Real-time 3D single-molecule localization using experimental point spread functions

Model-free machine learning-based 3D single molecule localisation microscopy

Single molecule localisation microscopy (SMLM) can provide two-dimensional super-resolved image data from conventional fluorescence microscopes, while three dimensional (3D) SMLM usually involves a modification of the microscope, for example, to engineer a predictable axial variation in the point spread function. Here we demonstrate a 3D SMLM approach (we call 'easyZloc') utilising a lightweight Convolutional Neural Network that is generally applicable, including with 'standard' (unmodified) fluorescence microscopes, and which we consider may be practically useful in a high throughput SMLM workflow. We demonstrate the reconstruction of nuclear pore complexes with comparable performance to previously reported methods but with a significant reduction in computational power and execution time. 3D reconstructions of the nuclear envelope and an actin sample over a larger axial range are also shown.

Image-based 3D active sample stabilization on the nanometer scale for optical microscopy

Super-resolution microscopy often entails long acquisition times of minutes to hours. Since drifts during the acquisition adversely affect data quality, active sample stabilization is commonly used for some of these techniques to reach their full potential. Although drifts in the lateral plane can often be corrected after acquisition, this is not always possible or may come with drawbacks. Therefore, it is appealing to stabilize sample position in three dimensions (3D) during acquisition. Various schemes for active sample stabilization have been demonstrated previously, with some reaching sub-nanometer stability in 3D. Here, we present a scheme for active drift correction that delivers the nanometer-scale 3D stability demanded by state-of-the-art super-resolution techniques and is straightforward to implement compared to previous schemes capable of reaching this level of stabilization precision. Using a refined algorithm that can handle various types of reference structure, without sparse signal peaks being mandatory, we stabilized sample position to ∼1 nm in 3D using objective lenses both with high and low numerical aperture. Our implementation requires only the addition of a simple widefield imaging path and we provide an open-source control software with graphical user interface to facilitate easy adoption of the module. Finally, we demonstrate how this has the potential to enhance data collection for diffraction-limited and super-resolution imaging techniques using single-molecule localization microscopy and cryo-confocal imaging as showcases.

Dark-based optical sectioning assists background removal in fluorescence microscopy

In fluorescence microscopy, a persistent challenge is the defocused background that obscures cellular details and introduces artifacts. Here, we introduce Dark sectioning, a method inspired by natural image dehazing for removing backgrounds that leverages dark channel prior and dual frequency separation to provide single-frame optical sectioning. Unlike denoising or deconvolution, Dark sectioning specifically targets and removes out-of-focus backgrounds, stably improving the signal-to-background ratio by nearly 10 dB and structural similarity index measure of images by approximately tenfold. Dark sectioning was validated using wide-field, confocal, two/three-dimensional structured illumination and one/two-photon microscopy with high-fidelity reconstruction. We further demonstrate its potential to improve the segmentation accuracy in deep tissues, resulting in better recognition of neurons in the mouse brain and accurate assessment of nuclei in prostate lesions or mouse brain sections. Dark sectioning is compatible with many other microscopy modalities, including light-sheet and light-field microscopy, as well as processing algorithms, including deconvolution and super-resolution optical fluctuation imaging.

Super-resolution imaging in whole cells and tissues via DNA-PAINT on a spinning disk confocal with optical photon reassignment

Single-Molecule Localization Microscopy (SMLM) has traditionally faced challenges to optimize signal-to-noise ratio, penetration depth, field-of-view (FOV), and spatial resolution simultaneously. Here, we show that DNA-PAINT imaging on a Spinning Disk Confocal with Optical Photon Reassignment (SDC-OPR) system overcomes these trade-offs, enabling high-resolution imaging across multiple cellular layers and large FOVs. We demonstrate the system's capability with DNA origami constructs and biological samples, including nuclear pore complexes, mitochondria, and microtubules, achieving a spatial resolution of 6 nm in the basal plane and sub-10 nm localization precision at depths of 9 µm within a 53 × 53 µm² FOV. Additionally, imaging of the developing Drosophila eye epithelium at depths up to 9 µm with sub-13 nm average localization precision, reveals distinct E-cadherin populations in adherens junctions. Quantitative analysis of Collagen IV deposition in this epithelium indicated an average of 46 ± 27 molecules per secretory vesicle. These results underscore the versatility of DNA-PAINT on an SDC-OPR for advancing super-resolution imaging in complex biological systems.

Landscape of the Epstein-Barr virus-host chromatin interactome and gene regulation

The three-dimensional (3D) chromatin structure of Epstein-Barr virus (EBV) within host cells and the underlying mechanisms of chromatin interaction and gene regulation, particularly those involving EBV's noncoding RNAs (ncRNAs), have remained incompletely characterized. In this study, we employed state-of-the-art techniques of 3D genome mapping, including protein-associated chromatin interaction analysis with paired-end tag sequencing (ChIA-PET), RNA-associated chromatin interaction technique (RDD), and super-resolution microscopy, to delineate the spatial architecture of EBV in human lymphoblastoid cells. We systematically analyzed EBV-to-EBV (E-E), EBV-to-host (E-H), and host-to-host (H-H) interactions linked to host proteins and EBV RNAs. Our findings reveal that EBV utilizes host CCCTC-binding factor (CTCF) and RNA polymerase II (RNAPII) to form distinct chromatin contact domains (CCDs) and RNAPII-associated interaction domains (RAIDs). The anchors of these chromatin domains serve as platforms for extensive interactions with host chromatin, thus modulating host gene expression. Notably, EBV ncRNAs, especially Epstein-Barr-encoded RNAs (EBERs), target and interact with less accessible regions of host chromatin to repress a subset of genes via the inhibition of RNAPII-associated chromatin loops. This process involves the cofactor nucleolin (NCL) and its RNA recognition motifs, and depletion of either NCL or EBERs alters expression of genes crucial for host infection control, immune response, and cell cycle regulation. These findings unveil a sophisticated interplay between EBV and host chromatin.

One-click image reconstruction in single-molecule localization microscopy via deep learning

Deep neural networks have led to significant advancements in microscopy image generation and analysis. In single-molecule localization based super-resolution microscopy, neural networks are capable of predicting fluorophore positions from high-density emitter data, thus reducing acquisition time, and increasing imaging throughput. However, neural network-based solutions in localization microscopy require intensive human intervention and computation expertise to address the compromise between model performance and its generalization. For example, researchers manually tune parameters to generate training images that are similar to their experimental data; thus, for every change in the experimental conditions, a new training set should be manually tuned, and a new model should be trained. Here, we introduce AutoDS and AutoDS3D, two software programs for reconstruction of single-molecule super-resolution microscopy data that are based on Deep-STORM and DeepSTORM3D, that significantly reduce human intervention from the analysis process by automatically extracting the experimental parameters from the imaging raw data. In the 2D case, AutoDS selects the optimal model for the analysis out of a set of pre-trained models, hence, completely removing user supervision from the process. In the 3D case, we improve the computation efficiency of DeepSTORM3D and integrate the lengthy workflow into a graphic user interface that enables image reconstruction with a single click. Ultimately, we demonstrate superior performance of both pipelines compared to Deep-STORM and DeepSTORM3D for single-molecule imaging data of complex biological samples, while significantly reducing the manual labor and computation time.

Joint estimation of point spread function and molecule positions in SMLM informed from multiple planes

The advent of single molecule localization microscopy (SMLM) has transformed our capacity to investigate biological structures at the nanoscale. While the research focus has long been on improving localization precision, systematic errors caused by optical aberrations are often overlooked. In the case of 3D SMLM, such errors have the potential to significantly impair the quality of the resulting images. In this paper, we present an imaging and data processing approach that jointly estimates both, molecule positions and optical aberrations in SMLM. Therefore, the method minimizes systematic errors in SMLM reconstructions without the necessity of additional experimental calibration steps, such as the recording of fluorescent bead z-stacks. We investigate the reliability of this approach, especially in situations where the joint retrieval can be expected to be ill-posed, i.e., whenever the sample is "flat" and provides little diversity among the captured single molecule images. To enhance the reliability of the inverse problem solution, we suggest utilizing small SMLM data sets acquired at one or more slightly defocused "auxiliary" planes. We investigate the effectiveness of our approach through numerical simulations and imaging experiments of a calibration probe and nuclear pore complexes. Our method is simple and integrates seamlessly into existing SMLM setups without necessitating modifications or added complexity to the system.

Advancing Super-Resolution Microscopy: Recent Innovations in Commercial Instruments

Super-resolution microscopy techniques have accelerated scientific progress, enabling researchers to explore cellular structures and dynamics with unprecedented detail. This review highlights the most recent developments in commercially available super-resolution microscopes, focusing on the most widely used techniques: confocal laser scanning systems, structured illumination microscopy (SIM), stimulated emission depletion (STED) microscopy, and single-molecule localization microscopy (SMLM). We detail the technological advancements of Confocal.NL's GAIA, Nikon's NSPARC, CSR Biotech's MI-SIM, Zeiss's Lattice SIM 5, Leica's STELLARIS STED, and abberior's STED and MINFLUX systems, as well as Abbelight's SAFe MN360 and Bruker's Vutara VXL SMLM platforms. These advancements address the need for enhanced resolution, reduced phototoxicity, and improved imaging capabilities in a range of sample types, while also aiming to enhance user friendliness.

Vortex Interference Enables Optimal 3D Interferometric Nanoscopy

Super-resolution imaging methods that combine interferometric axial (z) analysis with single-molecule localization microscopy (iSMLM) have achieved ultrahigh 3D precision and contributed to the elucidation of important biological ultrastructures. However, their dependence on imaging multiple phase-shifted output channels necessitates complex instrumentation and operation. To solve this problem, we develop an interferometric superresolution microscope capable of optimal direct axial nanoscopy, termed VILM (Vortex Interference Localization Microscopy). Using a pair of vortex phase plates with opposite orientation for each dual-opposed objective lens, the detection point-spread functions (PSFs) adopt a bilobed profile whose rotation encodes the axial position. Thus, direct 3D single-molecule coordinate determination can be achieved with a single output image. By reducing the number of output channels to as few as one and utilizing a simple 50∶50 beam splitter, the imaging system is significantly streamlined, while the optimal iSMLM imaging performance is retained, with axial precision 2 times better than the lateral. The capability of VILM is demonstrated by resolving the architecture of microtubules and probing the organization of tyrosine-phosphorylated signaling proteins in integrin-based cell adhesions.

Cryosectioning-enhanced super-resolution microscopy for single-protein imaging across cells and tissues

DNA-PAINT enables nanoscale imaging with virtually unlimited multiplexing and molecular counting. Here, we address challenges, such as variable imaging performance and target accessibility, that can limit its broader applicability. Specifically, we enhance its capacity for robust single-protein imaging and molecular counting by optimizing the integration of TIRF microscopy with physical sectioning, in particular, Tokuyasu cryosectioning. Our method, tomographic & kinetically enhanced DNA-PAINT (tkPAINT), achieves 3 nm localization precision across diverse samples, enhanced imager binding, and improved cellular integrity. tkPAINT can facilitate molecular counting with DNA-PAINT inside the nucleus, as demonstrated through its quantification of the in situ abundance of RNA Polymerase II in both HeLa cells as well as mouse tissues. Anticipating that tkPAINT could become a versatile tool for the exploration of biomolecular organization and interactions across cells and tissues, we also demonstrate its capacity to support multiplexing, multimodal targeting of proteins and nucleic acids, and 3D imaging.

The nuclear matrix stabilizes primed-specific genes in human pluripotent stem cells

The nuclear matrix, a proteinaceous gel composed of proteins and RNA, is an important nuclear structure that supports chromatin architecture, but its role in human pluripotent stem cells (hPSCs) has not been described. Here we show that by disrupting heterogeneous nuclear ribonucleoprotein U (HNRNPU) or the nuclear matrix protein, Matrin-3, primed hPSCs adopted features of the naive pluripotent state, including morphology and upregulation of naive-specific marker genes. We demonstrate that HNRNPU depletion leads to increased chromatin accessibility, reduced DNA contacts and increased nuclear size. Mechanistically, HNRNPU acts as a transcriptional co-factor that anchors promoters of primed-specific genes to the nuclear matrix with POLII to promote their expression and their RNA stability. Overall, HNRNPU promotes cell-type stability and when reduced promotes conversion to earlier embryonic states.

Enabling real-time reconstruction for large field-of-view single-molecule localization microscopy using discrete field-dependent point-spread function

Single-molecule localization microscopy (SMLM) is a powerful super-resolution imaging technique that offers resolution far beyond the optical diffraction limit. The commonly used high numerical-aperture (NA) objective lenses in SMLM can only provide a nearly ideal point-spread function (PSF) at the center of the field-of-view (FOV), whereas the off-axis PSF is often distorted due to optical aberrations. Since precision and accuracy of three-dimensional (3D) spatial localization of single molecules heavily depend on the system's PSF, the FOV of 3D SMLM is often restricted to about 50 µm × 50 µm limiting its applications in visualizing intra-/intercellular interactions and high-throughput single-molecule analysis. Here we present a systematic study to show the influence of optical aberrations on large FOV 3D SMLM using unmodified, astigmatic, and double-helix PSFs. Our results show that optical aberrations introduce significant localization errors during image reconstruction and thereby produce unreliable imaging results at the corner of the FOV. To maximize SMLM's FOV, we proposed and verified the potential of using discrete field-dependent PSFs to retain precise and accurate single-molecule localization and compare their reconstruction results using simulated resolution test patterns and biological structures. Moreover, GPU acceleration empowers a discrete PSF calibration model with high localization speed, which can provide real-time SMLM image reconstruction. We envision these results will further guide the development of strategies that can provide real-time and reliable image reconstruction in large FOV 3D SMLM.

Viscocohesive hyaluronan gel enhances stability of intravital multiphoton imaging with subcellular resolution

Multiphoton microscopy (MPM) has become a preferred technique for intravital imaging deep in living tissues with subcellular detail, where resolution and working depths are typically optimized utilizing high numerical aperture, water-immersion objectives with long focusing distances. However, this approach requires the maintenance of water between the specimen and the objective lens, which can be challenging or impossible for many intravital preparations with complex tissues and spatial arrangements. We introduce the novel use of cohesive hyaluronan gel (HG) as an immersion medium that can be used in place of water within existing optical setups to enable multiphoton imaging with equivalent quality and far superior stability. We characterize and compare imaging performance, longevity, and feasibility of preparations in various configurations. This combination of HG with MPM is highly accessible and opens the doors to new intravital imaging applications.

Calibration-free estimation of field dependent aberrations for single molecule localization microscopy across large fields of view

Image quality in single molecule localization microscopy (SMLM) depends largely on the accuracy and precision of the localizations. While under ideal imaging conditions the theoretically obtainable precision and accuracy are achieved, in practice this changes if (field dependent) aberrations are present. Currently there is no simple way to measure and incorporate these aberrations into the Point Spread Function (PSF) fitting, therefore the aberrations are often taken constant or neglected all together. Here we introduce a model-based approach to estimate the field-dependent aberration directly from single molecule data without a calibration step. This is made possible by using nodal aberration theory to incorporate the field-dependency of aberrations into our fully vectorial PSF model. This results in a limited set of aberration fit parameters that can be extracted from the raw frames without a bead calibration measurement, also in retrospect. The software implementation is computationally efficient, enabling fitting of a full 2D or 3D dataset within a few minutes. We demonstrate our method on 2D and 3D localization data of microtubuli and nuclear pore complexes over fields of view (FOV) of up to 180 μm and compare it with spline-based fitting and a deep learning based approach.

Whole-cell multi-target single-molecule super-resolution imaging in 3D with microfluidics and a single-objective tilted light sheet

Multi-target single-molecule super-resolution fluorescence microscopy offers a powerful means of understanding the distributions and interplay between multiple subcellular structures at the nanoscale. However, single-molecule super-resolution imaging of whole mammalian cells is often hampered by high fluorescence background and slow acquisition speeds, especially when imaging multiple targets in 3D. In this work, we have mitigated these issues by developing a steerable, dithered, single-objective tilted light sheet for optical sectioning to reduce fluorescence background and a pipeline for 3D nanoprinting microfluidic systems for reflection of the light sheet into the sample. This easily adaptable microfluidic fabrication pipeline allows for the incorporation of reflective optics into microfluidic channels without disrupting efficient and automated solution exchange. We combine these innovations with point spread function engineering for nanoscale localization of individual molecules in 3D, deep learning for analysis of overlapping emitters, active 3D stabilization for drift correction and long-term imaging, and Exchange-PAINT for sequential multi-target imaging without chromatic offsets. We then demonstrate that this platform, termed soTILT3D, enables whole-cell multi-target 3D single-molecule super-resolution imaging with improved precision and imaging speed.

Three-dimensional localization and tracking of chromosomal loci throughout the Escherichia coli cell cycle

The intracellular position of genes may impact their expression, but it has not been possible to accurately measure the 3D position of chromosomal loci. In 2D, loci can be tracked using arrays of DNA-binding sites for transcription factors (TFs) fused with fluorescent proteins. However, the same 2D data can result from different 3D trajectories. Here, we have developed a deep learning method for super-resolved astigmatism-based 3D localization of chromosomal loci in live E. coli cells which enables a precision better than 61 nm at a signal-to-background ratio of ~4 on a heterogeneous cell background. Determining the spatial localization of chromosomal loci, we find that some loci are at the periphery of the nucleoid for large parts of the cell cycle. Analyses of individual trajectories reveal that these loci are subdiffusive both longitudinally (x) and radially (r), but that individual loci explore the full radial width on a minute time scale.

High Refractive Index Imaging Buffer for Dual-Color 3D SMLM Imaging of Thick Samples

The current limitations of single-molecule localization microscopy (SMLM) in deep tissue imaging, primarily due to depth-dependent aberrations caused by refractive index (RI) mismatch, present a significant challenge in achieving high-resolution images at greater depths. To extend the imaging depth, we optimized the imaging buffer of SMLM with the RI matched to that of the objective immersion medium and systematically evaluated five different RI-matched buffers, focusing on their impact on the blinking behavior of red-absorbing dyes and the quality of reconstructed super-resolution images. Particularly, we found that clear unobstructed brain imaging cocktails-based imaging buffer could match the RI of oil and was able to clear the tissue samples. With the help of the RI-matched imaging buffers, we showed high-quality dual-color 3D SMLM images with imaging depths ranging from a few micrometers to tens of micrometers in both cultured cells and sectioned tissue samples. This advancement offers a practical and accessible method for high-resolution imaging at greater depths without the need for specialized optical equipment or expertise.

ECLiPSE: a versatile classification technique for structural and morphological analysis of 2D and 3D single-molecule localization microscopy data

Single-molecule localization microscopy (SMLM) has gained widespread use for visualizing the morphology of subcellular organelles and structures with nanoscale spatial resolution. However, analysis tools for automatically quantifying and classifying SMLM images have lagged behind. Here we introduce Enhanced Classification of Localized Point clouds by Shape Extraction (ECLiPSE), an automated machine learning analysis pipeline specifically designed to classify cellular structures captured through two-dimensional or three-dimensional SMLM. ECLiPSE leverages a comprehensive set of shape descriptors, the majority of which are directly extracted from the localizations to minimize bias during the characterization of individual structures. ECLiPSE has been validated using both unsupervised and supervised classification on datasets, including various cellular structures, achieving near-perfect accuracy. We apply two-dimensional ECLiPSE to classify morphologically distinct protein aggregates relevant for neurodegenerative diseases. Additionally, we employ three-dimensional ECLiPSE to identify relevant biological differences between healthy and depolarized mitochondria. ECLiPSE will enhance the way we study cellular structures across various biological contexts.

Aberration-robust monocular passive depth sensing using a meta-imaging camera

Depth sensing plays a crucial role in various applications, including robotics, augmented reality, and autonomous driving. Monocular passive depth sensing techniques have come into their own for the cost-effectiveness and compact design, offering an alternative to the expensive and bulky active depth sensors and stereo vision systems. While the light-field camera can address the defocus ambiguity inherent in 2D cameras and achieve unambiguous depth perception, it compromises the spatial resolution and usually struggles with the effect of optical aberration. In contrast, our previously proposed meta-imaging sensor1 has overcome such hurdles by reconciling the spatial-angular resolution trade-off and achieving the multi-site aberration correction for high-resolution imaging. Here, we present a compact meta-imaging camera and an analytical framework for the quantification of monocular depth sensing precision by calculating the Cramér-Rao lower bound of depth estimation. Quantitative evaluations reveal that the meta-imaging camera exhibits not only higher precision over a broader depth range than the light-field camera but also superior robustness against changes in signal-background ratio. Moreover, both the simulation and experimental results demonstrate that the meta-imaging camera maintains the capability of providing precise depth information even in the presence of aberrations. Showing the promising compatibility with other point-spread-function engineering methods, we anticipate that the meta-imaging camera may facilitate the advancement of monocular passive depth sensing in various applications.

Measurement precision bounds on aberrated single-molecule emission patterns

Single-molecule localization microscopy (SMLM) has revolutionized the study of biological phenomena by providing exquisite nanoscale spatial resolution. However, optical aberrations induced by sample and system imperfections distort the single-molecule emission patterns (i.e. PSFs), leading to reduced precision and resolution of SMLM, particularly in three-dimensional (3D) applications. While various methods, both analytical and instrumental, have been employed to mitigate these aberrations, a comprehensive analysis of how different types of commonly encountered aberrations affect single-molecule experiments and their image formation remains missing. In this study, we addressed this gap by conducting a quantitative study of the theoretical precision limit for position and wavefront distortion measurements in the presence of aberrations. Leveraging Fisher information and Cramér-Rao lower bound (CRLB), we quantitively analyzed and compared the effects of different aberration types, including index mismatch aberrations, on localization precision in both biplane and astigmatism 3D modalities as well as 2D SMLM imaging. Furthermore, we studied the achievable wavefront estimation precision from aberrated single-molecule emission patterns, a pivot step for successful adaptive optics in SMLM through thick specimens. This analysis lays a quantitative foundation for the development and application of SMLM in whole-cells, tissues and with a large field of view, providing in-depth insights into the behavior of different aberration types in single-molecule imaging and thus generating theoretical guidelines for developing highly efficient aberration correction strategies and enhancing the precision and reliability of 3D SMLM.

Build and operation of a custom 3D, multicolor, single-molecule localization microscope

Single-molecule localization microscopy (SMLM) enables imaging scientists to visualize biological structures with unprecedented resolution. Particularly powerful implementations of SMLM are capable of three-dimensional, multicolor and high-throughput imaging and can yield key biological insights. However, widespread access to these technologies is limited, primarily by the cost of commercial options and complexity of de novo development of custom systems. Here we provide a comprehensive guide for interested researchers who wish to establish a high-end, custom-built SMLM setup in their laboratories. We detail the initial configuration and subsequent assembly of the SMLM, including the instructions for the alignment of all the optical pathways, the software and hardware integration, and the operation of the instrument. We describe the validation steps, including the preparation and imaging of test and biological samples with structures of well-defined geometries, and assist the user in troubleshooting and benchmarking the system's performance. Additionally, we provide a walkthrough of the reconstruction of a super-resolved dataset from acquired raw images using the Super-resolution Microscopy Analysis Platform. Depending on the instrument configuration, the cost of the components is in the range US$95,000-180,000, similar to other open-source advanced SMLMs, and substantially lower than the cost of a commercial instrument. A builder with some experience of optical systems is expected to require 4-8 months from the start of the system construction to attain high-quality three-dimensional and multicolor biological images.

Monocular Metasurface for Structured Light Generation and 3D Imaging with a Large Field-of-View

Structured light three-dimensional (3D) imaging technology captures the geometric information on 3D objects by recording waves reflected from the objects' surface. The projection angle and point number of the laser dots directly determine the field-of-view (FOV) and the resolution of the reconstructed image. Conventionally, diffractive optical elements with micrometer-scale pixel size have been used to generate laser dot arrays, leading to limited FOV and point number within the projection optical path. Here, we theoretically put forward and experimentally demonstrate a monocular geometric phase metasurface composed of deep subwavelength meta-atoms to generate a 10 798 dot array within an FOV of 163°. Attributed to the vast number and high-density point cloud generated by the metasurface, the 3D reconstructed results showcase a maximum relative error in depth of 5.3 mm and a reconstruction error of 6.07%. Additionally, we propose a spin-multiplexed metasurface design method capable of doubling the number of lattice points. We demonstrate its application in the field of 3D imaging through experiments, where the 3D reconstructed results show a maximum relative depth error of 0.44 cm and a reconstruction error of 2.78%. Our proposed metasurface featuring advanced point cloud generation holds substantial potential for various applications such as facial recognition, autonomous driving, virtual reality, and beyond.

3D chromatin structures associated with ncRNA roX2 for hyperactivation and coactivation across the entire X chromosome

The three-dimensional (3D) organization of chromatin within the nucleus is crucial for gene regulation. However, the 3D architectural features that coordinate the activation of an entire chromosome remain largely unknown. We introduce an omics method, RNA-associated chromatin DNA-DNA interactions, that integrates RNA polymerase II (RNAPII)-mediated regulome with stochastic optical reconstruction microscopy to investigate the landscape of noncoding RNA roX2-associated chromatin topology for gene equalization to achieve dosage compensation. Our findings reveal that roX2 anchors to the target gene transcription end sites (TESs) and spreads in a distinctive boot-shaped configuration, promoting a more open chromatin state for hyperactivation. Furthermore, roX2 arches TES to transcription start sites to enhance transcriptional loops, potentially facilitating RNAPII convoying and connecting proximal promoter-promoter transcriptional hubs for synergistic gene regulation. These TESs cluster as roX2 compartments, surrounded by inactive domains for coactivation of multiple genes within the roX2 territory. In addition, roX2 structures gradually form and scaffold for stepwise coactivation in dosage compensation.

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.

Universal inverse modeling of point spread functions for SMLM localization and microscope characterization

The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single-molecule localization, aberration correction and deconvolution. Here we present universal inverse modeling of point spread functions (uiPSF), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single-molecule localization microscopy (SMLM). Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system- or sample-specific characteristics, for example, the bead size, field- and depth- dependent aberrations, and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single-molecule blinking data.

Temporal analysis of relative distances (TARDIS) is a robust, parameter-free alternative to single-particle tracking

In single-particle tracking, individual particles are localized and tracked over time to probe their diffusion and molecular interactions. Temporal crossing of trajectories, blinking particles, and false-positive localizations present computational challenges that have remained difficult to overcome. Here we introduce a robust, parameter-free alternative to single-particle tracking: temporal analysis of relative distances (TARDIS). In TARDIS, an all-to-all distance analysis between localizations is performed with increasing temporal shifts. These pairwise distances represent either intraparticle distances originating from the same particle, or interparticle distances originating from unrelated particles, and are fitted analytically to obtain quantitative measures on particle dynamics. We showcase that TARDIS outperforms tracking algorithms, benchmarked on simulated and experimental data of varying complexity. We further show that TARDIS performs accurately in complex conditions characterized by high particle density, strong emitter blinking or false-positive localizations, and is in fact limited by the capabilities of localization algorithms. TARDIS' robustness enables fivefold shorter measurements without loss of information.

Toward drift-free high-throughput nanoscopy through adaptive intersection maximization

Single-molecule localization microscopy (SMLM) often suffers from suboptimal resolution due to imperfect drift correction. Existing marker-free drift correction algorithms often struggle to reliably track high-frequency drift and lack the computational efficiency to manage large, high-throughput localization datasets. We present an adaptive intersection maximization-based method (AIM) that leverages the entire dataset's information content to minimize drift correction errors, particularly addressing high-frequency drift, thereby enhancing the resolution of existing SMLM systems. We demonstrate that AIM can robustly and efficiently achieve an angstrom-level tracking precision for high-throughput SMLM datasets under various imaging conditions, resulting in an optimal resolution in simulated and biological experimental datasets. We offer AIM as one simple, model-free software for instant resolution enhancement with standard CPU devices.

Correlative single molecule lattice light sheet imaging reveals the dynamic relationship between nucleosomes and the local chromatin environment

In the nucleus, biological processes are driven by proteins that diffuse through and bind to a meshwork of nucleic acid polymers. To better understand this interplay, we present an imaging platform to simultaneously visualize single protein dynamics together with the local chromatin environment in live cells. Together with super-resolution imaging, new fluorescent probes, and biophysical modeling, we demonstrate that nucleosomes display differential diffusion and packing arrangements as chromatin density increases whereas the viscoelastic properties and accessibility of the interchromatin space remain constant. Perturbing nuclear functions impacts nucleosome diffusive properties in a manner that is dependent both on local chromatin density and on relative location within the nucleus. Our results support a model wherein transcription locally stabilizes nucleosomes while simultaneously allowing for the free exchange of nuclear proteins. Additionally, they reveal that nuclear heterogeneity arises from both active and passive processes and highlight the need to account for different organizational principles when modeling different chromatin environments.

Traceable localization enables accurate integration of quantum emitters and photonic structures with high yield

In a popular integration process for quantum information technologies, localization microscopy of quantum emitters guides lithographic placement of photonic structures. However, a complex coupling of microscopy and lithography errors degrades registration accuracy, severely limiting device performance and process yield. We introduce a methodology to solve this widespread but poorly understood problem. A new foundation of traceable localization enables rapid characterization of lithographic standards and comprehensive calibration of cryogenic microscopes, revealing and correcting latent systematic effects. Of particular concern, we discover that scale factor deviation and complex optical distortion couple to dominate registration errors. These novel results parameterize a process model for integrating quantum dots and bullseye resonators, predicting higher yield by orders of magnitude, depending on the Purcell factor threshold as a quantum performance metric. Our foundational methodology is a key enabler of the lab-to-fab transition of quantum information technologies and has broader implications to cryogenic and correlative microscopy.

Filopodial protrusion driven by density-dependent Ena-TOCA-1 interactions

Filopodia are narrow actin-rich protrusions with important roles in neuronal development where membrane-binding adaptor proteins, such as I-BAR- and F-BAR-domain-containing proteins, have emerged as upstream regulators that link membrane interactions to actin regulators such as formins and proteins of the Ena/VASP family. Both the adaptors and their binding partners are part of diverse and redundant protein networks that can functionally compensate for each other. To explore the significance of the F-BAR domain-containing neuronal membrane adaptor TOCA-1 (also known as FNBP1L) in filopodia we performed a quantitative analysis of TOCA-1 and filopodial dynamics in Xenopus retinal ganglion cells, where Ena/VASP proteins have a native role in filopodial extension. Increasing the density of TOCA-1 enhances Ena/VASP protein binding in vitro, and an accumulation of TOCA-1, as well as its coincidence with Ena, correlates with filopodial protrusion in vivo. Two-colour single-molecule localisation microscopy of TOCA-1 and Ena supports their nanoscale association. TOCA-1 clusters promote filopodial protrusion and this depends on a functional TOCA-1 SH3 domain and activation of Cdc42, which we perturbed using the small-molecule inhibitor CASIN. We propose that TOCA-1 clusters act independently of membrane curvature to recruit and promote Ena activity for filopodial protrusion.

Large-FOV 3D localization microscopy by spatially variant point spread function generation

Accurate characterization of the microscopic point spread function (PSF) is crucial for achieving high-performance localization microscopy (LM). Traditionally, LM assumes a spatially invariant PSF to simplify the modeling of the imaging system. However, for large fields of view (FOV) imaging, it becomes important to account for the spatially variant nature of the PSF. Here, we propose an accurate and fast principal components analysis-based field-dependent 3D PSF generator (PPG3D) and localizer for LM. Through simulations and experimental three-dimensional (3D) single-molecule localization microscopy (SMLM), we demonstrate the effectiveness of PPG3D, enabling super-resolution imaging of mitochondria and microtubules with high fidelity over a large FOV. A comparison of PPG3D with a shift-variant PSF generator for 3D LM reveals a threefold improvement in accuracy. Moreover, PPG3D is approximately 100 times faster than existing PSF generators, when used in image plane-based interpolation mode. Given its user-friendliness, we believe that PPG3D holds great potential for widespread application in SMLM and other imaging modalities.

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.

Skp1 proteins are structural components of the synaptonemal complex in C. elegans

The synaptonemal complex (SC) is a zipper-like protein assembly that links homologous chromosomes to regulate recombination and segregation during meiosis. The SC has been notoriously refractory to in vitro reconstitution, thus leaving its molecular organization largely unknown. Here, we report a moonlighting function of two paralogous S-phase kinase-associated protein 1 (Skp1)-related proteins (SKR-1 and SKR-2), well-known adaptors of the Skp1-Cul1-F-box (SCF) ubiquitin ligase, as the key missing components of the SC in Caenorhabditis elegans. SKR proteins repurpose their SCF-forming interfaces to dimerize and interact with meiosis-specific SC proteins, thereby driving synapsis independent of SCF activity. SKR-1 enables the formation of the long-sought-after soluble complex with previously identified SC proteins in vitro, which we propose it to represent a complete SC building block. Our findings demonstrate how a conserved cell cycle regulator has been co-opted to interact with rapidly evolving meiotic proteins to construct the SC and provide a foundation for understanding its structure and assembly mechanisms.

Motion of VAPB molecules reveals ER-mitochondria contact site subdomains

To coordinate cellular physiology, eukaryotic cells rely on the rapid exchange of molecules at specialized organelle-organelle contact sites1,2. Endoplasmic reticulum-mitochondrial contact sites (ERMCSs) are particularly vital communication hubs, playing key roles in the exchange of signalling molecules, lipids and metabolites3,4. ERMCSs are maintained by interactions between complementary tethering molecules on the surface of each organelle5,6. However, due to the extreme sensitivity of these membrane interfaces to experimental perturbation7,8, a clear understanding of their nanoscale organization and regulation is still lacking. Here we combine three-dimensional electron microscopy with high-speed molecular tracking of a model organelle tether, Vesicle-associated membrane protein (VAMP)-associated protein B (VAPB), to map the structure and diffusion landscape of ERMCSs. We uncovered dynamic subdomains within VAPB contact sites that correlate with ER membrane curvature and undergo rapid remodelling. We show that VAPB molecules enter and leave ERMCSs within seconds, despite the contact site itself remaining stable over much longer time scales. This metastability allows ERMCSs to remodel with changes in the physiological environment to accommodate metabolic needs of the cell. An amyotrophic lateral sclerosis-associated mutation in VAPB perturbs these subdomains, likely impairing their remodelling capacity and resulting in impaired interorganelle communication. These results establish high-speed single-molecule imaging as a new tool for mapping the structure of contact site interfaces and reveal that the diffusion landscape of VAPB at contact sites is a crucial component of ERMCS homeostasis.

Measurement precision bounds on aberrated single molecule emission patterns

Single-Molecule Localization Microscopy (SMLM) has revolutionized the study of biological phenomena by providing exquisite nanoscale spatial resolution. However, optical aberrations induced by sample and system imperfections distort the single molecule emission patterns (i.e. PSFs), leading to reduced precision and resolution of SMLM, particularly in three-dimensional (3D) applications. While various methods, both analytical and instrumental, have been employed to mitigate these aberrations, a comprehensive analysis of how different types of commonly encountered aberrations affect single molecule experiments and their image formation remains missing. In this study, we addressed this gap by conducting a quantitative study of the theoretical precision limit for position and wavefront distortion measurements in the presence of aberrations. Leveraging Fisher information and Cramér-Rao lower bound (CRLB), we quantitively analyzed and compared the effects of different aberration types, including index mismatch aberrations, on localization precision in both biplane and astigmatism 3D modalities as well as 2D SMLM imaging. Furthermore, we studied the achievable wavefront estimation precision from aberrated single molecule emission patterns, a pivot step for successful adaptive optics in SMLM through thick specimens. This analysis lays a quantitative foundation for the development and application of SMLM in whole-cells, tissues and with large field of view, providing in-depth insights into the behavior of different aberration types in single molecule imaging and thus generating theoretical guidelines for developing highly efficient aberration correction strategies and enhancing the precision and reliability of 3D SMLM.

Optimal sampling rate for 3D single molecule localization

Resolution of single molecule localization microscopy (SMLM) depends on the localization accuracy, which can be improved by utilizing engineered point spread functions (PSF) with delicate shapes. However, the intrinsic pixelation effect of the detector sensor will deteriorate PSFs under different sampling rates. The influence of the pixelation effect to the achieved 3D localization accuracy for different PSF shapes under different signal to background ratio (SBR) and pixel dependent readout noise has not been investigated in detail so far. In this work, we proposed a framework to characterize the 3D localization accuracy of pixelated PSF at different sampling rates. Four different PSFs (astigmatic PSF, double helix (DH) PSF, Tetrapod PSF and 4Pi PSF) were evaluated and the pixel size with optimal 3D localization performance were derived. This work provides a theoretical guide for the optimal design of sampling rate for 3D super resolution imaging.

A phosphoinositide switch mediates exocyst recruitment to multivesicular endosomes for exosome secretion

Exosomes are secreted to the extracellular milieu when multivesicular endosomes (MVEs) dock and fuse with the plasma membrane. However, MVEs are also known to fuse with lysosomes for degradation. How MVEs are directed to the plasma membrane for exosome secretion rather than to lysosomes is unclear. Here we report that a conversion of phosphatidylinositol-3-phosphate (PI(3)P) to phosphatidylinositol-4-phosphate (PI(4)P) catalyzed sequentially by Myotubularin 1 (MTM1) and phosphatidylinositol 4-kinase type IIα (PI4KIIα) on the surface of MVEs mediates the recruitment of the exocyst complex. The exocyst then targets the MVEs to the plasma membrane for exosome secretion. We further demonstrate that disrupting PI(4)P generation or exocyst function blocked exosomal secretion of Programmed death-ligand 1 (PD-L1), a key immune checkpoint protein in tumor cells, and led to its accumulation in lysosomes. Together, our study suggests that the PI(3)P to PI(4)P conversion on MVEs and the recruitment of the exocyst direct the exocytic trafficking of MVEs for exosome secretion.

Universal inverse modelling of point spread functions for SMLM localization and microscope characterization

The point spread function (PSF) of a microscope describes the image of a point emitter. Knowing the accurate PSF model is essential for various imaging tasks, including single molecule localization, aberration correction and deconvolution. Here we present uiPSF (universal inverse modelling of Point Spread Functions), a toolbox to infer accurate PSF models from microscopy data, using either image stacks of fluorescent beads or directly images of blinking fluorophores, the raw data in single molecule localization microscopy (SMLM). The resulting PSF model enables accurate 3D super-resolution imaging using SMLM. Additionally, uiPSF can be used to characterize and optimize a microscope system by quantifying the aberrations, including field-dependent aberrations, and resolutions. Our modular framework is applicable to a variety of microscope modalities and the PSF model incorporates system or sample specific characteristics, e.g., the bead size, depth dependent aberrations and transformations among channels. We demonstrate its application in single or multiple channels or large field-of-view SMLM systems, 4Pi-SMLM, and lattice light-sheet microscopes using either bead data or single molecule blinking data.

Fluorescence Microscopy: a statistics-optics perspective

Fundamental properties of light unavoidably impose features on images collected using fluorescence microscopes. Modeling these features is ever more important in quantitatively interpreting microscopy images collected at scales on par or smaller than light's wavelength. Here we review the optics responsible for generating fluorescent images, fluorophore properties, microscopy modalities leveraging properties of both light and fluorophores, in addition to the necessarily probabilistic modeling tools imposed by the stochastic nature of light and measurement.

Presynapses contain distinct actin nanostructures

The architecture of the actin cytoskeleton that concentrates at presynapses remains poorly known, hindering our understanding of its roles in synaptic physiology. In this work, we measure and visualize presynaptic actin by diffraction-limited and super-resolution microscopy, thanks to a validated model of bead-induced presynapses in cultured neurons. We identify a major population of actin-enriched presynapses that concentrates more presynaptic components and shows higher synaptic vesicle cycling than their non-enriched counterparts. Pharmacological perturbations point to an optimal actin amount and the presence of distinct actin structures within presynapses. We directly visualize these nanostructures using Single Molecule Localization Microscopy (SMLM), defining three distinct types: an actin mesh at the active zone, actin rails between the active zone and deeper reserve pools, and actin corrals around the whole presynaptic compartment. Finally, CRISPR-tagging of endogenous actin allows us to validate our results in natural synapses between cultured neurons, confirming the role of actin enrichment and the presence of three types of presynaptic actin nanostructures.

Enhancing obSTORM imaging performance with cubic spline PSF modeling

Oblique plane microscopy-based single molecule localization microscopy (obSTORM) has shown great potential for super-resolution imaging of thick biological specimens. Despite its compatibility with tissues and small animals, prior uses of the Gaussian point spread function (PSF) model have resulted in limited imaging resolution and a narrow axial localization range. This is due to the poor fit of the Gaussian PSF model with the actual PSF shapes in obSTORM. To overcome these limitations, we have employed cubic splines for a more accurate modeling of the experimental PSF shapes. This refined PSF model enhances three-dimensional localization precision, leading to significant improvements in obSTORM imaging of mouse retina tissues, such as an approximately 1.2 times increase in imaging resolution, seamless stitching of single molecules between adjacent optical sections, and a doubling of the sectional interval in volumetric obSTORM imaging due to the extended axial range of usable section thickness. The cubic spline PSF model thus offers a path towards more accurate and faster volumetric obSTORM imaging of biological specimens.

Single-molecule tracking to determine the abundances and stoichiometries of freely-diffusing protein complexes in living cells: Past applications and future prospects

Most biological processes in living cells rely on interactions between proteins. Live-cell compatible approaches that can quantify to what extent a given protein participates in homo- and hetero-oligomeric complexes of different size and subunit composition are therefore critical to advance our understanding of how cellular physiology is governed by these molecular interactions. Biomolecular complex formation changes the diffusion coefficient of constituent proteins, and these changes can be measured using fluorescence microscopy-based approaches, such as single-molecule tracking, fluorescence correlation spectroscopy, and fluorescence recovery after photobleaching. In this review, we focus on the use of single-molecule tracking to identify, resolve, and quantify the presence of freely-diffusing proteins and protein complexes in living cells. We compare and contrast different data analysis methods that are currently employed in the field and discuss experimental designs that can aid the interpretation of the obtained results. Comparisons of diffusion rates for different proteins and protein complexes in intracellular aqueous environments reported in the recent literature reveal a clear and systematic deviation from the Stokes-Einstein diffusion theory. While a complete and quantitative theoretical explanation of why such deviations manifest is missing, the available data suggest the possibility of weighing freely-diffusing proteins and protein complexes in living cells by measuring their diffusion coefficients. Mapping individual diffusive states to protein complexes of defined molecular weight, subunit stoichiometry, and structure promises to provide key new insights into how protein-protein interactions regulate protein conformational, translational, and rotational dynamics, and ultimately protein function.

Single-molecule localization microscopy based on denoising, interpolation and local maxima

A single fluorescent molecule is highly likely to be located at the center pixel position of a raw image diffused spot in an ideal situation. Even if the molecule and the center pixel position do not completely overlap, they are very close. A single-molecule localization method based on denoising, interpolation and local maxima (DIL) is proposed. The low-resolution raw image is denoised and interpolated, and a new image with a pixel size equal to that of the super-resolution image is attained. The local maxima of the new image are extracted. With this method, it is found that the local maxima positions can be regarded as the fluorescent molecule positions. Simulation results demonstrate that the DIL single-molecule localization accuracy reaches ∼18 nm when the Gaussian noise variance is equal to 0.01. Experimental results demonstrate that the DIL localization methodology is comparable to the Gaussian fitting algorithm and is faster.

RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy

BACKGROUND

Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions.

RESULTS

We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software.

CONCLUSIONS

The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

Fluorescence-Based Detection of Proteins and Their Interactions in Live Cells

Recent advances in fluorescence-based microscopy techniques, such as single molecule fluorescence, Förster resonance energy transfer (FRET), fluorescence intensity fluctuations analysis, and super-resolution microscopy have expanded our ability to study proteins in greater detail within their native cellular environment and to investigate the roles that protein interactions play in biological functions, such as inter- and intracellular signaling and cargo transport. In this Perspective, we provide an up-to-date overview of the current state of the art in fluorescence-based detection of proteins and their interactions in living cells with an emphasis on recent developments that have facilitated the characterization of the spatial and temporal organization of proteins into oligomeric complexes in the presence and absence of natural and artificial ligands. Further advancements in this field will only deepen our understanding of the underlying mechanisms of biological processes and help develop new therapeutic targets.

Single-frame deep-learning super-resolution microscopy for intracellular dynamics imaging

Single-molecule localization microscopy (SMLM) can be used to resolve subcellular structures and achieve a tenfold improvement in spatial resolution compared to that obtained by conventional fluorescence microscopy. However, the separation of single-molecule fluorescence events that requires thousands of frames dramatically increases the image acquisition time and phototoxicity, impeding the observation of instantaneous intracellular dynamics. Here we develop a deep-learning based single-frame super-resolution microscopy (SFSRM) method which utilizes a subpixel edge map and a multicomponent optimization strategy to guide the neural network to reconstruct a super-resolution image from a single frame of a diffraction-limited image. Under a tolerable signal density and an affordable signal-to-noise ratio, SFSRM enables high-fidelity live-cell imaging with spatiotemporal resolutions of 30 nm and 10 ms, allowing for prolonged monitoring of subcellular dynamics such as interplays between mitochondria and endoplasmic reticulum, the vesicle transport along microtubules, and the endosome fusion and fission. Moreover, its adaptability to different microscopes and spectra makes it a useful tool for various imaging systems.

Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging

Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180 × 180 × 5 μm³, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.

Clathrin coats partially preassemble and subsequently bend during endocytosis

Eukaryotic cells use clathrin-mediated endocytosis to take up a large range of extracellular cargo. During endocytosis, a clathrin coat forms on the plasma membrane, but it remains controversial when and how it is remodeled into a spherical vesicle. Here, we use 3D superresolution microscopy to determine the precise geometry of the clathrin coat at large numbers of endocytic sites. Through pseudo-temporal sorting, we determine the average trajectory of clathrin remodeling during endocytosis. We find that clathrin coats assemble first on flat membranes to 50% of the coat area before they become rapidly and continuously bent, and this mechanism is confirmed in three cell lines. We introduce the cooperative curvature model, which is based on positive feedback for curvature generation. It accurately describes the measured shapes and dynamics of the clathrin coat and could represent a general mechanism for clathrin coat remodeling on the plasma membrane.

Semi-automated 3D fluorescence speckle analyzer (3D-Speckler) for microscope calibration and nanoscale measurement

The widespread use of fluorescence microscopy has prompted the ongoing development of tools aiming to improve resolution and quantification accuracy for study of biological questions. Current calibration and quantification tools for fluorescence images face issues with usability/user experience, lack of automation, and comprehensive multidimensional measurement/correction capabilities. Here, we developed 3D-Speckler, a versatile, and high-throughput image analysis software that can provide fluorescent puncta quantification measurements such as 2D/3D particle size, spatial location/orientation, and intensities through semi-automation in a single, user-friendly interface. Integrated analysis options such as 2D/3D local background correction, chromatic aberration correction, and particle matching/filtering are also encompassed for improved precision and accuracy. We demonstrate 3D-Speckler microscope calibration capabilities by determining the chromatic aberrations, field illumination uniformity, and response to nanometer-scale emitters above and below the diffraction limit of our imaging system using multispectral beads. Furthermore, we demonstrated 3D-Speckler quantitative capabilities for offering insight into protein architectures and composition in cells.