3-D reconstruction of microtubules from multi-angle total internal reflection fluorescence microscopy using Bayesian framework

Imaging Single-Vesicle Exocytosis with Total Internal Reflection Fluorescence Microscopy (TIRFM)

Total internal reflection fluorescence microscopy (TIRFM) provides extremely thin optical sectioning with excellent signal-to-noise ratios, which allows for visualization of membrane dynamics at the cell surface with superb spatiotemporal resolution. In this chapter, TIRFM is used to record and analyze exocytosis of single glucose transporter-4 (GLUT4) containing vesicles in 3T3-L1 adipocytes.

A survey on applications of deep learning in microscopy image analysis

Advanced microscopy enables us to acquire quantities of time-lapse images to visualize the dynamic characteristics of tissues, cells or molecules. Microscopy images typically vary in signal-to-noise ratios and include a wealth of information which require multiple parameters and time-consuming iterative algorithms for processing. Precise analysis and statistical quantification are often needed for the understanding of the biological mechanisms underlying these dynamic image sequences, which has become a big challenge in the field. As deep learning technologies develop quickly, they have been applied in bioimage processing more and more frequently. Novel deep learning models based on convolution neural networks have been developed and illustrated to achieve inspiring outcomes. This review article introduces the applications of deep learning algorithms in microscopy image analysis, which include image classification, region segmentation, object tracking and super-resolution reconstruction. We also discuss the drawbacks of existing deep learning-based methods, especially on the challenges of training datasets acquisition and evaluation, and propose the potential solutions. Furthermore, the latest development of augmented intelligent microscopy that based on deep learning technology may lead to revolution in biomedical research.

Three-dimensional total-internal reflection fluorescence nanoscopy with nanometric axial resolution by photometric localization of single molecules

Single-molecule localization microscopy enables far-field imaging with lateral resolution in the range of 10 to 20 nanometres, exploiting the fact that the centre position of a single-molecule’s image can be determined with much higher accuracy than the size of that image itself. However, attaining the same level of resolution in the axial (third) dimension remains challenging. Here, we present Supercritical Illumination Microscopy Photometric z-Localization with Enhanced Resolution (SIMPLER), a photometric method to decode the axial position of single molecules in a total internal reflection fluorescence microscope. SIMPLER requires no hardware modification whatsoever to a conventional total internal reflection fluorescence microscope and complements any 2D single-molecule localization microscopy method to deliver 3D images with nearly isotropic nanometric resolution. Performance examples include SIMPLER-direct stochastic optical reconstruction microscopy images of the nuclear pore complex with sub-20 nm axial localization precision and visualization of microtubule cross-sections through SIMPLER-DNA points accumulation for imaging in nanoscale topography with sub-10 nm axial localization precision.

Achieving high axial resolution is challenging in single-molecule localization microscopy. Here, the authors present a photometric method to decode the axial position of single molecules in a total internal reflection fluorescence microscope without hardware modification, and show nearly isotropic nanometric resolution.

Prior-Apprised Unsupervised Learning of Subpixel Curvilinear Features in Low Signal/Noise Images

Many biophysical problems involve molecular and nanoscale targets moving next to a curvilinear track, e.g., a cytosolic cargo transported by motor proteins moving along a microtubule. For this type of problem, fluorescence imaging is usually the primary tool of choice. There is, however, an ∼20-fold mismatch between target-localization precision and track-imaging resolution such that questions requiring high-fidelity definition of the target's track remain inaccessible. On the other hand, if the contextual image of the tracks can be refined to a level comparable to that of the target, many intuitive yet mechanistically important issues can begin to be addressed. This work demonstrates that it is possible to statistically infer, to subpixel precision, curvilinear features in a low signal/noise image. This is achieved by a framework that consists of three stages: the Hessian-based feature enhancement, the subimage feature sampling and registration, and the statistical learning of the underlying curvilinear structure using a new, to our knowledge, method developed here for inferring the principal curves. In each stage, the descriptive prior information that the features come from curvilinear elements is explicitly taken into account. It is fully automated without user supervision, which is distinctly different from approaches that require user seeding or well-defined training data sets. Computer simulations of realistic images are used to investigate the performance of the framework and its implementation. The characterization results suggest that curvilinear features are refined to the same order of precision as that of the target and that the bootstrap confidence intervals from the analysis allow an estimate for the statistical bounds of the simulated "true" curve. Also shown are analyses of experimental images from three different microscopy modalities: two-photon laser-scanning microscopy, epifluorescence microscopy, and total internal reflection fluorescence microscopy. The practical application of this prior-apprised unsupervised learning framework as well as its potential outlook are discussed.

Flat-top TIRF illumination boosts DNA-PAINT imaging and quantification

Super-resolution (SR) techniques have extended the optical resolution down to a few nanometers. However, quantitative treatment of SR data remains challenging due to its complex dependence on a manifold of experimental parameters. Among the different SR variants, DNA-PAINT is relatively straightforward to implement, since it achieves the necessary ‘blinking’ without the use of rather complex optical or chemical activation schemes. However, it still suffers from image and quantification artifacts caused by inhomogeneous optical excitation. Here we demonstrate that several experimental challenges can be alleviated by introducing a segment-wise analysis approach and ultimately overcome by implementing a flat-top illumination profile for TIRF microscopy using a commercially-available beam-shaping device. The improvements with regards to homogeneous spatial resolution and precise kinetic information over the whole field-of-view were quantitatively assayed using DNA origami and cell samples. Our findings open the door to high-throughput DNA-PAINT studies with thus far unprecedented accuracy for quantitative data interpretation.

The use of TIRF microscopy for DNA-PAINT experiments is limited by inhomogeneous illumination. Here the authors show that quantitative analysis of single-molecule TIRF experiments can be improved by using a segment-wise analysis approach and overcome by using a beam-shaping device to give a flat-top illumination profile.

Waveguide-PAINT offers an open platform for large field-of-view super-resolution imaging

Super-resolution microscopies based on the localization of single molecules have been widely adopted due to their demonstrated performance and their accessibility resulting from open software and simple hardware. The PAINT method for localization microscopy offers improved resolution over photoswitching methods, since it is less prone to sparse sampling of structures and provides higher localization precision. Here, we show that waveguides enable increased throughput and data quality for PAINT, by generating a highly uniform ~100 × 2000 µm² area evanescent field for TIRF illumination. To achieve this, we designed and fabricated waveguides optimized for efficient light coupling and propagation, incorporating a carefully engineered input facet and taper. We also developed a stable, low-cost microscope and 3D-printable waveguide chip holder for easy alignment and imaging. We demonstrate the capabilities of our open platform by using DNA-PAINT to image multiple whole cells or hundreds of origami structures in a single field of view.

TIRF imaging is limited by the size and uniformity of the illumination. Here the authors present a waveguide solution to create a large area of uniform evanescent illumination suitable for single molecule imaging coupled with a customised sample holder containing a reservoir for DNA-PAINT solutions.

Nanometric axial resolution of fibronectin assembly units achieved with an efficient reconstruction approach for multi-angle-TIRF microscopy

High resolution imaging of molecules at the cell-substrate interface is required for understanding key biological processes. Here we propose a complete pipeline for multi-angle total internal reflection fluorescence microscopy (MA-TIRF) going from instrument design and calibration procedures to numerical reconstruction. Our custom setup is endowed with a homogeneous field illumination and precise excitation beam angle. Given a set of MA-TIRF acquisitions, we deploy an efficient joint deconvolution/reconstruction algorithm based on a variational formulation of the inverse problem. This algorithm offers the possibility of using various regularizations and can run on graphics processing unit (GPU) for rapid reconstruction. Moreover, it can be easily used with other MA-TIRF devices and we provide it as an open-source software. This ensemble has enabled us to visualize and measure with unprecedented nanometric resolution, the depth of molecular components of the fibronectin assembly machinery at the basal surface of endothelial cells.

Inverse problem based on the fast alternating direction method of multipliers algorithm in multiangle total internal reflection fluorescence microscopy

Multiangle total internal reflection fluorescence microscopy (TIRFM) has become one of the most important techniques for achieving axial superresolution. The key process in this technique is solving the inverse problem. This paper applies an improved alternating direction method of multipliers algorithm to solve the inverse problem and validates the accuracy of the algorithm by reconstructing simulated microtubule structures in multiangle TIRFM images. The reconstruction times for different algorithms and the convergence speeds of the improved and original algorithms are compared. Experimental results show that the improved algorithm can achieve an axial resolution of 40 nm, reduce the influence of the penalty parameter on convergence, and improve the convergence speed of the iterative process while ensuring image reconstruction quality. Based on the algorithm, a three-dimensional image with the depth information of microtubules and mitochondria is reconstructed.

An integrated enhancement and reconstruction strategy for the quantitative extraction of actin stress fibers from fluorescence micrographs

BACKGROUND

The stress fibers are prominent organization of actin filaments that perform important functions in cellular processes such as migration, polarization, and traction force generation, and whose collective organization reflects the physiological and mechanical activities of the cells. Easily visualized by fluorescence microscopy, the stress fibers are widely used as qualitative descriptors of cell phenotypes. However, due to the complexity of the stress fibers and the presence of other actin-containing cellular features, images of stress fibers are relatively challenging to quantitatively analyze using previously developed approaches, requiring significant user intervention. This poses a challenge for the automation of their detection, segmentation, and quantitative analysis.

RESULT

Here we describe an open-source software package, SFEX (Stress Fiber Extractor), which is geared for efficient enhancement, segmentation, and analysis of actin stress fibers in adherent tissue culture cells. Our method made use of a carefully chosen image filtering technique to enhance filamentous structures, effectively facilitating the detection and segmentation of stress fibers by binary thresholding. We subdivided the skeletons of stress fiber traces into piecewise-linear fragments, and used a set of geometric criteria to reconstruct the stress fiber networks by pairing appropriate fiber fragments. Our strategy enables the trajectory of a majority of stress fibers within the cells to be comprehensively extracted. We also present a method for quantifying the dimensions of the stress fibers using an image gradient-based approach. We determine the optimal parameter space using sensitivity analysis, and demonstrate the utility of our approach by analyzing actin stress fibers in cells cultured on various micropattern substrates.

CONCLUSION

We present an open-source graphically-interfaced computational tool for the extraction and quantification of stress fibers in adherent cells with minimal user input. This facilitates the automated extraction of actin stress fibers from fluorescence images. We highlight their potential uses by analyzing images of cells with shapes constrained by fibronectin micropatterns. The method we reported here could serve as the first step in the detection and characterization of the spatial properties of actin stress fibers to enable further detailed morphological analysis.

Axial superresolution via multiangle TIRF microscopy with sequential imaging and photobleaching

We report superresolution optical sectioning using a multiangle total internal reflection fluorescence (TIRF) microscope. TIRF images were constructed from several layers within a normal TIRF excitation zone by sequentially imaging and photobleaching the fluorescent molecules. The depth of the evanescent wave at different layers was altered by tuning the excitation light incident angle. The angle was tuned from the highest (the smallest TIRF depth) toward the critical angle (the largest TIRF depth) to preferentially photobleach fluorescence from the lower layers and allow straightforward observation of deeper structures without masking by the brighter signals closer to the coverglass. Reconstruction of the TIRF images enabled 3D imaging of biological samples with 20-nm axial resolution. Two-color imaging of epidermal growth factor (EGF) ligand and clathrin revealed the dynamics of EGF-activated clathrin-mediated endocytosis during internalization. Furthermore, Bayesian analysis of images collected during the photobleaching step of each plane enabled lateral superresolution (<100 nm) within each of the sections.

Retrograde NGF axonal transport--motor coordination in the unidirectional motility regime

We present a detailed motion analysis of retrograde nerve growth factor (NGF) endosomes in axons to show that mechanical tugs-of-war and intracellular motor regulation are complimentary features of the near-unidirectional endosome directionality. We used quantum dots to fluorescently label NGF and acquired trajectories of retrograde quantum-dot-NGF-endosomes with <20-nm accuracy at 32 Hz in microfluidic neuron cultures. Using a combination of transient motion analysis and Bayesian parsing, we partitioned the trajectories into sustained periods of retrograde (dynein-driven) motion, constrained pauses, and brief anterograde (kinesin-driven) reversals. The data shows many aspects of mechanical tugs-of-war and multiple-motor mechanics in NGF-endosome transport. However, we found that stochastic mechanical models based on in vitro parameters cannot simulate the experimental data, unless the microtubule-binding affinity of kinesins on the endosome is tuned down by 10 times. Specifically, the simulations suggest that the NGF-endosomes are driven on average by 5-6 active dyneins and 1-2 downregulated kinesins. This is also supported by the dynamics of endosomes detaching under load in axons, showcasing the cooperativity of multiple dyneins and the subdued activity of kinesins. We discuss the possible motor coordination mechanism consistent with motor regulation and tugs-of-war for future investigations.

Polarised clathrin-mediated endocytosis of EGFR during chemotactic invasion

Directed cell migration is critical for numerous physiological processes including development and wound healing. However chemotaxis is also exploited during cancer progression. Recent reports have suggested links between vesicle trafficking pathways and directed cell migration. Very little is known about the potential roles of endocytosis pathways during metastasis. Therefore we performed a series of studies employing a previously characterised model for chemotactic invasion of cancer cells to assess specific hypotheses potentially linking endocytosis to directed cell migration. Our results demonstrate that clathrin-mediated endocytosis is indispensable for epidermal growth factor (EGF) directed chemotactic invasion of MDA-MB-231 cells. Conversely, caveolar endocytosis is not required in this mode of migration. We further found that chemoattractant receptor (EGFR) trafficking occurs by clathrin-mediated endocytosis and is polarised towards the front of migrating cells. However, we found no role for clathrin-mediated endocytosis in focal adhesion disassembly in this migration model. Thus, this study has characterised the role of endocytosis during chemotactic invasion and has identified functions mechanistically linking clathrin-mediated endocytosis to directed cell motility.

Fast high-resolution 3D total internal reflection fluorescence microscopy by incidence angle scanning and azimuthal averaging

Total internal reflection fluorescence microscopy (TIRFM) is the method of choice to visualize a variety of cellular processes in particular events localized near the plasma membrane of live adherent cells. This imaging technique not relying on particular fluorescent probes provides a high sectioning capability. It is, however, restricted to a single plane. We present here a method based on a versatile design enabling fast multiwavelength azimuthal averaging and incidence angles scanning to computationally reconstruct 3D images sequences. We achieve unprecedented 50-nm axial resolution over a range of 800 nm above the coverslip. We apply this imaging modality to obtain structural and dynamical information about 3D actin architectures. We also temporally decipher distinct Rab11a-dependent exocytosis events in 3D at a rate of seven stacks per second.

A novel multiple hypothesis based particle tracking method for clathrin mediated endocytosis analysis using fluorescence microscopy

In order to quantitatively analyze biological images and study underlying mechanisms of the cellular and subcellular processes, it is often required to track a large number of particles involved in these processes. Manual tracking can be performed by the biologists, but the workload is very heavy. In this paper, we present an automatic particle tracking method for analyzing an essential subcellular process, namely clathrin mediated endocytosis. The framework of the tracking method is an extension of the classical multiple hypothesis tracking (MHT), and it is designed to manage trajectories, solve data association problems, and handle pseudo-splitting/merging events. In the extended MHT framework, particle tracking becomes evaluating two types of hypotheses. The first one is the trajectory-related hypothesis, to test whether a recovered trajectory is correct, and the second one is the observation-related hypothesis, to test whether an observation from an image belongs to a real particle. Here, an observation refers to a detected particle and its feature vector. To detect the particles in 2D fluorescence images taken using total internal reflection microscopy, the images are segmented into regions, and the features of the particles are obtained by fitting Gaussian mixture models into each of the image regions. Specific models are developed according to the properties of the particles. The proposed tracking method is demonstrated on synthetic data under different scenarios and applied to real data.

Frealix: model-based refinement of helical filament structures from electron micrographs

The structures of many helical protein filaments can be derived from electron micrographs of their suspensions in thin films of vitrified aqueous solutions. The most successful and generally-applicable approach treats short segments of these filaments as independent "single particles", yielding near-atomic resolution for rigid and well-ordered filaments. The single-particle approach can also accommodate filament deformations, yielding sub-nanometer resolution for more flexible filaments. However, in the case of thin and flexible filaments, such as some amyloid-β (Aβ) fibrils, the single-particle approach may fail because helical segments can be curved or otherwise distorted and their alignment can be inaccurate due to low contrast in the micrographs. We developed new software called Frealix that allows the use of arbitrarily short filament segments during alignment to approximate even high curvatures. All segments in a filament are aligned simultaneously with constraints that ensure that they connect to each other in space to form a continuous helical structure. In this paper, we describe the algorithm and benchmark it against datasets of Aβ(1-40) fibrils and tobacco mosaic virus (TMV), both analyzed in earlier work. In the case of TMV, our algorithm achieves similar results to single-particle analysis. In the case of Aβ(1-40) fibrils, we match the previously-obtained resolution but we are also able to obtain reliable alignments and ∼8-Å reconstructions from curved filaments. Our algorithm also offers a detailed characterization of filament deformations in three dimensions and enables a critical evaluation of the worm-like chain model for biological filaments.

An expectation maximization based method for subcellular particle tracking using multi-angle TIRF microscopy

Multi-angle total internal reflection fluorescence microscopy (MA-TIRFM) is a new generation of TIRF microscopy to study cellular processes near dorsal cell membrane in 4 dimensions (3D+t). To perform quantitative analysis using MA-TIRFM, it is necessary to track subcellular particles in these processes. In this paper, we propose a method based on a MAP framework for automatic particle tracking and apply it to track clathrin coated pits (CCPs). The expectation maximization (EM) algorithm is employed to solve the MAP problem. To provide the initial estimations for the EM algorithm, we develop a forward filter based on the most probable trajectory (MPT) filter. Multiple linear models are used to model particle dynamics. For CCP tracking, we use two linear models to describe constrained Brownian motion and fluorophore variation according to CCP properties. The tracking method is evaluated on synthetic data and results show that it has high accuracy. The result on real data confirmed by human expert cell biologists is also presented.