Minimizing detection errors in single molecule localization microscopy

Optimized molecule detection in localization microscopy with selected false positive probability

Single-molecule localization microscopy (SMLM) allows imaging beyond the diffraction limit. Detection of molecules is a crucial initial step in SMLM. False positive detections, which are not quantitatively controlled in current methods, are a source of artifacts that affect the entire SMLM analysis pipeline. Furthermore, current methods lack standardization, which hinders reproducibility. Here, we present an optimized molecule detection method which combines probabilistic thresholding with theoretically optimal filtering. The probabilistic thresholding enables control over false positive detections while optimal filtering minimizes false negatives. A theoretically optimal Poisson matched filter is used as a performance benchmark to evaluate existing filtering methods. Overall, our approach allows the detection of molecules in a robust, single-parameter and user-unbiased manner. This will minimize artifacts and enable data reproducibility in SMLM.

Application of Super-resolution SPEED Microscopy in the Study of Cellular Dynamics

Super-resolution imaging techniques have broken the diffraction-limited resolution of light microscopy. However, acquiring three-dimensional (3D) super-resolution information about structures and dynamic processes in live cells at high speed remains challenging. Recently, the development of high-speed single-point edge-excitation subdiffraction (SPEED) microscopy, along with its 2D-to-3D transformation algorithm, provides a practical and effective approach to achieving 3D subdiffraction-limit information in subcellular structures and organelles with rotational symmetry. One of the major benefits of SPEED microscopy is that it does not rely on complex optical components and can be implemented on a standard, inverted epifluorescence microscope, simplifying the process of sample preparation and the expertise requirement. SPEED microscopy is specifically designed to obtain 2D spatial locations of individual immobile or moving fluorescent molecules inside submicrometer biological channels or cavities at high spatiotemporal resolution. The collected data are then subjected to postlocalization 2D-to-3D transformation to obtain 3D super-resolution structural and dynamic information. In recent years, SPEED microscopy has provided significant insights into nucleocytoplasmic transport across the nuclear pore complex (NPC) and cytoplasm-cilium trafficking through the ciliary transition zone. This Review focuses on the applications of SPEED microscopy in studying the structure and function of nuclear pores.

Spatial Organization of Dectin-1 and TLR2 during Synergistic Crosstalk Revealed by Super-resolution Imaging

Innate immune cells recognize and elicit responses against pathogens by integrating signals from different types of cell-surface receptors. How the receptors interact in the membrane to enable their signaling crosstalk is poorly understood. Here, we reveal the nanoscale organization of TLR2 and Dectin-1, a receptor pair known to cooperate in regulating antifungal immunity, through their synergistic signaling crosstalk at macrophage cell membranes. Using super-resolution single-molecule localization microscopy, we show that discrete noncolocalized nanoclusters of Dectin-1 and TLR2 are partially overlapped during their synergistic crosstalk. Compared to when one type of receptor is activated alone, the simultaneous activation of Dectin-1 and TLR2 leads to a higher percentage of both receptors being activated by their specific ligands and consequently an increased level of tyrosine phosphorylation. Our results depict, in nanoscale detail, how Dectin-1 and TLR2 achieve synergistic signaling through the spatial organization of their receptor nanoclusters.

Development of a New Approach for Low-Laser-Power Super-Resolution Fluorescence Imaging

The development of super-resolution fluorescence microscopy over the past decade has drastically improved the resolution of light microscopy to ∼10 nm. Stochastic optical reconstruction microscopy (STORM) can be used to achieve subdiffraction-limit resolution by sequentially imaging and localizing individual fluorophores. In principle, the super-resolution of STORM can be obtained by high-accuracy localization of photoswitchable fluorophores, which require fast photoswitching and bright fluorescence intensity from a single emitter. It is known that the switching rate of photoswitchable fluorophores depends on the laser power─a high laser power being required for the enhancement of imaging resolution. However, high laser power is usually harmful to biological specimens and limits the imaging time because of its photobleaching effects and high phototoxicity. In this study, we attempted to overcome this problem by improving the STORM resolution at a lower laser power. Through the quantitative analysis of the photoswitching behavior of single fluorophores under different laser power conditions, we developed a new approach to achieve super-resolution fluorescence images at a laser power 10 times lower than had previously been reported. This approach is expected to play an increasingly significant role in super-resolution imaging of power-sensitive samples.

Microclusters as T Cell Signaling Hubs: Structure, Kinetics, and Regulation

When T cell receptors (TCRs) engage with stimulatory ligands, one of the first microscopically visible events is the formation of microclusters at the site of T cell activation. Since the discovery of these structures almost 20 years ago, they have been studied extensively in live cells using confocal and total internal reflection fluorescence (TIRF) microscopy. However, due to limits in image resolution and acquisition speed, the spatial relationships of signaling components within microclusters, the kinetics of their assembly and disassembly, and the role of vesicular trafficking in microcluster formation and maintenance were not finely characterized. In this review, we will summarize how new microscopy techniques have revealed novel insights into the assembly of these structures. The sub-diffraction organization of microclusters as well as the finely dissected kinetics of recruitment and disassociation of molecules from microclusters will be discussed. The role of cell surface molecules in microcluster formation and the kinetics of molecular recruitment via intracellular vesicular trafficking to microclusters is described. Finally, the role of post-translational modifications such as ubiquitination in the downregulation of cell surface signaling molecules is also discussed. These results will be related to the role of these structures and processes in T cell activation.

Super-resolution Surface-Enhanced Raman Scattering Imaging of Single Particles in Cells

The ability to locate and identify molecular interactions in cells has significant importance for understanding protein function and molecular biology. Functionalized metallic nanoparticles have been used as probes for protein tracking and drug delivery because of their ability to carry therapeutic agents and readily functionalized surfaces. In this work, we present a super-resolution surface-enhanced Raman scattering (SERS) approach for imaging and tracking membrane receptors interacting with peptide-functionalized gold nanostars (AuNS). The αvβ3 integrin receptors in colon cancer cells are successfully targeted and imaged using AuNS with the high-affinity amino acid sequence arginine-glycine-aspartic acid-phenylalanine-cysteine (RGDFC) attached. The RGDFC peptide interaction with the integrin receptor provides a bright and fluctuating SERS signal that can be analyzed with localization microscopy algorithms. Additionally, the observed SERS spectrum is used to confirm protein-peptide interaction. Experiments with functionalized and bare AuNS illustrate specific and nonspecific binding events. Specific binding is monitored with a localization precision of ∼6 nm. The observed spatial resolution is associated with tight binding, which was confirmed by the slower diffusion coefficient measured from 4.4 × 10-11 cm2/s for the AuNS-RGDFC compared to 7.8 × 10-10 cm2/s for the bare AuNS. Super-resolution SERS images at different focal planes show evidence of internalized particles and suggest insights into protein orientation on the surface of cells. Our work demonstrates super-resolution SERS imaging to probe membrane receptor interactions in cells, providing chemical information and spatial resolution with potential for diverse applications in life science and biomedicine.

Super-resolution localization microscopy: Toward high throughput, high quality, and low cost

After nearly 15 years since its initial debut, super-resolution localization microscopy that surpasses the diffraction-limited resolution barrier of optical microscopy has rapidly gotten out of the ivory tower and entered a new phase to address various challenging biomedical questions. Recent advances in this technology greatly increased the imaging throughput, improved the imaging quality, simplified the sample preparation, and reduced the system cost, making this technology suitable for routine biomedical research. We will provide our perspective on the recent technical advances and their implications in serving the community of biomedical research.

Quantification of functional crosslinker reaction kinetics via super-resolution microscopy of swollen microgels

Super resolution microscopy (SRM) brings the advantages of optical microscopy to the imaging of nanostructured soft matter, and in colloidal microgels, promises to quantify variations of crosslink densities at unprecedented length scales. However, the distribution of all crosslinks does not coincide with that of dye-tagged crosslinks, and density quantification in SRM is not guaranteed due to over/under-counting dye molecules. Here we demonstrate that SRM images of microgels encode reaction rate constants of functional cross linkers, which hold the key to correlating these distributions. Combined with evolution of microgel particle radii, the functional cross linker distributions predict consumption versus time with high fidelity. Using a Bayesian regression approach, we extract reaction rate constants for homo and cross propagation of the functional crosslinker, which should be widely useful for predicting spatial variations in crosslink density of gels.

MemBright: A Family of Fluorescent Membrane Probes for Advanced Cellular Imaging and Neuroscience

The proper staining of the plasma membrane (PM) is critical in bioimaging as it delimits the cell. Herein, we developed MemBright, a family of six cyanine-based fluorescent turn-on PM probes that emit from orange to near infrared when reaching the PM, and enable homogeneous and selective PM staining with excellent contrast in mono- and two-photon microscopy. These probes are compatible with long-term live-cell imaging and immunostaining. Moreover, MemBright label neurons in a brighter manner than surrounding cells, allowing identification of neurons in acute brain tissue sections and neuromuscular junctions without any use of transfection or transgenic animals. In addition, MemBright probes were used in super-resolution imaging to unravel the neck of dendritic spines. 3D multicolor dSTORM in combination with immunostaining revealed en-passant synapse displaying endogenous glutamate receptors clustered at the axonal-dendritic contact site. MemBright probes thus constitute a universal toolkit for cell biology and neuroscience biomembrane imaging with a variety of microscopy techniques. VIDEO ABSTRACT.

Quantitative super-resolution single molecule microscopy dataset of YFP-tagged growth factor receptors

Background

Super-resolution single molecule localization microscopy (SMLM) is a method for achieving resolution beyond the classical limit in optical microscopes (approx. 200 nm laterally). Yellow fluorescent protein (YFP) has been used for super-resolution single molecule localization microscopy, but less frequently than other fluorescent probes. Working with YFP in SMLM is a challenge because a lower number of photons are emitted per molecule compared with organic dyes, which are more commonly used. Publically available experimental data can facilitate development of new data analysis algorithms.

Findings

Four complete, freely available single molecule super-resolution microscopy datasets on YFP-tagged growth factor receptors expressed in a human cell line are presented, including both raw and analyzed data. We report methods for sample preparation, for data acquisition, and for data analysis, as well as examples of the acquired images. We also analyzed the SMLM datasets using a different method: super-resolution optical fluctuation imaging (SOFI). The 2 modes of analysis offer complementary information about the sample. A fifth single molecule super-resolution microscopy dataset acquired with the dye Alexa 532 is included for comparison purposes.

Conclusions

This dataset has potential for extensive reuse. Complete raw data from SMLM experiments have typically not been published. The YFP data exhibit low signal-to-noise ratios, making data analysis a challenge. These datasets will be useful to investigators developing their own algorithms for SMLM, SOFI, and related methods. The data will also be useful for researchers investigating growth factor receptors such as ErbB3.

A High-Efficiency Super-Resolution Reconstruction Method for Ultrasound Microvascular Imaging

The emergence of super-resolution imaging makes it possible to display the microvasculatures clearly using ultrasound imaging, which is of great importance in the early diagnosis of cancer. At present, the super-resolution performance can only be achieved when the sampling signal is long enough (usually more than 10,000 frames). Thus, the imaging time resolution is not suitable for clinical use. In this paper, we proposed a novel super-resolution reconstruction method, which is proved to have a satisfactory resolution using shorter sampling signal sequences. In the microbubble localization step, the integrated form of the 2D Gaussian function is innovatively adopted for image deconvolution in our method, which enhances the accuracy of microbubble positioning. In the trajectory tracking step, for the first time the averaged shifted histogram technique is presented for the visualization, which greatly improves the precision of reconstruction. In vivo experiments on rabbits were conducted to verify the effectiveness of the proposed method. Compared to the conventional reconstruction method, our method significantly reduces the Full-Width-at-Half-Maximum (FWHM) by 50% using only 400-frame signals. Besides, there is no significant increase in the running time using the proposed method. Considering its imaging performance and used frame number, the conclusion can be drawn that the proposed method advances the application of super-resolution imaging to the clinical use with a much higher time resolution.

Unraveling the Thousand Word Picture: An Introduction to Super-Resolution Data Analysis

Super-resolution microscopy provides direct insight into fundamental biological processes occurring at length scales smaller than light's diffraction limit. The analysis of data at such scales has brought statistical and machine learning methods into the mainstream. Here we provide a survey of data analysis methods starting from an overview of basic statistical techniques underlying the analysis of super-resolution and, more broadly, imaging data. We subsequently break down the analysis of super-resolution data into four problems: the localization problem, the counting problem, the linking problem, and what we've termed the interpretation problem.

Super-resolution Analysis of TCR-Dependent Signaling: Single-Molecule Localization Microscopy

Single-molecule localization microscopy (SMLM) comprises methods that produce super-resolution images from molecular locations of single molecules. These techniques mathematically determine the center of a diffraction-limited spot produced by a fluorescent molecule, which represents the most likely location of the molecule. Only a small cohort of well-separated molecules is visualized in a single image, and then many images are obtained from a single sample. The localizations from all the images are combined to produce a super-resolution picture of the sample. Here we describe the application of two methods, photoactivation localization microscopy (PALM) and direct stochastic optical reconstruction microscopy (dSTORM), to the study of signaling microclusters in T cells.

Phase stretch transform for super-resolution localization microscopy

Super-resolution localization microscopy has revolutionized the observation of living structures at the cellular scale, by achieving a spatial resolution that is improved by more than an order of magnitude compared to the diffraction limit. These methods localize single events from isolated sources in repeated cycles in order to achieve super-resolution. The requirement for sparse distribution of simultaneously activated sources in the field of view dictates the acquisition of thousands of frames in order to construct the full super-resolution image. As a result, these methods have slow temporal resolution which is a major limitation when investigating live-cell dynamics. In this paper we present the use of a phase stretch transform for high-density super-resolution localization microscopy. This is a nonlinear frequency dependent transform that emulates the propagation of light through a physical medium with a specific warped diffractive property and applies a 2D phase function to the image in the frequency domain. By choosing properly the transform parameters and the phase kernel profile, the point spread function of each emitter can be sharpened and narrowed. This enables the localization of overlapping emitters, thus allowing a higher density of activated emitters as well as shorter data collection acquisition rates. The method is validated by numerical simulations and by experimental data obtained using a microtubule sample.

easySTORM: a robust, lower-cost approach to localisation and TIRF microscopy

TIRF and STORM microscopy are super-resolving fluorescence imaging modalities for which current implementations on standard microscopes can present significant complexity and cost. We present a straightforward and low-cost approach to implement STORM and TIRF taking advantage of multimode optical fibres and multimode diode lasers to provide the required excitation light. Combined with open source software and relatively simple protocols to prepare samples for STORM, including the use of Vectashield for non-TIRF imaging, this approach enables TIRF and STORM imaging of cells labelled with appropriate dyes or expressing suitable fluorescent proteins to become widely accessible at low cost.

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.

Separation of replication and transcription domains in nucleoli

In mammalian cells, active ribosomal genes produce the 18S, 5.8S and 28S RNAs of ribosomal particles. Transcription levels of these genes are very high throughout interphase, and the cell needs a special strategy to avoid collision of the DNA polymerase and RNA polymerase machineries. To investigate this problem, we measured the correlation of various replication and transcription signals in the nucleoli of HeLa, HT-1080 and NIH 3T3 cells using a specially devised software for analysis of confocal images. Additionally, to follow the relationship between nucleolar replication and transcription in living cells, we produced a stable cell line expressing GFP-RPA43 (subunit of RNA polymerase I, pol I) and RFP-PCNA (the sliding clamp protein) based on human fibrosarcoma HT-1080 cells. We found that replication and transcription signals are more efficiently separated in nucleoli than in the nucleoplasm. In the course of S phase, separation of PCNA and pol I signals gradually increased. During the same period, separation of pol I and incorporated Cy5-dUTP signals decreased. Analysis of single molecule localization microscopy (SMLM) images indicated that transcriptionally active FC/DFC units (i.e. fibrillar centers with adjacent dense fibrillar components) did not incorporate DNA nucleotides. Taken together, our data show that replication of the ribosomal genes is spatially separated from their transcription, and FC/DFC units may provide a structural basis for that separation.

ThunderSTORM: a comprehensive ImageJ plug-in for PALM and STORM data analysis and super-resolution imaging

Summary: ThunderSTORM is an open-source, interactive and modular plug-in for ImageJ designed for automated processing, analysis and visualization of data acquired by single-molecule localization microscopy methods such as photo-activated localization microscopy and stochastic optical reconstruction microscopy. ThunderSTORM offers an extensive collection of processing and post-processing methods so that users can easily adapt the process of analysis to their data. ThunderSTORM also offers a set of tools for creation of simulated data and quantitative performance evaluation of localization algorithms using Monte Carlo simulations.

Availability and implementation: ThunderSTORM and the online documentation are both freely accessible at https://code.google.com/p/thunder-storm/

Contact:guy.hagen@lf1.cuni.cz

Supplementary information:Supplementary data are available at Bioinformatics online.

SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy

Although there are many reconstruction algorithms for localization microscopy, their use is hampered by the difficulty to adjust a possibly large number of parameters correctly. We propose SimpleSTORM, an algorithm that determines appropriate parameter settings directly from the data in an initial self-calibration phase. The algorithm is based on a carefully designed yet simple model of the image acquisition process which allows us to standardize each image such that the background has zero mean and unit variance. This standardization makes it possible to detect spots by a true statistical test (instead of hand-tuned thresholds) and to de-noise the images with an efficient matched filter. By reducing the strength of the matched filter, SimpleSTORM also performs reasonably on data with high-spot density, trading off localization accuracy for improved detection performance. Extensive validation experiments on the ISBI Localization Challenge Dataset, as well as real image reconstructions, demonstrate the good performance of our algorithm.

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.

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.

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.

Wavelet analysis for single molecule localization microscopy

Localization of single molecules in microscopy images is a key step in quantitative single particle data analysis. Among them, single molecule based super-resolution optical microscopy techniques require high localization accuracy as well as computation of large data sets in the order of 10(5) single molecule detections to reconstruct a single image. We hereby present an algorithm based on image wavelet segmentation and single particle centroid determination, and compare its performance with the commonly used gaussian fitting of the point spread function. We performed realistic simulations at different signal-to-noise ratios and particle densities and show that the calculation time using the wavelet approach can be more than one order of magnitude faster than that of gaussian fitting without a significant degradation of the localization accuracy, from 1 nm to 4 nm in our range of study. We propose a simulation-based estimate of the resolution of an experimental single molecule acquisition.