Nanoscale

Proteins in solution adsorb to the corona of nanoparticles such as single-walled carbon nanotubes (SWCNTs), but these interactions are difficult to predict and analyze due to ambiguities in the structure of the latter. In this work, we employ ss(GT)15-DNA wrapped SWCNTs, a commonly used fluorescent sensor construct, to examine protein adsorption by quantifying binding dissociation constants and characterizing the corresponding photophysical effects. A library of 20 proteins are used to evaluate adsorption-induced changes in photoluminescence (PL) intensity ({Delta}I/I0) and emission wavelength upon solution phase binding. We find that 15 proteins produce monotonic dose-response behavior well described using a single-site Langmuir model. Alternatively, five proteins exhibited more complex, non-monotonic behavior consistent with a two-step binding model representing protein-protein interactions coupled to adsorption. The study reveals that metalloproteins, which comprised 12 of the 20 proteins in the library, induced greater PL quenching compared with metal-free proteins for this system, with maximum binding-associated quenching ({Delta}I/I0) of 94% for metalloproteins versus 20% for metal-free proteins. For metalloproteins, we introduce a proximity-based quenching framework in which protein size provides a coarse proxy for cofactor-SWCNT separation, offering a mechanistic interpretation of the observed quenching variation across proteins. Together, these results establish the use of metal coordination sites, such as those in metalloproteins, to assist the transduction of certain nanoparticle fluorescent sensors, helping with sensor probe design and interpretation in biological environments.

The first and critical reaction in magnetic hyperthermia to induce the death of cancer cells is the production of ROS (reactive oxygen species). We previously showed that it is possible to specifically deliver iron oxide magnetic nanoparticles (IONPs) in the lysosomes of cancer cells and eradicate them by targeted magnetic intra-lysosomal hyperthermia (MILH) via the application of a high frequency alternating magnetic field (AMF) without macroscopic temperature elevation. The mechanism involves a local temperature elevation at the IONPs surface which enhances the ROS production through the Fenton reaction; ROS then peroxide the proteins and lipids of the lysosomal membrane, inducing its permeabilization and leading to lysosomal enzymes release and cell death. Fe ions, critical to produce ROS in MILH, were assumed to be released by IONPs. We thus developed PEGylated multi-cores IONPs called NanoFlowers (NF@PEG) presenting or not a SiO2 shell (NF@SiO2@PEG), the later preventing the Fe3+ release from IONPs. NF@PEG released Fe ions and produced ROS production in vitro, in acidic medium mimicking lysosome upon AMF exposure, whereas NF@SiO2@PEG did not. Surprisingly, both nanoparticles increased the ROS production in cells, induced lysosome permeabilization and cell death, and slowed down the proliferation of cancer cells with the same efficacy, upon AMF application, indicating that MILH was efficient in absence of Fe3+ release from IONPs. In contrast, Ferristatin-II, an iron uptake inhibitor, prevented the ROS production and cell death in MILH induced by both IONPs, elucidating the role of endogenous iron cations responsible for the ROS production ROS in MILH to kill cancer cells.

The protein corona (PC), a layer of biomolecules that adsorbs onto nanoparticles (NPs) surfaces upon exposure to biological fluids, plays a key role in defining the biological identity and performance of nanomaterials. However, most current analytical approaches rely on pooled measurements of PC-coated NPs and therefore lack the resolution needed to detect subtle heterogeneity in PC composition, potentially masking important differences in NPs biological identity. Here, we used a high-sensitivity magnetic levitation (MagLev) platform capable of resolving extremely small density differences among nominally identical PC-coated NPs, enabling fractionation of particles based on subtle variations in PC composition. Compared with conventional standard MagLev systems (density resolution [~]10-3 g/cm3), the high-sensitivity MagLev improves density sensitivity by up to three orders of magnitude, allowing discrimination of density differences as small as 10-5 g/cm3. Using this approach, PC-coated NPs were separated along the MagLev column into multiple fractions corresponding to distinct density populations. Subsequent proteomic analysis across the extracted fractions identified more than 500 proteins and revealed a structured but continuous redistribution of protein composition across the column, including fraction-dependent differences in protein abundance, overlap, and biological identity. In particular, the fraction series captured hidden heterogeneity among nominally identical PC-coated NPs, with upper fractions retaining relatively stronger extracellular/plasma-associated signatures and lower fractions showing increasing representation of structural, membrane-associated, cytoskeletal, and metabolic proteins. These findings demonstrate that PC formation is intrinsically heterogeneous even on identical NPs and this heterogeneity is largely missed by conventional pooled analysis. High-sensitivity MagLev provides a simple, label-free framework for resolving PC heterogeneity and offers a new analytical approach for studying NP-biomolecule interactions, with important implications for nanomedicine design, biomarker discovery, and the clinical translation of NP-based systems. TOC O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/727410v1_ufig1.gif" ALT="Figure 1"> View larger version (41K): org.highwire.dtl.DTLVardef@155ad62org.highwire.dtl.DTLVardef@1ea3e3dorg.highwire.dtl.DTLVardef@19c40ceorg.highwire.dtl.DTLVardef@162ca8d_HPS_FORMAT_FIGEXP M_FIG C_FIG

Non-invasive tracking of natural killer (NK) cells remains a major challenge in cancer immunotherapy, limiting our understanding of their in vivo migration and persistence. Magnetic particle imaging (MPI) offers a quantitative, real-time method for visualizing labeled cells, yet optimal labeling protocols for NK cells have not been established. Here, we evaluate commercially available iron oxide nanoparticles (IONPs) for MPI labeling of both NK92MI cells and primary human NK cells. Labeled cells retained viability and cytotoxicity, including activity against three-dimensional tumor spheroids, and were detectable by MPI. To further examine imaging performance in a biologically relevant context, we employed mouse phantoms that recapitulate organ-specific signal distributions, enabling evaluation of quantification and liver spillover effects. We identify key tradeoffs between particle colloidal stability and per-cell iron content: VivoTrax and VivoTrax Plus provided higher MPI signal but required post-labeling purification, reducing cell recovery, whereas Synomag-D and Perimag were more stable and preserved cell yield despite lower signal intensity per cell. These results provide a framework for selecting nanoparticles that balance detection sensitivity, cell viability, and workflow practicality, advancing non-invasive NK cell tracking.

DNA-programmed assembly of plasmonic nanostructures provides a powerful route to couple molecular recognition with optical signal generation. Here, we report the sequence-specific assembly of DNA-functionalized gold nanorods using a sesame allergen-derived DNA biomarker as a molecular bridge. Target-induced assembly produces concentration-dependent assembly growth, plasmon coupling, and distinct assembly kinetics that are readily monitored by absorption spectroscopy, enabling label-free detection of the target DNA in the nanomolar concentration range. The assembled nanorods further produce strong surface-enhanced Raman scattering (SERS) signals arising from plasmonic coupling within the assemblies, extending detection to the picomolar regime without the use of Raman reporters. Quantitative analysis reveals that both the extent and rate of assembly formation are governed by target DNA concentration. These results establish a direct relationship between molecular recognition, assembly growth, plasmonic coupling, and spectroscopic response, highlighting DNA-programmed gold nanorod assembly as a versatile platform for investigating hybridization-driven plasmonic self-assembly and nucleic acid detection. Table of Content O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=112 SRC="FIGDIR/small/732610v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@6a8f42org.highwire.dtl.DTLVardef@1e36b9corg.highwire.dtl.DTLVardef@1ade546org.highwire.dtl.DTLVardef@1a787bd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Biomolecular condensates are important regulators of cellular compartmentalization and biochemical processes. Understanding their material properties is critical to elucidate how they control molecular organization and dynamics within cells. However, quantitatively probing these properties remains challenging due to the wide range of length scales, concentrations, and timescales over which condensates operate, as well as the limited force ranges accessible to current nanoscale mechanical mapping methods. We explored the use of a non-contact 3D imaging tool Scanning Ion Conductance Microscopy (SICM) for stiffness measurements of liquid-liquid phase-separated biomolecular condensates. We focus on the Dhh1 protein, which is a regulator of cytoplasmic processing bodies (PBs) membrane-less cytoplasmic condensates that control the storage and degradation of untranslated mRNA. In our study, we investigate the properties of mCherry2- or His-mCherry2- tagged full-length Dhh1 and N- or C-terminus tail-deletion constructs, as well as the catalytically inactive mutant DQAD, under different pH and incubation times. We mapped both spatial and temporal changes in the material properties of the condensates, highlighting the capabilities of the instrument. We found that the removal of either of the two tails led to an increase in condensate stiffness upon shifting the pH from a stress-associated cellular environment (pH 6.5) to physiological conditions (pH 7.5). Additionally, the choice of protein tags led to vastly different results depending on the pH where mCherry2-Dhh1 exhibited a stiffening going from pH 6.0 to 6.5 while the double-tagged His-mCherry2 did not. Our measurements are verified and corroborated by established techniques such as optical tweezer-based fusion assays and fluorescence recovery after photobleaching (FRAP). Furthermore, we were able to track the same biomolecular condensate sample for up to 20 days getting insights on the ageing and evolution of the condensates. Overall, our study demonstrates the applicability of SICM for direct measurement of the material properties of biomolecular condensate.

Intratumoral (i.t.) delivery of nanoparticles (NPs) is widely used to achieve high local NP concentrations. However, the temporal fate of i.t.-injected NPs remains poorly understood. We present a quantitative approach using whole-body magnetic particle imaging (MPI) to track magnetic NPs (MNPs) following i.t. injection. Using fiducial-calibrated imaging, we quantified MNP mass over time in subcutaneous 4T1 breast tumors. Longitudinal imaging revealed progressive loss of i.t. MNP content and heterogeneous systemic redistribution across animals despite standardized delivery conditions. Ex vivo MPI confirmed off-target accumulation primarily in the liver and spleen, consistent with reticuloendothelial clearance pathways. Histological analysis demonstrated spatially heterogeneous i.t. MNP deposition, potentially associated with local vascular features and tumor microenvironmental heterogeneity that may influence i.t. MNP retention or MNP clearance from the tumor. These findings highlight the importance of quantitative longitudinal whole-body MPI for understanding the fate of MNPs for informing localized nanotherapy.

Bridging molecular and emergent properties is essential for designing soft matter. Synthetic DNA materials are attractive in this context because their sequence design space supports a wide range of material properties. Targeted design of DNA materials is hindered by scale differences and manual exploration of vast design spaces. We address this challenge with a computational workflow that links sequence-level design to rheological material properties. Concretely, we use machine learning to parametrise scalable, DNA-sequence-aware simulations, which we then evaluate using graph-based rheology. In our example, we study materials composed of self-interacting, multivalent DNA nanostars assembled from single strands. Structure and flexibility of nanostars are quantified with nucleotide-level oxDNA simulations, enabling Bayesian optimisation of a more coarse-grained bead-spring model. The bead-spring model allows efficient simulation of network formation between nanostars, governed by hybridisation free energies, which are computed with oxDNA and NUPACK. Nanostar valency and network connectivity are translated into rheological material properties with a graph-based method that we extend to include hydrodynamic interactions, yielding good agreement with experimental reference data. We generalise our findings by analysing theoretical graph representations of DNA materials and show how machine learning can optimise sequence affinities to produce desired rheological responses. Our work illustrates how machine learning can bridge scales and automate coarse-graining to facilitate targeted design of DNA materials through sequence-property relationships. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=79 SRC="FIGDIR/small/728076v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@20b8c6org.highwire.dtl.DTLVardef@42f843org.highwire.dtl.DTLVardef@b90119org.highwire.dtl.DTLVardef@1f72d66_HPS_FORMAT_FIGEXP M_FIG C_FIG

This study presents the first investigation of the adsorption behaviour of Afatinib on gold nanoparticle (AuNP) monolayers, employing a combination of AFM-SEIRA and SERS techniques. Two types of AuNPs with distinct sizes, synthesized using different reagents, were employed to elucidate the influence of surface type on drug adsorption. The first type of AuNPs was synthesized using sodium borohydride (SB), whereas the second type was obtained using hydroxylamine hydrochloride (HH) as the reducing agent. AFM-SEIRA revealed that Afatinib interacts to the AuNPs primarily through the quinazoline ring, amide group, and amino moiety, with adsorption geometry strongly dependent on nanoparticle type. Contributions from CH3 and CH2 moieties were also identified, indicating their role in stabilizing the molecule/metal interface. Time-resolved SERS studies demonstrated that the adsorption process is dynamic and involves molecular reorientation, followed by gradual desorption, which is accelerated at physiological temperature (37 {degrees}C). Competitive adsorption experiments with phenylboronic acid (PBA) showed that Afatinib exhibits higher affinity toward AuNPs, however, co-adsorption leads to reduced stability of both species on the surface. The results reveal molecular insights into drug/nanoparticle interactions and emphasize the role of surface functionalization in efficient nanocarrier design. This work deepens understanding of adsorption at plasmonic interfaces for biomedical use.

Curcumin-functionalized gold nanoclusters are promising platforms for catalysis and drug delivery, yet the molecular determinants of their stability, morphology, and solvent response remain unclear. Here, microsecond all-atom molecular dynamics simulations are employed to investigate a 2 nm gold nanoparticle noncovalently coated with different curcumin forms, including neutral enol and trans-keto tautomers, the deprotonated enolate, and their mixtures in water-ethanol and water-methanol solvents. Layer-resolved analyses of radius of gyration, density profiles, and surface coverage reveal that neutral enol and trans forms generate compact assemblies with near-complete surface coverage, whereas enolate-rich systems adopt more expanded conformations with solvent-exposed molecules. Mixed systems preserve these intrinsic packing characteristics while improving overall coverage. Solvent substitution from ethanol to methanol reduces {pi}-{pi} stacking, strengthens Au-curcumin interactions, and increases surface coverage, yielding more compact nanostructures. Free energy and potential of mean force calculations indicate that deprotonated curcumin most effectively screens Au-Au interactions and stabilizes dispersed nanoparticles, while neutral tautomers provide moderate stabilization. Curcumin also enhances the loading of anticancer drug doxorubicin (DOX) onto Au nanoparticles, improving biocompatibility. Enolate(An)-containing systems produce extended structures with weaker membrane interactions, whereas neutral curcumin complexes form compact, positively charged assemblies that strongly bind to negatively charged cancer cell membranes. These findings clarify how tautomeric state and solvent environment cooperatively govern interfacial organization and colloidal stability, establish design guidelines for curcumin-based gold nanocarriers in catalysis, sensing, and drug delivery applications.

The protein corona influences the in vivo biodistribution of ionizable lipid nanoparticles (LNPs) in nucleic acid delivery, yet its structural architecture remains poorly defined. Using cryo-transmission electron microscopy, we visualized LNP-protein interactions in their native state. We show that, unlike the discrete "fuzzy" shells observed on hard nanoparticles, LNPs displayed no peripheral protein shell. Instead, controlled incubation and competitive "dual-particle" assays, supported by molecular dynamics simulations, indicate that LNP membranes undergo localized thickening and electron-dense remodeling consistent with lipoprotein integration rather than surface adsorption. Similar features were observed in extracellular vesicles, suggesting this behavior is shared among lipid-based carriers, and proteomic analysis identified apolipoproteins as the dominant associated proteins. Together, these findings support a model in which the biological identity of LNPs arises through membrane remodeling rather than shell-like adsorption, and provide a framework for the rational design of targeted nanomedicines. TOC Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=138 SRC="FIGDIR/small/695162v2_ufig1.gif" ALT="Figure 1"> View larger version (79K): org.highwire.dtl.DTLVardef@5275d3org.highwire.dtl.DTLVardef@1b59ae4org.highwire.dtl.DTLVardef@1cc290eorg.highwire.dtl.DTLVardef@9b7bfb_HPS_FORMAT_FIGEXP M_FIG C_FIG

Fluorescent nanodiamonds (FNDs) containing nitrogen-vacancy (NV) centers are promising quantum sensors for intracellular measurements, yet nuclear applications remained out of reach because optically detected magnetic resonance (ODMR) signals are weak and capillary delivery is inefficient. This study addresses both constraints by optimizing the electron irradiation dose to balance NV creation and charge-state stability, and by grafting hyperbranched polyglycerol with terminal carboxyl groups (HPGCOOH) to suppress aggregation and prevent needle clogging. The optimized dose yields strong ODMR contrast while preserving fluorescence suitable for microscopy. HPGCOOH surfaces enable smooth and reproducible microinjection through fine capillaries. Using this strategy, the microinjection of ODMR-active FNDs into the nuclei of living COS7 cells is achieved, and clear intranuclear spectra comparable to cytoplasmic readouts are obtained. Furthermore, field-of-view temperature sensing across multiple cell nuclei is demonstrated, enabling quantitative and spatially resolved thermal mapping within the genomic environment. This methodology provides a practical route to nuclear quantum sensing and opens opportunities for nanoscale physicochemical measurements within the genomic environment.

Light scattering in scintillators is a pervasive problem and a key factor limiting X-ray imaging resolution. Here, we shift scintillator radioluminescence from the traditional visible range into the short-wave infrared (SWIR) or near-infrared II (NIR-II, 1000-3000 nm) window to mitigate light scattering and thereby enhance light penetration and X-ray imaging resolution. We present an NIR II MgGa2O4:Ni2+ scintillator with peak emission at 1340 nm, achieving a threefold improvement in X-ray imaging resolution compared with visible scintillators owing to reduced light scattering. This heavy-metal-free NIR-II scintillator exhibits intense radioluminescence comparable to that of conventional visible-emitting CsI:Tl, achieving a detection limit of 56 nanograys per second, ~100-fold lower than typical doses used in medical imaging. We show that this NIR-II scintillator enables high-resolution X-ray radiography of electronic circuit boards and biological tissues.

Nanoscale lipid bilayer mimetics are powerful tools for research on lipid bilayer, membrane proteins or for drug delivery. Established nanoscale bilayer systems that are stabilized by short peptides or polymers produce a broad size distribution and are difficult to customize. Here we introduce a DNA nanotechnology-based lipid bilayer mimetic, in which we covalently conjugated established nanodisc-forming amphiphilic peptides to oligonucleotides. These peptide-DNA conjugates were then hybridized with a circular single-stranded scaffold to form stiff, circular PDC minicircles with 14 peptide modifications at the inner rim of the torus. Lipid reconstitution yielded defined nanodisc with a tightly controlled circumference and component stoichiometry. Molecular dynamics simulations further validated the structural stability and reveal an asymmetric migration of the DNA to one rim of the bilayer. To mimic membrane protein insertion, we co-reconstituted a transmembrane peptide coupled to a bulky quantum dot. In future applications, the size and peptide arrangement can easily be modified in these DNA-templated PDC nanodiscs.

Self-assembling protein cages are versatile nanoscale architectures with broad applications in drug delivery, vaccine development, and structural biology. Historically, two main strategies have been used to construct such cages: genetic fusion of oligomeric domains connected by helical linkers, and computational interface design using either physics-based or machine learning-based methods. Here, we extend the original fusion approach using modern AI algorithms and more sophisticated treatments of helix bending to create protein cages with novel architectures composed exclusively of trimeric building blocks arranged in tetrahedral symmetry. Of fifteen designs tested experimentally, multiple sequence variants of two of these designs assembled predominantly into soluble, monodisperse particles of the expected size, with native molecular masses of 633 kDa (T33-Fus-1A, B) and 638 kDa (T33-Fus-2). Cryo-electron microscopy (cryo-EM) structures of three distinct sequence variants spanning from 3.0-3.9 [A] in resolution confirmed the intended structures in atomic detail, with C-alpha RSMD values over the entire assemblies as low as 2 [A]. The predicted modes of helix bending were similarly validated. The results highlight the impact of methodological improvements for achieving a level of regularity and design precision that has largely evaded prior applications of the fusion approach. These findings expand the prospects and accessible design space for self-assembling protein nanomaterials.

Atomic force microscopy (AFM) enables nanometre-scale, label-free imaging of biomolecules and surfaces under near-native conditions, yet quantitative analysis of AFM data remains limited compared to other bioimaging modalities. This limitation largely arises from the absence of open, automated tools capable of addressing AFM-specific artefacts, data formats, and topographical outputs. Here, we present the latest version of TopoStats, an open-source Python package for automated and quantitative AFM image analysis, developed as a deep-learning enabled advancement of our original TopoStats software to support more complex samples and richer molecular characterisation. The pipeline integrates all key processing stages, including image flattening and noise correction, object detection and segmentation, morphometric feature extraction, and strand tracing with topological classification. Designed for accessibility and reproducibility, TopoStats adheres to the FAIR for Research Software (FAIR4RS) principles and provides configurable workflows adaptable to diverse biological samples. Combining high-resolution AFM and our analysis pipeline allows the quantification of subtle structural changes within a heterogeneous sample set, revealing properties not accessible with other structural biology techniques. We demonstrate the effectiveness of our pipeline to differentiate between plasmids with both different topology and sequence, by extracting meaningful quantitative descriptors that distinguish the samples with statistical significance. Collectively, these developments establish TopoStats as a versatile framework for high-throughput, quantitative AFM analysis, advancing AFM from a fundamentally qualitative visualisation technique toward a quantitative analytical tool.

This study presents the development and spectroscopic characterization of an erlotinib-functionalized gold nanoparticle (erlotinib:AuNP) nanosystem designed for targeted delivery to metastatic non-small cell lung cancer H1299 cells. Initial MTS assays demonstrated that free erlotinib induced a concentration-dependent reduction in cell viability, while 0.1 {micro}M erlotinib exhibited negligible cytotoxicity and was therefore selected for nanosystem fabrication. AuNPs alone showed minimal toxicity toward H1299 cells over the investigated concentration range. Following conjugation of erlotinib with AuNPs, the resulting nanosystems reduced cell viability to approximately 60%, indicating enhanced biological activity of the drug after nanoparticle-assisted delivery. Fluorescence microscopy confirmed the intracellular internalization of the nanosystems in H1299 cells, with nanoparticle aggregates predominantly localized in the perinuclear and perimitochondrial regions. Three-dimensional Raman spectroscopy (3D RS) mapping further verified the intracellular localization of the conjugates through characteristic Raman signatures of erlotinib:AuNPs. Importantly, 3D RS enabled detection of nanosystems at concentrations below the sensitivity limit of fluorescence imaging, demonstrating superior analytical performance for intracellular nanosystem tracking. Atomic force microscopy-infrared (AFM-IR) spectroscopy coupled with principal component analysis (PCA) demonstrated substantial biochemical modifications induced by the erlotinib:AuNP nanosystems, including enhanced lipid-related spectral features and significant alterations in protein secondary structure, particularly the increased contribution of unordered and antiparallel {beta}-turn conformations. The obtained results demonstrate that combining plasmonic nanocarriers with advanced vibrational spectroscopy enables highly sensitive monitoring of intracellular drug delivery and nanosystem-induced biochemical responses in cancer cells.

Lung cancer remains the leading cause of cancer-related mortality, and despite its limited efficacy, and low specificity, chemotherapy is still commonly used as a first-line treatment. Herein, we prepare and characterize poly(lactic-co-glycolic acid) (PLGA) nanoparticles (NPs) for the use as a non-targeted nanocarrier drug delivery system for the chemotherapeutic drug, paclitaxel (PTX). Drug loading capability, mono-dispersity, zeta potential, morphology, and drug release profile were determined for the PLGA NPs. Furthermore, the IC50 of the loaded nanoparticles was determined to be 20 times lower than the IC50 value of free PTX in cytotoxic studies in A549 lung cancer cells. The time dependent therapeutic efficacy of both free and encapsulated PTX was assess at several time points in A549 monolayers via label-free multiphoton metabolic imaging based on the fluorescence lifetime of the metabolic cofactor NAD(P)H. Specifically, the significant continuous metabolic shift towards oxidative phosphorylation in cells treated with NPs was correlated with the NPs drug release profile, highlighting the sustained and controlled behavior of the drug delivery system. These findings underscore not only the utility of PLGA NPs as a drug delivery system with enhanced efficacy compared to free drug treatments but also the use of label-free multiphoton fluorescence lifetime imaging microscopy for non-invasive imaging, allowing the tracking of metabolic changes on cellular and subcellular level. Additionally, the presented method is extended to include more complex in vitro models allowing for assessing the cellular bioenergetic response of individual cells in 3D models in real time, paving the way for improved therapeutic strategies against lung cancer.

Chronic infections persist in large part thanks to protection that biofilms afford their bacterial creators. The extracellular polymeric substance of biofilms is a hydrated matrix of DNA, polysaccharides, and structural proteins, amongst other components, through which nutrients, signalling molecules, and antimicrobial agents must diffuse to reach the bacteria within. Quantitative measurement of transport on the nanoscale within in vivo biofilms remains challenging due to optical heterogeneity, autofluorescence, active remodelling of biofilms and the ambiguity in trajectory reconstruction during single-particle tracking (SPT). Here, we present a methodological framework for measuring molecular transport in defined minimal extracellular matrix models using quantum dots as fluorescent nanoscale probes imaged with high-speed SlimVar microscopy. To establish conditions in which high-diffusivity particle trajectories can be reliably reconstructed, upper limits to quantum dot concentrations were estimated from Brownian motion. The 99th-percentile inter-frame jump distance was estimated from the three-dimensional Brownian jump distance distribution and used to define a target average nearest neighbour distance, and therefore a per-particle volume, used for calculating a concentration which minimises the probability of trajectory collision during data acquisition. Quantum dot movement was imaged at sub-millisecond frame rates and diffusion coefficients were calculated in a 20% glycerol control and in DNA nanostar hydrogels modelling minimal extracellular matrix scaffolds assembled at 250 M and 500 M. Median diffusion coefficients decreased from 94.9 m2*s-1 in glycerol to 15.9 m2*s-1 and 8.3 m2*s-1 in the 250 M and 500 M hydrogels, respectively. More broadly, this work establishes a workflow for quantitative SPT in minimal biofilm models. Rather than attempting to reproduce the full biological complexity of native biofilms, this approach provides the basis of a modular experimental framework in which individual extracellular matrix components can be incorporated sequentially and their effects on molecular transport quantified.

Extracellular vesicles (EVs) carry molecular signatures of their originating cells and have thus emerged as promising biomarkers. However, their clinical utility remains limited due to their low abundance and the modest sensitivity of current EV detection methods in complex biological environments. Here, we present a quantum well defect functionalized carbon nanotube sensor coupled with integrin-recognition RGD tripeptide for EV detection in human plasma. Leveraging the abundance of integrins on EV surfaces, we targeted 5{beta}1, V{beta}1, and V{beta}3 subtypes. The nanosensor exhibited robust hypsochromic shifts in defect emission upon integrin binding, achieving sub-picomolar detection limits for integrin subunits and quantifying EVs at concentrations as low as 104 EVs{middle dot}mL-1 for glioblastoma, ovarian cancer, and fibroblast cell-derived EV types. Molecular dynamics simulation indicated that integrin docking at the RGD-coupled quantum defect can substantially reshape the interfacial environments of the quantum defects, explaining the high sensitivity in EV detection in complex biological media. Finally, transmembrane protein analysis validated the expression of surface integrins across the tested EV types. The modular nanosensor construct can be targeted to detect disease-associated EV subpopulations, advancing EV-based diagnostics.