Biophysical Reports

Finite-bandgap charge-based FET biosensors are known to be insensitive to electronic tunneling noise in the semiconductor and interference from weakly-charged proteins in physiological samples. However, their signal is still corrupted by pH fluctuations and condensing counterions in the bulk electrolyte. Here, we demonstrate that electrolyte noise is screened by the Stern layer beyond the linear Debye-Huckel limit, when the interfacial potential drop is larger than the thermal voltage. Using a fully nonlinear Gouy-Chapman-Stern (GCS) theory, a universal calibration curve is derived and shown to collapse the low-noise FET signals for miRNA, extracellular vesicles (EV), and protein, captured either directly or through silica and magnetic nanoparticle charge reporters, over eleven decades of analyte concentration. The theory involves the dissociation constant KD and a gain parameter {chi}, which represents the competing effects of the reporter charge and buffer Debye screening that must be large to produce the low-noise GCS condition. The limit of detection (LOD) is shown to be KD/{chi}, which can be optimized by varying the probe density, charge reporter, and buffer solution. An optimized hybridization buffer then yields {chi} ~ 22 (LOD ~1 pM) for miRNA detection, and multivalent capture reduces KD by four orders of magnitude for EV detection (relative to protein detection) with {chi} ~ 34 (LOD ~10 fM).

Nanopore sensors have transformed single-molecule analysis, enabling real-time detection of biomolecules with unprecedented resolution. Understanding and modelling noise in nanopore sensing is essential to unlocking their full analytical potential. Although mechanistic models of nanopore noise exist, a rigorous statistical framework that characterises noise structure across diverse experimental conditions is still lacking. Here, we present a comprehensive empirical analysis of nanopore noise using twelve diverse experimental conditions acquired with different nanopores, analytes, and electronic recording systems. By employing multiple, mutually reinforcing statistical methods, we establish that nanopore noise is well described by a composite fractional model comprising multiple fractional Gaussian noise and fractional Brownian motion components. This finding is supported by confirmation of Gaussianity, rigorous interpretation of second-order exponents, multifractal analysis, and evidence that transient deviations are attributable to deterministic or experimental artefacts rather than alternative stochastic mechanisms. We further resolve quasi-deterministic structures, such as baseline trends and structural breaks, and demonstrate their separability from the underlying stochastic profile. Collectively, these results provide the first formal statistical validation of a composite fractional model for nanopore noise and establish a principled basis for noise reconstruction, realistic simulation, and statistically grounded algorithm design for nanopore sensing applications. This study provides the foundation for the creation of high-fidelity performance evaluation datasets for signal processing, and the controlled generation and augmentation of data for machine learning while maintaining statistical fidelity to experimental conditions.

With its unique spatiotemporal resolution at the single-molecule level, Forster resonance energy transfer (FRET) is a powerful tool for investigating biomolecular conformational dynamics and function. However, early photobleaching remains a key limitation restricting the achievable observation time and therefore the information gain and application range of single-molecule FRET. Here, we establish nanophotonic DyeCycling, which overcomes photobleaching by reversible fluorophore binding and efficient background suppression using zero-mode waveguides. Nanoscale conformational changes are sensitively resolved from milliseconds up to the hour range, enabling robust kinetic analyses at the single-molecule level. As a result, outlier molecules reflecting static heterogeneity and time-dependent kinetics within individual molecules reflecting dynamic heterogeneity are reliably resolved. The versatility of DyeCycling is demonstrated using DNA and protein systems. Altogether, nanophotonic DyeCycling extends smFRET observations beyond the conventional photobleaching limit, revealing previously inaccessible kinetic effects and providing deeper insight into biomolecular systems with multiple states and rates.

Fluorescent proteins are widely used as quantitative reporters of protein abundance in living cells. Here we show that this relationship can break down under translation inhibition: the cellular ribosomal abundance can increase while fluorescence intensity remains unchanged or even decreases. Using fluorescent reporters of ribosome abundance in Escherichia coli and Bacillus subtilis, we find that cellular fluorescence intensity quantitatively tracks ribosome abundance, inferred by RNA-to-protein ratio, under nutrient-limited growth but that is no longer true during chloramphenicol treatment. In E. coli, the discrepancy occurs with transcriptional and translational reporters and with both ribosomal protein and ribosomal RNA promoters. To distinguish changes in fluorescence output per reporter from changes in reporter abundance, we constructed a fluorescent protein-LacZ dual reporter in which fluorescence and an independent enzymatic estimate of reporter abundance are obtained from the same protein. Under translation inhibition, LacZ-derived reporter abundance increases whereas fluorescence intensity remains approximately constant, showing that fluorescence output per unit reporter abundance decreases. In a companion study, Bakshi and colleagues demonstrate that the effect extends to other fluorescent proteins and other translation inhibitors, indicating that it is not specific to a particular fluorescent protein or translation inhibitor. Our results show that fluorescent-protein calibration can be condition-dependent and cannot be assumed to transfer across physiological perturbations.

Transition-metal ion Forster resonance energy transfer (tmFRET) measures short-range distances and conformational changes in proteins, but its distance sensitivity depends on the donor-acceptor pair. Here, we establish the metal-chelating noncanonical amino acid bipyridylalanine (bpyA) as a flexible, genetically encoded acceptor platform whose Forster distance (R0) can be tuned without changing protein labeling. Cu2+, Cu2+ complexed with nitrilotriacetic acid (NTA), and Fe2+ form distinct acceptors at the same bpyA site, allowing distance sensitivity to be adjusted by changing the solution conditions. We incorporated bpyA at two sites in maltose binding protein (MBP) and labeled nearby engineered cysteines with the donor fluorophore BODIPY. The resulting [Cu(bpyA)]2+, [CuNTA(bpyA)]2+, and [Fe(bpyA)]2+ acceptors provided R0 values ranging from 13.9 to 21.2 [A]. Analysis of time-resolved tmFRET data recovered donor-acceptor distance distributions and maltose-dependent changes consistent with structural predictions. At the 237/295 site pair, both copper and iron acceptors resolved the maltose-dependent decrease in distance. At the 309/322 site pair, [Cu(bpyA)]2+ did not appreciably resolve the distance increase, whereas adding NTA shifted R0 toward shorter distances and enabled detection of an increase from 12.1 to 14.4 [A]. These results demonstrate that a single bpyA-labeled protein preparation supports multiple tmFRET acceptors, providing a cysteine-independent acceptor-labeling strategy with adjustable distance sensitivity.

Fluorescence microscopy is a powerful tool for dissecting the molecular mechanisms of T-cell antigen recognition in living cells, but its quantitative insight critically depends on non-perturbative, high-quality probes. Here, we repurpose a small (~15 kDa) CD3epsilon-binding DARPin (designed ankyrin repeat proteins) to a fluorescent label for T-cell receptor (TCR)/CD3 complexes on primary human CD8+ T-cells, with the aim of generating a powerful tool for quantitative analysis, single-molecule tracking, and advanced imaging of TCR dynamics. We show that the DARPin binds CD3{varepsilon} with high affinity and selectivity and using single molecule tracking and brightness analysis, we characterize the TCR-CD3 diffusion behavior and show that the DARPin binds to both CD3epsilon; subunits. Importantly, labeling preserves antigen sensitivity: on supported lipid bilayers presenting cognate pMHC, T-cells remain responsive, assemble synapses, form TCR microclusters, and initiate signaling similar to unlabeled controls. We further demonstrate compatibility with lattice light-sheet microscopy for volumetric imaging of T-cell - APC interactions in living cells. Together, these results establish DARPins as versatile, minimally perturbative probes for high resolution, quantitative studies of T cell synapse organization and signaling.

Lipid packing defects expose hydrophobic regions of membranes and influence protein-membrane recognition, membrane remodeling, and lipid sensing. Quantitative analysis of these defects remains challenging for heterogeneous membranes with strong curvature, mixed bilayer and monolayer organization, and substantial neutral-lipid content. Here, we present Lipid Defect Analyzer (LipiDeA), an open-source Python framework for quantitative analysis of lipid packing defects in complex membrane systems. LipiDeA reconstructs membrane surfaces using triangulated meshes that adapt to local membrane geometry and identifies packing defects from lipid coverage of the reconstructed surface. The framework further classifies bilayer and monolayer regions, resolves defect properties according to local membrane curvature, and distinguishes defects associated with phospholipid tails and neutral lipids. Validation using planar and buckled DPPC bilayers reproduced previously reported packing defect constants and showed that defects preferentially occur in positively curved membrane regions. Application to lipid-droplet-like membranes revealed composition- and topology-dependent defect formation, with the largest defects occurring during the transition from bilayer-dominated to monolayer-containing membranes and distinct defect properties for DAG- and TAG-containing systems. These results highlight the importance of local membrane geometry and composition in determining lipid packing defects.

Surface-tethered DNA, anchored to gold by the late 1990s, has matured into a versatile platform for biosensing, computing and actuation. Whereas the molecular behavior of DNA in bulk solution is well characterized, that of DNA at electrode interfaces remains far less understood. Here we investigate the mechanics and dynamics of electrode-tethered double-stranded DNA across surface chemistries, salt concentrations and applied potentials. Using GPU-accelerated all-atom molecular dynamics, we sample trajectory ensembles totaling 144 s over 620 simulations. The simulations reproduce key experimental observations yet uncover molecular behavior that had gone unnoticed despite nearly three decades of research. Ensemble statistics reveal a preferred orientation about the helical axis upon surface contact. Energetic analysis identifies a substantial role for Onsager-Samaras-type repulsion in opposing that contact. By reproducing experimental trends while resolving their molecular origins, these ensembles substantially narrow the longstanding simulation-experiment gap and provide a basis for engineering tethered-DNA devices.

Interpreting single-molecule force spectroscopy (SMFS) data from optical tweezers with steered molecular dynamics (SMD) is hindered by a severe timescale mismatch: SMD trajectories span nanoseconds to microseconds, while SMFS experiments unfold over milliseconds to seconds, forcing SMD to pull six to nine orders of magnitude faster than experiment and systematically distorting rupture forces, molecular pathways, and kinetic intermediates. We introduce force-probe coupled steered molecular dynamics (fp-SMD), which explicitly propagates the force probe (the optically trapped bead) alongside the biomolecule within a single classical Hamiltonian description of the coupled molecule-linker-bead system. A Predict-Correct Trajectory Propagation (PCTP) scheme resolves the resulting computational bottleneck by decoupling the fast molecular relaxation from the slow, macroscopic bead motion, using metadynamics and transition-state theory to identify the kinetically resolved molecular intermediate reached at each macroscopic time step. On the cholesterol-{beta}-cyclodextrin ({beta}-CD) host-guest complex, fp-SMD reproduces the experimental rupture-force histogram directly at matched conditions, without extrapolation from standard SMD. fp-SMD further resolves how applied force reshapes the molecular escape pathway itself--cholesterol dissociates via a high-angle route at low force and a direct, small-angle route at high force--providing, to our knowledge, the first direct structural evidence for force-induced pathway switching within small host-guest systems believed to underlie catch-slip bond behavior. fp-SMD/PCTP thus offers a general route to simulating force-probe-coupled biomolecular mechanics at experimentally relevant timescales.

Nanoplastic interactions with cell membranes can influence particle uptake, translocation, and biological effects, making their molecular description important for assessing the environmental and biological consequences of plastic pollution. However, molecular simulations generally represent nanoplastics using monodisperse polymer chains, whereas nanoplastics formed by environmental degradation are polydisperse. Here, we construct 4 nm polyethylene terephthalate (PET), polyethylene (PE), and polystyrene (PS) nanoparticles with lognormal molecular-weight distributions reported for degraded polymers, and with melt-like chain packing and entanglement. Using Martini coarse-grained molecular dynamics, we simulate their interactions for 10 microseconds with three lipid bilayers of increasing complexity. PE and PS insert rapidly and remain largely intact, whereas PET remains surface-associated and progressively releases individual chains. Fluid, polyunsaturated lipid environments undergo greater deformation and promote substantially more chain release, up to 56% of the chains for PET and 43% for PS. Release begins with the shortest chains and progressively extends to longer chains, while semicrystalline PE remains largely intact. In contrast, a size-matched monodisperse PS particle releases no chains and minimally perturbs the membrane. These results identify the molecular-weight distribution as a key determinant of nanoplastic-membrane interactions, and indicate that uniform model particles underestimate the release of polymer chains into biological membranes.

Surface plasmon resonance (SPR) and biolayer interferometry (BLI) are well-established label-free analytical technologies used to determine biomolecular interaction rates (ka, kd) and equilibrium constants (KD). While assays historically rely on multi-cycle kinetics (MCK), this approach requires intermediate surface regeneration, risking sensor degradation and increasing reagent consumption. Single-cycle kinetics (SCK) avoids regeneration by sequentially injecting increasing analyte concentrations. However, implementing SCK workflows on biosensor platforms like the OctetRR8 BLI instrument (Sartorius) remains a challenge due to the lack of built-in or online data analysis tools. To address this, we introduce SCKAnalyzer, a free, user-friendly web application designed for processing and fitting SCK datasets. SCKAnalyzer accepts column-based text files and native raw BLI data (.frd format) from Sartorius instruments, performing data pre-processing and non-linear fitting to a 1:1 interaction model. Additionally, it offers advanced simulation capabilities that allow variable association and dissociation durations across sequential injection steps, a feature absent in existing platforms such as BiacoreTMSimul8. The performance of SCKAnalyzer was experimentally validated using a 3-step SCK method (SCK3) for DNA/RNA duplex formation on a Biacore T200, as well as 4-step (SCK4) and 5-step (SCK5) methods for DNA/DNA duplex formation on an OctetRR8 BLI instrument. Binding parameters (ka, kd, KD) derived from SCKAnalyzer showed excellent agreement with values from established proprietary softwares such as Biacore T200 Evaluation Software v3.2.1 and BIAevaluation v4.1.1. SCKAnalyzer provides a practical alternative to MCK, reducing probe consumption and extending SCK utility across biosensor platforms. SCKAnalyzer is available at sck.iecb.u-bordeaux.fr (source code: github.com/synthaze/sckanalyzer).

Polyamines are cationic metabolites that support critical cellular processes including transcription, translation, and cell proliferation. Although polyamine synthesis and transport are emerging therapeutic targets in human disease, the polyamine transport system remains poorly understood. ATP13A2-4 were recently identified as endolysosomal polyamine exporters, transporting putrescine (PUT), spermidine (SPD), and spermine (SPM) from the vesicular lumen to the cytosol. Despite interest in pharmacologically targeting these transporters, existing assays of transporter function rely on indirect or endpoint measures that cannot resolve transport as it occurs. Here, we report the first voltage-clamp recordings of polyamine transporter activity, directly detecting ATP13A2- and ATP13A3-mediated charge movement across cell membranes. When expressed in Xenopus oocytes, these transporters localized to the plasma membrane, enabling electrophysiological measurements. Polyamines induced inward currents dependent on transporter catalytic activity, and their dose-dependence correlated with ATPase assays. ATP13A2 currents were largest in response to SPM, whereas ATP13A3 currents were largest with PUT, consistent with their established substrate preferences. Polyamine-induced currents were blocked by the polyamine transport inhibitor AMXT-1501. These findings establish ATP13A2 and ATP13A3 as electrogenic polyamine pumps and demonstrate the utility of two-electrode voltage clamp as a label-free, transporter-resolved assay to complement other approaches in polyamine transporter-targeted drug discovery.

Nanobubbles (NBs) have been widely investigated as ultrasound contrast and therapeutic agents, yet whether submicron gas bubbles can directly generate measurable ultrasound signals remains controversial. Because acoustic scattering scales with bubble size, several studies have recently attributed nanobubble echogenicity primarily to larger bubbles. A recent Science Advances publication, attributed the loss of ultrasound signal after filtration to the removal of residual larger bubbles [1]. This interpretation assumes that filtration acts solely as a size-selection process. Here, we show that filtration also alters the shell properties and stability of lipid-coated NBs, independent of size. Filtered NBs (F-NBs) show significant lipid loss and accumulation on the filters themselves, resulting in decreased bubble stability and greater susceptibility to destruction under acoustic excitation. To decouple size from processing effects, we generated NB populations size-matched to filtered samples using increased centrifugation (IC-NBs), a comparatively gentler separation method. Despite comparable size distributions, IC-NBs retained sustained ultrasound scattering, whereas filtered NBs showed significantly reduced acoustic persistence. Filtration through 5 m pores, ~5-fold larger than the largest bubbles, preserved acoustic persistence but still reduced bubble concentration by ~30%, suggesting that membrane contact may contribute to bubble loss beyond size exclusion. Complementary lipid and gas-content assays provided further evidence of filtration-induced changes. The 0.45 m filtration removed substantially more total lipid than could be attributed to free-lipid removal alone, while the lipid associated with the larger bubbles removed during filtration accounted for only ~5% of the additional lipid loss. Encapsulated gas content remained comparable to that of size-matched centrifuged controls, suggesting that the reduced acoustic stability of filtered NBs is more closely associated with changes in lipid content than with loss of the gas core. These results indicate that membrane filtration is not a purely size-selective process: it removes substantially more lipid than size exclusion predicts and yields bubbles that are less stable under ultrasound, most likely through changes in shell lipid content or in the surrounding lipid reservoir, underscoring the importance of processing-induced effects in interpreting nanobubble echogenicity.

Fluorescent base analogues have proven to be valuable probes of RNA structure. Pyrrolocytosine (pC), an analogue of cytosine, exhibits highly environment-sensitive fluorescence within RNA and is strongly quenched in hydrogen-bonded contexts. In this work, we incorporated pC into the aptamer domain of the B. subtilis guanine riboswitch to determine if this quenching mechanism could allow for sensing of RNA-small molecule interactions. We utilized complementary fluorescence spectroscopy methods to under-stand the functional impacts and ligand-sensitivity of pC substitutions distal to and within the binding pocket. We found that pC substitution within a static structural region exhibits the expected ligand-independent fluorescence properties. When placed in the ligand binding pocket, pC exhibits a moderately decreased affinity for the ligands guanine (G) and hypoxanthine (Hx) while maintaining high specificity for G. We found that pC behaves as a turn-off sensor, with distinct changes in fluorescence upon G or Hx binding. These differences are dependent on the number of hydrogen bonds formed with pC and are resolvable in both time-dependent and steady-state fluorescence measurements. Our results show that pC can be used to study RNA-small molecule interactions and can discriminate between similar ligands even when they have identical binding modes.

A cell's ability to maintain its volume is a prerequisite for its essential physiological functions. Changes in extracellular osmolarity induce the flux of water and solutes across the cell and nuclear membrane and, therefore, alters cell and nuclear volumes. Here, we investigated the equilibrium and dynamic volumetric responses of cells and nuclei in adherent human hepatoma (HepG2) cell monolayers using live three-dimensional imaging. Through image segmentation, 3D reconstruction, and volume rendering, precise cell and nuclear volumes were quantified. We found that during hypoosmotic stress, cells swell and lose osmolytes through active volume regulation, and nuclei undergo greater relative expansion than cells. During hyperosmotic stress, we found that both cells and nuclei shrink rapidly, develop irregular morphologies and undergo passive volume regulation. Furthermore, we explored the osmotic responses in these cells by restoring isosmotic conditions following osmotic stress and found that nuclei returned to volumes greater than their initial isosmotic volume, whereas cytoplasmic volume returned to a value below its initial isosmotic volume following swelling. By fitting coupled transport models water and solute permeability estimates for both cells and nuclei were made from their dynamic responses to different anisosmotic media with and without the presence of permeating solutes dimethyl sulfoxide and ethylene glycol. Together, these quantitative measurements of cell, cytoplasm, and nuclear volumetric responses and associated transport parameters have important implications for understanding cell physiology and the mechanism of osmoregulation.

Cardiac amyloidosis is caused by deposits of misfolded proteins in the heart and can lead to heart failure. Histological confirmation typically involves staining tissue sections with Congo red and examining them under polarized light for apple-green birefringence; however, achieving an interpretable, label-free, and observer-independent assessment remains challenging. Here we show that Pearson kurtosis of channel intensities within the amide I window provides an interpretable, fitting-free contrast for cardiac amyloid in unstained human cardiac tissue sections from ten participants imaged by ultrabroadband multiplex coherent anti-Stokes Raman scattering (CARS). In a discovery participant, high-kurtosis regions corresponded with Congo red-positive tissue in two CARS-histology pairs of adjacent sections (descriptive spatial AUCs, 0.79 and 0.73). Across 15 fields from nine additional participants, field-level kurtosis values did not overlap between severe-deposition and Congo red-negative tissue fields. Among the descriptors examined, kurtosis showed the largest standardized participant-level separation. Exploratory multivariate curve resolution yielded a deposit-associated component with aromatic-residue features shared with reagent transthyretin. In one field from the discovery participant, the second harmonic generation (SHG) signal, acquired simultaneously with CARS, showed a broadly inverse spatial pattern relative to the amide I kurtosis map, revealing distinct tissue-architectural contrast. Together, these findings support a potential label-free approach to detecting cardiac amyloid deposits that combines amide I kurtosis derived from broadband CARS with SHG-derived architectural contrast.

Fluorescent proteins (FPs) are the workhorse of modern-day cellular imaging and biosensing. However, rationally predicting and engineering the photophysical properties of FPs remain challenging, as these are governed by complex three-dimensional microenvironments and post-translational modifications. Here, we present a novel surface-mutation-guided engineering strategy combined with iterative rounds of rational mutagenesis to improve key photophysical properties of red fluorescent proteins (RFPs). This approach generated two bright, monomeric RFP variants from the mFruits lineage, mCranberry and mCranberry2, with quantum yields of 85% and 80%, respectively. Strategic incorporation of charged amino acid residues on the protein surface enhanced fluorescence quantum yield without compromising photostability. Comprehensive photophysical characterization and live-cell microscopy demonstrate enhanced performance of these variants and establish their utility as robust probes for fluorescence imaging. However, the exact mechanism of such improvements is subject to further investigation. Collectively, this work introduces a strategy for optimizing fluorescence properties and provides a framework that may be broadly applicable to other pigment-protein systems such as genetically encoded metal-ion indicators and photosynthetic light-harvesting complexes.

Drug-target association and dissociation rates often determine in vivo efficacy more than affinity alone. The association rate constant, however, is computationally challenging to predict since the productive encounter is a rare event in a large translational and orientational search space, which lies beyond the reach of conventional atomistic simulations. We present PySTARC (Python Simulation Toolkit for Association Rate Constants), a GPU-accelerated Brownian dynamics engine for estimating bimolecular association rate constants. PySTARC is a Python reimplementation of the BrownDye engine that converges even small reaction probabilities on a single GPU, at a throughput that would otherwise require a large CPU cluster. The current framework resolves reactions between integration steps with a closed-form Brownian bridge, models the internal flexibility of the solute through a coarse-grained bead chain, distributes trajectories across multiple GPUs, checkpoints long runs, monitors convergence, and automates the entire workflow from input structures to rate estimates. PySTARC is validated against protein-ligand and protein-protein complexes spanning five orders of magnitude in the association rate constant, with most estimates within one order of magnitude of experiment.

Fluorescence microscopy is routinely used in the quantitative analysis of Giant Unilamellar Vesicles (GUVs) because it makes GUVs visible and easy to detect. Nonetheless, it requires fluorescence-equipped microscopes and, especially, the addition of tagged molecules that may introduce undesired and unaccounted variations compared to the pristine formulation. Alternatively, phase-contrast microscopy can be used to observe GUVs without labelling, yet the automatic detection of GUVs in phase-contrast images by standard image processing techniques is difficult. To overcome this limitation, we developed a framework for the automated detection and size characterization of label-free GUVs based on YOLOv11 model, a state-of-art lightweight Convolutional Neural Network (CNN) for object detection. Trained on 164 phase-contrast microscope images containing 4666 manually annotated vesicles, the model enables robust detection under low contrast-to-noise ratio and heterogeneous illumination. The trained model is implemented in a stand-alone desktop application supporting multiple analysis in a single run, pixel-to-micrometer calibration, and automated size distribution analysis. This approach achieves high detection accuracy and competitive cross-microscope generalization using YOLOv11 nano on greyscale image. Retraining scripts compatible with standard hardware are also provided to enhance adaptability. This tool enables fast and reliable analysis of GUVs without altering their composition, supporting experimental reproducibility.

High-speed atomic force microscopy (HS-AFM) experiments provide unique insights into nanoscale biological processes by directly visualizing the surface topography dynamics of biomolecules, such as proteins, over extended periods of time and generating large imaging data sets. However, the lack of computational methods for automated data analysis has thus far limited the full utilization of these data for the quantitative understanding of single-molecule dynamic. Here, we present the AFM Analysis Arena (A3) within BioAFMviewer as a software platform for automated, high-throughput quantitative analysis of AFM topographic imaging data within a user-friendly interactive graphical interface, implementing a convenient workflow from raw HS-AFM (AFM) imaging to statistical analysis. In several applications to HS-AFM imaging data, we demonstrate methods of automatized molecule detection and dynamic tracking to highlight the available capacities, aiming to transform HS-AFM measurements from observations towards an analytical tool in nanoscale biology.