Frontiers in Physics

Cellular monolayers often exhibit orientational order, with nematic alignment of cell shape and cytoskeletal structures governing tissue-scale collective dynamics. Despite extensive studies, a unified analysis framework for characterizing active nematics in living systems remains partial, and key discrepancies with theory persist. Here, we present a systematic and comparative analysis of nematic order and tissue flow dynamics across twelve distinct cell types. We quantify the impact of analysis parameters and provide data-driven guidelines to improve reproducibility and cross-study comparability. Across all nematic systems, we uncover remarkably consistent static properties, supporting the universality of nematic behavior in living tissues. By combining orientation-field analysis with velocity-field measurements and numerical simulations, we show that all examined systems display contractile active nematic signatures, with characteristic flow structures around topological defects. However, direct tracking of individual defects reveals subdiffusive dynamics, in stark contrast with the superdiffusive, self-propelled motion predicted by the hydrodynamic theory of active nematics. Our results establish a standardized framework for nematic analysis in biological systems and highlight fundamental limitations of current active nematic models in describing defect dynamics in living tissues.

Injectable biomaterials with aligned microstructures play a critical role in tissue engineering and drug-delivery applications where control over the position and orientation of cells and nano/micron-scale architectures enhance intervention efficacy. Patients are often subject to MRI scans; for patient safety and treatment efficacy, we investigated the effects of MRI on a biomaterial treatment consisting of aligned magnetic microstructures being developed for guiding cell growth. Under MRI exposure, potential movement of aligned structures could be detrimental to nearby cells, and potential MRI-induced heating could adversely affect traumatized tissue. In this work, the alignment state and heat conduction of such a treatment were studied using a 9.4 T preclinical MRI. The treatment comprises short magnetic rod-shaped polycaprolactone fibers (rods) with embedded magnetic nanoparticles in a surrounding hydrogel (gelatin methacrylate), with rod alignment observed before and after a 45-minute MRI scan. No change in rod alignment state was observed, and no heat generation was measured. A theoretical framework was developed which supports the experimental observation that the biomaterial is stable under MRI. This work can be extended to other biomaterial systems with aligned architectures used in tissue engineering applications such as spinal cord, muscle and tendon.

There is an urgent need for better treatment options for many neurological conditions, including Alzheimer's disease, Parkinson's disease, depression, and epilepsy. Transcranial electrical stimulation (tES) is a non-invasive, safe, inexpensive, and promising method that could address some of this unmet need. The therapeutic value of tES has been well demonstrated, but the effect is highly variable. To enable tES to reach its full potential requires a better understanding of how tES modulates neural activity so that tES treatments can be tailored to specific neurological conditions and individual patients. The neural response to tES is, however, highly complex, and the parameter space involved in optimizing tES treatments is daunting. This has made it difficult to obtain general insights into how tES modulates neural activity, and a central challenge lies in the cell-type-specific and frequency-dependent nature of these responses. In this study, we investigate cell-type-specific neuronal responses to tES over a broad frequency range, using a large database of biophysically detailed neuron models. We find that pyramidal cells respond strongly to low-frequency tES, but their responses drop sharply with frequency. In contrast, inhibitory neurons show a smaller reduction and, on average, become more responsive than pyramidal cells above ~60 Hz. By leveraging a reciprocity theorem we demonstrate that the effect of tES on a given cell-type is proportional to the frequency-dependent current-dipole moment that determines the EEG-signal contribution of this cell-type. We further identified the dendritic asymmetry as key in determining tES responses across the frequency spectrum. Counterintuitively, we also found that while total cell length increases tES sensitivity at low frequencies, it can have the opposite effect at high frequencies. Furthermore, we derived an analytical formula for idealized neuron models which can approximately predict the tES sensitivity of different cell types at any given frequency. By characterizing the role of morphology and stimulation frequency in determining tES responses of single cells, this is an important step towards a better understanding of tES at the fundamental level. These results also provide an efficient and accurate method for characterizing and comparing the tES responses of different neural populations across the frequency spectrum, which facilitates optimizing tES for cell-type specific targeting.

Within many bacterial colonies, persister cells exist as a subpopulation that is tolerant to antibiotics and other stressors, yet not genetically distinct from the rest of the colony. A recent study has proposed epigenetic inheritance as a mechanism that leads to the presence of persister cells. We analyze a nonlocal PDE--ODE model introduced in that study to describe the epigenetic inheritance process and establish its mathematical well-posedness, including existence, uniqueness, and nonnegativity of solutions. We identify a sharp parameter threshold delineating extinction from persistence of the colony: below this threshold the washout equilibrium is globally asymptotically stable, while above it a unique positive equilibrium exists and the population is weakly persistent. Notably, this threshold is independent of the internal community structure.

AO_SCPLOWBSTRACTC_SCPLOWSpatially resolved characterization of nanomaterial (NM) distribution within cellular ultrastructure is essential for understanding NM fate and activity in biological systems. Volume electron microscopy (vEM) is uniquely positioned to address this challenge, yet fully documented quantitative pipelines that simultaneously segment NMs and cellular structures remain scarce. Here, an end-to-end analytical pipeline is presented based on the example of serial block-face scanning electron microscopy (SBF-SEM) data of tumor spheroids containing nanoparticles (NPs). A hybrid segmentation strategy is adopted: a fine-tuned Cellpose-SAM model for cells and nuclei, and an empirical Bayes approach for AuNPs. The fine-tuned model outperforms both the pre-trained baseline and benchmark experiments in Amira, and shows good generalization to 2D EM datasets of varying sample types, suggesting potential as a general-purpose segmentation model for electron microscopy. Full 3D reconstruction of NP distributions reveals preferential clustering in the perinuclear region, with a median nucleus-to-NP distance of 2.57 {micro}m and NM uptake spanning several orders of magnitude across cells. Furthermore, morphological analysis of segmented cells and nuclei using 3D shape descriptors and local curvature metrics provides quantitative access to features inaccessible from single sections. Together, these results establish a reproducible, open framework for the joint quantitative analysis of NM distribution and cellular morphology in vEM data.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continuously evolved since its emergence in the human population in 2019. As of 1st August 2025, more than 1,700 Omicron subvariants have been designated by the Pango nomenclature system. The Pango nomenclature system designates a new lineage based on genetic and epidemiological information of SARS-CoV-2 strains. However, there is a possibility that strains that have similar genetic backgrounds and the same phenotype are given different Pango lineage names. In this paper, we propose a new algorithm, called FindPart-w, which can identify groups of viral lineages that share the same relative effective reproduction numbers. We introduced a new lineage replacement model, called the constrained RelRe model, which constrains groups of lineages to have the same relative effective reproduction numbers. The FindPart-w algorithm searches the equality constraints that minimise the Akaike Information Criterion of constrained RelRe models. Using hypothetical observation count data created by simulation, we found that the FindPart-w algorithm can identify groups of lineages having the same relative effective reproduction number in a practical computational time. Applying FindPart-w to actual real-world data of time-stamped lineage counts from the United States, we found that the Pango lineage nomenclature system may have given different lineage names to SARS-CoV-2 strains even if they have the same relative effective reproduction number and similar genetic backgrounds. In conclusion, this study showed that viruses that had the same relative effective reproduction number were identifiable from temporal count data of viral sequences. These findings will contribute to the future development of lineage designation systems that consider both genetic backgrounds and transmissibilities of lineages.

Knowing the mortality rates associated with infection by a pathogen is essential for effective preparedness and response. Here, harnessing the flexibility of a Bayesian approach, we produce an estimate of the Infection Fatality Ratio (IFR) for A(H5N1) conditional on explicit assumptions, and quantify the uncertainty thereof. We also apply the method to first-wave COVID-19 data up to March 2020, demonstrating the estimates that could be obtained were the model available then. Our analysis uses World Development Indicators (WDI) from the World Bank, the A(H5N1) WHO confirmed cases and deaths tracker by country (2003-2024), and COVID-19 cases and deaths data from John Hopkins University (January and February 2020). Since infectious disease dynamics are typically influenced by local socio-economic factors rather than political borders, individual countries are placed within clusters of countries sharing similar WDIs relevant to respiratory viral diseases, with clusters derived by performing Hierarchical Clustering. To estimate the IFR, we fit a Negative Binomial Bayesian Hierarchical Model for A(H5N1) and COVID-19 separately. We explicitly modelled key unobserved parameters with informative priors from expert opinion and literature. By modelling underreporting, our analysis suggests lower fatality (15.3%) compared to WHO's Case Fatality Ratio estimate (54%) on lab-confirmed cases. However, credible intervals are wide ([0.5%, 64.2%] 95% CrI). Therefore, good preparedness for a potential A(H5N1) pandemic implies adopting scenario planning under our central estimate, as well as for IFRs as high as 70%. Our approach also returns a COVID-19 IFR estimate of 2.8% with [2.5%, 3.1%] 95% CrI which is consistent with literature.

Hippocampal sclerosis (HS) is the most common pathology in drug-resistant temporal lobe epilepsy (TLE). However, clinical diagnosis, prevalent epileptogenicity, and drug drug-resistance in individuals with HS remain an ongoing challenge demanding multidisciplinary research efforts. In this study, we examined the mechanical properties of neurosurgically en bloc resected HS specimens (n=8) ex vivo under compression, tension, and torsional shear. We fitted a two-term Ogden hyperelastic model to the measured mechanical responses to quantify nonlinear mechanical tissue properties. The resulting parameters revealed higher strain stiffening under compression in HS compared to hippocampus obtained post mortem (n=7). The distinction was most noticeable in the large-strain regime, which has important implications for using mechanical tissue properties as valuable diagnostic biomarker. Furthermore, we correlated the tissue microstructure with mechanical parameters. We trained a deep-learning histopathology classifier to detect and classify neurons and glial cells from hematoxylin-stained whole slide images (WSI). We identified a strong association between the small-strain stiffness (shear modulus {micro}) and the overall cell density as well as the glial cell density. The negative relationship between the neuron-to-glia ratio and shear modulus is consistent with the hypothesis that neuronal cell loss and gliosis drives tissue stiffening, respectively. Magnetic resonance imaging (MRI) analysis of the specimens confirmed the previously reported negative association between MRI-derived fractional anisotropy and shear modulus {micro}. Taken together, our study establishes a direct link between tissue mechanics and microstructure, suggesting nonlinear continuum mechanics models as promising new tools for clinical diagnosis and novel research strategies.

Understanding the dynamics of HIV epidemics is important to control them effectively. Classical methods that mainly rely on occurrence data are limited by the fact that an unknown part of the epidemic eludes sampling. Since the early 2000s, phylodynamic methods have enabled the estimation of key epidemiological parameters from virus genetic sequence data. These methods have the advantage of being less sensitive to partial sampling and to provide insights about epidemic history that even predates the first samples. In this study, we analysed 2,205 HIV sequences from the French ANRS PRIMO C06 cohort. We identified and were able to reconstruct the temporal dynamics of two large clades that represent the HIV-1 epidemics in the country. Using Bayesian phylodynamic inference models, we found that the first clade, from subtype B, originated in the end of 1970s, grew rapidly during the 80s before decreasing from 2000 to 2015 and stagnating since then. The second clade, from circulating recombinant form CRF02_AG, emerged and spread in the 80s, grew again in the early 2000s, before declining slightly. We also estimated key epidemiological parameters associated with each clade. Finally, using numerical simulations, we investigated prospective scenarios and assessed the possibility to meet the 2030 UNAIDS targets. This is one of the rare studies to analyse the HIV epidemic in France using molecular epidemiology methods. It highlights the value of routine HIV sequence data for studying past epidemic trends or designing public health policies.

Across the last three decades, functional magnetic resonance imaging (fMRI) research - through both resting-state (rsfMRI) and task-based (tfMRI) studies - has greatly advanced our understanding regarding the neural basis of cognition. Yet the mechanistic relationship between rsfMRI and tfMRI is still poorly understood. In particular, it remains unclear how and why the brain activation patterns observed during the resting state are linked to cognitive functioning and individual differences present during task performance. Here, we test a unifying computational account which postulates that task contexts modulate the nonlinear attractor landscape and associated dynamical properties of the brain present under resting conditions, and further that the nature of this modulation is impacted by meaningful cognitive individual differences. To test this account, we develop a joint rsfMRI-tfMRI modeling and analysis framework called Mesoscale Individualized NeuroDynamics with eXogenous inputs (MINDy-X) and apply it to resting and N-back working memory task data from the Human Connectome Project. We first validated that the joint model can simulate and predict both rsfMRI and tfMRI data accurately, consistent with a common underlying dynamical system. Analyses of this joint model revealed that task-related modulation bifurcated the predominantly multistable attractor dynamics present during the resting state towards a predominantly monostable dynamics observed during N-back task states. This topological shift was also accompanied by a geometric reconfiguration, with the task state characterized by an enrichment of dynamical attractor "motifs" clustered around the frontoparietal (FPN) and default mode (DMN) networks. Task-related modulations of this attractor landscape were further subject to clear individual differences, such that individuals who did not exhibit a shift in attractor topology were more error-prone and less cautious in responding, while closer geometric proximity to the FPN and DMN motifs explained additional aspects of task performance. N-back behavior was best characterized by the combination of topological and geometric properties present in both task and rest states, suggesting that they each account for unique aspects of individual variability. The current work supports a novel computational framework for understanding the whole-brain neural activity patterns observed during rsfMRI and tfMRI as reflecting different states within a common non-linear dynamical system. This framework provides a new vocabulary for characterizing cognitive functioning in terms of the unique geometric and topological configuration of the associated attractor landscapes, with the potential for wide application in many domains of basic and clinical neuroscience research.

In dense urban settings, delays between diagnostic sampling and effective isolation can sustain transmission during peak infectiousness. We define a waiting-window transmission externality arising when infectious individuals remain mobile while awaiting results, formalized as E = N{middle dot}P{middle dot}TR{middle dot}D, where N is daily testing volume, P test positivity, TR transmission during the waiting period, and D turnaround time. Using Monte Carlo simulation and a susceptible-infectious-recovered (SIR) framework, we quantify excess infections per 1,000 tests/day under multiple diagnostic workflows. A surge scenario incorporates positive coupling between TR and D ({rho} = 0.45), reflecting co-occurrence of laboratory saturation and elevated contacts during system stress. Under centralized 48-hour workflows, excess infections reach [~]80 at P = 10% and [~]401 at P = 50%, increasing to [~]628 under surge conditions. In contrast, near-patient rapid testing and home sampling reduce this to [~]5 and [~]25-26, respectively. Workflows that eliminate the waiting window--either through immediate isolation at sampling or through home-based PCR that returns results at the point of collection--effectively collapse the transmission term. These findings identify diagnostic delay as a modifiable driver of epidemic dynamics. Operational redesign of testing workflows, including decentralized sampling and home-based molecular diagnostics, offers a scalable pathway to improve epidemic controllability and reduce inequities in dense urban environments.

Transparent and composite surfaces pose a fundamental challenge for stereo photogrammetry: optically smooth glass produces no detectable surface features under visible illumination, making three-dimensional reconstruction impossible without surface preparation. This excludes optical components such as lenses and cover glasses, composite assemblies, and semi-translucent biological specimens from non-contact geometric measurement. Here we show that coherent speckle illumination at 266 nm overcomes this limitation by exploiting wavelength-dependent scatter enhancement, generating sufficient backscattered signal on surfaces that are entirely invisible under visible illumination. We developed a multispectral stereo system and evaluated three illumination modalities under identical acquisition conditions. On transparent glass, both visible modalities produce complete reconstruction failure, recovering only non-transparent holder structures. Ultraviolet speckle illumination at 266 nm enables dense reconstruction of the same surfaces. We demonstrate recovery of an uncoated plano-convex lens with a fitted radius of 30.946 mm and point-cloud standard deviation of 106.5 {micro}m, defect detection on a transparent cover glass without surface preparation, and reconstruction of a semi-translucent biological specimen. On metrology-grade reference objects, ultraviolet speckle achieves a standard deviation of 116 {micro}m and completeness exceeding 93%, approaching the performance of optimised visible structured illumination. These results establish ultraviolet speckle photogrammetry as an enabling approach of optical metrology to otherwise uncooperative surfaces, with relevance to optical manufacturing inspection and biological surface analysis.

Accurate shell shape quantification is critical for studying biodiversity and evolution, yet intraspecific variability in bivalves makes morphology-based identification difficult. Traditional methods, including landmark-based analyses and elliptic Fourier descriptors, suffer either from subjectivity in homologous point selection or from limited use of contour information. Here, we introduce Morpho-VAE, a deep generative framework integrating a variational autoencoder with a supervised classifier, to analyze shell images of five Anadara species. Morpho-VAE outperforms conventional approaches in species classification by embedding morphological variation into a low-dimensional space where species cluster distinctly. To highlight species-specific morphological patterns, we develop a patch masking assay, revealing the hinge line as a shared morphological marker across species and species-specific regions near the umbo and anterior ventral margin. The decoder further enables morphological visualization via image reconstruction and interpolation. Our results show that Morpho-VAE can automatically extract species-defining morphological patterns from raw images, providing complementary or novel insights beyond traditional morphometric methods.

1Three-dimensional organoid cultures have emerged as powerful models for studying human tissue biology, disease mechanisms, and drug responses. Fluorescence confocal microscopy of organoids generates complex volumetric image data that is increasingly analyzed using deep learning pipelines for cell segmentation, morphometry, and phenotyping. However, training and benchmarking such pipelines requires large annotated datasets, the manual curation of which is prohibitively expensive and time-consuming. Here we present a parametric, physics-based computational framework for generating synthetic 3D fluorescence organoid images with exact ground-truth cell body and nucleus label masks. The framework models cell placement using force-directed sphere packing with optional hollow lumen exclusion for cyst-forming organoids, cell morphology using power-diagram (Laguerre) tessellation with apical-basal elongation and surface flattening for polarized epithelial cells, membrane curvature using low-frequency coordinate displacement, nuclear shape using irregular ellipsoid deformation with smooth radial eccentricity direction blending, and optical effects using depth-dependent point-spread function broadening, a physically motivated staining diffusion gradient with residual interior plateau, z-attenuation, haze, shot noise, and channel crosstalk. The necrotic core model uses a three-phenotype nuclear population, pyknotic, ghost, and karyorrhectic, reflecting the histological diversity of real necrotic zones. Five condition-specific presets are provided, calibrated to published biological measurements and covering PDAC osmotic stress, HMECyst normal and cyst-forming organoids, and a large primary PDAC organoid with a necrotic core. Unlike generative adversarial network approaches, our method requires no training data, produces exact ground truth by construction, and allows systematic and interpretable control over every morphological and optical parameter. The framework is released as open-source Python software with a PyQt5 graphical interface and produces OME-TIFF output compatible with arivis Pro, FIJI, and napari, as well as most other microscopy image analysis software.

Functional spinal cord repair in zebrafish is governed by regeneration-favorable biochemical and mechanical cues within the lesion microenvironment. Alterations in extracellular matrix composition and stiffness are closely associated with axon regeneration. However, experimentally dissecting the interplay between mechanical signals and axonal regrowth in vivo remains technically challenging. Here, we present an agent-based modeling framework to simulate stiffness-mediated axonal growth trajectories across the lesion. We use this model to explore potential mechanisms underlying the characteristic growth patterns observed during zebrafish spinal cord regeneration. Computational predictions were qualitatively compared with confocal imaging data obtained from larval zebrafish. These phenomenological comparisons revealed a close agreement between simulated and experimentally observed axon growth, indicating that experimentally observed patterns could be governed by transient changes in the stiffness profile of the spinal cord and lesion microenvironment. Hence, our computational framework provides an in silico platform for investigating the role of mechanical cues in axon regeneration in the injured spinal cord.

Stratified epithelial tissues such as the skin epidermis maintain barrier integrity during development and homeostasis through the coordinated action of cell proliferation, differentiation, delamination, and tissue-scale mechanical forces. During development, the orientation of cell division within the basal layer plays a pivotal role in tissue stratification; however, the mechanical principles linking the orientation of the division plane to these processes across developmental stages remain poorly understood. Here, we expand a recently developed three-dimensional vertex model for stratified epithelia, composed of the basement membrane, basal, and suprabasal layers, to study the mechanical and structural impact of cell divisions with a wider range of orientations. The model integrates developmental stage via specific changes in heterotypic interfacial tensions (arising from actomyosin cortical contractility and adhesion molecules at the basal-suprabasal interface) and tissue stiffness that have been quantified previously in experiments. By systematically varying background mechanical parameters, we investigate how heterotypic tension, division orientation, and tissue fluidity collectively influence the outcome of cell division. Our goal is to uncover the strategies that the embryo may employ to generate stratified phenotypes at different developmental stages, recognizing that these strategies might evolve over time. Although our focus is on the embryonic developmental stages of the epidermis, this framework may also be extended to investigate transformed cells, such as in cancer, to explore how altered division orientation contributes to precancerous or transformed phenotypes.

Interpreting serological data remains challenging, particularly in low prevalence or cross reactive contexts, where antibody responses often show substantial overlap between exposed and unexposed individuals and may depart from normal distributional assumptions. Conventional cutoff based approaches often yield inconsistent or biased estimates of seroprevalence. Here, we present a decisional framework based on finite mixture models (FMMs) that enhances the robustness and interpretability of serological analyses. Beyond simply applying mixture models, our framework integrates multiple methodological innovations : (i) systematic comparison of Gaussian and skew normal mixture models to accommodate asymmetric antibody distributions; (ii) rigorous model selection using the Cramer von Mises test (p > 0.01) combined with a parsimonious score (APS) to prioritize models with well separated clusters; and (iii) hierarchical clustering of posterior probabilities to collapse latent components into biologically meaningful seronegative and seropositive groups. Applied to chikungunya virus (CHIKV) data from Bangladesh, the framework produced prevalence estimates consistent with ROC based methods while probabilistically identifying borderline cases. Validation on SARS CoV 2 and dengue datasets further demonstrated its generalizability: for SARS CoV 2, the approach identified up to five latent clusters with high sensitivity (up to 100%) and specificity (up to 100%), enabling discrimination by disease severity. For dengue, it revealed interpretable subgrouping consistent with background exposure and subclinical infection, despite limited confirmed cases. By integrating distributional flexibility, robust goodness of fit testing, and biologically guided cluster consolidation, this decisional FMM framework provides a reproducible and scalable method for serological interpretation across pathogens and epidemiological settings, addressing key limitations of threshold based classification.

Background: Nigeria bears one of the highest maternal mortality burdens globally, with skilled birth attendance (SBA) remaining critically low in many regions. Understanding the independent determinants of SBA is essential for designing targeted interventions. Methods: This cross sectional study analyzed 21,465 births from the 2018 Nigeria Demographic and Health Survey (NDHS), a nationally representative household survey using stratified two stage cluster sampling. SBA was defined as delivery attended by a doctor, nurse, midwife, or auxiliary midwife. Multivariable logistic regression was used to estimate adjusted odds ratios (aOR) with 95% confidence intervals for the associations between SBA and maternal education, household wealth, place of residence, geopolitical region, maternal age, parity, and antenatal care (ANC) utilization, after accounting for confounding. Results: The overall prevalence of SBA was 44.9%. In the fully adjusted model, higher education (aOR = 7.01, 95% CI: 5.68-8.67), richest wealth quintile (aOR = 6.27, 95% CI: 5.27-7.46), and attending [≥]4 ANC visits (aOR = 3.80, 95% CI: 3.51-4.11) were the strongest independent predictors of SBA. Regional inequalities were pronounced, with SBA prevalence ranging from 17.7% in the North West to 85.6% in the South West. Crude effect estimates for education and wealth were substantially attenuated after adjustment, indicating large confounding by correlated socioeconomic factors. Conclusions: Maternal education, household wealth, ANC utilization, and geopolitical region are independent determinants of SBA in Nigeria. Scaling up ANC programs represents the most immediately actionable intervention, while long term gains require investment in girls' education and wealth equity. Targeted strategies for the northern regions are urgently needed. Keywords: skilled birth attendance, maternal mortality, Nigeria, DHS, antenatal care, logistic regression, health equity

High level of protein expression is usually welcomed in industry and research, and codon optimization is widely used to achieve high expression. Methods of implementing codon optimization can be divided into two branches, one is classical methods which develop cost functions based on empirical law, another is AI methods which learn the codon choice principles from endogenous genes with neural networks. Here we develop two codon optimization tools based on two branches respectively, namely OptimWiz 2.1 and OptimWiz 3.0. Results of fusion protein fluorescence detection indicate that both OptimWiz 2.1 and OptimWiz 3.0 are superior to all the other commercially available codon optimization tools. Principles of codon optimization are revealed in the process of machine learning on both tools.

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.