Analytical Chemistry

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.

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.

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles that mediate intercellular communication through the transfer of bioactive molecules. Their growing relevance in translational applications demands downstream purification workflows that are selective, scalable, and compatible with robust impurity control. Conventional EV isolation methods primarily rely on physicochemical properties such as size, density, or charge and therefore co-enrich overlapping EV fractions together with non-vesicular impurities. Here, we establish a Nanofitin(R)-based affinity chromatography workflow for selective enrichment of a CD81-positive EV fraction under EV-compatible elution conditions. Nanofitin(R) candidate NF06 was identified by ribosome display against the large extracellular loop of CD81 and combined nanomolar affinity with favorable release behavior while retaining binding after repeated regeneration cycles. Static screening with recombinant CD81 and HEK293-derived EVs identified 1 M arginine at pH 10 as the most suitable elution condition. Dynamic chromatography on a 1 mL column using tangential flow filtration-concentrated HEK293 conditioned medium achieved 66.9% overall recovery with an elution step yield of 57.7%. In parallel, dsDNA, host cell protein, and total protein were reduced by 2 to 3 log relative to conditioned medium. Nano flow cytometry showed enrichment of the CD81-positive EV fraction from 40% in conditioned medium to more than 90% in the eluates, together with a smaller and narrower particle size distribution. These results demonstrate that Nanofitin(R)-based affinity chromatography provides a practical route toward marker-defined EV enrichment that combines selective capture, EV-compatible release, and substantial impurity clearance in a chromatography-compatible process format.

Aim: To comprehensively characterize the salivary proteome in periodontitis using Orbitrap Astral data-independent acquisition mass spectrometry (DIA-MS), identify an atlas of differentially expressed proteins (DEPs), and develop a machine learning-derived multi-protein biomarker panel for non-invasive diagnosis of stage III/IV periodontitis. Materials and Methods: Unstimulated saliva samples from 199 participants (periodontal health/gingivitis, n=120; stage III/IV periodontitis, n=79) were analyzed by Orbitrap Astral DIA-MS. DEPs were identified, and pathway enrichment analysis was performed. A two-tier machine learning pipeline, integrating pathway-based feature selection with cross-validated evaluation, was applied to identify the optimal diagnostic panel. Results: Orbitrap Astral DIA-MS quantified 5,597 salivary proteins and 1,966 DEPs (|log2FC|>0.5, FDR<0.05). Pathway analysis identified 14 periodontitis-relevant KEGG pathways, including Th17 cell differentiation, IL-17 signaling, neutrophil extracellular trap formation, and complement and coagulation cascades. A four-protein panel (TEC, RAC1, MAPK14, KRT17) achieved an area under the curve (AUC) of 0.985 plus-or-minus sign 0.010, with 83% sensitivity and 100% specificity. The panel was corroborated using public datasets. Conclusions: To our knowledge, this study represents the first application of Orbitrap Astral DIA mass spectrometry in periodontitis research, establishing a disease-specific DEPs atlas and a salivary biomarker panel with high diagnostic accuracy for stage III/IV periodontitis, providing a foundation for future external validation studies.

The immunohistochemistry (IHC) methods widely used in diagnostic medicine and biomedical research are kinetically complex reaction-diffusion processes that, ideally, produce stain intensities correlated with the local antigen concentration. Yet after 75 years of use, practical theoretical tools to rigorously plan and interpret IHC experiments are still lacking. Because modeling the reactions requires time-consuming computer simulation, impractical for regular use, most protocols are optimized empirically, without detailed knowledge of the reaction rates and antigen-antibody equilibria. The resulting stain intensities can be calibrated against standards with known antigen abundance, but they are typically not interpretable in terms of chemical antigen concentrations. To address these limitations, we developed a fast interpolation method to model reaction-diffusion behavior, and experimental methods to characterize IHC kinetic parameters in formalin-fixed paraffin-embedded (FFPE) samples. Used together, these allow experimental measurement of both the chemical concentration of antigen in the sample and the reaction-diffusion parameters consistent with the assay results. Results show 1) direct immunofluorescent detection has low nanomolar sensitivity with >1000-fold dynamic range, and 2) antibody diffusion rates in FFPE samples can be >1000-fold slower than in aqueous solutions, producing diffusion-limited conditions in which the IHC reaction time course may depend on the sample antigen concentration. Awareness of these details is necessary to avoid potential underestimation of both the absolute and relative antigen concentrations in different samples that may occur if staining is stopped before reaching equilibrium. Software tools are provided to allow users to rapidly model IHC reaction time courses and to fit experimental time course data with candidate reaction parameters. The principles described here apply equally to other tissue-based "spatial omics" analyses and should be considered when designing and interpreting experiments requiring any macromolecule to diffuse into and react in a tissue section. SIGNIFICANCEThe theoretical and experimental framework described here advances IHC staining from a qualitative or semi-quantitative method towards a more rigorously quantitative assay. The practical ability to predict IHC reaction kinetics and fit reaction parameters to experimental data has the potential to advance IHC applications in diagnostic medicine and biomedical research in three ways: 1) interpretation of experimental and diagnostic samples stained under different conditions can be more objective, facilitating comparison of results from different protocols and different laboratories; 2) IHC staining can be interpreted as molar chemical antigen-antibody concentrations calculated from the reaction parameters measured in the studied sample; 3) the correlation between antigen concentration and biological behavior can be examined more reliably. Practical software tools are provided.

1.1Hair has applications in biomarker discovery and forensics, yet the influence of proteomics software tools on hair proteome characterisation remains underexplored. This study compares four bottom-up proteomics workflows (MaxQuant, FragPipe, MetaMorpheus, and SearchGUI/PeptideShaker). Publicly available hair proteomes were analysed following extraction with 1-dodecyl-3-methylimidazolium chloride (DMC), sodium dodecanoate (SDD), sodium dodecyl sulfate (SDS), and urea. Data were acquired on Orbitrap-based DDA platforms. Peptide identification, protein inference, functional annotation, physicochemical properties, and label-free quantification (LFQ) were evaluated. Peptide-level performance differed across tools. MS-GF+ and FragPipe identified the most unique peptides, while X!Tandem reported the fewest. Protein inference showed a dissociation from peptide-level results. MetaMorpheus reported the highest number of protein groups despite only the third highest peptide counts. FragPipe and MaxQuant followed, while PeptideShaker consistently inferred the fewest proteins. Protein-level concordance was low, with only 30.3% overlap across tools and extraction methods. These differences extended to downstream analyses. Functional enrichment showed moderate concordance (38.25% overlap). Physicochemical profiles varied, with MetaMorpheus identifying more hydrophobic proteomes and PeptideShaker more hydrophilic profiles. At the quantitative level, reproducibility depended on extraction buffer. SDS and urea showed lower variability (CV =< 0.025), while DMC and SDD showed higher variability (up to 0.10). Absolute LFQ intensities and differential expression outputs varied across tools despite moderate to strong correlation (r = 0.77 to 0.93). Overall, software choice influences proteome coverage, physicochemical profiles, and quantitative outcomes. Relative trends were partially conserved, but magnitude and significance varied. These findings support careful method selection and multi-tool validation in hair proteomics

This protocol describes a computational approach for constructing correlation-based molecular networks from untargeted metabolomics data using MetVAE, a variational autoencoder-based framework. Complementing spectral similarity networks, it captures functional relationships re-flected in cross-sample correlations. The workflow imports metabolomics features and sample metadata, adjusts for compositionality, missingness, confounding, and high-dimensionality, esti-mates sparse metabolite correlations, and exports GraphML files for network visualization. In a hepatocellular carcinoma mouse model, it links lipid classes in high-fat-diet animals, suggesting an endogenous "auto-brewery" route to lipotoxic metabolites.

Technological advances in biomedical sciences have accelerated multi-omics research, enabling high-resolution spatial mapping of diverse molecular compound classes. However, integrating spatial omics often requires serial tissue sections, limiting the alignment correlation across modalities. We present a single-section integrative multi-omics (SIMO) workflow that combines metabolite and lipid imaging with histopathology and region-specific proteomics. Using MALDI-MSI, tissue staining, and laser microdissection (LMD), SIMO delivers comprehensive metabolic, lipidomic, and proteomic insight from the same sample. Using mouse cardiac tissue we develop, control, and validate the methodology resulting in [~]60 imaged lipids and [~]60 imaged metabolites at 20 {micro}m pixel size and subsequently spatial proteomics by LMD, detecting over 5,000 proteins from the same tissue. To demonstrate the capabilities of the workflow in preclinical context, we apply SIMO to a metastasizing melanoma PDX model, identifying over 100 spatially localized lipids and metabolites, and over 5,000 proteins across metastases and non-tumor tissues in liver. SIMO enables precise ROI selection, statistical comparison of protein regulation, and alignment of metabolic and lipidomics pathways across spatial omics and region-specific proteomics, demonstrating its value as a spatial multi-omics platform.

Advances in ultra-fast mass analyzer technology and procedural automation have enabled proteomics screening at the throughput of hundreds of proteomes per day. However, these approaches often require expensive instrumentation upgrades and robotic automation that remain inaccessible to many research laboratories and core facilities. In this study we address the feasibility of scaling up proteomic screening capabilities with minimal upgrade cost by focusing on (a) strategies for non-automated high-throughput sample preparation from 96-well cell culture, (b) data acquisition on sub-50Hz scan speed hybrid and tribrid Orbitrap instruments and (c) data analysis strategies for label-free and labeled proteomic screening. We find that the 96-well format STrap, in combination with C18 plates, provides the most robust throughput for a non-automated sample preparation workflow. Furthermore, we show that for static proteomes, an isobaric tandem mass tag-based (TMT) multiplexing approach provides deeper and more precise proteome coverage whereas label-free data-independent acquisition (DIA) is more accurate, albeit with a reduced dynamic range and more missing values. Finally, we extend the optimized workflow to proteome turnover studies using pulsed stable isotope labeling by amino acids in cell culture (pSILAC), highlighting the key advantages and trade-offs of DIA and TMT data-dependent acquisition strategies for capturing protein translation. Together, these results provide a practical framework for designing high-throughput proteomics experiments that balance throughput, depth, and quantitative accuracy using existing instrumentation, without requiring major hardware upgrades or automation.

Plants produce the most diverse blends of specialized metabolites on earth. Natural products derived from plants are valuable resources for drug development, food chemistry, and crop resistance breeding. Phenotypes of specialized metabolite profiles can be captured by untargeted mass-spectrometry across species phylogeny, tissues, and genotypes. Here, we collected metabolic fingerprints of 17 Brassicaceae species across three tissues (paired leaf and root; flower) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in positive and negative ionization mode. Corresponding metadata has been refined for reuse according to ReDU guidelines, and for integration with public genomic and transcriptomic data. Standardization of in vitro growth conditions, and data processing workflows enables integration of acquired raw and processed data across platforms for single- and multi-omics analysis. Further, the inclusion of tissue-specific metabolic profiles across ploidy levels, as well as across crop species and wild relatives, makes this dataset a valuable resource for natural product discovery.

Automation of multi-step mRNA imaging protocols increases reproducibility and throughput in spatial biology, as many workflows require repeated buffer exchanges, precise timing, and controlled reaction conditions. Commercial automation platforms can be expensive, proprietary, and difficult to customise, limiting their use in most laboratories. Here, we present two open-source robots for the Rapid Amplified Multiplexed Fluorescent In-Situ Hybridization (RAM-FISH) workflow based on programmable delivery of fluids and integrated thermal control with no dedicated bubble trap requirement. The first robot is designed to perform the steps necessary for signal localization (Multiplexer), and the second performs signal removal (RemBot). Both robots function without manual supervision and conduct precise, repeatable buffer exchanges, temperature regulation, and timed reactions. Both can operate on free-floating and gel-embedded tissues and can be assembled using widely available components. The robots support iterative imaging workflows, enabling detection of multiple genes across sequential hybridization rounds within the same sample. By providing customizable and accessible robots, we lower the technical know-how barriers that need to be overcome to perform complex spatial imaging experiments and enable scalable, hands-free execution of multi-step multiplex-FISH.

High-parameter flow cytometry is essential for dissecting the intricate landscape of T-cell diversity. In this study, we directly compare conventional flow cytometry (CFC) and spectral flow cytometry (SFC) for high-dimensional T-cell phenotyping, assessing how spectral detection and panel-design strategies influence analytical performance. Using peripheral blood mononuclear cells from healthy donors stained with both an established (v1) and an optimized (v2) fluorochrome-labelled antibody panel, and analyzed through manual gating and unsupervised approaches, we found that CFC reliably identified major T-cell subsets. However, spectral acquisition consistently delivered clear technical advantages, including improved signal-to-noise ratios, higher staining index values, and superior resolution of low-intensity and co-expressed markers. These improvements translated into more sharply delineated multidimensional clusters and a markedly enhanced resolution of T-cell differentiation states. Moreover, the optimized spectral panel enhanced the unsupervised detection of rare populations, such as cytotoxic CD4 T-cells (PD-1GZMB). However, despite the overall increase in data quality achieved with SFC, the selection of antibody clones may influence the measured frequencies of the identified populations. Finally, SFC - particularly when coupled with rational panel optimization and the use of advanced fluorophores - consistently delivers superior, higher-quality measurements and improved multidimensional resolution, thereby substantially enhancing the robustness and sensitivity of high-parameter T-cell phenotyping for comprehensive immunological studies.

Grapevine (Vitis vinifera L.) is highly prone to viral infections that pose a significant threat to global viticulture sustainability. Traditional detection methods, such as PCR and ELISA, are limited to well-known pathogens, highlighting the need for more comprehensive and unbiased approaches. Here, we present the development of a cost-effective viral enrichment system adapted to next-generation sequencing (NGS) for the detection and characterization of grapevine viruses. Our strategy leverages hybridization-based capture using biotin-labeled cDNA probes hereafter named "Chloro-Zero") designed to selectively deplete highly abundant host transcripts particularly plastid and ribosomal RNAs while preserving viral RNA. Probe design was informed by transcriptomic analysis of V. vinifera. We evaluated different subtractor-to-target RNA ratios, observing a consistent reduction of host RNA and a moderate enrichment of viral sequences. NGS analysis revealed improved recovery of low-abundance viral transcripts, with coverage levels comparable, to a certain extent, to those obtained using previously available commercial kits, but at a significantly lower cost. Although variability in depletion efficiency was observed, the results demonstrate the potential of this scalable and locally adaptable protocol for virome profiling in grapevines. By addressing key limitations of current depletion methods, our approach facilitates the detection of emerging viral threats and supports the development of more effective certification programs and sustainable management practices. Ongoing improvements in probe design and bioinformatic workflows are expected to enhance performance, providing a robust platform for broader applications in plant virology.

Extracellular vesicles (EVs) mediate intercellular transfer of lipids, proteins, and nucleic acids between nearly all cell types. We previously showed that astrocyte-derived EVs modulate neuronal mitochondria in vitro, but whether endogenous astrocytic EVs are trafficked to neuronal mitochondria in vivo remained unknown. To address this, we generated an EV reporter mouse, Aldh1l1-Cre; CD9-tGFPfl/fl, in which astrocyte-secreted EVs are labeled with a CD9-turboGFP fusion protein (CD9-tGFP). Astrocyte-specific expression of CD9-tGFP was verified in brain tissue and isolated EVs, comprising 13.2 {+/-} 1.6% of total brain EVs. In primary glial cultures, CD9-tGFP was restricted to astrocytes, localizing to vesicular compartments and cell protrusions (filopodia and cilia), with 89.3 {+/-} 2.2% of astrocyte-derived EVs carrying the label. These EVs were enriched with the sphingolipid ceramide, consistent with its co-distribution with CD9-tGFP in astrocytic cell protrusions. In the cortex, hippocampus, and cerebellum, CD9-tGFP was predominantly detected in astrocytic processes co-labeled with GLAST1 and GFAP, forming contacts with laminin-positive capillaries and parvalbumin-positive neurons. CD9-tGFP-labeled EVs were detected inside capillaries and neurons, and super-resolution STED microscopy revealed partial overlap with neuronal mitochondria. Live-cell spinning disk confocal imaging and AI-assisted proximity analysis confirmed uptake of CD9-tGFP EVs by neuronal cells and trafficking of their cargo to mitochondria in vitro. Biochemical isolation of synaptic and non-synaptic mitochondria confirmed EV-derived cargo on mitochondria in vivo, with 3-fold higher association of CD9-tGFP with synaptic than non-synaptic mitochondria. Together, these findings validate the Aldh1l1-Cre; CD9-tGFPfl/fl reporter mouse as a powerful tool for tracking astrocyte-derived EVs in vivo and provide direct evidence that their cargo is preferentially trafficked to synaptic mitochondria. Graphical AbstractAstrocyte-derived extracellular vesicles target neuronal mitochondria in vivo O_FIG O_LINKSMALLFIG WIDTH=156 HEIGHT=200 SRC="FIGDIR/small/718987v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@174d92aorg.highwire.dtl.DTLVardef@5d8248org.highwire.dtl.DTLVardef@114483borg.highwire.dtl.DTLVardef@924d55_HPS_FORMAT_FIGEXP M_FIG C_FIG

Massively multiplexed qPCR is primarily constrained by increasing primer dimer formation as the number of distinct primers in a single reaction increases. Previous multiplex primer design algorithms either fail to sufficiently suppress primer dimers at 100+ plex, or take exceedingly high amounts of computational resources to complete. Here, we present DIMPLE, a linear-runtime primer design algorithm that effectively generates 10,000+ primers to amplify thousands of potential amplicons in a single qPCR reaction. As one clinical demonstration of this algorithm, we designed an assay to detect 2,302 distinct KMT2A gene fusion subtypes using 204 primers in a single tube. In contrast to FISH and convention NGS approaches with 2% variant allele frequency (VAF) limit of detection, our DIMPLE qPCR assay was able to analytically detect gene fusions down to 0.05% VAF. We also constructed proof-of-concept multiplex qPCR panels for additional oncology gene fusions, multiplex pathogen detection, and DNA methylation markers. The scalability and low computational cost DIMPLE are complementary to new instrument platforms for massively multiplex qPCR readout for enabling rapid, point-of-care nucleic acid testing.

Per- and polyfluoroalkyl substances (PFAS) are highly resistant to enzymatic C-F bond cleavage, and hydrolytic defluorination of long-chain PFAS has rarely been demonstrated. Here, we report selective hydrolytic defluorination of branched perfluorooctanoic acid (PFOA) isomers by a haloacid dehalogenase (4A) from Delftia acidovorans strain D4B. A fluoride-specific riboswitch biosensor was used for initial substrate screening, followed by scaled-up assays in which fluoride release was quantified using a fluoride ion-selective electrode. Defluorination products were subsequently identified by liquid chromatography-mass spectrometry (LC-MS). Although purified 4A (10 M) readily catalyzed hydrolytic defluorination of fluoroacetic acid, incubation of PFOA (0.5 mM) with purified 4A resulted in a statistically significant increase in fluoride release at elevated enzyme loading (500 M). High-resolution LC-MS/MS analysis revealed that defluorination products originated from minor branched PFOA isomers rather than linear PFOA. Molecular docking analyses supported catalytically plausible binding geometries for branched PFOA isomers, positioning the substrate -carbon within [~]4 [A] of the catalytic aspartate residue. These findings demonstrate previously unrecognized hydrolytic reactivity of a haloacid dehalogenase toward branched PFAS isomers and expand the known catalytic scope of the haloacid dehalogenase family. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=109 SRC="FIGDIR/small/719434v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1c12fb1org.highwire.dtl.DTLVardef@224ae3org.highwire.dtl.DTLVardef@16293b7org.highwire.dtl.DTLVardef@d014b7_HPS_FORMAT_FIGEXP M_FIG C_FIG SYNOPSISEnzymatic defluorination of PFAS is rarely observed in environmental systems. This study identifies hydrolytic defluorination of branched PFOA isomers, improving understanding of PFAS defluorination at the enzyme level.

Cell-penetrating peptides (CPPs) can deliver diverse cargos into cells. However, designing CPPs with receptor-selective interaction profiles remains difficult because interactions with individual cell-surface components cannot be tuned independently. Here, we developed a closed-loop in silico framework for receptor-selective CPP design, in which receptor interactions are formulated as explicit objectives in a multi-objective optimization problem. We first constructed a CPP-like candidate library using a sequence generative model fine-tuned on known CPPs. The framework then evaluated candidate peptides by receptor-wise docking, molecular dynamics simulations, and MM/GBSA to compute receptor-wise binding scores. These scores were used iteratively to propose subsequent candidates by multi-objective Bayesian optimization. Applied to a CXCR4/NRP1 design setting, the framework identified candidates with more favorable predicted interaction profiles, characterized by higher CXCR4 binding scores and lower NRP1 binding scores. We selected 10 peptides from the computationally identified candidates for cell-based imaging and found that 4 showed higher enrichment in CXCR4-positive regions than in NRP1-positive regions under the tested conditions. These results show that the proposed framework provides a practical in silico approach for designing CPPs with receptor-selective interaction profiles.

Exacerbations of respiratory viral infections significantly contribute to morbidity and healthcare burden. Among these viruses, Human Rhinoviruses (HRVs) are the most frequent causative agents of upper respiratory tract infections. To date, over 150 HRV serotypes have been identified, classified into three species: HRV-A, HRV-B, and HRV-C. No antiviral therapies are currently available against this viral family, largely due to the high serotype diversity and limited cross-protection. The major group of HRVs relies on the Intercellular Adhesion Molecule-1 (ICAM-1) receptor to infect airway epithelial cells, making ICAM-1 an attractive target for broad-spectrum therapeutic interventions. Here, we report the development of nucleic acid-based aptamers designed to disrupt ICAM-1-HRV binding and thereby prevent viral infection. Aptamers are single-stranded DNA molecules that fold into precise three-dimensional structures, enabling highly specific protein recognition. Using a Systematic Evolution of Ligands by EXponential Enrichment (SELEX) approach guided by a minimal peptide mimicking the ICAM-1 viral binding interface, a library of >1024 random single-stranded DNA sequences was screened. Through iterative rounds of selection, we identified eight candidate 77-nt DNA aptamers, which were subsequently evaluated for their potential using in silico and in vitro assays, as well as functional assays in human epithelial cells. From this strategy, two lead aptamers were selected that effectively inhibited HRV-A16 replication in a concentration-dependent manner, as measured by viral titers (TCID assay) and viral RNA quantification by RT-PCR. These findings demonstrate the potential of ICAM-1-targeting aptamers as antiviral agents capable of preventing HRV entry. By targeting a host receptor and creating a protective barrier at the cell surface, this approach may offer a broadly applicable strategy against multiple HRV serotypes, paving the way for the development of novel antiviral interventions. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=131 SRC="FIGDIR/small/717810v1_ufig1.gif" ALT="Figure 1"> View larger version (26K): org.highwire.dtl.DTLVardef@1f0c564org.highwire.dtl.DTLVardef@2f5035org.highwire.dtl.DTLVardef@3b063eorg.highwire.dtl.DTLVardef@116ed49_HPS_FORMAT_FIGEXP M_FIG C_FIG

Myelin is a highly complex membranous structure wrapped around axons by oligodendrocytes or Schwann cells in the central and peripheral nervous system, respectively. Fluorescent labeling is widely used to study the structure and dynamics of myelin. Combining structural with functional imaging requires labeling of myelin with red fluorescence, as many functional sensors, including Ca2+ indicators and genetically encoded metabolite sensors, fluoresce in the green spectral range. However, in vivo tools enabling red fluorescent labeling of myelinating cells and their myelin sheaths remain limited. Here, we generated a set of seven transgenic mouse lines expressing a membrane-targeted variant of the red fluorescent protein tdTomato in myelinating oligodendrocytes and Schwann cells throughout the nervous system. The mouse lines provide a variety of expression patterns ranging from wide-spread labeling of myelin to a rather sparse expression, the latter enabling visualization of individual oligodendrocytes and their associated myelin sheaths. In the peripheral nervous system, the pattern of fluorescence in sciatic nerves indicates predominant localization of tdTomato to non-compact myelin compartments including the inner and outer tongues, paranodal loops and Schmidt-Lanterman incisures. In summary, our work provides a set of novel mouse lines with myelin labeled by red fluorescence, which are compatible with diverse imaging modalities in the green spectral range enabling integrated structural and functional imaging. Main PointsO_LITransgenic mouse lines expressing membrane-targeted tdTomato in myelin enable imaging of myelin in the red spectral range C_LIO_LIDistinct expression patterns range from wide-spread labeling to sparse single-cell resolution, supporting diverse imaging applications C_LI