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Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10–20 nm. SMLM thus enables imaging single molecul...
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... is based on two parameters for detecting and segmenting the clusters in SMLM data. It requires a neighborhood radius ε and the minimum number of localizations/points (MinPts) within ε to qualify as a cluster (Figure 7). The algorithm can start from any molecular localization that has not been visited. ...
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... Single-molecule localization microscopy (SMLM) approaches such as direct stochastic optical reconstruction microscopy (dSTORM) can be used to image Cav1 structures sized below the diffraction limit [8, 9,91,92]. Using the time-resolved blinking patterns of individual fluorophores to identify their precise location within 2D or 3D space, dSTORM imaging produces a point cloud of localizations that can be used to reconstruct Cav1 structures with a lateral resolution as low as 20 nm [93]. Machine learning network analyses of dSTORM-generated SMLM point clouds, comparing PC3 prostate cancer cells expressing Cav1, but lacking cavin-1 and caveolae, with PC3 cells stably transfected with cavin-1, enabled the microscopic detection and identification of caveolae and three types of scaffolds in the plasma membrane [9]. ...
Caveolin-1 (Cav1) is a 22 kDa intracellular protein that is the main protein constituent of bulb-shaped membrane invaginations known as caveolae. Cav1 can be also found in functional non-caveolar structures at the plasma membrane called scaffolds. Scaffolds were originally described as SDS-resistant oligomers composed of 10–15 Cav1 monomers observable as 8S complexes by sucrose velocity gradient centrifugation. Recently, cryoelectron microscopy (cryoEM) and super-resolution microscopy have shown that 8S complexes are interlocking structures composed of 11 Cav1 monomers each, which further assemble modularly to form higher-order scaffolds and caveolae. In addition, Cav1 can act as a critical signaling regulator capable of direct interactions with multiple client proteins, in particular, the endothelial nitric oxide (NO) synthase (eNOS), a role believed by many to be attributable to the highly conserved and versatile scaffolding domain (CSD). However, as the CSD is a hydrophobic domain located by cryoEM to the periphery of the 8S complex, it is predicted to be enmeshed in membrane lipids. This has led some to challenge its ability to interact directly with client proteins and argue that it impacts signaling only indirectly via local alteration of membrane lipids. Here, based on recent advances in our understanding of higher-order Cav1 structure formation, we discuss how the Cav1 CSD may function through both lipid and protein interaction and propose an alternate view in which structural modifications to Cav1 oligomers may impact exposure of the CSD to cytoplasmic client proteins, such as eNOS.
... 29 On the other hand, STORM and PALM require the use of photoactivatable and photoswitchable fluorophores for molecular photo-localisation of biomolecules, which can be expensive and are limited to the observation of thick samples. 30,31 In contrast, SIM ( Figure 1C) allows thicker samples to be observed, and it is possible to use conventional fluorophores to resolve biological structures of up to 100 nm. 24 This technique is suitable for the low-cost nanostructural characterisation of cellulose fibres, monitoring pulping processes, and isolating cellulose fibres with a better resolution regarding CLSM. ...
... This size is obviously related to a cluster of accumulated emitters instead of single molecules attached to the sample. 31 Furthermore, green dots in the samples represent the single-molecule localisation for a calcofluor white molecule attached to the surface of a cellulose chain. However, as fluorescence follows a Gaussian distribution, isolated dots were analysed with higher precision showing nanometric sizes below 30 nm for a single-molecule (see W SMLM in Table 1). ...
Nowadays, the use of super‐resolution microscopy (SRM) is increasing globally due to its potential application in several fields of life sciences. However, a detailed and comprehensive guide is necessary for understanding a single‐frame image's resolution limit. This study was performed to provide information about the structural organisation of isolated cellulose fibres from garlic and agave wastes through fluorophore‐based techniques and image analysis algorithms. Confocal microscopy provided overall information on the cellulose fibres’ microstructure, while techniques such as total internal reflection fluorescence microscopy facilitated the study of the plant fibres’ surface structures at a sub‐micrometric scale. Furthermore, SIM and single‐molecule localisation microscopy (SMLM) using the PALM reconstruction wizard can resolve the network of cellulose fibres at the nanometric level. In contrast, the mean shift super‐resolution (MSSR) algorithm successfully determined nanometric structures from confocal microscopy images. Atomic force microscopy was used as a microscopy technique for measuring the size of the fibres. Similar fibre sizes to those evaluated with SIM and SMLM were found using the MSSR algorithm and AFM. However, the MSSR algorithm must be cautiously applied because the selection of thresholding parameters still depends on human visual perception. Therefore, this contribution provides a comparative study of SRM techniques and MSSR algorithm using cellulose fibres as reference material to evaluate the performance of a mathematical algorithm for image processing of bioimages at a nanometric scale. In addition, this work could act as a simple guide for improving the lateral resolution of single‐frame fluorescence bioimages when SRM facilities are unavailable.
... The advancement of machine learning-based approaches [32][33][34][35][36][37][38][39][40][41][42] has been instrumental for quantitative image analysis 30,43,44 and has the potential to resolve the bottleneck of extraction of assemblies of interest in super-resolution data. In general, these approaches can be broadly categorised as either supervised or unsupervised, each of which has advantages and limitations 30 . ...
... Their performance however is often limited by a onesize-fits-all approach. This often results in laborious human intervention in model tuning, restricting their adaptation to heterogeneity in localization densities and assembly sizes in varying experimental data 43,49 . While these approaches support multidimensional data input, temporal information is not incorporated into distance coordinate systems, rendering clustering algorithms ineffective for handling a temporal axis. ...
The morphology of protein assemblies impacts their behaviour and contributes to beneficial and aberrant cellular responses. While single-molecule localization microscopy provides the required spatial resolution to investigate these assemblies, the lack of universal robust analytical tools to extract and quantify underlying structures limits this powerful technique. Here we present SEMORE, a semi-automatic machine learning framework for universal, system- and input-dependent, analysis of super-resolution data. SEMORE implements a multi-layered density-based clustering module to dissect biological assemblies and a morphology fingerprinting module for quantification by multiple geometric and kinetics-based descriptors. We demonstrate SEMORE on simulations and diverse raw super-resolution data: time-resolved insulin aggregates, and published data of dSTORM imaging of nuclear pore complexes, fibroblast growth receptor 1, sptPALM of Syntaxin 1a and dynamic live-cell PALM of ryanodine receptors. SEMORE extracts and quantifies all protein assemblies, their temporal morphology evolution and provides quantitative insights, e.g. classification of heterogeneous insulin aggregation pathways and NPC geometry in minutes. SEMORE is a general analysis platform for super-resolution data, and being a time-aware framework can also support the rise of 4D super-resolution data.
... Through novel optical designs to manipulate the PSFs, researchers also achieved 3D SMLM [29][30][31]. As SMLM images are histograms of discontinuous localizations of discrete fluorescence emissions, SMLM provides unique information about local protein distribution and density [32]. This can be particularly interesting when evaluating mitochondrial outer membrane cohesion, closely associated with mitochondrial health [33,34]. ...
Mitochondrial morphology provides unique insights into their integrity and function. Among fluorescence microscopy techniques, 3D super-resolution microscopy uniquely enables the analysis of mitochondrial morphological features individually. However, there is a lack of tools to extract morphological parameters from super-resolution images of mitochondria. We report a quantitative method to extract mitochondrial morphological metrics, including volume, aspect ratio, and local protein density, from 3D single-molecule localization microscopy images, with single-mitochondrion sensitivity. We validated our approach using simulated ground-truth SMLM images of mitochondria. We further tested our morphological analysis on mitochondria that have been altered functionally and morphologically in controlled manners. This work sets the stage to quantitatively analyze mitochondrial morphological alterations associated with disease progression on an individual basis.
... Consequently, the research power provided by these capabilities comes with an accompanying increase in responsibility to perform reliable and reproducible science (Munafò et al., 2017). This imperative has led to an abundance of guides in the literature for every stage of a microscopy project from experimental conceptualization (Jost and Waters, 2019;Wait et al., 2020) to image acquisition (Jonkman et al., 2020;North, 2006) and quantification (Khater et al., 2020;Waters and Swedlow, 2008). Journals, reviewers and funding agencies can encourage the use of these best practices, but ultimately, implementation of these procedures falls on the observer. ...
... There is a wealth of guidance in the literature on experimental design (Jost and Waters, 2019;Wait et al., 2020), sample preparation (Reiche et al., 2022), image acquisition (Lee et al., 2018;North, 2006;Waters and Swedlow, 2008), image analysis (Aaron et al., 2018;Aaron et al., 2019; see 2019 article by Cimini at https://carpenter-singh-lab.broadinstitute.org/blog/when-tosay-good-enough; Khater et al., 2020) and statistical techniques (Bishop, 2020;Krzywinski and Altman, 2013;Makin and Orban de Xivry, 2019;Nuzzo, 2014;Pollard et al., 2019) take advantage of these resources. Take advantage of computational tools Computational tools can minimize the hazards of choosing experimental parameters and challenge naïve perceptions. ...
... Similarly, clusters of events in time instinctively seem meaningful, but can be the natural result of random chance (Gilovich et al., 1985). Quantitative approaches for testing randomness, such as the use of Ripley's K function (Khater et al., 2020), can be used to minimize this perceptual distortion. In other cases, illusory relationships might appear due to global trends in the studied biological system. ...
The visual allure of microscopy makes it an intuitively powerful research tool. Intuition, however, can easily obscure or distort the reality of the information contained in an image. Common cognitive biases, combined with institutional pressures that reward positive research results, can quickly skew a microscopy project towards upholding, rather than rigorously challenging, a hypothesis. The impact of these biases on a variety of research topics is well known. What might be less appreciated are the many forms in which bias can permeate a microscopy experiment. Even well-intentioned researchers are susceptible to bias, which must therefore be actively recognized to be mitigated. Importantly, although image quantification has increasingly become an expectation, ostensibly to confront subtle biases, it is not a guarantee against bias and cannot alone shield an experiment from cognitive distortions. Here, we provide illustrative examples of the insidiously pervasive nature of bias in microscopy experiments – from initial experimental design to image acquisition, analysis and data interpretation. We then provide suggestions that can serve as guard rails against bias.
... Other approaches, such as high-throughput genome sequencing [9] and chemometric analysis, as well as various microscopic and spectroscopic techniques (e.g., fluorescence, confocal, Raman, and Fourier Transform Infrared Spectroscopy), have been employed to explore genetic variations, biochemical changes, and cellular dynamics [10]. However, these methods often require extensive sample preparation, have limited sample lifetimes, lengthy processing times (e.g., several days), and rely on external probes [11]- [17]. Furthermore, the anisotropic nature of biological cells and the time sensitivity of cellular dehydration during sample preparation pose additional challenges. ...
This study investigated the use of terahertz (THz) imaging as a rapid, high-fidelity technique for discriminating between genetic variants of the
Allium
genus based on cellular water dynamics. It has been demonstrated earlier that plant genetic variations can be related to the biochemical and bio-mechanical alterations of the cell and that in turn they affect the water dynamics within the cell. In this article we show that the water dynamics, when considered in the form of the temporal evolution of the trajectory of the plant's response to terahertz radiation probe, and measured by a coherent terahertz transceiver, provides unique signature of the genetic makeup of the plant. Therefore by exploring these trajectories, we discriminate between closely related variants of the same genus. The technique used for THz probing was the laser feedback interferometry with THz quantum cascade lasers which enabled fast acquisition of high-resolution THz amplitude and phase images, which were processed into evaporation profiles describing the time-dependent dehydration of the samples. The trajectory of this profile in amplitude-phase reflectivity domain discriminates between different members of the
Allium
genus. This enables real-time genetic discrimination in agricultural and genome conservation applications.
... SMLM achieves SR imaging by sparse activation of the fluorophores. A graphic overview of SMLM is illustrated in Figure 2 [9]. In each frame of the image, only a small fraction of fluorophores are randomly activated, while others remain dark [10]. ...
... The non-overlapping images of individual fluorophores are processed with Gaussian fitting algorithms to locate the centers of each bright spot, i.e., the probable positions of the fluorescent molecules. The SR image of the full field-of-view (FOV) is then reconstructed from thousands of frames [9]. DNA-PAINT is a typical SMLM method that utilizes the well-understood, robust, and specific binding between complementary DNA strands to locate target molecules [12]. ...
Neurodegenerative diseases (NDs) are closely associated with the amyloid aggregation of proteins like Amyloid-, -synuclein, and tau. Understanding the pathogenesis of NDs requires studying the structures and morphological features of these aggregates, which are typically below 100 nm in size. Traditional fluorescence microscopy is limited by the diffraction limit of light (~250 nm). Single-molecule localization microscopy (SMLM) offers a resolution down to ~20 nm, enabling the visualization of these aggregates. However, issues such as non-specific binding (NSB) of fluorophores and background noise degrade the quality of SMLM images. This study presents a U-net-based convolutional neural network (CNN) to denoise SMLM images of protein aggregates. The training dataset includes noise-free super-resolution images of aggregates and their noisy counterparts with non-specific binding signals. Various imaging conditions are simulated to mimic real-world scenarios. The U-net's output is evaluated against ground-truth images for denoising performance. Post-processing techniques further enhance denoised images. The fine-tuned U-net model achieves a validation loss of 0.0042 and low prediction errors of 0.32% and 3.71% in the area and number of aggregates, respectively. This research offers a powerful tool for denoising SMLM images, facilitating accurate characterization of protein aggregate structures and morphological features.
... However, SR microscopy techniques provide ways to gain the nm spatial resolution by introducing additional molecular contrast on the imaged items by using special features of the fluorescent dyes, such as on-off fluorescence blinking which is at the heart of single molecule localization microscopy (SMLM) techniques [3][4][5] , or by using special optics, such as in stimulated emission depletion (STED) microscopy [6][7][8][9][10] or minimal flux (MINFLUX) microscopy 11-13 . Imaging interactions between different biomolecules is more complex. ...
... As described above, we studied four different FRET reporter parameter channels (see Eqs. [1][2][3][4]. Then, we searched these images to detect local maxima in parameter channels 1 and 3, or local minima in parameter channels 2 and 4 (Fig. S1). ...
Super-resolution light microscopy techniques facilitate the observation of nanometer-size biomolecules, which are 1-2 orders of magnitude smaller than the diffraction limit of light. Using super-resolution microscopy techniques it is possible to observe fluorescence from two biomolecules in close proximity, however not necessarily in direct interaction. Using FRET-sensitized acceptor emission localization (FRETsael), we localize biomolecular interactions exhibiting FRET with nanometer accuracy, from two color fluorescence lifetime imaging data. The concepts of FRETsael were tested first against simulations, in which the recovered localization accuracy is 20-30 nm for true-positive detections of FRET pairs. Further analyses of the simulation results report the conditions in which true-positive rates are maximal. We then show the capabilities of FRETsael on simulated samples of Actin-Vinculin and ER-ribosomes interactions, as well as on experimental samples of Actin-Myosin two-color confocal imaging. Conclusively, the FRETsael approach paves the way towards studying biomolecular interactions with improved spatial resolution from laser scanning confocal two color fluorescence lifetime imaging.
... Although relatively uncommon in microscopy, this type of dataset has been analysed for decades in fields such as ecology and epidemiology. This has provided a baseline for the development of a vast range of cluster analysis tools dedicated to SMLM (Khater et al., 2020). Here, we will discuss the dos and don'ts when analysing SMLM SPP for cluster quantification. ...
... There are a wide variety of cluster analysis algorithms available, even when only considering those that have been tested and validated for use on SMLM data (Khater et al., 2020). While they all have advantages and disadvantages compared to each other, one property that almost all share is the use of user-defined analysis settings. ...
Many proteins display a non-random distribution on the cell surface. From dimers to nanoscale clusters to large, micron-scale aggregations, these distributions regulate protein-protein interactions and signalling. Although these distributions show organisation on length-scales below the resolution limit of conventional optical microscopy, single molecule localisation microscopy (SMLM) can map molecule locations with nanometre precision. The data from SMLM is not a conventional pixelated image and instead takes the form of a point-pattern—a list of the x, y coordinates of the localised molecules. To extract the biological insights that researchers require cluster analysis is often performed on these data sets, quantifying such parameters as the size of clusters, the percentage of monomers and so on. Here, we provide some guidance on how SMLM clustering should best be performed.
... This is paralleled by extremely efficient and precise methods for genome editing using CRISPR-Cas9, which allow the introduction of almost any genetic change into genomes of almost any organism, including plants (Nasti & Voytas, 2021). Cell biology has been revolutionised with the improvement of the spatial resolution of light microscopy using superresolution imaging and temporal resolution of observations using genetically encoded sensors and optogenetics, i.e., the use of light to manipulate cellular parameters (Goglia & Toettcher, 2019;Khater, Nabi, & Hamarneh, 2020;Kim, Ju, Lee, Chun, & Seong, 2021). Another rapidly evolving area is the application of soft matter physics in cell biology with the study of liquid-liquid phase separation and the formation of membrane-less organelles and their role in cellular metabolism and signalling (Shin & Brangwynne, 2017). ...
