Fast, single-molecule localization that achieves theoretically minimum uncertainty

An automated Bayesian pipeline for rapid analysis of single-molecule binding data

Single-molecule binding assays enable the study of how molecular machines assemble and function. Current algorithms can identify and locate individual molecules, but require tedious manual validation of each spot. Moreover, no solution for high-throughput analysis of single-molecule binding data exists. Here, we describe an automated pipeline to analyze single-molecule data over a wide range of experimental conditions. In addition, our method enables state estimation on multivariate Gaussian signals. We validate our approach using simulated data, and benchmark the pipeline by measuring the binding properties of the well-studied, DNA-guided DNA endonuclease, TtAgo, an Argonaute protein from the Eubacterium Thermus thermophilus. We also use the pipeline to extend our understanding of TtAgo by measuring the protein’s binding kinetics at physiological temperatures and for target DNAs containing multiple, adjacent binding sites.

Analysis of single-molecule binding assays still requires substantial manual user intervention. Here, the authors present a pipeline for rapid, automated analysis of co-localization single-molecule spectroscopy images, with a modular user interface that can be adjusted to a range of experimental conditions.

A Method to Visualize the Nanoscopic Morphology of Astrocytes In Vitro and In Situ

In recent years it has become apparent that astroglia are not only essential players in brain development, homeostasis, and metabolic support but are also important for the formation and regulation of synaptic circuits. Fine astrocytic processes that can be found in the vicinity of synapses undergo considerable structural plasticity associated with age- and use-dependent changes in neural circuitries. However, due to the extraordinary complex, essentially nanoscopic morphology of astroglia, the underlying cellular mechanisms remain poorly understood.Here we detail a super-resolution microscopy approach, based on the single-molecule localisation microscopy (SMLM) technique direct stochastic optical reconstruction microscopy (dSTORM) to visualize astroglial morphology on the nanoscale. This approach enables visualization of key morphological changes that occur in nanoscopic astrocyte processes, whose characteristic size falls below the diffraction limit of conventional optical microscopy.

Temporal Filtering to Improve Single Molecule Identification in High Background Samples

Single molecule localization microscopy is currently revolutionizing the life sciences as it offers, for the first time, insights into the organization of biological samples below the classical diffraction limit of light microscopy. While there have been numerous examples of new biological findings reported in the last decade, the technique could not reach its full potential due to a set of limitations immanent to the samples themselves. Particularly, high background signals impede the proper performance of most single-molecule identification and localization algorithms. One option is to exploit the characteristic blinking of single molecule signals, which differs substantially from the residual brightness fluctuations of the fluorescence background. To pronounce single molecule signals, we used a temporal high-pass filtering in Fourier space on a pixel-by-pixel basis. We evaluated the performance of temporal filtering by assessing statistical parameters such as true positive rate and false discovery rate. For this, ground truth signals were generated by simulations and overlaid onto experimentally derived movies of samples with high background signals. Compared to the nonfiltered case, we found an improvement of the sensitivity by up to a factor 3.5 while no significant change in the localization accuracy was observable.

Excitation-multiplexed multicolor superresolution imaging with fm-STORM and fm-DNA-PAINT

Recent advancements in single-molecule-based superresolution microscopy have made it possible to visualize biological structures with unprecedented spatial resolution. Determining the spatial coorganization of these structures within cells under physiological and pathological conditions is an important biological goal. This goal has been stymied by the current limitations of carrying out superresolution microscopy in multiple colors. Here, we develop an approach for simultaneous multicolor superresolution imaging which relies solely on fluorophore excitation, rather than fluorescence emission properties. By modulating the intensity of the excitation lasers at different frequencies, we show that the color channel can be determined based on the fluorophore's response to the modulated excitation. We use this frequency multiplexing to reduce the image acquisition time of multicolor superresolution DNA-PAINT while maintaining all its advantages: minimal color cross-talk, minimal photobleaching, maximal signal throughput, ability to maintain the fluorophore density per imaged color, and ability to use the full camera field of view. We refer to this imaging modality as "frequency multiplexed DNA-PAINT," or fm-DNA-PAINT for short. We also show that frequency multiplexing is fully compatible with STORM superresolution imaging, which we term fm-STORM. Unlike fm-DNA-PAINT, fm-STORM is prone to color cross-talk. To overcome this caveat, we further develop a machine-learning algorithm to correct for color cross-talk with more than 95% accuracy, without the need for prior information about the imaged structure.

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.

What we talk about when we talk about nanoclusters

Superresolution microscopy results have sparked the idea that many membrane proteins are not randomly distributed across the plasma membrane but are instead arranged in nanoclusters. Frequently, these new results seemed to confirm older data based on biochemical and electron microscopy experiments. Recently, however, it was recognized that multiple countings of the very same fluorescently labeled protein molecule can be easily confused with true protein clusters. Various strategies have been developed, which are intended to solve the problem of discriminating true protein clusters from imaging artifacts. We believe that there is currently no perfect algorithm for this problem; instead, different approaches have different strengths and weaknesses. In this review, we discuss single molecule localization microscopy in view of its ability to detect nanoclusters of membrane proteins. To capture the different views on nanoclustering, we chose an unconventional style for this article: we placed its scientific content in the setting of a fictive conference, where five researchers from different fields discuss the problem of detecting and quantifying nanoclusters. Using this style, we feel that the different approaches common for different research areas can be well illustrated. Similarities to a short story by Raymond Carver are not unintentional.

Analyzing complex single-molecule emission patterns with deep learning

A fluorescent emitter simultaneously transmits its identity, location, and cellular context through its emission pattern. We developed smNet, a deep neural network for multiplexed single-molecule analysis to enable retrieving such information with high accuracy. We demonstrate that smNet can extract three-dimensional molecule location, orientation, and wavefront distortion with precision approaching the theoretical limit and therefore will allow multiplexed measurements through the emission pattern of a single molecule.

The deep neural network smNet enables extraction of multiplexed parameters such as 3D position, orientation and wavefront distortion from emission patterns of single molecules.

Advanced 3D Analysis and Optimization of Single-Molecule FISH in Drosophila Muscle

Single-molecule fluorescence in situ hybridization (smFISH) provides direct access to the spatial relationship between nucleic acids and specific subcellular locations. The ability to precisely localize a messenger RNA can reveal key information about its regulation. Although smFISH is well established in cell culture or thin sections, the utility of smFISH is hindered in thick tissue sections due to the poor probe penetration of fixed tissue, the inaccessibility of target mRNAs for probe hybridization, high background fluorescence, spherical aberration along the optical axis, and the lack of methods for image segmentation of organelles. Studying mRNA localization in 50 μm thick Drosophila larval muscle sections, these obstacles are overcome using sample-specific optimization of smFISH, particle identification based on maximum likelihood testing, and 3D multiple-organelle segmentation. The latter allows independent thresholds to be assigned to different regions of interest within an image stack. This approach therefore facilitates accurate measurement of mRNA location in thick tissues.

Molecular imaging with neural training of identification algorithm (neural network localization identification)

Superresolution localization microscopy strongly relies on robust identification algorithms for accurate reconstruction of the biological systems it is used to measure. The fields of machine learning and computer vision have provided promising solutions for automated object identification, but usually rely on well represented training sets to learn object features. However, using a static training set can result in the learned identification algorithm making mistakes on data that is not well represented by the training set. Here, we present a method for training an artificial neural network without providing a training set in advance. This method uses the data to be analyzed, and the fitting algorithm to train an artificial neural network tailored to that data set. We show that the same artificial neural network can learn to identify at least two types of molecular emissions: the regular point spread functions (PSFs), and the astigmatism PSF. Simulations indicate that this method can be extremely reliable in extracting molecular emission signatures. Additionally, we implemented the artificial neural network calculation to be performed on a graphics processing unit (GPU) for massively parallelized calculation which drastically reduces the time required for the identification process. By implementing the neural identification on a GPU, we allow this method of identification to be used in a real time analysis algorithm. RESEARCH HIGHLIGHTS: Here, we present a machine learning algorithm for identifying point-spread functions without the need for an a priori training set. We show that this method can detect over 90% of molecules with less than 1% false positive identification in simulations. We further show that because this algorithm does not make assumptions of about the shape of molecular emission, it is compatible with models beyond the symmetric 2D Gaussian.

Sequential super-resolution imaging using DNA strand displacement

Sequential labeling and imaging in fluorescence microscopy allows the imaging of multiple structures in the same cell using a single fluorophore species. In super-resolution applications, the optimal dye suited to the method can be chosen, the optical setup can be simpler and there are no chromatic aberrations between images of different structures. We describe a method based on DNA strand displacement that can be used to quickly and easily perform the labeling and removal of the fluorophores during each sequence. Site-specific tags are conjugated with unique and orthogonal single stranded DNA. Labeling for a particular structure is achieved by hybridization of antibody-bound DNA with a complimentary dye-labeled strand. After imaging, the dye is removed using toehold-mediated strand displacement, in which an invader strand competes off the dye-labeled strand than can be subsequently washed away. Labeling and removal of each DNA-species requires only a few minutes. We demonstrate the concept using sequential dSTORM super-resolution for multiplex imaging of subcellular structures.

Hessian single-molecule localization microscopy using sCMOS camera

Single-molecule localization microscopy (SMLM) has the highest spatial resolution among the existing super-resolution imaging techniques, but its temporal resolution needs further improvement. An sCMOS camera can effectively increase the imaging rate due to its large field of view and fast imaging speed. Using an sCMOS camera for SMLM imaging can significantly improve the imaging time resolution, but the unique single-pixel-dependent readout noise of sCMOS cameras severely limits their application in SMLM imaging. This paper develops a Hessian-based SMLM (Hessian-SMLM) method that can correct the variance, gain, and offset of a single pixel of a camera and effectively eliminate the pixel-dependent readout noise of sCMOS cameras, especially when the signal-to-noise ratio is low. Using Hessian-SMLM to image mEos3.2-labeled actin was able to significantly reduce the artifacts due to camera noise.

Systematic Nanoscale Analysis of Endocytosis Links Efficient Vesicle Formation to Patterned Actin Nucleation

Clathrin-mediated endocytosis is an essential cellular function in all eukaryotes that is driven by a self-assembled macromolecular machine of over 50 different proteins in tens to hundreds of copies. How these proteins are organized to produce endocytic vesicles with high precision and efficiency is not understood. Here, we developed high-throughput superresolution microscopy to reconstruct the nanoscale structural organization of 23 endocytic proteins from over 100,000 endocytic sites in yeast. We found that proteins assemble by radially ordered recruitment according to function. WASP family proteins form a circular nanoscale template on the membrane to spatially control actin nucleation during vesicle formation. Mathematical modeling of actin polymerization showed that this WASP nano-template optimizes force generation for membrane invagination and substantially increases the efficiency of endocytosis. Such nanoscale pre-patterning of actin nucleation may represent a general design principle for directional force generation in membrane remodeling processes such as during cell migration and division.

Mechanisms of inside-out signaling of the high-affinity IgG receptor FcγRI

Fc receptors (FcRs) are an important bridge between the innate and adaptive immune system. Fc gamma receptor I (FcγRI; CD64), the high-affinity receptor for immunoglobulin G (IgG), plays roles in inflammation, autoimmune responses, and immunotherapy. Stimulation of myeloid cells with cytokines, such as tumor necrosis factor-α ( TNFα) and interferon-γ ( IFNγ), increases the binding of FcγRI to immune complexes (ICs), such as antibody-opsonized pathogens or tumor cells, through a process known as "inside-out" signaling. Using super-resolution imaging, we found that stimulation of cells with IL-3 also enhanced the clustering of FcγRI both before and after exposure to ICs. This increased clustering was dependent on an intact actin cytoskeleton. We found that chemical inhibition of the activity of the phosphatase PP1 reduced FcγRI inside-out signaling, although the phosphorylation of FcγRI itself was unaffected. Furthermore, the antibody-dependent cytotoxic activity of human neutrophils toward CD20-expressing tumor cells was increased after stimulation with TNFα and IFNγ. These results suggest that nanoscale reorganization of FcγRI, stimulated by cytokine-induced, inside-out signaling, enhances FcγRI cellular effector functions.

Active PSF shaping and adaptive optics enable volumetric localization microscopy through brain sections

Single Molecule Switching Nanoscopy (SMSN) beyond the coverslip surface poses significant challenges. The sample-induced aberrations distort and blur single-molecule emission patterns. Combining active point-spread function shaping and efficient adaptive optics enables robust 3D-SMSN imaging at large depths. This development allowed us to image through 30-μm thick brain sections, visualize the morphology and the nanoscale details of amyloid-β filaments in a mouse model of Alzheimer’s disease and reconstruct their volumetric arrangements.

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.

Light sheet approaches for improved precision in 3D localization-based super-resolution imaging in mammalian cells [Invited]

The development of imaging techniques beyond the diffraction limit has paved the way for detailed studies of nanostructures and molecular mechanisms in biological systems. Imaging thicker samples, such as mammalian cells and tissue, in all three dimensions, is challenging due to increased background and volumes to image. Light sheet illumination is a method that allows for selective irradiation of the image plane, and its inherent optical sectioning capability allows for imaging of biological samples with reduced background, photobleaching, and photodamage. In this review, we discuss the advantage of combining single-molecule imaging with light sheet illumination. We begin by describing the principles of single-molecule localization microscopy and of light sheet illumination. Finally, we present examples of designs that successfully have married single-molecule super-resolution imaging with light sheet illumination for improved precision in mammalian cells.

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

We present a real-time fitter for 3D single-molecule localization microscopy using experimental point spread functions (PSFs) that achieves minimal uncertainty in 3D on any microscope and is compatible with any PSF engineering approach. We used this method to image cellular structures and attained unprecedented image quality for astigmatic PSFs. The fitter compensates for most optical aberrations and makes accurate 3D super-resolution microscopy broadly accessible, even on standard microscopes without dedicated 3D optics.

Protein machineries defining pathways of nanocarrier exocytosis and transcytosis

The transport of nanocarriers through barriers like the gut in a living organism involves the transcytosis of these nanocarriers through the cell layer dividing two compartments. Understanding how this process works is not only essential to further developing strategies for a more effective nanocarrier transport system but also for providing fundamental insights into the barrier function as a means of protection against micro- and nanoplastics in the food chain. We therefore set out to investigate the different uptake mechanisms, intracellular trafficking and the routes for exocytosis for small polystyrene nanoparticles (PS-NPs ca. 100 nm) as mimicking nanocarriers in a Caco-2 cell model for gut-blood transition. We used label-free, quantitative mass spectrometry (MS) for determining the proteome that adhered to transversed nanoparticles. From this rich proteomics dataset, as well as previous studies, we generated stable-transfected Caco-2 cell lines carrying the green fluorescent protein (GFP) coupled to proteins of interest for uptake, early, late and exocytotic endosomes. We detected the spatial and temporal overlap of such marked endosomes with the nanocarrier signal in confocal laser scanning and super-resolution microscopy. There was a clear distinction in the time course of nanoparticle trafficking between groups of proteins for endocytosis, intracellular storage and putatively transcytosis and we identified several key transcytotic markers like Rab3 and Copine1. Moreover, we postulate the necessity of a certain protein composition on endosomes for successful transcytosis of nanocarriers. Finally, we define the two-sided impasse of the lysosome as a dead end for nano-plastic and the limit of nanocarriers in the 100 nm range.

STATEMENT OF SIGNIFICANCE

Here we focus on mechanisms of transcytosis and how we can follow these with methods not used before. First, we use mass spectrometry of transcytosed nanoparticles to pick proteins of the transcytosis machinery describing key proteins involved. We can detect the complex mixtures of proteins. As this is a dynamic process involving whole families of proteins interacting with each other and as this is an orchestrated process we coined the term protein machineries for this active interplay. By genetically modifying the proteins attaching GFP we are able to follow the transcytosis pathway. We evaluate the process in a quantitative manner over time. This reveals that the most obvious obstacle to transcytosis is a routing of the nanocarriers to the lysosomes.

Antimicrobial agent triclosan disrupts mitochondrial structure, revealed by super-resolution microscopy, and inhibits mast cell signaling via calcium modulation

The antimicrobial agent triclosan (TCS) is used in products such as toothpaste and surgical soaps and is readily absorbed into oral mucosa and human skin. These and many other tissues contain mast cells, which are involved in numerous physiologies and diseases. Mast cells release chemical mediators through a process termed degranulation, which is inhibited by TCS. Investigation into the underlying mechanisms led to the finding that TCS is a mitochondrial uncoupler at non-cytotoxic, low-micromolar doses in several cell types and live zebrafish. Our aim was to determine the mechanisms underlying TCS disruption of mitochondrial function and of mast cell signaling. We combined super-resolution (fluorescence photoactivation localization) microscopy and multiple fluorescence-based assays to detail triclosan's effects in living mast cells, fibroblasts, and primary human keratinocytes. TCS disrupts mitochondrial nanostructure, causing mitochondria to undergo fission and to form a toroidal, "donut" shape. TCS increases reactive oxygen species production, decreases mitochondrial membrane potential, and disrupts ER and mitochondrial Ca²⁺ levels, processes that cause mitochondrial fission. TCS is 60 × more potent than the banned uncoupler 2,4-dinitrophenol. TCS inhibits mast cell degranulation by decreasing mitochondrial membrane potential, disrupting microtubule polymerization, and inhibiting mitochondrial translocation, which reduces Ca²⁺ influx into the cell. Our findings provide mechanisms for both triclosan's inhibition of mast cell signaling and its universal disruption of mitochondria. These mechanisms provide partial explanations for triclosan's adverse effects on human reproduction, immunology, and development. This study is the first to utilize super-resolution microscopy in the field of toxicology.

Machine learning approach for single molecule localisation microscopy

Single molecule localisation (SML) microscopy is a fundamental tool for biological discoveries; it provides sub-diffraction spatial resolution images by detecting and localizing "all" the fluorescent molecules labeling the structure of interest. For this reason, the effective resolution of SML microscopy strictly depends on the algorithm used to detect and localize the single molecules from the series of microscopy frames. To adapt to the different imaging conditions that can occur in a SML experiment, all current localisation algorithms request, from the microscopy users, the choice of different parameters. This choice is not always easy and their wrong selection can lead to poor performance. Here we overcome this weakness with the use of machine learning. We propose a parameter-free pipeline for SML learning based on support vector machine (SVM). This strategy requires a short supervised training that consists in selecting by the user few fluorescent molecules (∼ 10-20) from the frames under analysis. The algorithm has been extensively tested on both synthetic and real acquisitions. Results are qualitatively and quantitatively consistent with the state of the art in SML microscopy and demonstrate that the introduction of machine learning can lead to a new class of algorithms competitive and conceived from the user point of view.

Non-heuristic automatic techniques for overcoming low signal-to-noise-ratio bias of localization microscopy and multiple signal classification algorithm

Localization microscopy and multiple signal classification algorithm use temporal stack of image frames of sparse emissions from fluorophores to provide super-resolution images. Localization microscopy localizes emissions in each image independently and later collates the localizations in all the frames, giving same weight to each frame irrespective of its signal-to-noise ratio. This results in a bias towards frames with low signal-to-noise ratio and causes cluttered background in the super-resolved image. User-defined heuristic computational filters are employed to remove a set of localizations in an attempt to overcome this bias. Multiple signal classification performs eigen-decomposition of the entire stack, irrespective of the relative signal-to-noise ratios of the frames, and uses a threshold to classify eigenimages into signal and null subspaces. This results in under-representation of frames with low signal-to-noise ratio in the signal space and over-representation in the null space. Thus, multiple signal classification algorithms is biased against frames with low signal-to-noise ratio resulting into suppression of the corresponding fluorophores. This paper presents techniques to automatically debias localization microscopy and multiple signal classification algorithm of these biases without compromising their resolution and without employing heuristics, user-defined criteria. The effect of debiasing is demonstrated through five datasets of invitro and fixed cell samples.

Plasmon-Assisted Selective and Super-Resolving Excitation of Individual Quantum Emitters on a Metal Nanowire

Hybrid systems composed of multiple quantum emitters coupled with plasmonic waveguides are promising building blocks for future integrated quantum nanophotonic circuits. The techniques that can super-resolve and selectively excite contiguous quantum emitters in a diffraction-limited area are of great importance for studying the plasmon-mediated interaction between quantum emitters and manipulating the single plasmon generation and propagation in plasmonic circuits. Here we show that multiple quantum dots coupled with a silver nanowire can be controllably excited by tuning the interference field of surface plasmons on the nanowire. Because of the period of the interference pattern is much smaller than the diffraction limit, we demonstrate the selective excitation of two quantum dots separated by a distance as short as 100 nm. We also numerically demonstrate a new kind of super-resolution imaging method that combines the tunable surface plasmon interference pattern on the NW with the structured illumination microscopy technique. Our work provides a novel high-resolution optical excitation and imaging method for the coupled systems of multiple quantum emitters and plasmonic waveguides, which adds a new tool for studying and manipulating single quantum emitters and single plasmons for quantum plasmonic circuitry applications.

OligoMiner provides a rapid, flexible environment for the design of genome-scale oligonucleotide in situ hybridization probes

Oligonucleotide (oligo)-based FISH has emerged as an important tool for the study of chromosome organization and gene expression and has been empowered by the commercial availability of highly complex pools of oligos. However, a dedicated bioinformatic design utility has yet to be created specifically for the purpose of identifying optimal oligo FISH probe sequences on the genome-wide scale. Here, we introduce OligoMiner, a rapid and robust computational pipeline for the genome-scale design of oligo FISH probes that affords the scientist exact control over the parameters of each probe. Our streamlined method uses standard bioinformatic file formats, allowing users to seamlessly integrate new and existing utilities into the pipeline as desired, and introduces a method for evaluating the specificity of each probe molecule that connects simulated hybridization energetics to rapidly generated sequence alignments using supervised machine learning. We demonstrate the scalability of our approach by performing genome-scale probe discovery in numerous model organism genomes and showcase the performance of the resulting probes with diffraction-limited and single-molecule superresolution imaging of chromosomal and RNA targets. We anticipate that this pipeline will make the FISH probe design process much more accessible and will more broadly facilitate the design of pools of hybridization probes for a variety of applications.

Mechanism of Stx17 recruitment to autophagosomes via IRGM and mammalian Atg8 proteins

Autophagy is a conserved eukaryotic process with metabolic, immune, and general homeostatic functions in mammalian cells. Mammalian autophagosomes fuse with lysosomes in a SNARE-driven process that includes syntaxin 17 (Stx17). How Stx17 translocates to autophagosomes is unknown. In this study, we show that the mechanism of Stx17 recruitment to autophagosomes in human cells entails the small guanosine triphosphatase IRGM. Stx17 directly interacts with IRGM, and efficient Stx17 recruitment to autophagosomes requires IRGM. Both IRGM and Stx17 directly interact with mammalian Atg8 proteins, thus being guided to autophagosomes. We also show that Stx17 is significant in defense against infectious agents and that Stx17-IRGM interaction is targeted by an HIV virulence factor Nef.

Single-molecule techniques in biophysics: a review of the progress in methods and applications

Single-molecule biophysics has transformed our understanding of biology, but also of the physics of life. More exotic than simple soft matter, biomatter lives far from thermal equilibrium, covering multiple lengths from the nanoscale of single molecules to up to several orders of magnitude higher in cells, tissues and organisms. Biomolecules are often characterized by underlying instability: multiple metastable free energy states exist, separated by levels of just a few multiples of the thermal energy scale k _B T, where k _B is the Boltzmann constant and T absolute temperature, implying complex inter-conversion kinetics in the relatively hot, wet environment of active biological matter. A key benefit of single-molecule biophysics techniques is their ability to probe heterogeneity of free energy states across a molecular population, too challenging in general for conventional ensemble average approaches. Parallel developments in experimental and computational techniques have catalysed the birth of multiplexed, correlative techniques to tackle previously intractable biological questions. Experimentally, progress has been driven by improvements in sensitivity and speed of detectors, and the stability and efficiency of light sources, probes and microfluidics. We discuss the motivation and requirements for these recent experiments, including the underpinning mathematics. These methods are broadly divided into tools which detect molecules and those which manipulate them. For the former we discuss the progress of super-resolution microscopy, transformative for addressing many longstanding questions in the life sciences, and for the latter we include progress in 'force spectroscopy' techniques that mechanically perturb molecules. We also consider in silico progress of single-molecule computational physics, and how simulation and experimentation may be drawn together to give a more complete understanding. Increasingly, combinatorial techniques are now used, including correlative atomic force microscopy and fluorescence imaging, to probe questions closer to native physiological behaviour. We identify the trade-offs, limitations and applications of these techniques, and discuss exciting new directions.

Super-Resolution Monitoring of Mitochondrial Dynamics upon Time-Gated Photo-Triggered Release of Nitric Oxide

Nitric oxide (NO) potentially plays a regulatory role in mitochondrial fusion and fission, which are vital to cell survival and implicated in health, disease, and aging. Molecular tools facilitating the study of the relationship between NO and mitochondrial dynamics are in need. We have recently developed a novel NO donor (NOD550). Upon photoactivation, NOD550 decomposes to release two NO molecules and a fluorophore. The NO release could be spatially mapped with subdiffraction resolution and with a temporal resolution of 10 s. Due to the preferential localization of NOD550 at mitochondria, morphology and dynamics of mitochondria could be monitored upon NO release from NOD550.

DNA-PAINT Super-Resolution Imaging for Nucleic Acid Nanostructures

Far-field super-resolution fluorescence microscopy has allowed observation of biomolecular and synthetic nanoscale systems with features on the nanometre scale, with chemical specificity and multiplexing capability. DNA-PAINT (DNA-based point accumulation for imaging in nanoscale topography) is a super-resolution method that exploits programmable transient hybridization between short oligonucleotide strands, and allows multiplexed, single-molecule, single-label visualization with down to ~5-10 nm resolution. DNA-PAINT provides a method for structural characterisation of nucleic acid nanostructures with high spatial resolution and single-strand visibility.

Dual-Color and 3D Super-Resolution Microscopy of Multi-protein Assemblies

Breaking the resolution limit of conventional microscopy by super-resolution microscopy (SRM) led to many new biological insights into protein assemblies at the nanoscale. Here we provide detailed protocols for single-molecule localization microscopy (SMLM) to image the structure of a protein complex. As examples, we show how to acquire single- and dual-color super-resolution images of the nuclear pore complex (NPC) and dual-color 3D data on actin and paxillin in focal adhesions.

SpotLearn: Convolutional Neural Network for Detection of Fluorescence In Situ Hybridization (FISH) Signals in High-Throughput Imaging Approaches

DNA fluorescence in situ hybridization (FISH) is the technique of choice to map the position of genomic loci in three-dimensional (3D) space at the single allele level in the cell nucleus. High-throughput DNA FISH methods have recently been developed using complex libraries of fluorescently labeled synthetic oligonucleotides and automated fluorescence microscopy, enabling large-scale interrogation of genomic organization. Although the FISH signals generated by high-throughput methods can, in principle, be analyzed by traditional spot-detection algorithms, these approaches require user intervention to optimize each interrogated genomic locus, making analysis of tens or hundreds of genomic loci in a single experiment prohibitive. We report here the design and testing of two separate machine learning-based workflows for FISH signal detection in a high-throughput format. The two methods rely on random forest (RF) classification or convolutional neural networks (CNNs), respectively. Both workflows detect DNA FISH signals with high accuracy in three separate fluorescence microscopy channels for tens of independent genomic loci, without the need for manual parameter value setting on a per locus basis. In particular, the CNN workflow, which we named SpotLearn, is highly efficient and accurate in the detection of DNA FISH signals with low signal-to-noise ratio (SNR). We suggest that SpotLearn will be useful to accurately and robustly detect diverse DNA FISH signals in a high-throughput fashion, enabling the visualization and positioning of hundreds of genomic loci in a single experiment.

Quantitative performance evaluation of a back-illuminated sCMOS camera with 95% QE for super-resolution localization microscopy

Scientific Complementary Metal Oxide Semiconductor (sCMOS) cameras were introduced into the market in 2009 and are now becoming a major type of commercial cameras for low-light imaging. sCMOS cameras provide simultaneously low read noise, high readout speed, and large pixel array; however, the relatively low quantum efficiency (QE) of sCMOS cameras has been a major limitation for its application in single molecule imaging, especially super-resolution localization microscopy which requires high detection sensitivity. Here we report the imaging performance of a newly released back-illuminated sCMOS camera (called Dhyana 95 from Tucsen) which is claimed to be the world's first 95% QE sCMOS camera. The imaging performance evaluation is based on a new methodology which is designed to provide paired images from two tested cameras under almost identical experimental conditions. We verified that this new 95% QE sCMOS camera is able to provide superior imaging performance over a representative front-illuminated sCMOS camera (Hamamatsu Flash 4.0 V2) and a popular back-illuminated EMCCD camera (Andor iXon 897 Ultra) in a wide signal range. We hope this study will inspire more studies on using sCMOS cameras in super-resolution localization microscopy, or even single molecule imaging. © 2017 International Society for Advancement of Cytometry.

Gpufit: An open-source toolkit for GPU-accelerated curve fitting

We present a general purpose, open-source software library for estimation of non-linear parameters by the Levenberg-Marquardt algorithm. The software, Gpufit, runs on a Graphics Processing Unit (GPU) and executes computations in parallel, resulting in a significant gain in performance. We measured a speed increase of up to 42 times when comparing Gpufit with an identical CPU-based algorithm, with no loss of precision or accuracy. Gpufit is designed such that it is easily incorporated into existing applications or adapted for new ones. Multiple software interfaces, including to C, Python, and Matlab, ensure that Gpufit is accessible from most programming environments. The full source code is published as an open source software repository, making its function transparent to the user and facilitating future improvements and extensions. As a demonstration, we used Gpufit to accelerate an existing scientific image analysis package, yielding significantly improved processing times for super-resolution fluorescence microscopy datasets.

Spatial cues and not spindle pole maturation drive the asymmetry of astral microtubules between new and preexisting spindle poles

The distinct behavior of the spindle pole bodies (SPBs) during spindle orientation in yeast metaphase does not result from them being differently mature, but astral microtubule organization correlates with the subcellular position rather than the age of the SPBs.

In many asymmetrically dividing cells, the microtubule-organizing centers (MTOCs; mammalian centrosome and yeast spindle pole body [SPB]) nucleate more astral microtubules on one of the two spindle poles than the other. This differential activity generally correlates with the age of MTOCs and contributes to orienting the mitotic spindle within the cell. The asymmetry might result from the two MTOCs being in distinctive maturation states. We investigated this model in budding yeast. Using fluorophores with different maturation kinetics to label the outer plaque components of the SPB, we found that the Cnm67 protein is mobile, whereas Spc72 is not. However, these two proteins were rapidly as abundant on both SPBs, indicating that SPBs mature more rapidly than anticipated. Superresolution microscopy confirmed this finding for Spc72 and for the γ-tubulin complex. Moreover, astral microtubule number and length correlated with the subcellular localization of SPBs rather than their age. Kar9-dependent orientation of the spindle drove the differential activity of the SPBs in astral microtubule organization rather than intrinsic differences between the spindle poles. Together, our data establish that Kar9 and spatial cues, rather than the kinetics of SPB maturation, control the asymmetry of astral microtubule organization between the preexisting and new SPBs.

Differential mast cell outcomes are sensitive to FcεRI-Syk binding kinetics

Cross-linking of immunoglobulin E-bound FcεRI triggers multiple cellular responses, including degranulation and cytokine production. Signaling is dependent on recruitment of Syk via docking of its dual SH2 domains to phosphorylated tyrosines within the FcεRI immunoreceptor tyrosine-based activation motifs. Using single-molecule imaging in live cells, we directly visualized and quantified the binding of individual mNeonGreen-tagged Syk molecules as they associated with the plasma membrane after FcεRI activation. We found that Syk colocalizes transiently to FcεRI and that Syk-FcεRI binding dynamics are independent of receptor aggregate size. Substitution of glutamic acid for tyrosine between the Syk SH2 domains (Syk-Y130E) led to an increased Syk-FcεRI off-rate, loss of site-specific Syk autophosphorylation, and impaired downstream signaling. Genome edited cells expressing only Syk-Y130E were deficient in antigen-stimulated calcium release, degranulation, and production of some cytokines (TNF-a, IL-3) but not others (MCP-1, IL-4). We propose that kinetic discrimination along the FcεRI signaling pathway occurs at the level of Syk-FcεRI interactions, with key outcomes dependent upon sufficiently long-lived Syk binding events.

CRISPR-Cas9 Mediated Labelling Allows for Single Molecule Imaging and Resolution

Single molecule imaging approaches like dSTORM and PALM resolve structures at 10–20 nm, and allow for unique insights into protein stoichiometry and spatial relationships. However, key obstacles remain in developing highly accurate quantitative single molecule approaches. The genomic tagging of PALM fluorophores through CRISPR-Cas9 offers an excellent opportunity for generating stable cell lines expressing a defined single molecule probe at endogenous levels, without the biological disruption and variability inherent to transfection. A fundamental question is whether these comparatively low levels of expression can successfully satisfy the stringent labelling demands of super-resolution SMLM. Here we apply CRISPR-Cas9 gene editing to tag a cytoskeletal protein (α-tubulin) and demonstrate a relationship between expression level and the subsequent quality of PALM imaging, and that spatial resolutions comparable to dSTORM can be achieved with CRISPR-PALM. Our approach shows a relationship between choice of tag and the total expression of labelled protein, which has important implications for the development of future PALM tags. CRISPR-PALM allows for nanoscopic spatial resolution and the unique quantitative benefits of single molecule localization microscopy through endogenous expression, as well as the capacity for super-resolved live cell imaging.

Interference based localization of single emitters

The ability to localize precisely a single optical emitter is important for particle tracking applications and super resolution microscopy. It is known that for a traditional microscope the ability to localize such an emitter is limited by the photon count. Here we analyze the ability to improve such localization by imposing interference fringes. We show here that a simple grating interferometer can introduce such improvement in certain circumstances and analyze what is required to increase the localization precision further.

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.

Eigen-analysis reveals components supporting super-resolution imaging of blinking fluorophores

This paper presents eigen-analysis of image stack of blinking fluorophores to identify the components that enable super-resolved imaging of blinking fluorophores. Eigen-analysis reveals that the contributions of spatial distribution of fluorophores and their temporal photon emission characteristics can be completely separated. While cross-emitter cross-pixel information of spatial distribution that permits super-resolution is encoded in two matrices, temporal statistics weigh the contribution of these matrices to the measured data. The properties and conditions of exploitation of these matrices are investigated. Con-temporary super-resolution imaging methods that use blinking for super-resolution are studied in the context of the presented analysis. Besides providing insight into the capabilities and limitations of existing super-resolution methods, the analysis shall help in designing better super-resolution techniques that directly exploit these matrices.

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.

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.

Single Particle Tracking: From Theory to Biophysical Applications

After three decades of developments, single particle tracking (SPT) has become a powerful tool to interrogate dynamics in a range of materials including live cells and novel catalytic supports because of its ability to reveal dynamics in the structure-function relationships underlying the heterogeneous nature of such systems. In this review, we summarize the algorithms behind, and practical applications of, SPT. We first cover the theoretical background including particle identification, localization, and trajectory reconstruction. General instrumentation and recent developments to achieve two- and three-dimensional subdiffraction localization and SPT are discussed. We then highlight some applications of SPT to study various biological and synthetic materials systems. Finally, we provide our perspective regarding several directions for future advancements in the theory and application of SPT.

Single-molecule and super-resolution imaging of transcription in living bacteria

In vivo single-molecule and super-resolution techniques are transforming our ability to study transcription as it takes place in its native environment in living cells. This review will detail the methods for imaging single molecules in cells, and the data-analysis tools which can be used to extract quantitative information on the spatial organization, mobility, and kinetics of the transcription machinery from these experiments. Furthermore, we will highlight studies which have applied these techniques to shed new light on bacterial transcription.

Refining particle positions using circular symmetry

Particle and object tracking is gaining attention in industrial applications and is commonly applied in: colloidal, biophysical, ecological, and micro-fluidic research. Reliable tracking information is heavily dependent on the system under study and algorithms that correctly determine particle position between images. However, in a real environmental context with the presence of noise including particular or dissolved matter in water, and low and fluctuating light conditions, many algorithms fail to obtain reliable information. We propose a new algorithm, the Circular Symmetry algorithm (C-Sym), for detecting the position of a circular particle with high accuracy and precision in noisy conditions. The algorithm takes advantage of the spatial symmetry of the particle allowing for subpixel accuracy. We compare the proposed algorithm with four different methods using both synthetic and experimental datasets. The results show that C-Sym is the most accurate and precise algorithm when tracking micro-particles in all tested conditions and it has the potential for use in applications including tracking biota in their environment.

Measurement-based estimation of global pupil functions in 3D localization microscopy

We report the use of a phase retrieval procedure based on maximum likelihood estimation (MLE) to produce an improved, experimentally calibrated model of a point spread function (PSF) for use in three-dimensional (3D) localization microscopy experiments. The method estimates a global pupil phase function (which includes both the PSF and system aberrations) over the full axial range from a simple calibration scan. The pupil function is used to refine the PSF model and hence enable superior localizations from experimental data. To demonstrate the utility of the procedure, we apply it to experimental data acquired with a microscope employing a tetrapod PSF with a 6 µm axial range. The phase-retrieved model demonstrates significant improvements in both accuracy and precision of 3D localizations relative to the model based on scalar diffraction theory. The localization precision of the phase-retrieved model is shown to be near the limits imposed by estimation theory, and the reproducibility of the procedure is characterized and discussed. Code which performs the phase retrieval algorithm is provided.

Tuning membrane protein mobility by confinement into nanodomains

High-speed atomic force microscopy (HS-AFM) can be used to visualize function-related conformational changes of single soluble proteins. Similar studies of single membrane proteins are, however, hampered by a lack of suitable flat, non-interacting membrane supports and by high protein mobility. Here we show that streptavidin crystals grown on mica-supported lipid bilayers can be used as porous supports for membranes containing biotinylated lipids. Using SecYEG (protein translocation channel) and GlpF (aquaglyceroporin), we demonstrate that the platform can be used to tune the lateral mobility of transmembrane proteins to any value within the dynamic range accessible to HS-AFM imaging through glutaraldehyde-cross-linking of the streptavidin. This allows HS-AFM to study the conformation or docking of spatially confined proteins, which we illustrate by imaging GlpF at sub-molecular resolution and by observing the motor protein SecA binding to SecYEG.

Automated tracking of colloidal clusters with sub-pixel accuracy and precision

Quantitative tracking of features from video images is a basic technique employed in many areas of science. Here, we present a method for the tracking of features that partially overlap, in order to be able to track so-called colloidal molecules. Our approach implements two improvements into existing particle tracking algorithms. Firstly, we use the history of previously identified feature locations to successfully find their positions in consecutive frames. Secondly, we present a framework for non-linear least-squares fitting to summed radial model functions and analyze the accuracy (bias) and precision (random error) of the method on artificial data. We find that our tracking algorithm correctly identifies overlapping features with an accuracy below 0.2% of the feature radius and a precision of 0.1 to 0.01 pixels for a typical image of a colloidal cluster. Finally, we use our method to extract the three-dimensional diffusion tensor from the Brownian motion of colloidal dimers.

Cryogenic photoluminescence imaging system for nanoscale positioning of single quantum emitters

We report a photoluminescence imaging system for locating single quantum emitters with respect to alignment features. Samples are interrogated in a 4 K closed-cycle cryostat by a high numerical aperture (NA = 0.9, 100× magnification) objective that sits within the cryostat, enabling high efficiency collection of emitted photons without image distortions due to the cryostat windows. The locations of single InAs/GaAs quantum dots within a >50 μm × 50 μm field of view are determined with ≈4.5 nm uncertainty (one standard deviation) in a 1 s long acquisition. The uncertainty is determined through a combination of a maximum likelihood estimate for localizing the quantum dot emission, and a cross correlation method for determining the alignment mark center. This location technique can be an important step in the high-throughput creation of nanophotonic devices that rely upon the interaction of highly confined optical modes with single quantum emitters.

Single-pixel interior filling function approach for detecting and correcting errors in particle tracking

We present a general method for detecting and correcting biases in the outputs of particle-tracking experiments. Our approach is based on the histogram of estimated positions within pixels, which we term the single-pixel interior filling function (SPIFF). We use the deviation of the SPIFF from a uniform distribution to test the veracity of tracking analyses from different algorithms. Unbiased SPIFFs correspond to uniform pixel filling, whereas biased ones exhibit pixel locking, in which the estimated particle positions concentrate toward the centers of pixels. Although pixel locking is a well-known phenomenon, we go beyond existing methods to show how the SPIFF can be used to correct errors. The key is that the SPIFF aggregates statistical information from many single-particle images and localizations that are gathered over time or across an ensemble, and this information augments the single-particle data. We explicitly consider two cases that give rise to significant errors in estimated particle locations: undersampling the point spread function due to small emitter size and intensity overlap of proximal objects. In these situations, we show how errors in positions can be corrected essentially completely with little added computational cost. Additional situations and applications to experimental data are explored in SI Appendix In the presence of experimental-like shot noise, the precision of the SPIFF-based correction achieves (and can even exceed) the unbiased Cramér-Rao lower bound. We expect the SPIFF approach to be useful in a wide range of localization applications, including single-molecule imaging and particle tracking, in fields ranging from biology to materials science to astronomy.