A framework for evaluating the performance of SMLM cluster analysis algorithms

Extracellular vesicles adhere to cells primarily by interactions of integrins and GM1 with laminin

Tumor-derived extracellular vesicles (EVs) have attracted significant attention, yet the molecular mechanisms that govern their specific binding to recipient cells remain elusive. Our in vitro study utilizing single-particle tracking demonstrated that integrin heterodimers comprising α6β4 and α6β1 and ganglioside, GM1, are responsible for the binding of small EV (sEV) subtypes to laminin. EVs derived from four distinct tumor cell lines, regardless of size, exhibited high binding affinities for laminin but not for fibronectin, although fibronectin receptors are abundant in EVs and have functional roles in EV-secreting cells. Our findings revealed that integrins in EVs bind to laminin via the conventional molecular interface, facilitated by CD151 rather than by inside-out signaling of talin-1 and kindlin-2. Super-resolution movie observation revealed that sEV integrins bind only to laminin on living recipient cells. Furthermore, sEVs bound to HUVEC and induced cell branching morphogenesis in a laminin-dependent manner. Thus, we demonstrated that EVs predominantly bind to laminin on recipient cells, which is indispensable for cell responses.

Treatment of acute myeloid leukemia models by targeting a cell surface RNA-binding protein

Immunotherapies for acute myeloid leukemia (AML) and other cancers are limited by a lack of tumor-specific targets. Here we discover that RNA-binding proteins and glycosylated RNAs (glycoRNAs) form precisely organized nanodomains on cancer cell surfaces. We characterize nucleophosmin (NPM1) as an abundant cell surface protein (csNPM1) on a variety of tumor types. With a focus on AML, we observe csNPM1 on blasts and leukemic stem cells but not on normal hematopoietic stem cells. We develop a monoclonal antibody to target csNPM1, which exhibits robust anti-tumor activity in multiple syngeneic and xenograft models of AML, including patient-derived xenografts, without observable toxicity. We find that csNPM1 is expressed in a mutation-agnostic manner on primary AML cells and may therefore offer a general strategy for detecting and treating AML. Surface profiling and in vivo work also demonstrate csNPM1 as a target on solid tumors. Our data suggest that csNPM1 and its neighboring glycoRNA-cell surface RNA-binding protein (csRBP) clusters may serve as an alternative antigen class for therapeutic targeting or cell identification.

RNA-binding proteins and glycoRNAs form domains on the cell surface for cell-penetrating peptide entry

The composition and organization of the cell surface determine how cells interact with their environment. Traditionally, glycosylated transmembrane proteins were thought to be the major constituents of the external surface of the plasma membrane. Here, we provide evidence that a group of RNA-binding proteins (RBPs) is present on the surface of living cells. These cell-surface RBPs (csRBPs) precisely organize into well-defined nanoclusters enriched for multiple RBPs and glycoRNAs, and their clustering can be disrupted by extracellular RNase addition. These glycoRNA-csRBP clusters further serve as sites of cell-surface interaction for the cell-penetrating peptide trans-activator of transcription (TAT). Removal of RNA from the cell surface, or loss of RNA-binding activity by TAT, causes defects in TAT cell internalization. Together, we provide evidence of an expanded view of the cell surface by positioning glycoRNA-csRBP clusters as a regulator of communication between cells and the extracellular environment.

Three-color single-molecule localization microscopy in chromatin

Super-resolution microscopy has revolutionized our ability to visualize structures below the diffraction limit of conventional optical microscopy and is particularly useful for investigating complex biological targets like chromatin. Chromatin exhibits a hierarchical organization with structural compartments and domains at different length scales, from nanometers to micrometers. Single molecule localization microscopy (SMLM) methods, such as STORM, are essential for studying chromatin at the supra-nucleosome level due to their ability to target epigenetic marks that determine chromatin organization. Multi-label imaging of chromatin is necessary to unpack its structural complexity. However, these efforts are challenged by the high-density nuclear environment, which can affect antibody binding affinities, diffusivity and non-specific interactions. Optimizing buffer conditions, fluorophore stability, and antibody specificity is crucial for achieving effective antibody conjugates. Here, we demonstrate a sequential immunolabeling protocol that reliably enables three-color studies within the dense nuclear environment. This protocol couples multiplexed localization datasets with a robust analysis algorithm, which utilizes localizations from one target as seed points for distance, density and multi-label joint affinity measurements to explore complex organization of all three targets. Applying this multiplexed algorithm to analyze distance and joint density reveals that heterochromatin and euchromatin are not-distinct territories, but that localization of transcription and euchromatin couple with the periphery of heterochromatic clusters. This work is a crucial step in molecular imaging of the dense nuclear environment as multi-label capacity enables for investigation of complex multi-component systems like chromatin with enhanced accuracy.

Uptake of small extracellular vesicles by recipient cells is facilitated by paracrine adhesion signaling

Small extracellular vesicles (sEVs) play crucial roles in intercellular communication. However, the internalization of individual sEVs by recipient cells has not been directly observed. Here, we examined these mechanisms using state-of-the-art imaging techniques. Single-molecule imaging shows that tumor-derived sEVs can be classified into several subtypes. Simultaneous single-sEV particle tracking and observation of super-resolution movies of membrane invaginations in living cells reveal that all sEV subtypes are internalized via clathrin-independent endocytosis mediated by galectin-3 and lysosome-associated membrane protein-2C, while some subtypes that recruited raft markers are internalized through caveolae. Integrin β1 and talin-1 accumulate in recipient cell plasma membranes beneath all sEV subtypes. Paracrine, but not autocrine, sEV binding triggers Ca2+ mobilization induced by the activation of Src family kinases and phospholipase Cγ. Subsequent Ca2+-induced activation of calcineurin-dynamin promotes sEV internalization, leading to the recycling pathway. Thus, we clarified the detailed mechanisms of sEV internalization driven by paracrine adhesion signaling.

Kovács BB, Novák T, Erdélyi M. Cluster parameter-based DBSCAN maps for image characterization

Single-molecule localization microscopy techniques are one of the most powerful methods in biological studies, allowing the visualization of nanoclusters. Cluster analysis algorithms are used for quantitative evaluation, with DBSCAN being one of the most widely used. Clustering results are extremely sensitive to the initial parameters; thus, several methods including DBSCAN maps, have been developed for parameter optimization. Here, we introduce cluster parameter-based DBSCAN maps, which are directly applicable to measured datasets. These maps can be used for image characterization and parameter optimization through sensitivity studies. We show the applicability of these maps to simulated and measured datasets and compare our results with the recently implemented lacunarity analysis for SMLM measurements.

Simple quantitation and spatial characterization of label free cellular images

Label-free imaging is routinely used during cell culture because of its minimal interference with intracellular biology and capability of observing cells over time. However, label-free image analysis is challenging due to the low contrast between foreground signals and background. So far various deep learning tools have been developed for label-free image analysis and their performance depends on the quality of training data. In this study, we developed a simple computational pipeline that requires no training data and is suited to run on images generated using high-content microscopy equipment. By combining classical image processing functions, Voronoi segmentation, Gaussian mixture modeling and automatic parameter optimization, our pipeline can be used for cell number quantification and spatial distribution characterization based on a single label-free image. We demonstrated the applicability of our pipeline in four morphologically distinct cell types with various cell densities. Our pipeline is implemented in R and does not require excessive computational power, providing novel opportunities for automated label-free image analysis for large-scale or repeated cell culture experiments.

Annotation and automated segmentation of single-molecule localisation microscopy data

Single Molecule Localisation Microscopy (SMLM) is becoming a widely used technique in cell biology. After processing the images, the molecular localisations are typically stored in a table as xy (or xyz) coordinates, with additional information, such as number of photons, etc. This set of coordinates can be used to generate an image to visualise the molecular distribution, for example, a 2D or 3D histogram of localisations. Many different methods have been devised to analyse SMLM data, among which cluster analysis of the localisations is popular. However, it can be useful to first segment the data, to extract the localisations in a specific region of a cell or in individual cells, prior to downstream analysis. Here we describe a pipeline for annotating localisations in an SMLM dataset in which we compared membrane segmentation approaches, including Otsu thresholding and machine learning models, and subsequent cell segmentation. We used an SMLM dataset derived from dSTORM images of sectioned cell pellets, stained for the membrane proteins EGFR (epidermal growth factor receptor) and EREG (epiregulin) as a test dataset. We found that a Cellpose model retrained on our data performed the best in the membrane segmentation task, allowing us to perform downstream cluster analysis of membrane versus cell interior localisations. We anticipate this will be generally useful for SMLM analysis.

Heterogeneous density-based clustering with a dual-functional memristive array

In the big data era, the requirement for data clustering methods that can handle massive and heterogeneous datasets with varying distributions increases. This study proposes a clustering algorithm for data sets with heterogeneous density using a dual-mode memristor crossbar array for data clustering. The array consists of a Ta/HfO2/RuO2 memristor operating in analog or digital modes, controlled by the reset voltage. The digital mode shows low dispersion and a high resistance ratio, and the analog mode enables precise conductance tuning. The local outlier factor is introduced to handle a heterogeneous density, and the required Euclidean and K-distances within the given dataset are calculated in the analog mode in parallel. In the digital mode, clustering is performed based on the connectivity among data points after excluding the detected outliers. The proposed algorithm boasts linear time complexity for the entire process. Extensive evaluations of synthetic datasets demonstrate significant improvement over representative density-based algorithms, and the datasets with heterogeneous density are clustered feasibly. Finally, the proposed algorithm is used to cluster the single-molecule localization microscopy data, demonstrating the feasibility of the suggested method for real-world problems.

Anisotropic DBSCAN for 3D SMLM Data Clustering

Single-molecule localization microscopy (SMLM) advanced biological discoveries beyond the diffraction limit. Various implementations enable 3D SMLM to reconstruct volumetric cell images. Yet, the inherent anisotropic point spread function of optical microscopes often limits the localization precision in the axial direction compared to the lateral precision. Such localization anisotropy could also expand spherical cellular structures to ellipsoidal cellular structures. Structure identification, however, is often performed using DBSCAN cluster algorithms, considering an isotropic search volume. Here, we show that an anisotropic DBSCAN search volume identifies anisotropic clusters more reliably using simulated ground truth data sets. Given experimental localization precisions, we suggest optimized search parameters based on an expanded computational grid search and show an enhanced performance of anisotropic DBSCAN amidst variations in localization precision. We demonstrate the capability of anisotropic DBSCAN on experimental data and anticipate that the algorithm allows for a more rigorous identification of clusters in cells, considering the anisotropic localization precisions of astigmatism-based 3D SMLM.

A supervised graph-based deep learning algorithm to detect and quantify clustered particles

Considerable efforts are currently being devoted to characterizing the topography of membrane-embedded proteins using combinations of biophysical and numerical analytical approaches. In this work, we present an end-to-end (i.e., human intervention-independent) algorithm consisting of two concatenated binary Graph Neural Network (GNNs) classifiers with the aim of detecting and quantifying dynamic clustering of particles. As the algorithm only needs simulated data to train the GNNs, it is parameter-independent. The GNN-based algorithm is first tested on datasets based on simulated, albeit biologically realistic data, and validated on actual fluorescence microscopy experimental data. Application of the new GNN method is shown to be faster than other currently used approaches for high-dimensional SMLM datasets, with the additional advantage that it can be implemented on standard desktop computers. Furthermore, GNN models obtained via training procedures are reusable. To the best of our knowledge, this is the first application of GNN-based approaches to the analysis of particle aggregation, with potential applications to the study of nanoscopic particles like the nanoclusters of membrane-associated proteins in live cells.

Mapping the nuclear landscape with multiplexed super-resolution fluorescence microscopy

The nucleus coordinates many different processes. Visualizing how these are spatially organized requires imaging protein complexes, epigenetic marks, and DNA across scales from single molecules to the whole nucleus. To accomplish this, we developed a multiplexed imaging protocol to localize 13 different nuclear targets with nanometer precision in single cells. We show that nuclear specification into active and repressive states exists along a spectrum of length scales, emerging below one micron and becoming strengthened at the nanoscale with unique organizational principles in both heterochromatin and euchromatin. HP1-α was positively correlated with DNA at the microscale but uncorrelated at the nanoscale. RNA Polymerase II, p300, and CDK9 were positively correlated at the microscale but became partitioned below 300 nm. Perturbing histone acetylation or transcription disrupted nanoscale organization but had less effect at the microscale. We envision that our imaging and analysis pipeline will be useful to reveal the organizational principles not only of the cell nucleus but also other cellular compartments.

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

Membrane topography and the overestimation of protein clustering in single molecule localisation microscopy - identification and correction

According to single-molecule localisation microscopy almost all plasma membrane proteins are clustered. We demonstrate that clusters can arise from variations in membrane topography where the local density of a randomly distributed membrane molecule to a degree matches the variations in the local amount of membrane. Further, we demonstrate that this false clustering can be differentiated from genuine clustering by using a membrane marker to report on local variations in the amount of membrane. In dual colour live cell single molecule localisation microscopy using the membrane probe DiI alongside either the transferrin receptor or the GPI-anchored protein CD59, we found that pair correlation analysis reported both proteins and DiI as being clustered, as did its derivative pair correlation-photoactivation localisation microscopy and nearest neighbour analyses. After converting the localisations into images and using the DiI image to factor out topography variations, no CD59 clusters were visible, suggesting that the clustering reported by the other methods is an artefact. However, the TfR clusters persisted after topography variations were factored out. We demonstrate that membrane topography variations can make membrane molecules appear clustered and present a straightforward remedy suitable as the first step in the cluster analysis pipeline.

Distinguishment of different varieties of rhubarb based on UPLC fingerprints and chemometrics

Rhubarb, a widely used traditional Chinese medicine (TCM), is primarily used for purging in practice. It is derived from the dried roots and rhizomes of R. tanguticum Maxim. ex Balf. (RT), Rheum officinale Baill. (RO) and R. palmatum L. (RP). To date, although the three varieties of rhubarb have been used as the same medicine in clinical, studies have found that they have different chemical compositions and pharmacological effects. To ensure the stability of rhubarb for clinical use, a simple and effective method should be built to compare and discriminate three varieties of rhubarb. Here, ultra-performance liquid chromatography-diode array detection (UPLC-DAD) fingerprints combined with chemometric methods were developed to evaluate and discriminate 29 batches of rhubarb. Similarity evaluation, hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed that the chemical constituents of the three varieties of rhubarb were significantly different, and the three varieties could be effectively distinguished. Finally, all the 14 common peaks were identified by ultra-performance liquid chromatography coupled with quadrupole-time-of-flight mass spectrometry (UPLC-Q-TOF-MS). In this research, the developed UPLC fingerprints offer a simple, reliable and specific approach for distinguishing different varieties of rhubarb. This research aims to promote the scientific and appropriate clinical application of rhubarb from three varieties.

Cloud migration framework clustering method for social decision support in modernizing the legacy system

Cloud solutions accelerate the large‐scale acceptance of IoT projects. By diminishing the need for maintaining on‐premises infrastructure, the cloud has enabled corporations to surpass the traditional applications of IoT (e.g., in‐home appliances) and opened the doors for large‐scale deployment of IoT applications on the cloud. However, shifting legacy systems to the cloud environment can be considerably difficult. Accordingly, this article proposes a method that may support organizations in deciding to modernize their legacy systems. The main concept of this study is to discuss the modernization strategies in detail and to support organizations in selecting the most accurate and appropriate cloud migration strategy, based on their requirements of the legacy system. This article introduces a novel research process, called the K‐means cosine cloud clustering method (K3CM). K3CM is a statistical knowledge‐based method for identifying and clustering the most relevant and similar cloud migration strategies. The quality of a cluster is evaluated by measuring intra‐cohesiveness. Simulation experiments statistically analyzed, evaluated, and verified the quality of K3CM clusters. Correspondence analysis explored the similarity and relationship among cloud migration frameworks and validated the proposed technique. The statistical and simulation results of this study focus on the analytics and decision support system implementation that provides a reliable, valid, and robust clustering method for modernizing the legacy system.

SEMORE: SEgmentation and MORphological fingErprinting by machine learning automates super-resolution data analysis

The morphology of protein assemblies impacts their behaviour and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system- and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data: time-resolved insulin aggregates, and published data of dSTORM imaging of nuclear pore complexes, fibroblast growth receptor 1, sptPALM of Syntaxin 1a and dynamic live-cell PALM of ryanodine receptors. SEMORE extracts and quantifies all protein assemblies, their temporal morphology evolution and provides quantitative insights, e.g. classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being a time-aware framework can also support the rise of 4D super-resolution data.

Single-molecule localization microscopy reveals STING clustering at the trans-Golgi network through palmitoylation-dependent accumulation of cholesterol

Stimulator of interferon genes (STING) is critical for the type I interferon response to pathogen- or self-derived DNA in the cytosol. STING may function as a scaffold to activate TANK-binding kinase 1 (TBK1), but direct cellular evidence remains lacking. Here we show, using single-molecule imaging of STING with enhanced time resolutions down to 5 ms, that STING becomes clustered at the trans-Golgi network (about 20 STING molecules per cluster). The clustering requires STING palmitoylation and the Golgi lipid order defined by cholesterol. Single-molecule imaging of TBK1 reveals that STING clustering enhances the association with TBK1. We thus provide quantitative proof-of-principle for the signaling STING scaffold, reveal the mechanistic role of STING palmitoylation in the STING activation, and resolve the long-standing question of the requirement of STING translocation for triggering the innate immune signaling.

Biological Applications of Synthetic Binders Isolated from a Conceptually New Adhiron Library

BACKGROUND

Adhirons are small (10 kDa) synthetic ligands that might represent an alternative to antibody fragments and to alternative scaffolds such as DARPins or affibodies.

METHODS

We prepared a conceptionally new adhiron phage display library that allows the presence of cysteines in the hypervariable loops and successfully panned it against antigens possessing different characteristics.

RESULTS

We recovered binders specific for membrane epitopes of plant cells by panning the library directly against pea protoplasts and against soluble C-Reactive Protein and SpyCatcher, a small protein domain for which we failed to isolate binders using pre-immune nanobody libraries. The best binders had a binding constant in the low nM range, were produced easily in bacteria (average yields of 15 mg/L of culture) in combination with different tags, were stable, and had minimal aggregation propensity, independent of the presence or absence of cysteine residues in their loops.

DISCUSSION

The isolated adhirons were significantly stronger than those isolated previously from other libraries and as good as nanobodies recovered from a naïve library of comparable theoretical diversity. Moreover, they proved to be suitable reagents for ELISA, flow cytometry, the western blot, and also as capture elements in electrochemical biosensors.

Nanoscale CAR Organization at the Immune Synapse Correlates with CAR-T Effector Functions

T cells expressing chimeric antigen receptors (CARs) are at the forefront of clinical treatment of cancers. Still, the nanoscale organization of CARs at the interface of CAR-Ts with target cells, which is essential for TCR-mediated T cell activation, remains poorly understood. Here, we studied the nanoscale organization of CARs targeting CD138 proteoglycans in such fixed and live interfaces, generated optimally for single-molecule localization microscopy. CARs showed significant self-association in nanoclusters that was enhanced in interfaces with on-target cells (SKOV-3, CAG, FaDu) relative to negative cells (OVCAR-3). CARs also segregated more efficiently from the abundant membrane phosphatase CD45 in CAR-T cells forming such interfaces. CAR clustering and segregation from CD45 correlated with the effector functions of Ca++ influx and target cell killing. Our results shed new light on the nanoscale organization of CARs on the surfaces of CAR-Ts engaging on- and off-target cells, and its potential significance for CAR-Ts' efficacy and safety.

clusterBMA: Bayesian model averaging for clustering

Various methods have been developed to combine inference across multiple sets of results for unsupervised clustering, within the ensemble clustering literature. The approach of reporting results from one 'best' model out of several candidate clustering models generally ignores the uncertainty that arises from model selection, and results in inferences that are sensitive to the particular model and parameters chosen. Bayesian model averaging (BMA) is a popular approach for combining results across multiple models that offers some attractive benefits in this setting, including probabilistic interpretation of the combined cluster structure and quantification of model-based uncertainty. In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms. We use clustering internal validation criteria to develop an approximation of the posterior model probability, used for weighting the results from each model. From a combined posterior similarity matrix representing a weighted average of the clustering solutions across models, we apply symmetric simplex matrix factorisation to calculate final probabilistic cluster allocations. In addition to outperforming other ensemble clustering methods on simulated data, clusterBMA offers unique features including probabilistic allocation to averaged clusters, combining allocation probabilities from 'hard' and 'soft' clustering algorithms, and measuring model-based uncertainty in averaged cluster allocation. This method is implemented in an accompanying R package of the same name. We use simulated datasets to explore the ability of the proposed technique to identify robust integrated clusters with varying levels of separation between subgroups, and with varying numbers of clusters between models. Benchmarking accuracy against four other ensemble methods previously demonstrated to be highly effective in the literature, clusterBMA matches or exceeds the performance of competing approaches under various conditions of dimensionality and cluster separation. clusterBMA substantially outperformed other ensemble methods for high dimensional simulated data with low cluster separation, with 1.16 to 7.12 times better performance as measured by the Adjusted Rand Index. We also explore the performance of this approach through a case study that aims to identify probabilistic clusters of individuals based on electroencephalography (EEG) data. In applied settings for clustering individuals based on health data, the features of probabilistic allocation and measurement of model-based uncertainty in averaged clusters are useful for clinical relevance and statistical communication.

Optimizing Voronoi-based quantifications for reaching interactive analysis of 3D localizations in the million range

Over the last decade, single-molecule localization microscopy (SMLM) has revolutionized cell biology, making it possible to monitor molecular organization and dynamics with spatial resolution of a few nanometers. Despite being a relatively recent field, SMLM has witnessed the development of dozens of analysis methods for problems as diverse as segmentation, clustering, tracking or colocalization. Among those, Voronoi-based methods have achieved a prominent position for 2D analysis as robust and efficient implementations were available for generating 2D Voronoi diagrams. Unfortunately, this was not the case for 3D Voronoi diagrams, and existing methods were therefore extremely time-consuming. In this work, we present a new hybrid CPU-GPU algorithm for the rapid generation of 3D Voronoi diagrams. Voro3D allows creating Voronoi diagrams of datasets composed of millions of localizations in minutes, making any Voronoi-based analysis method such as SR-Tesseler accessible to life scientists wanting to quantify 3D datasets. In addition, we also improve ClusterVisu, a Voronoi-based clustering method using Monte-Carlo simulations, by demonstrating that those costly simulations can be correctly approximated by a customized gamma probability distribution function.

Introducing Diinamic, a flexible and robust method for clustering analysis in single-molecule localization microscopy

Super-resolution microscopy allowed major improvements in our capacity to describe and explain biological organization at the nanoscale. Single-molecule localization microscopy (SMLM) uses the positions of molecules to create super-resolved images, but it can also provide new insights into the organization of molecules through appropriate pointillistic analyses that fully exploit the sparse nature of SMLM data. However, the main drawback of SMLM is the lack of analytical tools easily applicable to the diverse types of data that can arise from biological samples. Typically, a cloud of detections may be a cluster of molecules or not depending on the local density of detections, but also on the size of molecules themselves, the labeling technique, the photo-physics of the fluorophore, and the imaging conditions. We aimed to set an easy-to-use clustering analysis protocol adaptable to different types of data. Here, we introduce Diinamic, which combines different density-based analyses and optional thresholding to facilitate the detection of clusters. On simulated or real SMLM data, Diinamic correctly identified clusters of different sizes and densities, being performant even in noisy datasets with multiple detections per fluorophore. It also detected subdomains ("nanodomains") in clusters with non-homogeneous distribution of detections.

Super-resolved trajectory-derived nanoclustering analysis using spatiotemporal indexing

Single-molecule localization microscopy techniques are emerging as vital tools to unravel the nanoscale world of living cells by understanding the spatiotemporal organization of protein clusters at the nanometer scale. Current analyses define spatial nanoclusters based on detections but neglect important temporal information such as cluster lifetime and recurrence in "hotspots" on the plasma membrane. Spatial indexing is widely used in video games to detect interactions between moving geometric objects. Here, we use the R-tree spatial indexing algorithm to determine the overlap of the bounding boxes of individual molecular trajectories to establish membership in nanoclusters. Extending the spatial indexing into the time dimension allows the resolution of spatial nanoclusters into multiple spatiotemporal clusters. Using spatiotemporal indexing, we found that syntaxin1a and Munc18-1 molecules transiently cluster in hotspots, offering insights into the dynamics of neuroexocytosis. Nanoscale spatiotemporal indexing clustering (NASTIC) has been implemented as a free and open-source Python graphic user interface.

Error-Correction Method for High-Throughput Sizing of Nanoscale Vesicles with Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) allows super-resolution imaging, mapping, counting, and sizing of biological nanostructures such as cell organelles and extracellular vesicles (EVs), but sizing structures smaller than ∼100 nm can be inaccurate due to single-molecule localization error caused by distortion of the point spread function and limited photon number. Here we demonstrate a method to correct localization error when sizing vesicles and other spherical nanoparticles with SMLM and compare sizing results using two vesicle labeling schemes. We use mean approximation theory to derive a simple equation using full width at half-maximum (FWHM) for correcting particle sizes measured by two-dimensional SMLM, validate the method by sizing streptavidin-coated polystyrene nanobeads with the SMLM technique dSTORM with and without error correction, using transmission electron microscopy (TEM) for comparison, and then apply the method to sizing small seminal EVs. Nanobead sizes measured by dSTORM became increasingly less accurate (larger than TEM values) for beads smaller than 50 nm. The error-correction method reduced the size difference versus TEM from 15% without error correction to 7% with error correction for 40 nm beads, from 44% to 9% for 30 nm beads, and from 66% to 15% for 20 nm beads. Seminal EVs were labeled with a lipophilic membrane dye (MemBright 700) and with an Alexa Fluor 488-anti-CD63 antibody conjugate, and were sized separately using both dyes by dSTORM. Error-corrected exosome diameters were smaller than uncorrected values: 72 nm vs 79 nm mean diameter with membrane dyes; 84 nm vs 97 nm with the antibody-conjugated dyes. The mean error-corrected diameter was 12 nm smaller when using the membrane dye than when using the antibody-conjugated dye likely due to the large size of the antibody. Thus, both the error-correction method and the compact membrane labeling scheme reduce overestimation of vesicle size by SMLM. This error-correction method has a low computational cost as it does not require correction of individual blinking events, and it is compatible with all SMLM techniques (e.g., PALM, STORM, and DNA-PAINT).