-to-person linkage in future digital twin workflows.

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.

Cholesterol is a key component of cellular membranes, regulating membrane organization, fluidity, and signaling. However, cholesterol analysis remains technically challenging, as no single method currently allows both accurate quantification and spatially resolved visualization. Biochemical assays provide accurate quantification but lack spatial resolution, whereas imaging strategies can perturb membrane organization or cholesterol accessibility. Here, we describe optimized protocols using fluorescent D4 probes derived from the cholesterol-binding domain of perfringolysin O (D4-mCherry and D4-GFP) to detect, visualize, and quantify cholesterol in biological samples. We detail procedures for probe production, purification, and application, and establish conditions that ensure robust and reproducible labeling of membrane-accessible cholesterol. By combining fluorescence-based imaging with quantitative analysis, this approach enables the assessment of cholesterol distribution while preserving its native membrane environment. The proposed methodology provides a versatile and reliable framework for studying cholesterol in a wide range of experimental systems.

A recent clinical study tested the effects of two different monoclonal antibodies (mAbs) (siltuximab, anti-IL6; tocilizumab, anti-IL6R) on the fate and function of T-cells in people with type 1 diabetes. While both mAbs affect the response of T-cells to stimulation, they have very different, sometimes opposing mechanisms. Here, we use mass-spectrometry based proteomics to analyze longitudinal serum samples (baseline and two weeks post-treatment) from 20 clinical trial participants to examine the effects of siltuximab and tocilizumab on extracellular vesicles. To accomplish this, serum samples were enriched for extracellular vesicles with Mag-Net and analyzed by LC-MS/MS to identify significantly differentially abundant protein groups and pathways. Proteome analysis confirmed highly reproducible measurements across multiple draw dates. In total, we quantified >3300 protein groups of which 46 protein groups had significantly altered abundance after mAb treatment. Tocilizumab altered pathways associated with proteostasis (neddylation) and pre-notch transcription and translation. Siltuximab altered FCGR activation pathway members. In addition, quantitation of the monoclonal antibody therapies themselves enabled the measurement of the correlation between drug amounts and impacted proteins. Taken together, this work demonstrates the utility of the Mag-Net method to evaluate the impacts of therapeutic interventions on serum extracellular vesicles.

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

Affinity purification is a essential technique for isolating highly purified proteins; however, generating affinity ligands require significant time and financial investment. To address these limitations, this study proposes a novel affinity chromatography method utilizing in silico-designed cyclic peptides as ligands. Targeting Complement C1q (C1q), a plasma protein that plays crucial roles in classical complement pathway, we employed the biomolecular structure prediction model, AlphaFold2, to design specific binding cyclic peptides. Based on these designs, we synthesized lariat-type cyclic peptides characterized by disulfide cyclization and biotinylation, which were subsequently immobilized on streptavidin carriers. Performance tests confirmed that the resulting column specifically captured C1q, allowing for elution via a standard NaCl concentration gradient. Notably, high selectivity was preserved even in the presence of plasma, underscoring the ligands practical robustness. By overcoming traditional constraints through (1) rapid and simple design, (2) high specificity, and (3) universal versatility without genetic modification, this de novo design strategy represents a potential breakthrough in protein purification technologies. HighlightsO_LIAI-driven de novo design generated a specific cyclic peptide ligand for Complement C1q C_LIO_LIThe synthetic ligand enabled one-step purification of Complement C1q directly from human plasma C_LIO_LIMild elution conditions preserved the targets oligomeric structure and native interactome C_LIO_LIThis label-free strategy offers a rapid, low-cost alternative to antibody-based chromatography C_LI

Inositols are a family of cyclic sugar alcohols comprising nine stereoisomers. Myo-inositol is the most abundant isomer found in humans and has been studied most extensively. It plays an important role in osmoregulation and is incorporated into membrane-anchored phosphatidylinositols. Scyllo-inositol is the second most abundant inositol isomer in the human brain and aberrant concentrations are associated with various diseases; however, its biological functions remain poorly understood. Here, the development and application of [13C6]scyllo-inositol as an isotopic tracer to study its metabolism is reported. A concise and robust synthetic route was established to obtain [13C6]scyllo-inositol from [13C6]myo-inositol in good yield. The uptake of [13C6]scyllo-inositol and responses of endogenous inositol isomers were measured in multiple cell lines by HILIC-MS/MS, showcasing the advantages of isotopic tracing. [13C6]scyllo-inositol proved to be a versatile isotopic tracer, when coupled with MS-based lipidomics and 2D NMR experiments. These experiments provide evidence that scyllo-inositol is incorporated into phosphatidylinositols in different cell lines. The results suggest a previously underappreciated role of scyllo-inositol in mammalian cells. The utilization of [13C6]scyllo-inositol will help to elucidate the role of scyllo-inositol metabolism in healthy and diseased states. SignificanceScyllo-inositol is a cyclic sugar alcohol found predominantly in the human brain. Changes in its concentration are associated with different diseases, and scyllo-inositol has been investigated as a potential drug against Alzheimers disease in clinical trials. However, its metabolic fate in mammalian cells is not well understood. We report here a synthetic strategy to obtain [13C6]scyllo-inositol and demonstrate, through isotopic tracing, its incorporation into phosphatidylinositols in different human-derived cell lines. This new stable isotopic tracer enables the investigation of the biological role of scyllo-inositol in mammals and beyond. HighlightsO_LIConcise synthesis of [13C6]scyllo-inositol C_LIO_LI[13C6]scyllo-inositol uptake and response of endogenous inositol isomers studied in multiple cell lines C_LIO_LIUse of [13C6]scyllo-inositol as an isotopic tracer in metabolomics and lipidomics experiments C_LIO_LIEvidence for scyllo-inositol incorporation into phosphatidylinositol in mammalian cells C_LI

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.

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.

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.

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

Predicting high-concentration viscosity of monoclonal antibodies such as IgG1 is crucial for their development as therapeutics for subcutaneous delivery. Unfortunately, traditional experimental rheometry methods for assessing viscosity are low-throughput. This study evaluates Self-Interaction Nanoparticle Spectroscopy (SINS) assays--specifically charge-stabilized SINS (CS-SINS) and PEG-stabilized SINS (PS-SINS)--for high-throughput viscosity prediction. We characterized 96 IgG1 antibodies, assessing SINS against in silico descriptors and dynamic light scattering (DLS) data. CS-SINS showed strong correlation with charge, offering limited additional utility. In contrast, PS-SINS provided orthogonal information; integrating it with in silico data and DLS significantly improved random forest model accuracy for binary viscosity classification. PS-SINS measurements in multiple buffers captured complementary information, achieving comparable accuracy without DLS. Importantly, PS-SINS scores exhibited a strong logarithmic relationship (r=0.98) with high-concentration viscosity in Fc variants of clinical antibodies, suggesting a direct mechanistic link. Furthermore, PS-SINS performed reliably with one column purified (protein A) samples, supporting its early-stage application. These findings establish PS-SINS as a high-throughput tool to accelerate the developability assessment of antibody candidates.

Extracellular vesicles (EVs) are cell-secreted biological nanoparticles that play a crucial role in intercellular communication and are gaining increasing attention as diagnostic biomarkers, therapeutic agents, and drug delivery vehicles. Consequently, the development of robust and sensitive methods for their characterization is essential. Herein we present the use of a microscope-mounted nanofluidic device for direct size determination and multi-parametric (3-color) fluorescence-based phenotyping of single biological nanoparticles that are in the size range of 20-200 nm in a method we denote Nano-SMF (SMF; size and multiplexed fluorescence). We demonstrate that it is possible to accurately determine the size of nanoparticles by analyzing their one-dimensional Brownian motion during directional flow through nanochannels, achieving size distributions for monodisperse nanoparticle solutions that are on par with TEM analysis, and size discrimination of nanoparticle mixtures that is significantly improved compared to conventional nanoparticle tracking analysis (NTA). Furter, we demonstrate that the method can be applied to analyze EVs directly in minute volumes of cell supernatant, avoiding pre-isolation or concentration steps. The method was applied to phenotype CD63- and CD81-positive EVs from a human embryonic kidney cell model, demonstrating that vesicle sub-populations defined by these two tetraspanin biomarkers differ significantly in size.

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

Understanding allelic variability is crucial for elucidating intrinsic bacterial mechanisms and distinguishing phenotypic profiles. However, such variability poses a major challenge for the reliable identification of proteins in data-independent acquisition (DIA) proteomics. To address this, we developed an analytical workflow that integrates protein sequence variability to enhance proteome coverage. Fifteen Legionella pneumophila isolates were analyzed using DIA-NN, with spectral libraries generated either from a reference proteome or incorporating allelic variability. Our workflow includes protein clustering and subsequent protein inference from these clusters, allowing the accurate assignment of shared and variant-specific peptides. Integration of variability enabled the identification of a comparable number of proteins as the reference proteome while capturing between 28 and 77 % of variant-specific sequences in each isolate, all while maintaining a low false positive rate. These findings demonstrate that accounting for allelic variability substantially improves proteomic coverage and identification confidence, providing a more comprehensive view of the proteome. This approach facilitates a deeper understanding of biological mechanisms and enables precise bacterial proteotyping of Legionella pneumophila isolates.

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

Osmotic pressure has been known to play essential roles in living systems from single cells to complex tissues. However, direct in-situ measurements of osmotic pressures in biosystems have remained challenging, especially in complicated heterogeneous systems in which osmotic pressure gradients could exist and induce directed forces. Bacterial biofilms -- organized communities of bacteria encased in a self-produced extracellular matrix -- are a major mode of bacterial life. It has, however, remained unexplored how the osmotic pressure is distributed in the biofilm and how this distribution contributes to biofilm growth and activity. Here, liposomal nano-sensors are developed for the in-situ mapping of osmotic pressures at an unprecedented microscale resolution in real time using Escherichia coli. biofilm as a model system that develops at the surface of a hydrogel containing the nutrients. The measurements reveal osmotic pressure gradients with a radially increasing trend from the inner regions to the outer regions of the biofilm, which is associated with biofilm formation, morphology, and metabolism. The gradients likely contribute to mechanical properties, internal stresses, and nutrient transport. The sensor readouts also show that there is an osmotic pressure difference between the biofilm and the adjacent medium, which may promote biofilm expansion through matrix swelling and bacteria growth via water and nutrient uptake from the surroundings. Our novel approach based on in-situ osmotic pressure mapping in a growing biofilm reveals a sophisticated spatial regulation of physical forces, which may inspire new models and approaches in the field of mechanobiology.

Data-independent acquisition (DIA) proteomics enables reproducible and systematic peptide detection and quantification, and trapped ion mobility spectrometry (TIMS) on the timsTOF platform further improves DIA by synchronizing ion mobility separation with quadrupole precursor sampling. Analyzing the highly multiplexed spectra generated by DIA typically relies on spectral libraries, and fully leveraging the additional ion mobility dimension requires these libraries to include accurate retention time, fragment ion intensity, and ion mobility annotations. Existing in silico spectral library generation tools either lack ion mobility support entirely or rely on models trained on data-dependent acquisition (DDA) data, that can introduce a mismatch that may not capture unique experiment-specific biases when applied to each respective timsTOF dataset. Carafe is a software tool that uses deep learning models to generate high-quality, experiment-specific in silico libraries by training directly on DIA data. In this study, we extend Carafe to generate libraries for timsTOF DIA data, which involves fine-tuning retention time (RT), fragment ion intensity, and ion mobility prediction models using timsTOF DIA data. Carafe2 operates directly on native timsTOF raw data (Bruker .d directories) without the need for data conversion. We demonstrate the performance of Carafe2 across a wide range of DIA applications, including global proteome, phosphoproteome, and plasma proteome datasets. Comparing Carafe2 fine-tuned RT, fragment ion intensity, and ion mobility prediction models with pretrained DDA models, we find that Carafe2 models outperform pretrained models on a variety of DIA datasets. We then demonstrate the utility of in silico libraries generated by Carafe2 for peptide detection on several different types of timsTOF DIA datasets by comparing with the libraries generated with DDA-trained AlphaPeptDeep models, DIA-NN built-in models, and empirical spectral libraries generated from DDA experiments.