Inferring Stochastic Rates from Heterogeneous Snapshots of Particle Positions.

Many imaging techniques for biological systems-like fixation of cells coupled with fluorescence microscopy-provide sharp spatial resolution in reporting locations of individuals at a single moment in time but also destroy the dynamics they intend to capture. These snapshot observations contain no information about individual trajectories, but still encode information about movement and demographic dynamics, especially when combined with a well-motivated biophysical model. The relationship between spatially evolving populations and single-moment representations of their collective locations is well-established with partial differential equations (PDEs) and their inverse problems. However, experimental data is commonly a set of locations whose number is insufficient to approximate a continuous-in-space PDE solution. Here, motivated by popular subcellular imaging data of gene expression, we embrace the stochastic nature of the data and investigate the mathematical foundations of parametrically inferring demographic rates from snapshots of particles undergoing birth, diffusion, and death in a nuclear or cellular domain. Toward inference, we rigorously derive a connection between individual particle paths and their presentation as a Poisson spatial process. Using this framework, we investigate the properties of the resulting inverse problem and study factors that affect quality of inference. One pervasive feature of this experimental regime is the presence of cell-to-cell heterogeneity. Rather than being a hindrance, we show that cell-to-cell geometric heterogeneity can increase the quality of inference on dynamics for certain parameter regimes. Altogether, the results serve as a basis for more detailed investigations of subcellular spatial patterns of RNA molecules and other stochastically evolving populations that can only be observed for single instants in their time evolution.

Answers to fundamental questions in superresolution microscopy.

This article presents answers to the questions on superresolution and structured illumination microscopy (SIM) as raised in the editorial of this collection of articles (https://doi.org/10.1098/rsta.2020.0143). These answers are based on my personal views on superresolution in light microscopy, supported by reasoning. Discussed are the definition of superresolution, Abbe's resolution limit and the classification of superresolution methods into nonlinear-, prior knowledge- and near-field-based superresolution. A further focus is put on the capabilities and technical aspects of present and future SIM methods. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Structured illumination microscopy artefacts caused by illumination scattering.

Despite its wide application in live-cell super-resolution (SR) imaging, structured illumination microscopy (SIM) suffers from aberrations caused by various sources. Although artefacts generated from inaccurate reconstruction parameter estimation and noise amplification can be minimized, aberrations due to the scattering of excitation light on samples have rarely been investigated. In this paper, by simulating multiple subcellular structure with the distinct refractive index from water, we study how different thicknesses of this subcellular structure scatter incident light on its optical path of SIM excitation. Because aberrant interference light aggravates with the increase in sample thickness, the reconstruction of the 2D-SIM SR image degraded with the change of focus along the axial axis. Therefore, this work may guide the future development of algorithms to suppress SIM artefacts caused by scattering in thick samples. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Extended mechanical force measurements using structured illumination microscopy.

Quantifying cell generated mechanical forces is key to furthering our understanding of mechanobiology. Traction force microscopy (TFM) is one of the most broadly applied force probing technologies, but its sensitivity is strictly dependent on the spatio-temporal resolution of the underlying imaging system. In previous works, it was demonstrated that increased sampling densities of cell derived forces permitted by super-resolution fluorescence imaging enhanced the sensitivity of the TFM method. However, these recent advances to TFM based on super-resolution techniques were limited to slow acquisition speeds and high illumination powers. Here, we present three novel TFM approaches that, in combination with total internal reflection, structured illumination microscopy and astigmatism, improve the spatial and temporal performance in either two-dimensional or three-dimensional mechanical force quantification, while maintaining low illumination powers. These three techniques can be straightforwardly implemented on a single optical set-up offering a powerful platform to provide new insights into the physiological force generation in a wide range of biological studies. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

mmSIM: an open toolbox for accessible structured illumination microscopy.

Since the first practical super-resolution structured illumination fluorescence microscopes (SIM) were demonstrated more than two decades ago, the method has become increasingly popular for a wide range of bioimaging applications. The high cost and relative inflexibility of commercial systems, coupled with the conceptual simplicity of the approach and the desire to exploit and customize existing hardware, have led to the development of a large number of home-built systems. Several detailed hardware designs are available in the scientific literature, complemented by open-source software tools for SIM image validation and reconstruction. However, there remains a lack of simple open-source software to control these systems and manage the synchronization between hardware components, which is critical for effective SIM imaging. This article describes a new suite of software tools based on the popular Micro-Manager package, which enable the keen microscopist to develop and run a SIM system. We use the software to control two custom-built, high-speed, spatial light modulator-based SIM systems, evaluating their performance by imaging a range of fluorescent samples. By simplifying the process of SIM hardware development, we aim to support wider adoption of the technique. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Improving axial resolution in Structured Illumination Microscopy using deep learning.

Structured Illumination Microscopy (SIM) is a widespread methodology to image live and fixed biological structures smaller than the diffraction limits of conventional optical microscopy. Using recent advances in image up-scaling through deep learning models, we demonstrate a method to reconstruct 3D SIM image stacks with twice the axial resolution attainable through conventional SIM reconstructions. We further demonstrate our method is robust to noise and evaluate it against two-point cases and axial gratings. Finally, we discuss potential adaptions of the method to further improve resolution. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Structured illumination microscopy and image scanning microscopy: a review and comparison of imaging properties.

Structured illumination microscopy and image scanning microscopy are two microscopical tech- niques, rapidly increasing in practical application, that can result in improvement in transverse spatial resolution, and/or improvement in axial imaging performance. The history and principles of these techniques are reviewed, and the imaging properties of the two methods compared. This article is part of the Theo Murphy meeting issue 'Super-resolution structured illumination microscopy (part 1)'.

Quantitative analysis of fluorescent ligand binding to dopamine D3 receptors using live-cell microscopy.

Dopamine receptors are G protein-coupled receptors that have several essential functions in the central nervous system. A better understanding of the regulatory mechanisms of ligand binding to the receptor may open new possibilities to affect the downstream signal transduction pathways. The majority of the available ligand binding assays use either membrane preparations, cell suspensions, or genetically modified receptors, which may give at least partially incorrect understanding of ligand binding. In this study, we implemented an assay combining fluorescence and bright-field microscopy to measure ligand binding to dopamine D3 receptors in live mammalian cells. For membrane fluorescence intensity quantification from microscopy images, we developed a machine learning-based user-friendly software membrane tools and incorporated it into a data management software aparecium that has been previously developed in our workgroup. For the experiments, a fluorescent ligand NAPS-Cy3B was synthesized by conjugating a dopaminergic antagonist N-(p-aminophenethyl)spiperone with a fluorophore Cy3B. The subnanomolar affinity of NAPS-Cy3B makes it a suitable ligand for the characterization of D3 receptors in live HEK293 cells. Using a microplate compatible automated widefield fluorescence microscope, together with the membrane tools software, enables the detection and quantification of ligand binding with a high-throughput. The live cell assay is suitable for the characterization of fluorescent ligand binding and also in the competition experiments for the screening of novel unlabeled dopaminergic ligands. We propose that this simple yet more native-like approach is feasible in GPCR research, as it enables the detection of ligand binding in an environment containing more components involved in the signal transduction cascade.

Testing independence between two random sets for the analysis of colocalization in bioimaging.

Colocalization aims at characterizing spatial associations between two fluorescently tagged biomolecules by quantifying the co-occurrence and correlation between the two channels acquired in fluorescence microscopy. Colocalization is presented either as the degree of overlap between the two channels or the overlays of the red and green images, with areas of yellow indicating colocalization of the molecules. This problem remains an open issue in diffraction-limited microscopy and raises new challenges with the emergence of superresolution imaging, a microscopic technique awarded by the 2014 Nobel prize in chemistry. We propose GcoPS, for Geo-coPositioning System, an original method that exploits the random sets structure of the tagged molecules to provide an explicit testing procedure. Our simulation study shows that GcoPS unequivocally outperforms the best competitive methods in adverse situations (noise, irregularly shaped fluorescent patterns, and different optical resolutions). GcoPS is also much faster, a decisive advantage to face the huge amount of data in superresolution imaging. We demonstrate the performances of GcoPS on two biological real data sets, obtained by conventional diffraction-limited microscopy technique and by superresolution technique, respectively.

A two-stage method for automated detection of ring-like endosomes in fluorescent microscopy images.

Endosomes are subcellular organelles which serve as important transport compartments in eukaryotic cells. Fluorescence microscopy is a widely applied technology to study endosomes at the subcellular level. In general, a microscopy image can contain a large number of organelles and endosomes in particular. Detecting and annotating endosomes in fluorescence microscopy images is a critical part in the study of subcellular trafficking processes. Such annotation is usually performed by human inspection, which is time-consuming and prone to inaccuracy if carried out by inexperienced analysts. This paper proposes a two-stage method for automated detection of ring-like endosomes. The method consists of a localization stage cascaded by an identification stage. Given a test microscopy image, the localization stage generates a voting-map by locally comparing the query endosome patches and the test image based on a bag-of-words model. Using the voting-map, a number of candidate patches of endosomes are determined. Subsequently, in the identification stage, a support vector machine (SVM) is trained using the endosome patches and the background pattern patches. Each of the candidate patches is classified by the SVM to rule out those patches of endosome-like background patterns. The performance of the proposed method is evaluated with real microscopy images of human myeloid endothelial cells. It is shown that the proposed method significantly outperforms several state-of-the-art competing methods using multiple performance metrics.

Temperature imaging using a cationic linear fluorescent polymeric thermometer and fluorescence lifetime imaging microscopy.

Temperature is one of the most important of the physiological parameters that determine the biological status of living organisms. However, intracellular temperature was not imaged at the single-cell level until recently because of the lack of a molecular thermometer that can be applied to living cells. We have recently developed a method for imaging intracellular temperature using a cationic linear fluorescent polymeric thermometer (FPT) and fluorescence lifetime imaging microscopy (FLIM). The cationic linear FPT exhibits cell permeability in various mammalian cell lines and yeast cells, entering live cells within 10 min of incubation. Intracellular thermometry using the cationic linear FPT and FLIM can be used to image temperature with high temperature resolution (0.3-1.29 °C within a temperature range of 25-35 °C). The diffuse intracellular localization of the cationic linear FPT allows a high spatial resolution (i.e., the light microscope's diffraction limit, 200 nm), enabling the detection of temperature distributions at the subcellular level. This protocol, including the construction of a calibration curve and intracellular temperature imaging, requires ~14 h. Experience in handling cultured mammalian cells and use of a confocal laser-scanning microscope (CLSM) is required.

Biobeam-Multiplexed wave-optical simulations of light-sheet microscopy.

Sample-induced image-degradation remains an intricate wave-optical problem in light-sheet microscopy. Here we present biobeam, an open-source software package that enables simulation of operational light-sheet microscopes by combining data from 105-106 multiplexed and GPU-accelerated point-spread-function calculations. The wave-optical nature of these simulations leads to the faithful reproduction of spatially varying aberrations, diffraction artifacts, geometric image distortions, adaptive optics, and emergent wave-optical phenomena, and renders image-formation in light-sheet microscopy computationally tractable.

Deep learning massively accelerates super-resolution localization microscopy.

The speed of super-resolution microscopy methods based on single-molecule localization, for example, PALM and STORM, is limited by the need to record many thousands of frames with a small number of observed molecules in each. Here, we present ANNA-PALM, a computational strategy that uses artificial neural networks to reconstruct super-resolution views from sparse, rapidly acquired localization images and/or widefield images. Simulations and experimental imaging of microtubules, nuclear pores, and mitochondria show that high-quality, super-resolution images can be reconstructed from up to two orders of magnitude fewer frames than usually needed, without compromising spatial resolution. Super-resolution reconstructions are even possible from widefield images alone, though adding localization data improves image quality. We demonstrate super-resolution imaging of >1,000 fields of view containing >1,000 cells in ∼3 h, yielding an image spanning spatial scales from ∼20 nm to ∼2 mm. The drastic reduction in acquisition time and sample irradiation afforded by ANNA-PALM enables faster and gentler high-throughput and live-cell super-resolution imaging.

Sensitive detection of fluorescence in western blotting by merging images.

The western blotting technique is widely used to analyze protein expression levels and protein molecular weight. The chemiluminescence method is mainly used for detection due to its high sensitivity and ease of manipulation, but it is unsuitable for detailed analyses because it cannot be used to detect multiple proteins simultaneously. Recently, more attention has been paid to the fluorescence detection method because it is more quantitative and is suitable for the detection of multiple proteins simultaneously. However, fluorescence detection can be limited by poor image resolution and low detection sensitivity. Here, we describe a method to detect fluorescence in western blots using fluorescence microscopy to obtain high-resolution images. In this method, filters and fluorescent dyes are optimized to enhance detection sensitivity to a level similar to that of the chemiluminescence method.

Quantifying protein densities on cell membranes using super-resolution optical fluctuation imaging.

Quantitative approaches for characterizing molecular organization of cell membrane molecules under physiological and pathological conditions profit from recently developed super-resolution imaging techniques. Current tools employ statistical algorithms to determine clusters of molecules based on single-molecule localization microscopy (SMLM) data. These approaches are limited by the ability of SMLM techniques to identify and localize molecules in densely populated areas and experimental conditions of sample preparation and image acquisition. We have developed a robust, model-free, quantitative clustering analysis to determine the distribution of membrane molecules that excels in densely labeled areas and is tolerant to various experimental conditions, i.e. multiple-blinking or high blinking rates. The method is based on a TIRF microscope followed by a super-resolution optical fluctuation imaging (SOFI) analysis. The effectiveness and robustness of the method is validated using simulated and experimental data investigating nanoscale distribution of CD4 glycoprotein mutants in the plasma membrane of T cells.

Analyzing optical imaging of Ca2+ signals via TIRF microscopy: The limits on resolution due to chemical rates and depth of the channels.

High resolution total internal reflection (TIRF) microscopy (TIRFM) together with detailed computational modeling provides a powerful approach towards the understanding of a wide range of Ca2+ signals mediated by the ubiquitous inositol 1,4,5-trisphosphate (IP3) receptor (IP3R) channel. Exploiting this fruitful collaboration further requires close agreement between the models and observations. However, elementary Ca2+ release events, puffs, imaged through TIRFM do not show the rapid single-channel openings and closings during and between puffs as are present in simulated puffs using data-driven single channel models. TIRFM also shows a rapid equilibration of 10ms after a channel opens or closes which is not achievable in simulation using standard Ca2+ diffusion coefficients and reaction rates between indicator dye and Ca2+. Furthermore, TIRFM imaging cannot decipher the depth of the channel with respect to the microscope, which will affect the change in fluorescence that the microscope detects, thereby affecting its sensitivity to fast single-channel activity. Using the widely used Ca2+ diffusion coefficients and reaction rates, our simulations show equilibration rates that are eight times slower than TIRFM imaging. We show that to get equilibrium rates consistent with observed values, the diffusion coefficients and reaction rates have to be significantly higher than the values reported in the literature, and predict the channel depth to be 200-250nm. Finally, we show that with the addition of noise, short events due to 1-2ms opening and closing of channels that are observed in computational models can be missed in TIRFM.

Implementation of fluorescence confocal mosaicking microscopy by "early adopter" Mohs surgeons and dermatologists: recent progress.

Confocal mosaicking microscopy (CMM) enables rapid imaging of large areas of fresh tissue ex vivo without the processing that is necessary for conventional histology. When performed in fluorescence mode using acridine orange (nuclear specific dye), it enhances nuclei-to-dermis contrast that enables detection of all types of basal cell carcinomas (BCCs), including micronodular and thin strands of infiltrative types. So far, this technique has been mostly validated in research settings for the detection of residual BCC tumor margins with high sensitivity of 89% to 96% and specificity of 99% to 89%. Recently, CMM has advanced to implementation and testing in clinical settings by "early adopter" Mohs surgeons, as an adjunct to frozen section during Mohs surgery. We summarize the development of CMM guided imaging of ex vivo skin tissues from bench to bedside. We also present its current state of application in routine clinical workflow not only for the assessment of residual BCC margins in the Mohs surgical setting but also for some melanocytic lesions and other skin conditions in clinical dermatology settings. Last, we also discuss the potential limitations of this technology as well as future developments. As this technology advances further, it may serve as an adjunct to standard histology and enable rapid surgical pathology of skin cancers at the bedside.

Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.

An Overview of data science uses in bioimage informatics.

This review aims at providing a practical overview of the use of statistical features and associated data science methods in bioimage informatics. To achieve a quantitative link between images and biological concepts, one typically replaces an object coming from an image (a segmented cell or intracellular object, a pattern of expression or localisation, even a whole image) by a vector of numbers. They range from carefully crafted biologically relevant measurements to features learnt through deep neural networks. This replacement allows for the use of practical algorithms for visualisation, comparison and inference, such as the ones from machine learning or multivariate statistics. While originating mainly, for biology, in high content screening, those methods are integral to the use of data science for the quantitative analysis of microscopy images to gain biological insight, and they are sure to gather more interest as the need to make sense of the increasing amount of acquired imaging data grows more pressing.

Model-independent counting of molecules in single-molecule localization microscopy.

Most biomolecular processes rely on tightly controlled stoichiometries, from the formation of molecular assemblies to cellular signaling. Single-molecule localization micro-scopy studies of fluorophore blinking offer a promising route to probe oligomeric states. Here we show that the distribution of the number of blinking events assumes a universal functional form, independent of photophysics, under relatively mild assumptions. The number of photophysical states, the kinetics of interconversion, and the fraction of active fluorophores enter as two or three constants. This essentially model-independent formulation allows us to determine molecule counts from fluorophore blinking statistics. The formulas hold even if the fluorophores have many different yet unresolved dark states, as long as there is only a single fluorescent state, or if there are different yet unresolvable fluorescent states, as long as there is only a single dark state. We demonstrate the practical applicability of this approach by quantifying the oligomerization states of membrane proteins tagged with the mEos2 fluorescent protein. We find that the model parameters, obtained by likelihood maximization, are transferable. With the counting statistics being independent of the detailed photophysics and its parameters being transferable, the method should be robust and broadly applicable to counting colocalized molecules in vivo and in vitro.