PALMER: a method capable of parallel localization of multiple emitters for high-density localization microscopy

Instant in situ highlighting of latent fingerprints by a green fluorescent probe based on aggregation-induced emission

Fluorescence sensing of latent fingerprints (LFPs) has gained extensive attention due to its high sensitivity, non-destructive testing, low biotoxicity, ease of operation, and the potential for in situ visualization. However, the realization of in situ visualization of LFPs especially with green emission and rapid speed is still a challenge. Herein, we synthesized an amphibious green-emission AIE-gen TPE-NI-AOH (PLQY = 62%) for instant in situ LFP detecting, which integrates the excellent fluorescence properties of naphthalimide (NI) with a hydrophilic head and the AIE character as well as the donating property of tetraphenylethene (TPE). TPE-NI-AOH in ethanol/water binary solvent was used as an environmentally friendly LFP developer and achieved in situ green-fluorescence visualization of LFPs. The fluorescence signal achieves its 60% saturated intensity in 0.37 s and nearly 100% in 2.50 s, which is an instant process for the naked eye. Moreover, level 3 details and super-resolution images of LFPs could be observed clearly. Besides, the TPE-NI-AOH developer could be stored for at least 6 months, suitable for long-term storage. This instant in situ highlighting method does not require post-processing operations, providing a more convenient, rapid, and efficient detection method of LFPs. This work would inspire the further advancement of fluorescent sensors for fingerprint imaging.

PCIe-based FPGA-GPU heterogeneous computation for real-time multi-emitter fitting in super-resolution localization microscopy

Real-time multi-emitter fitting is a key technology for advancing super-resolution localization microscopy (SRLM), especially when it is necessary to achieve dynamic imaging quality control and/or optimization of experimental conditions. However, with the increase of activation densities, the requirements in the computing resources would increase rapidly due to the complexity of the fitting algorithms, making it difficult to realize real-time multi-emitter fitting for emitter density more than 0.6 mol/µm² in large field of view (FOV), even after acceleration with the popular Graphics Processing Unit (GPU) computation. Here we adopt the task parallelism strategy in computer science to construct a Peripheral Component Interconnect Express (PCIe) based all-in-one heterogeneous computing platform (AIO-HCP), where the data between two major parallel computing hardware, Field Programmable Gate Array (FPGA) and GPU, are interacted directly and executed simultaneously. Using simulated and experimental data, we verify that AIO-HCP could achieve a data throughput of up to ∼ 1.561 GB/s between FPGA and GPU. With this new platform, we develop a multi-emitter fitting method, called AIO-STORM, under big data stream parallel scheduling. We show that AIO-STORM is capable of providing real-time image processing on raw images with 100 µm × 100 µm FOV, 10 ms exposure time and 5.5 mol/µm² structure density, without scarifying image quality. This study overcomes the data throughput limitation of heterogeneous devices, demonstrates the power of the PCIe-based heterogeneous computation platform, and offers opportunities for multi-scale stitching of super-resolution images.

Accelerating multi-emitter localization in super-resolution localization microscopy with FPGA-GPU cooperative computation

The real-time multi-emitter localization method is essential for advancing high-throughput super-resolution localization microscopy (HT-SRLM). In the past decade, the graphics processing unit (GPU) computation has been dominantly used to accelerate the execution speed of the multi-emitter localization method. However, if HT-SRLM is combined with a scientific complementary metal-oxide-semiconductor (sCMOS) camera working at full frame rate, real-time image processing is still difficult to achieve using this acceleration approach, thus resulting in a massive data storage challenge and even system crash. Here we take advantage of the cooperative acceleration power of field programming gate array (FPGA) computation and GPU computation, and propose a method called HCP-STORM to enable real-time multi-emitter localization. Using simulated images, we verified that HCP-STORM is capable of providing real-time image processing for raw images from a representative Hamamatsu Flash 4 V3 sCMOS camera working at full frame rate (that is, 2048×2048 pixels @ 10 ms exposure time). Using experimental images, we prove that HCP-STORM is 25 times faster than QC-STORM and 295 times faster than ThunderSTORM, with a small but acceptable degradation in image quality. This study shows the potential of FPGA-GPU cooperative computation in accelerating multi-emitter localization, and pushes a significant step toward the maturity of HT-SRLM technology.

Potential quality improvement of stochastic optical localization nanoscopy images obtained by frame by frame localization algorithms

A data movie of stochastic optical localization nanoscopy contains spatial and temporal correlations, both providing information of emitter locations. The majority of localization algorithms in the literature estimate emitter locations by frame-by-frame localization (FFL), which exploit only the spatial correlation and leave the temporal correlation into the FFL nanoscopy images. The temporal correlation contained in the FFL images, if exploited, can improve the localization accuracy and the image quality. In this paper, we analyze the properties of the FFL images in terms of root mean square minimum distance (RMSMD) and root mean square error (RMSE). It is shown that RMSMD and RMSE can be potentially reduced by a maximum fold equal to the square root of the average number of activations per emitter. Analyzed and revealed are also several statistical properties of RMSMD and RMSE and their relationship with respect to a large number of data frames, bias and variance of localization errors, small localization errors, sample drift, and the worst FFL image. Numerical examples are taken and the results confirm the prediction of analysis. The ideas about how to develop an algorithm to exploit the temporal correlation of FFL images are also briefly discussed. The results suggest development of two kinds of localization algorithms: the algorithms that can exploit the temporal correlation of FFL images and the unbiased localization algorithms.

Fast Super-Resolution Imaging Technique and Immediate Early Nanostructure Capturing by a Photoconvertible Fluorescent Protein

Low temporal resolution and limited photocontrollable fluorescent protein probes have restricted the widespread application of single-molecule localization microscopy (SMLM). In the current study, we developed a new photoconvertible fluorescent protein (PCFP), pcStar, and quick single molecule-guided Bayesian localization microscopy (Quick-SIMBA). The combination of pcStar and Quick-SIMBA achieved the highest temporal resolution (0.1-0.25 s) with large field-of-view (76 × 9.4 μm² -76 × 31.4 μm²) among the SMLM methods, which enabled the dynamic movements of the endoplasmic reticulum dense tubular matrix to be resolved. Moreover, pcStar extended the application of SMLM to imaging the immediate early nanostructures in Drosophila embryos and revealed a specific "parallel three-pillar" structure in the neuronal-glial cell junction, helping to elucidate glial cell "locking" and support of neurons during Drosophila embryogenesis.

WindSTORM: Robust online image processing for high-throughput nanoscopy

High-throughput nanoscopy becomes increasingly important for unraveling complex biological processes from a large heterogeneous cell population at a nanoscale resolution. High-density emitter localization combined with a large field of view and fast imaging frame rate is commonly used to achieve a high imaging throughput, but the image processing speed and the presence of heterogeneous background in the dense emitter scenario remain a bottleneck. Here, we present a simple non-iterative approach, referred to as WindSTORM, to achieve high-speed high-density emitter localization with robust performance for various image characteristics. We demonstrate that WindSTORM improves the computation speed by two orders of magnitude on CPU and three orders of magnitude upon GPU acceleration to realize online image processing, without compromising localization accuracy. Further, WindSTORM is highly robust to maximize the localization accuracy and minimize the image artifacts in the presence of nonuniform background. WindSTORM paves the way for next generation high-throughput nanoscopy.

Accelerating single molecule localization microscopy through parallel processing on a high-performance computing cluster

Super-resolved microscopy techniques have revolutionized the ability to study biological structures below the diffraction limit. Single molecule localization microscopy (SMLM) techniques are widely used because they are relatively straightforward to implement and can be realized at relatively low cost, e.g. compared to laser scanning microscopy techniques. However, while the data analysis can be readily undertaken using open source or other software tools, large SMLM data volumes and the complexity of the algorithms used often lead to long image data processing times that can hinder the iterative optimization of experiments. There is increasing interest in high throughput SMLM, but its further development and application is inhibited by the data processing challenges. We present here a widely applicable approach to accelerating SMLM data processing via a parallelized implementation of ThunderSTORM on a high-performance computing (HPC) cluster and quantify the speed advantage for a four-node cluster (with 24 cores and 128 GB RAM per node) compared to a high specification (28 cores, 128 GB RAM, SSD-enabled) desktop workstation. This data processing speed can be readily scaled by accessing more HPC resources. Our approach is not specific to ThunderSTORM and can be adapted for a wide range of SMLM software. LAY DESCRIPTION: Optical microscopy is now able to provide images with a resolution far beyond the diffraction limit thanks to relatively new super-resolved microscopy (SRM) techniques, which have revolutionized the ability to study biological structures. One approach to SRM is to randomly switch on and off the emission of fluorescent molecules in an otherwise conventional fluorescence microscope. If only a sparse subset of the fluorescent molecules labelling a sample can be switched on at a time, then each emitter will be, on average, spaced further apart than the diffraction-limited resolution of the conventional microscope and the separate bright spots in the image corresponding to each emitter can be localised to high precision by finding the centre of each feature using a computer program. Thus, a precise map of the emitter positions can be recorded by sequentially mapping the localisation of different subsets of emitters as they are switched on and others switched off. Typically, this approach, described as single molecule localisation microscopy (SMLM), results in large image data sets that can take many minutes to hours to process, depending on the size of the field of view and whether the SMLM analysis employs a computationally-intensive iterative algorithm. Such a slow workflow makes it difficult to optimise experiments and to analyse large numbers of samples. Faster SMLM experiments would be generally useful and automated high throughput SMLM studies of arrays of samples, such as cells, could be applied to drug discovery and other applications. However, the time required to process the resulting data would be prohibitive on a normal computer. To address this, we have developed a method to run standard SMLM data analysis software tools in parallel on a high-performance computing cluster (HPC). This can be used to accelerate the analysis of individual SMLM experiments or it can be scaled to analyse high throughput SMLM data by extending it to run on an arbitrary number of HPC processors in parallel. In this paper we outline the design of our parallelised SMLM software for HPC and quantify the speed advantage when implementing it on four HPC nodes compared to a powerful desktop computer.

A multi-emitter fitting algorithm for potential live cell super-resolution imaging over a wide range of molecular densities

Multi-emitter fitting algorithms have been developed to improve the temporal resolution of single-molecule switching nanoscopy, but the molecular density range they can analyse is narrow and the computation required is intensive, significantly limiting their practical application. Here, we propose a computationally fast method, wedged template matching (WTM), an algorithm that uses a template matching technique to localise molecules at any overlapping molecular density from sparse to ultrahigh density with subdiffraction resolution. WTM achieves the localization of overlapping molecules at densities up to 600 molecules μm^-2 with a high detection sensitivity and fast computational speed. WTM also shows localization precision comparable with that of DAOSTORM (an algorithm for high-density super-resolution microscopy), at densities up to 20 molecules μm^-2 , and better than DAOSTORM at higher molecular densities. The application of WTM to a high-density biological sample image demonstrated that it resolved protein dynamics from live cell images with subdiffraction resolution and a temporal resolution of several hundred milliseconds or less through a significant reduction in the number of camera images required for a high-density reconstruction. WTM algorithm is a computationally fast, multi-emitter fitting algorithm that can analyse over a wide range of molecular densities. The algorithm is available through the website. https://doi.org/10.17632/bf3z6xpn5j.1.

Quantum-Optically Enhanced STORM (QUEST) for Multi-Emitter Localization

Super-resolution imaging has introduced new capabilities to investigate processes at the nanometer scale by optical means. However, most super-resolution techniques require either sparse excitation of few emitters or analysis of high-order cumulants in order to identify several emitters in close vicinity. Here, we present an approach that draws upon methods from quantum optics to perform localization super-resolution imaging of densely packed emitters and determine their number automatically: Quantum-optically enhanced STORM (QUEST). By exploiting normalized photon correlations, we predict a localization precision below 30 nm or better even for closely spaced emitter up to a density of 125 emitters per μm at photon emission rates of 10⁵ photons per second and emitter. Our technique does not require complex experimental arrangements and relies solely on spatially resolved time streams of photons and subsequent data analysis.

Molecular Counting with Localization Microscopy: A Bayesian Estimate Based on Fluorophore Statistics

Superresolved localization microscopy has the potential to serve as an accurate, single-cell technique for counting the abundance of intracellular molecules. However, the stochastic blinking of single fluorophores can introduce large uncertainties into the final count. Here we provide a theoretical foundation for applying superresolved localization microscopy to the problem of molecular counting based on the distribution of blinking events from a single fluorophore. We also show that by redundantly tagging single molecules with multiple, blinking fluorophores, the accuracy of the technique can be enhanced by harnessing the central limit theorem. The coefficient of variation then, for the number of molecules M estimated from a given number of blinks B, scales like ∼1/N_l, where N_l is the mean number of labels on a target. As an example, we apply our theory to the challenging problem of quantifying the cell-to-cell variability of plasmid copy number in bacteria.

Three-Dimensional Localization of Single Molecules for Super-Resolution Imaging and Single-Particle Tracking

Single-molecule super-resolution fluorescence microscopy and single-particle tracking are two imaging modalities that illuminate the properties of cells and materials on spatial scales down to tens of nanometers or with dynamical information about nanoscale particle motion in the millisecond range, respectively. These methods generally use wide-field microscopes and two-dimensional camera detectors to localize molecules to much higher precision than the diffraction limit. Given the limited total photons available from each single-molecule label, both modalities require careful mathematical analysis and image processing. Much more information can be obtained about the system under study by extending to three-dimensional (3D) single-molecule localization: without this capability, visualization of structures or motions extending in the axial direction can easily be missed or confused, compromising scientific understanding. A variety of methods for obtaining both 3D super-resolution images and 3D tracking information have been devised, each with their own strengths and weaknesses. These include imaging of multiple focal planes, point-spread-function engineering, and interferometric detection. These methods may be compared based on their ability to provide accurate and precise position information on single-molecule emitters with limited photons. To successfully apply and further develop these methods, it is essential to consider many practical concerns, including the effects of optical aberrations, field dependence in the imaging system, fluorophore labeling density, and registration between different color channels. Selected examples of 3D super-resolution imaging and tracking are described for illustration from a variety of biological contexts and with a variety of methods, demonstrating the power of 3D localization for understanding complex systems.

Quantum correlation enhanced super-resolution localization microscopy enabled by a fibre bundle camera

Despite advances in low-light-level detection, single-photon methods such as photon correlation have rarely been used in the context of imaging. The few demonstrations, for example of subdiffraction-limited imaging utilizing quantum statistics of photons, have remained in the realm of proof-of-principle demonstrations. This is primarily due to a combination of low values of fill factors, quantum efficiencies, frame rates and signal-to-noise characteristic of most available single-photon sensitive imaging detectors. Here we describe an imaging device based on a fibre bundle coupled to single-photon avalanche detectors that combines a large fill factor, a high quantum efficiency, a low noise and scalable architecture. Our device enables localization-based super-resolution microscopy in a non-sparse non-stationary scene, utilizing information on the number of active emitters, as gathered from non-classical photon statistics.

Complementarity of PALM and SOFI for super-resolution live-cell imaging of focal adhesions

Live-cell imaging of focal adhesions requires a sufficiently high temporal resolution, which remains a challenge for super-resolution microscopy. Here we address this important issue by combining photoactivated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI). Using simulations and fixed-cell focal adhesion images, we investigate the complementarity between PALM and SOFI in terms of spatial and temporal resolution. This PALM-SOFI framework is used to image focal adhesions in living cells, while obtaining a temporal resolution below 10 s. We visualize the dynamics of focal adhesions, and reveal local mean velocities around 190 nm min⁻¹. The complementarity of PALM and SOFI is assessed in detail with a methodology that integrates a resolution and signal-to-noise metric. This PALM and SOFI concept provides an enlarged quantitative imaging framework, allowing unprecedented functional exploration of focal adhesions through the estimation of molecular parameters such as fluorophore densities and photoactivation or photoswitching kinetics.

Live cell super-resolution imaging requires a high temporal resolution, which remains a challenge. Here the authors combine photo-activated localization microscopy (PALM) with super-resolution optical fluctuation imaging (SOFI) to achieve high spatiotemporal resolution and quantitative imaging of focal adhesion dynamics.

Multifluorophore localization as a percolation problem: limits to density and precision

We show that the maximum desirable density of activated fluorophores in a superresolution experiment can be determined by treating the overlapping point spread functions as a problem in percolation theory. We derive a bound on the density of activated fluorophores, taking into account the desired localization accuracy and precision, as well as the number of photons emitted. Our bound on density is close to that reported in experimental work, suggesting that further increases in the density of imaged fluorophores will come at the expense of localization accuracy and precision.

Precise multi-emitter localization method for fast super-resolution imaging

We present a method that can simultaneously locate positions of overlapped multi-emitters at the theoretical-limit precision. We derive a set of simple equations whose solutions give the maximum likelihood estimator of multi-emitter positions. We compare the performance of our simultaneous localization analysis with the conventional single-molecule analysis for simulated images and show that our method can improve the time-resolution of super-resolution microscopy by an order of magnitude. In particular, we derive the information-theoretic bound on time resolution of localization-based super-resolution microscopy and demonstrate that the bound can be asymptotically attained by our analysis.

A review of progress in single particle tracking: from methods to biophysical insights

Optical microscopy has for centuries been a key tool to study living cells with minimum invasiveness. The advent of single molecule techniques over the past two decades has revolutionized the field of cell biology by providing a more quantitative picture of the complex and highly dynamic organization of living systems. Amongst these techniques, single particle tracking (SPT) has emerged as a powerful approach to study a variety of dynamic processes in life sciences. SPT provides access to single molecule behavior in the natural context of living cells, thereby allowing a complete statistical characterization of the system under study. In this review we describe the foundations of SPT together with novel optical implementations that nowadays allow the investigation of single molecule dynamic events with increasingly high spatiotemporal resolution using molecular densities closer to physiological expression levels. We outline some of the algorithms for the faithful reconstruction of SPT trajectories as well as data analysis, and highlight biological examples where the technique has provided novel insights into the role of diffusion regulating cellular function. The last part of the review concentrates on different theoretical models that describe anomalous transport behavior and ergodicity breaking observed from SPT studies in living cells.

Quantitative evaluation of software packages for single-molecule localization microscopy

The quality of super-resolution images obtained by single-molecule localization microscopy (SMLM) depends largely on the software used to detect and accurately localize point sources. In this work, we focus on the computational aspects of super-resolution microscopy and present a comprehensive evaluation of localization software packages. Our philosophy is to evaluate each package as a whole, thus maintaining the integrity of the software. We prepared synthetic data that represent three-dimensional structures modeled after biological components, taking excitation parameters, noise sources, point-spread functions and pixelation into account. We then asked developers to run their software on our data; most responded favorably, allowing us to present a broad picture of the methods available. We evaluated their results using quantitative and user-interpretable criteria: detection rate, accuracy, quality of image reconstruction, resolution, software usability and computational resources. These metrics reflect the various tradeoffs of SMLM software packages and help users to choose the software that fits their needs.

3D multifocus astigmatism and compressed sensing (3D MACS) based superresolution reconstruction

Single molecule based superresolution techniques (STORM/PALM) achieve nanometer spatial resolution by integrating the temporal information of the switching dynamics of fluorophores (emitters). When emitter density is low for each frame, they are located to the nanometer resolution. However, when the emitter density rises, causing significant overlapping, it becomes increasingly difficult to accurately locate individual emitters. This is particularly apparent in three dimensional (3D) localization because of the large effective volume of the 3D point spread function (PSF). The inability to precisely locate the emitters at a high density causes poor temporal resolution of localization-based superresolution technique and significantly limits its application in 3D live cell imaging. To address this problem, we developed a 3D high-density superresolution imaging platform that allows us to precisely locate the positions of emitters, even when they are significantly overlapped in three dimensional space. Our platform involves a multi-focus system in combination with astigmatic optics and an ℓ 1-Homotopy optimization procedure. To reduce the intrinsic bias introduced by the discrete formulation of compressed sensing, we introduced a debiasing step followed by a 3D weighted centroid procedure, which not only increases the localization accuracy, but also increases the computation speed of image reconstruction. We implemented our algorithms on a graphic processing unit (GPU), which speeds up processing 10 times compared with central processing unit (CPU) implementation. We tested our method with both simulated data and experimental data of fluorescently labeled microtubules and were able to reconstruct a 3D microtubule image with 1000 frames (512×512) acquired within 20 seconds.

High density 3D localization microscopy using sparse support recovery

Single-molecule localization microscopy methods offer high spatial resolution, but they are not always suitable for live cell imaging due to limited temporal resolution. One strategy is to increase the density of photoactivated molecules present in each image, however suitable analysis algorithms for such data are still lacking. We present 3denseSTORM, a new algorithm for localization microscopy which is able to recover 2D or 3D super-resolution images from a sequence of diffraction limited images with high densities of photoactivated molecules. The algorithm is based on sparse support recovery and uses a Poisson noise model, which becomes critical in low-light conditions. For 3D data reconstruction we use the astigmatism and biplane imaging methods. We derive the theoretical resolution limits of the method and show examples of image reconstructions in simulations and in real 2D and 3D biological samples. The method is suitable for fast image acquisition in densely labeled samples and helps facilitate live cell studies with single molecule localization microscopy.

A general strategy for developing cell-permeable photo-modulatable organic fluorescent probes for live-cell super-resolution imaging

Single-molecule localization microscopy (SMLM) achieves super-resolution imaging beyond the diffraction limit but critically relies on the use of photo-modulatable fluorescent probes. Here we report a general strategy for constructing cell-permeable photo-modulatable organic fluorescent probes for live-cell SMLM by exploiting the remarkable cytosolic delivery ability of a cell-penetrating peptide (rR)₃R₂. We develop photo-modulatable organic fluorescent probes consisting of a (rR)₃R₂ peptide coupled to a cell-impermeable organic fluorophore and a recognition unit. Our results indicate that these organic probes are not only cell permeable but can also specifically and directly label endogenous targeted proteins. Using the probes, we obtain super-resolution images of lysosomes and endogenous F-actin under physiological conditions. We resolve the dynamics of F-actin with 10 s temporal resolution in live cells and discern fine F-actin structures with diameters of ~80 nm. These results open up new avenues in the design of fluorescent probes for live-cell super-resolution imaging.

Single-molecule localization microscopy depends on the use of photo-modulatable fluorescent probes; however, many cannot be used in live-cell studies due to poor cell permeability. Pan et al. present a strategy for constructing cell-permeable probes and use it to image actin filament dynamics and lysosomes.

Quantitative analysis of single-molecule superresolution images

This review highlights the quantitative capabilities of single-molecule localization-based superresolution imaging methods. In addition to revealing fine structural details, the molecule coordinate lists generated by these methods provide the critical ability to quantify the number, clustering, and colocalization of molecules with 10-50 nm resolution. Here we describe typical workflows and precautions for quantitative analysis of single-molecule superresolution images. These guidelines include potential pitfalls and essential control experiments, allowing critical assessment and interpretation of superresolution images.

Superresolution microscope image reconstruction by spatiotemporal object decomposition and association: application in resolving t-tubule structure in skeletal muscle

One key factor that limits resolution of single-molecule superresolution microscopy relates to the localization accuracy of the activated emitters, which is usually deteriorated by two factors. One originates from the background noise due to out-of-focus signals, sample auto-fluorescence, and camera acquisition noise; and the other is due to the low photon count of emitters at a single frame. With fast acquisition rate, the activated emitters can last multiple frames before they transiently switch off or permanently bleach. Effectively incorporating the temporal information of these emitters is critical to improve the spatial resolution. However, majority of the existing reconstruction algorithms locate the emitters frame by frame, discarding or underusing the temporal information. Here we present a new image reconstruction algorithm based on tracklets, short trajectories of the same objects. We improve the localization accuracy by associating the same emitters from multiple frames to form tracklets and by aggregating signals to enhance the signal to noise ratio. We also introduce a weighted mean-shift algorithm (WMS) to automatically detect the number of modes (emitters) in overlapping regions of tracklets so that not only well-separated single emitters but also individual emitters within multi-emitter groups can be identified and tracked. In combination with a maximum likelihood estimator method (MLE), we are able to resolve low to medium density of overlapping emitters with improved localization accuracy. We evaluate the performance of our method with both synthetic and experimental data, and show that the tracklet-based reconstruction is superior in localization accuracy, particularly for weak signals embedded in a strong background. Using this method, for the first time, we resolve the transverse tubule structure of the mammalian skeletal muscle.

A multiemitter localization comparison of 3D superresolution imaging modalities

Single-molecule localization-based superresolution imaging is complicated by emission from multiple emitters overlapping at the detector. The potential for overlapping emitters is even greater for 3D imaging than for 2D imaging due to the large effective "volume" of the 3D point spread function. Overlapping emission can be accounted for in the estimation model, recovering the ability to localize the emitters, but with the caveat that the localization precision has a dependence on the amount of overlap from other emitters. Whether a particular 3D imaging modality has a significant advantage in facilitating the position estimation of overlapping emitters is investigated. The variants of two commonly used and easily implemented imaging modalities for 3D single-molecule imaging are compared: astigmatic imaging; dual focal plane imaging; and the combination of the two approaches, dual focal plane imaging with astigmatism. The Cramér-Rao lower bound is used to quantify the multiemitter estimation performance by calculating the theoretical best localization precision under a multiemitter estimation model. The performance of these 3D modalities is investigated under a wide range of conditions including various distributions of collected photons per emitter, background counts, pixel sizes, and camera readout noise values. Differences between modalities are small and it is therefore concluded that multiemitter fitting performance should not be a primary factor in selecting between these modalities.

A divide and conquer strategy for the maximum likelihood localization of low intensity objects

In cell biology and other fields the automatic accurate localization of sub-resolution objects in images is an important tool. The signal is often corrupted by multiple forms of noise, including excess noise resulting from the amplification by an electron multiplying charge-coupled device (EMCCD). Here we present our novel Nested Maximum Likelihood Algorithm (NMLA), which solves the problem of localizing multiple overlapping emitters in a setting affected by excess noise, by repeatedly solving the task of independent localization for single emitters in an excess noise-free system. NMLA dramatically improves scalability and robustness, when compared to a general purpose optimization technique. Our method was successfully applied for in vivo localization of fluorescent proteins.

Superresolution localization methods

Superresolution localization microscopy methods produce nanoscale images via a combination of intermittently active fluorescent probes and algorithms that can precisely determine the positions of these probes from single-molecule or few-molecule images. These algorithms vary widely in their underlying principles, complexity, and accuracy. In this review, we begin by surveying the principles of localization microscopy and describing the fundamental limits to localization precision. We then examine several different families of fluorophore localization algorithms, comparing their complexity, performance, and range of applicability (e.g., whether they require particular types of experimental information, are optimized for specific situations, or are more general). Whereas our focus is on the localization of single isotropic emitters in two dimensions, we also consider oriented dipoles, three-dimensional localization, and algorithms that can handle overlapping images of several nearby fluorophores. Throughout the review, we try to highlight practical advice for users of fluorophore localization algorithms, as well as open questions.

Fast and efficient molecule detection in localization-based super-resolution microscopy by parallel adaptive histogram equalization

In localization-based super-resolution microscopy, individual fluorescent markers are stochastically photoactivated and subsequently localized within a series of camera frames, yielding a final image with a resolution far beyond the diffraction limit. Yet, before localization can be performed, the subregions within the frames where the individual molecules are present have to be identified-oftentimes in the presence of high background. In this work, we address the importance of reliable molecule identification for the quality of the final reconstructed super-resolution image. We present a fast and robust algorithm (a-livePALM) that vastly improves the molecule detection efficiency while minimizing false assignments that can lead to image artifacts.

Imaging cells at the nanoscale

Recently developed super-resolution techniques in optical microscopy have pushed the length scale at which cellular structure can be observed down to tens of nanometres. A wide array of methods have been described that fall under the umbrella term of super-resolution microscopy and each of these methods has different requirements for acquisition speed, experimental complexity, fluorophore requirements and post-processing of data. For example, experimental complexity can be decreased by using a standard widefield microscope for acquisition, but this requires substantial processing of the data to extract the super-resolution information. These powerful techniques are bringing new insights into the nanoscale structure of sub-cellular assemblies such as podosomes, which are an ideal system to observe with super-resolution microscopy as the structures are relatively thin and they form and dissociate over a period of several minutes. Here we discuss the major classes of super-resolution microscopy techniques, and demonstrate their relative performance by imaging podosomes.

Real-time analysis and visualization for single-molecule based super-resolution microscopy

Accurate multidimensional localization of isolated fluorescent emitters is a time consuming process in single-molecule based super-resolution microscopy. We demonstrate a functional method for real-time reconstruction with automatic feedback control, without compromising the localization accuracy. Compatible with high frame rates of EM-CCD cameras, it relies on a wavelet segmentation algorithm, together with a mix of CPU/GPU implementation. A combination with Gaussian fitting allows direct access to 3D localization. Automatic feedback control ensures optimal molecule density throughout the acquisition process. With this method, we significantly improve the efficiency and feasibility of localization-based super-resolution microscopy.

Visualizing cell structure and function with point-localization superresolution imaging

Fundamental to the success of cell and developmental biology is the ability to tease apart molecular organization in cells and tissues by localizing specific proteins with respect to one another in a native cellular context. However, many key cellular structures (from mitochondrial cristae to nuclear pores) lie below the diffraction limit of visible light, precluding analysis of their organization by conventional approaches. Point-localization superresolution microscopy techniques, such as PALM and STORM, are poised to resolve, with unprecedented clarity, the organizational principles of macromolecular complexes within cells, thus leading to deeper insights into cellular function in both health and disease.

Quantifying spatial organization in point-localization superresolution images using pair correlation analysis

The distinctive distributions of proteins within subcellular compartments both at steady state and during signaling events have essential roles in cell function. Here we describe a method for delineating the complex arrangement of proteins within subcellular structures visualized using point-localization superresolution (PL-SR) imaging. The approach, called pair correlation photoactivated localization microscopy (PC-PALM), uses a pair-correlation algorithm to precisely identify single molecules in PL-SR imaging data sets, and it is used to decipher quantitative features of protein organization within subcellular compartments, including the existence of protein clusters and the size, density and number of proteins in these clusters. We provide a step-by-step protocol for PC-PALM, illustrating its analysis capability for four plasma membrane proteins tagged with photoactivatable GFP (PAGFP). The experimental steps for PC-PALM can be carried out in 3 d and the analysis can be done in ∼6-8 h. Researchers need to have substantial experience in single-molecule imaging and statistical analysis to conduct the experiments and carry out this analysis.