Biosensors and Bioelectronics

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.

Multivalent interactions are fundamental to many biological systems, including how antibodies bind to their respective antigens. Still, their accumulated binding strength, i.e. avidity, resulting from binding and rebinding events of multiple interacting points, remains difficult to measure. Classical assay platforms like ELISA and SPR lack control over antigen positioning, limiting the resolution of the binding characteristics that shape biological outcomes at nanoscale. Here, we present PANMAP, a planar and plate-based assay inspired by ELISA that uses DNA origami to present antigens at defined nanoscale patterns, enabling direct measurements of spatially resolved binding events, termed avidity profiles. Using IgG antibodies as a model system, PANMAP distinguishes between monovalent and bivalent binding states by combining equilibrium absorbance measurements with a simple biophysical model. The avidity profiles reveal how the balance shifts between monovalent and bivalent interactions as a function of antibody concentration and antigen spacing, with intermediate separation distances favoring bivalent binding events and excluded at both near and far distances. This spatial profiling allows decoupling of affinity dependency from avidity profiles to reveal how spatial constraints influence binding equilibria. Our approach fills a longstanding gap in multivalent interaction measurement and offers a new tool for antibody engineering, development of multivalent reagents, therapeutic screening, and mechanistic immunology.

Rapid pathogen evolution, exemplified by SARS-CoV-2 during the COVID-19 pandemic, threatens public health by eroding the effectiveness of vaccines, therapeutics, and diagnostic tools through continuous viral mutation. Although spike protein targeting monoclonal antibodies (mAbs) were developed within 10-12 months of the initial outbreak to serve as key theranostic agents, their redesign has struggled to keep pace with viral evolution, rendering many neutralizing antibodies ineffective. Here we demonstrate a novel platform that integrates a random-rational hybrid library diversification with high-throughput MiSeq screening to evolve aptamers as highly versatile recognition elements that can be easily reprogrammed to bind the spike proteins of emerging SARS-CoV-2 strains. Using a repurposed next-generation sequencing (NGS) platform, interactions between 3 different spike proteins and 11,806 unique aptamer variant designs can be effectively screened within a few days. Our starting point is a 40-nt aptamer that binds strongly to the wild-type (WT) spike protein but shows reduced and no affinity toward its Delta and Omicron strains, respectively. With this starting aptamer diversified, our rapid screening method yielded one double mutant that exhibits 4-fold improvement in binding to the Delta spike protein and another double mutant that converts its binding to the Omicron spike protein from no detectable affinity to the kd of nanomolar range. A selective WT binder was also identified with no binding the two variants of interest. Using this pipeline, we identified bases not previously recognized as part of the motif that contribute critically to spike protein binding. Moreover, our pipeline integrates screening data analysis with molecular dynamics simulations, providing insights into aptamer-protein binding interactions. A sensor was developed based on the identified WT-selective binder, enabling highly specific detection of the WT spike protein with minimal cross-reactivity and robust performance in 40% serum. Together, these results demonstrate that aptamers can be rapidly optimized to bind new variants or selectively recognize a specific strain using the repurposed NGS platform. This work highlights the platform as a highly adaptive technology capable of obtaining aptamers within days to keep pace with rapidly evolving pathogens in future pandemics. TeaserA novel high-throughput MiSeq screening platform to rapidly evolve spike-protein-binding aptamers to keep pace with viral evolution.

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.

For a vision system, estimating the speed and direction of movement at the retinal input stage is an essential function for survival in many organisms. Retinal ganglion cells specific to this movement function were identified using multi-electrode array recordings in neonatal chick retina. Motion-evoked "visual streaks" and direction selective responses were observed in chick ganglion cells upon sequential activation as a response to moving bar stimuli. These characteristics were preserved in the sub-retinal prosthetic consisting of a semiconductor polymer film coupled to the blind chick retina which generated spatiotemporal activity patterns resembling those in natural vision. The motion parameters of direction and speed inferred from these recordings demonstrate that polymer-based prostheses can evoke physiologically relevant activity patterns, suggesting their potential to restore motion perception in degenerative retinae.

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.

Continuous, non-invasive cardiovascular monitoring is limited by the superficial sensing depth of Photoplethysmography (PPG), which is susceptible to peripheral artifacts. This study evaluates a wearable dual-modality prototype integrating dryelectrode Impedance Plethysmography (IPG) and PPG within a smartwatch form factor. Results from a pilot study (N=2) demonstrate that IPG signals exhibit a temporal lead over PPG across ventral and dorsal sites, supporting its greater penetration depth. During brachial artery modulation, IPG showed superior sensitivity to arterial recovery on the ventral forearm. Furthermore, 60-minute napping sessions revealed that while PPG remained morphologically stable, IPG signals underwent significant evolution, capturing distinct pulsewave archetypes. These findings suggest that wearable IPG provides a high-fidelity window into deep systemic hemodynamics typically reserved for clinical instrumentation.

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.

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.

Background. Breast cancer (BC) remains the most diagnosed malignancy and leading cancer-related cause of mortality in women worldwide. Although blood-based untargeted metabolomics has emerged as a promising modality for detecting early-stage BC, the clinical translation of this approach has been bottlenecked by two unresolved issues: (i) the field has almost exclusively relied on serum or plasma, which require venipuncture and cold-chain logistics, and (ii) machine-learning models reported on such data are frequently validated with protocols that are blind to analytical batch structure, producing optimistically biased performance estimates. Methods. We present a breast cancer detection study based on dried blood spots (DBS), an analytical matrix that enables self-collection and ambient-temperature shipping. A cohort of 2,734 participants (114 biopsy-confirmed BC cases; 2,620 non-cancer controls) was profiled by untargeted LC-MS/MS on a Thermo Scientific Orbitrap IQ-X coupled to a Vanquish UHPLC. A 39-metabolite panel meeting MSI Level 1 identification criteria was pre-specified a priori from the published breast-cancer metabolomics literature, frozen prior to LC-MS acquisition, and applied to the present cohort without any feature selection on the data. Six standard supervised-learning architectures (LASSO, Elastic Net, Linear SVM, PLS-DA, OPLS-DA, XGBoost) were evaluated on this pre-specified panel; OPLS-DA is reported only in the sex-matched subgroup analysis where a single-seed 5-fold stratified protocol permits a directly comparable fit. Per-batch control-median normalization is applied upstream; kNN imputation, log transform, and robust scaling are fit within each training fold. The evaluation battery comprises batch-aware StratifiedGroupKFold CV at single-seed (seed=42) with inter-seed SD quantified across 10 independent seeds, batch-aware nested CV, a 100-seed held-out 20%-batch validation with disjoint-batch isotonic probability calibration (30% calibration partition), PPV/NPV reporting at multiple operating points and three deployment prevalences, subgroup analyses by TNM stage and tumor grade, pathway-ablation sensitivity analysis, and a 1,000-iteration permutation test. Results. Under batch-aware evaluation (StratifiedGroupKFold, single-seed=42), AUC ranged from 0.914 to 0.949 across classifiers, with LASSO achieving 0.928 and XGBoost 0.949; inter-seed SD across 10 seeds was 0.002-0.006. At 95% specificity, LASSO reached 75.4% sensitivity and XGBoost 81.6%. Held-out batch validation (100 seeds) yielded mean AUC 0.912 for Elastic Net and 0.935 for XGBoost, confirming robust generalization. All 39 panel features showed high coefficient stability, and permutation testing on representative classifiers (LASSO, Linear SVM, PLS-DA) yielded p <= 0.001. Subgroup analyses showed weaker detection of stage IIA tumors (AUC 0.87, n=40) compared with stage IIB/IIIA (AUC 0.95), consistent with stronger metabolic signatures in more advanced disease. Bootstrap coefficient consistency of the Elastic Net classifier confirmed that all 39 panel features received a non-zero multivariate weight in >=80% of 100 stratified bootstraps. Conclusions. On this cohort of diagnosed, pre-treatment breast-cancer cases, DBS LC-MS metabolomic profiling delivers classification performance (AUC 0.928 for LASSO and 0.949 for XGBoost under batch-aware GroupKFold CV at single-seed=42; held-out AUC 0.912-0.935) that is robust across classifier families and biological pathways. The DBS matrix is non-radiating, self-collectable by finger-prick, and mailable at ambient temperature. Performance is weaker on stage IIA than on more advanced disease, and prospective validation in an independent asymptomatic screening cohort is required before clinical positioning as a decentralized triage modality.

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.

Advances in multi-dimensional imaging method and probe developments have brought super-resolution fluorescence microscopy into a functional era. They capture additional single-molecule fluorescence information concurrently with spatial localization, enabling simultaneous identification of molecular species and interrogation of nanoscale environments with rich, high-dimensional imaging information. However, the adoption of multi-dimensional imaging has been hindered by fragmented analysis workflows, complex parameter tuning, and limited integration of advanced computational methods. Here, we introduce an agentic single-molecule multi-dimensional bioimaging AI, referred to as SIMBA, an AI-driven platform that unifies single-molecule localization, spectral processing and deep learning-based denoising within a single agentic and interactive framework. SIMBA incorporates large language model-based agents capable of interpreting user intent, orchestrating analysis pipelines, and dynamically selecting computational tools for automated data processing. We demonstrate that SIMBA enables supports standard single-molecule localization workflow, functional mapping of nanoscale environmental heterogeneity through single-molecule spectral analysis and denoising using developed supervised learning methods. By integrating extensible tool architectures with human language-guided workflows, SIMBA establishes a new paradigm for intelligent microscopy analysis, lowering barriers to multi-dimensional imaging adoption while enabling scalable, reproducible, and adaptive analysis of complex imaging datasets.

Nowadays N95 facial mask has gain huge attention due to COVID19 pandemic situation and it serves as the prime PPE. Though the microbes can be restricted to get inside the human body due to the presence of mask temporarily, but over the time, bacteria and other microbes may get entrapped into the threads of the mask itself and thus acting as a storage chamber of microbes. It is necessary to eliminate them from the mask surface. To do so different floral structured Nano-ZnO with variable oriented arrangement of petals were fabricated on the surface of the N95 mask and further characterized through instrumentations including XRD, FTIR,UV-Vis, Fluorescence-Spectroscopy, SEM, DLS. The average crystallite size calculated for synthesized four different ZnO nanoflower were 25.19 nm, 23.46 nm, 27.27 nm and 31.78 nm (for glycerol, PEG, EDTA, Chitosan assisted) respectively. The antimicrobial activity was investigated by standard microbial broth dilution assay and Kirby-Bauer test which assured the inhibition of the bacterial growth. The MIC-MBC value of ZnO nanoflowers for E.coli and B. subtilis were found to be effective at dilution of 250 {micro}g/ml and 100 {micro}g/ml. Additionally a modified Kirby-Bauer assay has been designed to investigate the killing efficiency of the bacteria (E.coli). O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=145 SRC="FIGDIR/small/719592v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@48e5ecorg.highwire.dtl.DTLVardef@1ef03c5org.highwire.dtl.DTLVardef@e089ddorg.highwire.dtl.DTLVardef@17b2850_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOFig. - Graphical AbstractC_FLOATNO 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.

Cancer cell membrane coated biomimetic nanoparticles have been shown to be highly efficient in cellular uptake, homotypic tumour targeting, and the ability to suppress tumour growth compared to uncoated nanoparticles. Long duration anti-cancer treatment regimens require highly stable cancer cell membrane coated biomimetic nanoparticle. To manufacture such highly stable cancer cell membrane coated biomimetic nanoparticle, we used "Click-chemistry" to encapsulate cancer cell membrane on nanoparticles. In situ characterization was done to confirm the functionality of the novel Click-chemistry based formulation to encapsulate cancer cell membrane on nanoparticles. Gold nanoparticles were encapsulated with the cell membranes of cell lines of lung adenocarcinoma, malignant melanoma, high-grade serous epithelial ovarian cancer, colorectal cancer, oral cancer, esophageal adenocarcinoma, adenoid cystic carcinoma of salivary gland, and breast cancer. Functional group analysis, size, morphology, and surface charge confirmed long-stability of the biomimetic nanoparticles after incubating in complete growth medium for 12-months.

Esophageal squamous cell carcinoma (ESCC) is still lack of clinically molecular subtyping and effective therapeutic strategies. Herein, a total of 46 paired tissue samples of esophageal squamous cell carcinoma (ESCC) were collected and subjected to a systematic proteogenomic evaluation. Consensus assessment of the ESCC-related transcriptomes and TCGA dataset revealed several consensual modes of gene expression related to ESCC specificity, with 8 plasma-detectable hub proteins that could discriminate ESCC from others. Three ESCC molecular subtypes were defined and validated based on proteome data, including pCC1 with activated immune response and best survival outcome, pCC2 as cell cycle subtype with relative worse outcome, and pCC3 with worst outcome that expressed more cell adhesion related proteins. Furthermore, we proposed potential therapeutic strategies for improving survival outcomes in patients with different ESCC molecular subtypes. This integrative proteogenomic analysis provided a novel view of ESCC-dependent molecular information.

Inflammasomes are cytosolic innate immune sensors that, once activated by a pathogenic threat, lead to activation of the inflammatory Caspase-1. Inflammasome activation and its consequences have been studied extensively in myeloid cells and in overexpression systems. Recent studies have identified cell type specific effects that are not fully explained by the known cleavage targets of Caspase-1. Here, we identified targets of caspase cleavage using mass spectrometry in primary intestinal epithelial cells by specifically activating the NAIP-NLRC4 inflammasome. We have taken an unbiased approach and developed a novel method for analyzing mass spectrometry data for evidence of caspase activity. Our approach can also be applied to existing proteomic datasets to establish the presence of caspase activity under various biological conditions. These results lay the groundwork for future studies on mechanisms of caspase-induced processes such as intestinal epithelial cell extrusion.

Breast cancer is globally the most common cancer among women. Although the five-year survival rate exceeds 80% for patients with localized disease, it drops to approximately 30% once metastasis occurs, underscoring the urgent need to define mechanisms that drive metastatic progression. Breast is a highly innervated organ and most of its innervation is sensory. However, whether sensory neurons can directly impact breast cancer cells remains an understudied topic. Here, we show that mammary tumors have increased CGRP sensory innervation. Using our novel microfluidic Device for Cancer cell-Axon Interaction Testing (DACIT), we demonstrate that the presence of axons strongly inhibits ECM-degrading ability of cancer cells. The sensory neuron secretome suppresses assembly and function of invadopodia, which are cancer cell protrusions controlling ECM degradation, and essential for intravasation and metastasis. We identify calcitonin gene-related peptide (CGRP) as the key component of the sensory neuron secretome responsible for the inhibitory effect. CGRP signaling occurs through the CRLR/RAMP1 receptor complex expressed by breast cancer cells, inducing a rapid increase in intracellular cAMP levels in breast cancer cells, followed by an increase in RhoC activity and suppression of invadopodia and ECM degradation. Loss of RAMP1 function enhances 3D spheroid invasion, cancer cell motility in vivo and significantly increases the number and the size of lung metastatic foci. Consistently, in silico analyses of both mouse and human RNASeq data point to a link between increasingly invasive subtypes with a gradual decrease in expression of RAMP1 and CRLR. To validate in silico findings, we compare RAMP1 expression in the patient breast tumors with adjacent normal tissues, confirming the invasive breast tumors have reduced levels of RAMP1. Together, our findings identify a protective role for the paracrine CGRP signaling in limiting breast cancer invasion and metastasis. We also demonstrate how cancer cells circumvent CGRP inhibition by suppressing RAMP1 expression, highlighting CGRP-RAMP1-cAMP axis as a potential therapeutic target in breast cancer.

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.

Respiratory virus infections affect both humans and livestock, causing considerable mortality and morbidity. While respiratory pathogens such as swine influenza A virus (pH1N1) and porcine respiratory coronavirus (PRCV) often present with overlapping clinical symptoms, their pathological trajectories and outcomes differ. Given the propensity for pathogen spillover and the use of pigs as a physiologically relevant large-animal translational model, we aimed to characterise host serum protein signatures that detect and differentiate pH1N1 from PRCV, enabling improved disease monitoring and control. Using high-resolution mass spectrometry- based proteomics, we identified 162 serum proteins that were significantly dysregulated across 3 infection timepoints (1, 5, and 12 days post-infection (DPI)), with signatures correlating with viral shedding and lung pathology as early as 1 DPI. Notably, multiplexed targeted analysis of a subset of proteins in an independent cohort from a different breed and geographic location demonstrated detection, femtomole-level targeted quantitation, and validation of SRGN as a diagnostic marker for pH1N1 and PRCV (AUC=0.85). Further, SOD1 was validated as an early marker for PRCV, increasing as early as 1 DPI (AUC= 0.9). Finally, a multi-peptide signature composed of SRGN, SOD1, and RAN demonstrated reasonable predictive power for pH1N1 (AUC=0.75) and PRCV (AUC=0.65) at 1 DPI. Our data validate the proteomic screening, provide insights into the role of early protein markers in distinguishing respiratory viral infections, and pave the way for the development of point-of-care diagnostics and targeted prevention strategies, enhancing preparedness against emerging zoonotic threats.