High-density localization of active molecules using Structured Sparse Model and Bayesian Information Criterion

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.

Advanced imaging techniques for tracking drug dynamics at the subcellular level

Optical microscopes are an important imaging tool that have effectively advanced the development of modern biomedicine. In recent years, super-resolution microscopy (SRM) has become one of the most popular techniques in the life sciences, especially in the field of living cell imaging. SRM has been used to solve many problems in basic biological research and has great potential in clinical application. In particular, the use of SRM to study drug delivery and kinetics at the subcellular level enables researchers to better study drugs' mechanisms of action and to assess the efficacy of their targets in vivo. The purpose of this paper is to review the recent advances in SRM and to highlight some of its applications in assessing subcellular drug dynamics.

Snapshot volumetric imaging with engineered point-spread functions

The biological world involves intracellular and intercellular interactions that occur at high speed, at multiple scales and in three dimensions. Acquiring 3D images, however, typically requires a compromise in either spatial or temporal resolution compared to 2D imaging. Conventional 2D fluorescence imaging provides high spatial resolution but requires plane-by-plane imaging of volumes. Conversely, snapshot methods such as light-field microscopy allow video-rate imaging, but at the cost of spatial resolution. Here we introduce 3D engineered point-spread function microscopy (3D-EPM), enabling snapshot imaging of real-world 3D extended biological structures while retaining the native resolution of the microscope in space and time. Our new computational recovery strategy is the key to volumetrically reconstructing arbitrary 3D structures from the information encapsulated in 2D raw EPM images. We validate our technique on both point-like and extended samples, and demonstrate its power by imaging the intracellular motion of chloroplasts undergoing cyclosis in a sample of Egeria densa. Our technique represents a generalised computational methodology for 3D image recovery which is readily adapted to a diverse range of existing microscopy platforms and engineered point-spread functions. We therefore expect it to find broad applicability in the study of rapid biological dynamics in 3D.

Practical Aspects of Super-Resolution Imaging and Segmentation of Macromolecular Complexes by dSTORM

Super-resolution fluorescence microscopy allows imaging macromolecular complexes down to the nanoscopic scale and thus is a great tool to combine and integrate cellular imaging in the native cellular environment with structural analysis by X-ray crystallography or high-resolution cryo electron microscopy or tomography. Here we describe practical aspects of SMLM imaging by dSTORM, from the initial sample preparation using mounting media, antibodies and fluorescent markers, the experimental setup for data acquisition including multi-color colocalization and 3D data acquisition, and finally tips and clues on advanced data processing that includes image reconstruction and data segmentation using 2D or 3D clustering methods. This approach opens the path toward multi-resolution integration in cellular structural biology.

Live-SIMBA: an ImageJ plug-in for the universal and accelerated single molecule-guided Bayesian localization super resolution microscopy (SIMBA) method

Live-cell super-resolution fluorescence microscopy techniques allow biologists to observe subcellular structures, interactions and dynamics at the nanoscale level. Among of them, single molecule-guided Bayesian localization super resolution microscopy (SIMBA) and its derivatives produce an appropriate 50 nm spatial resolution and a 0.1-2s temporal resolution in living cells with simple off-the-shelf total internal reflection fluorescence (TIRF) equipment. However, SIMBA and its derivatives are limited by the requirement for dual-channel dataset or single-channel dataset with special design, the time-consuming calculation for extended field of view and the lack of real-time visualization tool. Here, we propose a universal and accelerated SIMBA ImageJ plug-in, Live-SIMBA, for time-series analysis in living cells. Live-SIMBA circumvents the requirement of dual-channel dataset using intensity-based sampling algorithm and improves the computing speed using multi-core parallel computing technique. Live-SIMBA also better resolves the weak signals inside the specimens with adjustable background estimation and distance-threshold filter. With improved fidelity on reconstructed structures, greatly accelerated computation, and real-time visualization, Live-SIMBA demonstrates its extended capabilities in live-cell super-resolution imaging.

DLBI: deep learning guided Bayesian inference for structure reconstruction of super-resolution fluorescence microscopy

Motivation

Super-resolution fluorescence microscopy with a resolution beyond the diffraction limit of light, has become an indispensable tool to directly visualize biological structures in living cells at a nanometer-scale resolution. Despite advances in high-density super-resolution fluorescent techniques, existing methods still have bottlenecks, including extremely long execution time, artificial thinning and thickening of structures, and lack of ability to capture latent structures.

Results

Here, we propose a novel deep learning guided Bayesian inference (DLBI) approach, for the time-series analysis of high-density fluorescent images. Our method combines the strength of deep learning and statistical inference, where deep learning captures the underlying distribution of the fluorophores that are consistent with the observed time-series fluorescent images by exploring local features and correlation along time-axis, and statistical inference further refines the ultrastructure extracted by deep learning and endues physical meaning to the final image. In particular, our method contains three main components. The first one is a simulator that takes a high-resolution image as the input, and simulates time-series low-resolution fluorescent images based on experimentally calibrated parameters, which provides supervised training data to the deep learning model. The second one is a multi-scale deep learning module to capture both spatial information in each input low-resolution image as well as temporal information among the time-series images. And the third one is a Bayesian inference module that takes the image from the deep learning module as the initial localization of fluorophores and removes artifacts by statistical inference. Comprehensive experimental results on both real and simulated datasets demonstrate that our method provides more accurate and realistic local patch and large-field reconstruction than the state-of-the-art method, the 3B analysis, while our method is more than two orders of magnitude faster.

Availability and implementation

The main program is available at https://github.com/lykaust15/DLBI.

Supplementary information

Supplementary data are available at Bioinformatics online.

Bayesian Multiple Emitter Fitting using Reversible Jump Markov Chain Monte Carlo

In single molecule localization-based super-resolution imaging, high labeling density or the desire for greater data collection speed can lead to clusters of overlapping emitter images in the raw super-resolution image data. We describe a Bayesian inference approach to multiple-emitter fitting that uses Reversible Jump Markov Chain Monte Carlo to identify and localize the emitters in dense regions of data. This formalism can take advantage of any prior information, such as emitter intensity and density. The output is both a posterior probability distribution of emitter locations that includes uncertainty in the number of emitters and the background structure, and a set of coordinates and uncertainties from the most probable model.

Strategy to Lengthen the On-Time of Photochromic Rhodamine Spirolactam for Super-resolution Photoactivated Localization Microscopy

Rhodamine derivatives and analogues have been widely used for different super-resolution imaging techniques, including photoactivated localization microscopy (PALM). Among them, rhodamine spirolactams exhibit great superiority for PALM imaging due to a desirable bright-dark contrast during the photochromic switching process. Although considerable attention has been paid to the chemical modifications on rhodamine spirolactams, the on-time of photochromic switching, one of the key characteristics for PALM imaging, has never been optimized in previous developments. In this study, we proposed that simply installing a carboxyl group close to the lactam site could impose an intramolecular acidic environment, stabilize the photoactivated zwitterionic structure, and thus effectively increase the on-time. On the basis of this idea, we have synthesized a new rhodamine spirolactam, Rh-Gly, that demonstrated considerably longer on-time than the other tested analogues, as well as an enhancement of single-molecule brightness, an improvement on signal-to-noise ratio and an enlargement of total collected photons of a single molecule before photobleaching. Finally, super-resolution images of live cell mitochondria stained with Rh-Gly have been obtained with a good temporal resolution of 10 s, as well as a satisfactory localization precision of ∼25 nm. Through self-labeling protein tags, Rh-Gly modified with a HaloTag ligand enabled super-resolution imaging of histone H2B proteins in live HeLa cells; through immunostaining antibodies labeled with an isothiocyanate-substituted Rh-Gly, super-resolution imaging of microtubules was achieved in fixed cells. Therefore, our simple and effective strategy provides novel insight for developing further enhanced rhodamine spirolactams recommendable for PALM imaging.

Fast analysis method for stochastic optical reconstruction microscopy using multiple measurement vector model sparse Bayesian learning

Compressed sensing (CS) can be used in fluorescence microscopy to improve the temporal resolution of stochastic optical reconstruction microscopy (STORM). Currently, most algorithms used in CS-STORM belong to the single measurement vector (SMV) model, where each super-resolution image is recovered individually from a raw frame, thereby prolonging the computational time. Here, we apply the multiple measurement vector (MMV) model CS algorithm to STORM, wherein all raw images are converted into a matrix and recovered by solving the simultaneous sparse recovery problem. We use the MMV model-based sparse Bayesian learning (SBL) algorithm to reconstitute the raw images of STORM, then compare its imaging resolution and run time with the SMV model CS algorithms. The simulated and experimentally recovered super-resolution images prove that the resolution of MMV model SBL (M-SBL) is comparable with the SMV model algorithm, while the run time is far less and decreases from several hours to several minutes. The high resolution and shorter reconstitution time make M-SBL a promising real-time image reconstruction method for CS-STORM.

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.

Blind sparse inpainting reveals cytoskeletal filaments with sub-Nyquist localization

Single-molecule localization microscopy (SMLM), such as stochastic optical reconstruction microscopy and (fluorescence) photoactivated localization microscopy, has enabled superresolution microscopy beyond the diffraction limit. However, the temporal resolution of SMLM is limited by the time needed to acquire sufficient sparse single-molecule activation events to successfully construct a superresolution image. Here, a novel fast SMLM technique is developed to achieve superresolution imaging within a much shortened duration. This technique does not require a faster switching rate or a higher activation density, which may cause signal degradation or photodamage/bleaching, but relies on computational algorithms to reconstruct a high-density superresolution image from a low-density one using the concept of blind image inpainting. Our results demonstrate that the technique reduces the acquisition time by up to two orders of magnitude compared to the conventional method while achieving the same high resolution. We anticipate our technique to enable future real-time live cell imaging with even higher resolution.

Stochastic Optical Reconstruction Microscopy (STORM)

Super-resolution (SR) fluorescence microscopy, a class of optical microscopy techniques at a spatial resolution below the diffraction limit, has revolutionized the way we study biology, as recognized by the Nobel Prize in Chemistry in 2014. Stochastic optical reconstruction microscopy (STORM), a widely used SR technique, is based on the principle of single molecule localization. STORM routinely achieves a spatial resolution of 20 to 30 nm, a ten-fold improvement compared to conventional optical microscopy. Among all SR techniques, STORM offers a high spatial resolution with simple optical instrumentation and standard organic fluorescent dyes, but it is also prone to image artifacts and degraded image resolution due to improper sample preparation or imaging conditions. It requires careful optimization of all three aspects-sample preparation, image acquisition, and image reconstruction-to ensure a high-quality STORM image, which will be extensively discussed in this unit. © 2017 by John Wiley & Sons, Inc.

Reconstruction of super-resolution STORM images using compressed sensing based on low-resolution raw images and interpolation

Single-molecule-localization-based super-resolution microscopic technologies, such as stochastic optical reconstruction microscopy (STORM), require lengthy runtimes. Compressed sensing (CS) can partially overcome this inherent disadvantage, but its effect on super-resolution reconstruction has not been thoroughly examined. In CS, measurement matrices play more important roles than reconstruction algorithms. Larger measurement matrices have better restricted isometry properties (RIPs). This paper proposes, analyzes, and compares uses of higher resolution cameras and interpolation to achieve better outcomes. Statistical results demonstrate that super-resolution reconstructions is significantly improved by interpolating low-resolution STORM raw images and using point-spread-function-based measurement matrices with better RIPs. The analysis of publically accessible experimental data confirms this conclusion.

State space approach to single molecule localization in fluorescence microscopy

Single molecule super-resolution microscopy enables imaging at sub-diffraction-limit resolution by producing images of subsets of stochastically photoactivated fluorophores over a sequence of frames. In each frame of the sequence, the fluorophores are accurately localized, and the estimated locations are used to construct a high-resolution image of the cellular structures labeled by the fluorophores. Many methods have been developed for localizing fluorophores from the images. The majority of these methods comprise two separate steps: detection and estimation. In the detection step, fluorophores are identified. In the estimation step, the locations of the identified fluorophores are estimated through an iterative approach. Here, we propose a non-iterative state space-based localization method which combines the detection and estimation steps. We demonstrate that the estimated locations obtained from the proposed method can be used as initial conditions in an estimation routine to potentially obtain improved location estimates. The proposed method models the given image as the frequency response of a multi-order system obtained with a balanced state space realization algorithm based on the singular value decomposition of a Hankel matrix. The locations of the poles of the resulting system determine the peak locations in the frequency domain, and the locations of the most significant peaks correspond to the single molecule locations in the original image. The performance of the method is validated using both simulated and experimental data.

A state space based approach to localizing single molecules from multi-emitter images

Single molecule super-resolution microscopy is a powerful tool that enables imaging at sub-diffraction-limit resolution. In this technique, subsets of stochastically photoactivated fluorophores are imaged over a sequence of frames and accurately localized, and the estimated locations are used to construct a high-resolution image of the cellular structures labeled by the fluorophores. Available localization methods typically first determine the regions of the image that contain emitting fluorophores through a process referred to as detection. Then, the locations of the fluorophores are estimated accurately in an estimation step. We propose a novel localization method which combines the detection and estimation steps. The method models the given image as the frequency response of a multi-order system obtained with a balanced state space realization algorithm based on the singular value decomposition of a Hankel matrix, and determines the locations of intensity peaks in the image as the pole locations of the resulting system. The locations of the most significant peaks correspond to the locations of single molecules in the original image. Although the accuracy of the location estimates is reasonably good, we demonstrate that, by using the estimates as the initial conditions for a maximum likelihood estimator, refined estimates can be obtained that have a standard deviation close to the Cramér-Rao lower bound-based limit of accuracy. We validate our method using both simulated and experimental multi-emitter images.

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.

STED and STORM Superresolution Imaging of Primary Cilia

The characteristic lengths of molecular arrangement in primary cilia are below the diffraction limit of light, challenging structural and functional studies of ciliary proteins. Superresolution microscopy can reach up to a 20 nm resolution, significantly improving the ability to map molecules in primary cilia. Here we describe detailed experimental procedure of STED microscopy imaging and dSTORM imaging, two of the most powerful superresolution imaging techniques. Specifically, we emphasize the use of these two methods on imaging proteins in primary cilia.

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.

3D localization of high particle density images using sparse recovery

If particles are too close in space, their images may be overlapped when they are observed with microscopes because of diffraction limitation, which makes them difficult to be distinguished or localized. This limitation also affects the efficiency of localization of those single-particle-localization microcopies, such as stochastic optical reconstruction microscopy (STORM) and (fluorescence) photoactivated localization microscopy [(F)PALM]. In this work, we developed a 3D sparse recovery (3D-SR) method, with the aim of localizing particles with high density in three dimensions, which cannot be resolved using original STROM or (F)PALM. A cylindrical lens was introduced to a traditional wide-field microscope in order to form the 3D point spread function for 3D-SR. The performance of the 3D-SR method was evaluated using simulation. Simulated results demonstrated that, even for particle densities as high as 4  μm^-2 on a transversal projection, particles could still be localized with high accuracy. The standard deviations were found to be 25.59 nm along the transverse direction and 50.42 nm along the axial direction. Compared with the existing 3D localization methods used in high particle density cases, such as 3D-DAOSTORM, 3D-SR allows a higher activated fluorophore density per frame.

Sequential superresolution imaging of multiple targets using a single fluorophore

Fluorescence superresolution (SR) microscopy, or fluorescence nanoscopy, provides nanometer scale detail of cellular structures and allows for imaging of biological processes at the molecular level. Specific SR imaging methods, such as localization-based imaging, rely on stochastic transitions between on (fluorescent) and off (dark) states of fluorophores. Imaging multiple cellular structures using multi-color imaging is complicated and limited by the differing properties of various organic dyes including their fluorescent state duty cycle, photons per switching event, number of fluorescent cycles before irreversible photobleaching, and overall sensitivity to buffer conditions. In addition, multiple color imaging requires consideration of multiple optical paths or chromatic aberration that can lead to differential aberrations that are important at the nanometer scale. Here, we report a method for sequential labeling and imaging that allows for SR imaging of multiple targets using a single fluorophore with negligible cross-talk between images. Using brightfield image correlation to register and overlay multiple image acquisitions with ~10 nm overlay precision in the x-y imaging plane, we have exploited the optimal properties of AlexaFluor647 for dSTORM to image four distinct cellular proteins. We also visualize the changes in co-localization of the epidermal growth factor (EGF) receptor and clathrin upon EGF addition that are consistent with clathrin-mediated endocytosis. These results are the first to demonstrate sequential SR (s-SR) imaging using direct stochastic reconstruction microscopy (dSTORM), and this method for sequential imaging can be applied to any superresolution technique.

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.

Bayesian localization microscopy based on intensity distribution of fluorophores

Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data (3B) method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fluorophores in the cell to fit the data. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensity at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution (FID3B). We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the performance of our method using both simulated data and experimental data from cellular structures. The results confirm the effectiveness of our method.

High-density 3D single molecular analysis based on compressed sensing

Single molecule fitting-based superresolution microscopy achieves sub-diffraction-limit image resolution but suffers from a need for long acquisition times to gather enough molecules. Several methods have recently been developed that analyze high molecule density images but most are only applicable to two dimensions. In this study, we implemented a high-density superresolution localization algorithm based on compressed sensing and a biplane approach that provides three-dimensional information about molecules, achieving super-resolution imaging at higher molecule densities than those achieved using the conventional single molecule fitting method.

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.

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.

PSF decomposition of nanoscopy images via Bayesian analysis unravels distinct molecular organization of the cell membrane

The spatial organization of membrane receptors at the nanoscale has major implications in cellular function and signaling. The advent of super-resolution techniques has greatly contributed to our understanding of the cellular membrane. Yet, despite the increased resolution, unbiased quantification of highly dense features, such as molecular aggregates, remains challenging. Here we describe an algorithm based on Bayesian inference of the marker intensity distribution that improves the determination of molecular positions inside dense nanometer-scale molecular aggregates. We tested the performance of the method on synthetic images representing a broad range of experimental conditions, demonstrating its wide applicability. We further applied this approach to STED images of GPI-anchored and model transmembrane proteins expressed in mammalian cells. The analysis revealed subtle differences in the organization of these receptors, emphasizing the role of cortical actin in the compartmentalization of the cell membrane.

Single-molecule detection and tracking in plants

Combining optical properties with a limited choice of fluorophores turns single-molecule imaging in plants into a challenging task. This explains why the technique, despite its success in the field of animal cell biology, is far from being routinely applied in plant cell research. The same challenges, however, also apply to the application of single-molecule microscopy to any intact tissue or multicellular 3D cell culture. As recent and upcoming progress in fluorescence microscopy will permit single-molecule detection in the context of multicellular systems, plant tissue imaging will experience a huge benefit from this progress. In this review, we address every step of a single-molecule experiment, highlight the critical aspects of each and elaborate on optimizations and developments required for improvements. We relate each step to recent achievements, which have so far been conducted exclusively on the root epidermis of Arabidopsis thaliana seedlings with inclined illumination and show examples of single-molecule measurements using different cells or illumination schemes.

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 compressed sensing analysis for super-resolution imaging using L1-homotopy

In super-resolution imaging techniques based on single-molecule switching and localization, the time to acquire a super-resolution image is limited by the maximum density of fluorescent emitters that can be accurately localized per imaging frame. In order to increase the imaging rate, several methods have been recently developed to analyze images with higher emitter densities. One powerful approach uses methods based on compressed sensing to increase the analyzable emitter density per imaging frame by several-fold compared to other reported approaches. However, the computational cost of this approach, which uses interior point methods, is high, and analysis of a typical 40 µm x 40 µm field-of-view super-resolution movie requires thousands of hours on a high-end desktop personal computer. Here, we demonstrate an alternative compressed-sensing algorithm, L1-Homotopy (L1H), which can generate super-resolution image reconstructions that are essentially identical to those derived using interior point methods in one to two orders of magnitude less time depending on the emitter density. Moreover, for an experimental data set with varying emitter density, L1H analysis is ~300-fold faster than interior point methods. This drastic reduction in computational time should allow the compressed sensing approach to be routinely applied to super-resolution image analysis.

Chemical biology-based approaches on fluorescent labeling of proteins in live cells

Recently, significant advances have been made in live cell imaging owing to the rapid development of selective labeling of proteins in vivo. Green fluorescent protein (GFP) was the first example of fluorescent reporters genetically introduced to protein of interest (POI). While GFP and various types of engineered fluorescent proteins (FPs) have been actively used for live cell imaging for many years, the size and the limited windows of fluorescent spectra of GFP and its variants set limits on possible applications. In order to complement FP-based labeling methods, alternative approaches that allow incorporation of synthetic fluorescent probes to target POIs were developed. Synthetic fluorescent probes are smaller than fluorescent proteins, often have improved photochemical properties, and offer a larger variety of colors. These synthetic probes can be introduced to POIs selectively by numerous approaches that can be largely categorized into chemical recognition-based labeling, which utilizes metal-chelating peptide tags and fluorophore-carrying metal complexes, and biological recognition-based labeling, such as (1) specific non-covalent binding between an enzyme tag and its fluorophore-carrying substrate, (2) self-modification of protein tags using substrate variants conjugated to fluorophores, (3) enzymatic reaction to generate a covalent binding between a small molecule substrate and a peptide tag, and (4) split-intein-based C-terminal labeling of target proteins. The chemical recognition-based labeling reaction often suffers from compromised selectivity of metal-ligand interaction in the cytosolic environment, consequently producing high background signals. Use of protein-substrate interactions or enzyme-mediated reactions generally shows improved specificity but each method has its limitations. Some examples are the presence of large linker protein, restriction on the choice of introducible probes due to the substrate specificity of enzymes, and competitive reaction mediated by an endogenous analogue of the introduced protein tag. These limitations have been addressed, in part, by the split-intein-based labeling approach, which introduces fluorescent probes with a minimal size (~4 amino acids) peptide tag. In this review, the advantages and the limitations of each labeling method are discussed.

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.

Fluorescence nanoscopy. Methods and applications

Fluorescence nanoscopy refers to the experimental techniques and analytical methods used for fluorescence imaging at a resolution higher than conventional, diffraction-limited, microscopy. This review explains the concepts behind fluorescence nanoscopy and focuses on the latest and promising developments in acquisition techniques, labelling strategies to obtain highly detailed super-resolved images and in the quantitative methods to extract meaningful information from them.

Single molecule methods with applications in living cells

Our knowledge about dynamic processes in biological cells systems has been obtained roughly on two levels of detail; molecular level experiments with purified components in test tubes and system wide experiments with indirect readouts in living cells. However, with the development of single molecule methods for application in living cells, this partition has started to dissolve. It is now possible to perform detailed biophysical experiments at high temporal resolution and to directly observe processes at the level of molecules in living cells. In this review we present single molecule methods that can easily be implemented by readers interested to venture into this exciting and expanding field. We also review some recent studies where single molecule methods have been used successfully to answer biological questions as well as some of the most common pitfalls associated with these methods.

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.

Development in the STORM

The recent invention of superresolution microscopy has brought up much excitement in the biological research community. Here, we focus on stochastic optical reconstruction microscopy/photoactivated localization microscopy (STORM/PALM) to discuss the challenges in applying superresolution microscopy to the study of developmental biology, including tissue imaging, sample preparation artifacts, and image interpretation. We also summarize new opportunities that superresolution microscopy could bring to the field of developmental biology.

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.

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

Developing methods for high-density localization of multiple emitters is a promising approach for enhancing the temporal resolution of localization microscopy while maintaining a desired spatial resolution, but the widespread use of this approach is thus far mainly obstructed by the slow image analysis speed. Here we present a high-density localization method based on the combination of Graphics Processing Unit (GPU) parallel computation, multiple-emitter fitting, and model recommendation via Bayesian Information Criterion (BIC). This method, called PALMER, exhibits satisfactory localization accuracy comparable with the previous reported SSM_BIC method, while executes more than two orders of magnitudes faster. Meanwhile, compared to the conventional localization microscopy which is based on sparse emitter localization, high-density localization microscopy based the PALMER method allows a speed gain of up to ~14-fold in obtaining a super-resolution image with the same Nyquist resolution.

Optical nanoscopy: from acquisition to analysis

Recent advances in far-field microscopy have demonstrated that fluorescence imaging is possible at resolutions well below the long-standing diffraction limit. By exploiting photophysical properties of fluorescent probe molecules, this new class of methods yields a resolving power that is fundamentally diffraction unlimited. Although these methods are becoming more widely used in biological imaging, they must be complemented by suitable data analysis approaches if their potential is to be fully realized. Here we review the basic principles of diffraction-unlimited microscopy and how these principles influence the selection of available algorithms for data analysis. Furthermore, we provide an overview of existing analysis strategies and discuss their application.

Faster STORM using compressed sensing

In super-resolution microscopy methods based on single-molecule switching, the rate of accumulating single-molecule activation events often limits the time resolution. Here we developed a sparse-signal recovery technique using compressed sensing to analyze images with highly overlapping fluorescent spots. This method allows an activated fluorophore density an order of magnitude higher than what conventional single-molecule fitting methods can handle. Using this method, we demonstrated imaging microtubule dynamics in living cells with a time resolution of 3 s.

Super resolution for common probes and common microscopes

A sophisticated analysis approach based on the concept of fluorophore localization provides dynamic super-resolution data of GFP-labeled live cells using a common, arc lamp–based wide-field fluorescence microscope.

Localization-based super-resolution microscopy with an sCMOS camera

In the community of localization-based super-resolution microscopy (or called localization microscopy), it is generally believed that the emission of single molecules is so weak that an EMCCD (electron multiplying charge coupled device) camera is necessary to be used as the detector by eliminating read noise. Here we evaluate the possibility of a new kind of low light detector, scientific complementary metal-oxide-semiconductor (sCMOS) camera in localization microscopy. We demonstrate experimentally that sCMOS is capable of imaging actin bundles with FWHM diameter of 37 nm, evidencing the capability of sCMOS in localization microscopy. We further characterize the noise performance of sCMOS and find out that, with the use of a bright fluorescence probe such as d2EosFP, localization microscopy imaging is now working in the shot noise limited region.