Single-molecule localization microscopy analysis with ImageJ

Advanced image-free analysis of the nano-organization of chromatin and other biomolecules by Single Molecule Localization Microscopy (SMLM)

The advancement of high-content optical microscopy has enabled the acquisition of very large three-dimensional (3D) image datasets. The analysis of these image volumes requires more computational resources than a biologist may have access to in typical desktop or laptop computers. This is especially true if machine learning tools are being used for image analysis. With the increased amount of data analysis and computational complexity, there is a need for a more accessible, easy-to-use, and efficient network-based 3D image processing system. The distributed and networked analysis of volumetric image data (DINAVID) system was developed to enable remote analysis of 3D microscopy images for biologists. We present an overview of the DINAVID system and compare it to other tools currently available for microscopy image analysis. DINAVID is designed using open-source tools and has two main sub-systems, a computational system for 3D microscopy image processing and analysis and a 3D visualization system. DINAVID is a network-based system with a simple web interface that allows biologists to upload 3D volumes for analysis and visualization. DINAVID enables the image access model of a center hosting image volumes and remote users analyzing those volumes, without the need for remote users to manage any computational resources. The DINAVID system, designed and developed using open-source tools, enables biologists to analyze and visualize 3D microscopy volumes remotely without the need to manage computational resources. DINAVID also provides several image analysis tools, including pre-processing and several segmentation models.

The advent of single molecule localization microscopy (SMLM) has transformed our capacity to investigate biological structures at the nanoscale. While the research focus has long been on improving localization precision, systematic errors caused by optical aberrations are often overlooked. In the case of 3D SMLM, such errors have the potential to significantly impair the quality of the resulting images. In this paper, we present an imaging and data processing approach that jointly estimates both, molecule positions and optical aberrations in SMLM. Therefore, the method minimizes systematic errors in SMLM reconstructions without the necessity of additional experimental calibration steps, such as the recording of fluorescent bead z-stacks. We investigate the reliability of this approach, especially in situations where the joint retrieval can be expected to be ill-posed, i.e., whenever the sample is "flat" and provides little diversity among the captured single molecule images. To enhance the reliability of the inverse problem solution, we suggest utilizing small SMLM data sets acquired at one or more slightly defocused "auxiliary" planes. We investigate the effectiveness of our approach through numerical simulations and imaging experiments of a calibration probe and nuclear pore complexes. Our method is simple and integrates seamlessly into existing SMLM setups without necessitating modifications or added complexity to the system.

Robust Nonparametric Descriptors for Clustering Quantification in Single-Molecule Localization Microscopy

Cancer is a major health threat worldwide. Options for targeted cancer therapy, however, are often limited, in a large part due to our incomplete understanding of how key processes including oncogenesis and drug response are mediated at the molecular level. New imaging techniques for visualizing biomolecules and their interactions at the nanometer and single molecule scales, collectively named fluorescence nanoscopy, hold the promise to transform biomedical research by providing direct mechanistic insight into cellular processes. We discuss the principles of quantitative single-molecule localization microscopy (SMLM), a subset of fluorescence nanoscopy, and their applications to cancer biomedicine. In particular, we will examine oncogenesis and drug resistance mediated by mutant Ras, which is associated with ~1/3 of all human cancers but has remained an intractable drug target. At ~20 nm spatial and single-molecule stoichiometric resolutions, SMLM clearly showed that mutant Ras must form dimers to activate its effector pathways and drive oncogenesis. SMLM further showed that the Raf kinase, one of the most important effectors of Ras, also forms dimers upon activation by Ras. Moreover, treatment of cells expressing wild type Raf with Raf inhibitors induces Raf dimer formation in a manner dependent on Ras dimerization. Together, these data suggest that Ras dimers mediate oncogenesis and drug resistance in tumors with hyperactive Ras and can potentially be targeted for cancer therapy. We also discuss recent advances in SMLM that enable simultaneous imaging of multiple biomolecules and their interactions at the nanoscale. Our work demonstrates the power of quantitative SMLM in cancer biomedicine.

P-glycoprotein is implicated in clinical drug resistance; thus, rapid quantitative analysis of its expression and activity is of paramout importance to the design and success of novel therapeutics. The scope for the application of quantitative imaging and image analysis tools in this field is reported here at "proof of concept" level. P-glycoprotein expression was utilized as a model for quantitative immunofluorescence and subsequent spatial intensity distribution analysis (SpIDA). Following expression studies, p-glycoprotein inhibition as a function of verapamil concentration was assessed in two cell lines using live cell imaging of intracellular Calcein retention and a routine monolayer fluorescence assay. Intercellular and sub-cellular distributions in the expression of the p-glycoprotein transporter between parent and MDR1-transfected Madin-Derby Canine Kidney cell lines were examined. We have demonstrated that quantitative imaging can provide dose-response parameters while permitting direct microscopic analysis of intracellular fluorophore distributions in live and fixed samples. Analysis with SpIDA offers the ability to detect heterogeniety in the distribution of labeled species, and in conjunction with live cell imaging and immunofluorescence staining may be applied to the determination of pharmacological parameters or analysis of biopsies providing a rapid prognostic tool.

Super-resolution fluorescence imaging based on single-molecule localization microscopy (SMLM) enables visualizing cellular structures with nanometric precision. However, its spatial and temporal resolution largely relies on the brightness of ON/OFF switchable fluorescent dyes. Moreover, in cell plasma membranes, the single-molecule localization is hampered by the fast lateral diffusion of membrane probes. Here, to address these two fundamental problems, we propose a concept of ON/OFF switchable probes for SMLM (points accumulation for imaging in nanoscale topography, PAINT) based on fluorogenic dimers of bright cyanine dyes. In these probes, the two cyanine units connected with a linker were modified at their extremities with low-affinity membrane anchors. Being self-quenched in water due to intramolecular dye H-aggregation, they displayed light up on reversible binding to lipid membranes. The charged group in the linker further decreased the probe affinity to the lipid membranes, thus accelerating its dynamic reversible ON/OFF switching. The concept was validated on cyanines 3 and 5. SMLM of live cells revealed that the new probes provided higher brightness and ∼10-fold slower diffusion at the cell surface, compared to reference probes Nile Red and DiD, which boosted axial localization precision >3-fold down to 31 nm. The new probe allowed unprecedented observation of nanoscale fibrous protrusions on plasma membranes of live cells with 40 s time resolution, revealing their fast dynamics. Thus, going beyond the brightness limit of single switchable dyes by cooperative dequenching in fluorogenic dimers and slowing down probe diffusion in biomembranes open the route to significant enhancement of super-resolution fluorescence microscopy of live cells.

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine and deep learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine and deep learning approaches to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-picked features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand selected features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches. Additional information about individual files is in the accompanying CSV file (item_metadata.csv). This dataset was originally deposited in the Simon Fraser University institutional repository.

As one of the super-resolved optical imaging techniques, single molecule localization microscopy (SMLM) received considerable attention due to its impressive spatial resolution. Compared with other fluorescence imaging techniques, SMLM has one particular request for the fluorophores, that is, continuous ‘on’ and ‘off’ behaviors of their signals (referred to as ‘blinking’). Hence, we present here a kind of super blinking and biocompatible nanoprobes (denoted as SBNs) for SMLM. The SBNs have two main advantages, first, they possess an outstanding fluorescence blinking. Second, they are biocompatible since they are based on bovine serum albumin (BSA). The SBNs are fabricated by doping organic dyes into BSA nanoparticles. We fabricated two kinds of SBNs, one was doped with Alexa Fluor 647 (A647) and the other was doped with Alexa Fluor 594 (A594). Especially for A594 doped SBNs, the improved blinking of A594 doped SBNs induced a better localization precision as compared with A594 alone. Moreover, SMLM imaging of breast cancer cells and exosomes using the SBNs was successfully realized with high spatial resolutions. The work demonstrated here provides a new strategy to prepare novel kinds of super blinking fluorescent agents for SMLM, which broadens the selection of suitable fluorophores for SMLM.

To better understand the interactions between biological molecules, a high optical resolution in all three dimensions is crucial. The intrinsically lower axial resolution of microscopes however, is a limiting factor in fluorescence imaging, correspondingly in fluorescence based single molecule localization microscopy (SMLM). Here, we present a method to improve the axial localization precision in SMLM by combining point-spread-function engineering with total internal reflection fluorescence (TIRF) fields with decay lengths that vary within the on-time of a fluorophore. Such time-varying illumination field intensity allows one to extract additional axial location information from the emitted photons. With this time varying illumination approach, we show that axial localization is improved two-fold over TIRF-based SMLM using astigmatic PSFs. We calculate theoretical resolution gains for various imaging conditions via the Cramér Rao Lower Bound (CRLB), a commonly used metric to compute the best attainable localization precision in SMLM.

Image analysis tools are currently in use for a variety of applications in the fields of forensics and security printing. Objective, quantitative data allow for characterization and comparison of a variety of image attributes.In forensics, document examiners can use quantitative image analysis systems in authentication, detection and analysis of forgeries, counterfeits and altered documents. Statistical analysis enables population characterization that can help in forgery detection and supports the potential for source identification. In security printing, image analysis tools can be applied to measure the quality of printed output and assess wear for bank notes.Flexible hardware options allow for a broad application of image quality measurements in a variety of image analysis situations. Camera and scanner-based image quality systems can be used for quantitative analysis of image content. A laser-based system can be used for height and depth profiling. Oblique angle illumination and scanning can be used for impression detection. A flatbed scanner can be used in transmission mode for the evaluation of bank note wear and for watermark analysis.In this paper, we will be discussing the use of a variety of hardware configurations for image analysis in forensics and security printing.

Expanding the Spectral Resolution of Single-Molecule Localization Microscopy with Bodipy-Based Photoswitchable Fluorophores

Single-molecule localization microscopy (SMLM) has revolutionized the study of biological phenomena by providing exquisite nanoscale spatial resolution. However, optical aberrations induced by sample and system imperfections distort the single-molecule emission patterns (i.e. PSFs), leading to reduced precision and resolution of SMLM, particularly in three-dimensional (3D) applications. While various methods, both analytical and instrumental, have been employed to mitigate these aberrations, a comprehensive analysis of how different types of commonly encountered aberrations affect single-molecule experiments and their image formation remains missing. In this study, we addressed this gap by conducting a quantitative study of the theoretical precision limit for position and wavefront distortion measurements in the presence of aberrations. Leveraging Fisher information and Cramér-Rao lower bound (CRLB), we quantitively analyzed and compared the effects of different aberration types, including index mismatch aberrations, on localization precision in both biplane and astigmatism 3D modalities as well as 2D SMLM imaging. Furthermore, we studied the achievable wavefront estimation precision from aberrated single-molecule emission patterns, a pivot step for successful adaptive optics in SMLM through thick specimens. This analysis lays a quantitative foundation for the development and application of SMLM in whole-cells, tissues and with a large field of view, providing in-depth insights into the behavior of different aberration types in single-molecule imaging and thus generating theoretical guidelines for developing highly efficient aberration correction strategies and enhancing the precision and reliability of 3D SMLM.

Single-molecule localization microscopy provides subdiffraction resolution images with virtually molecular resolution. Through the availability of commercial instruments and open-source reconstruction software, achieving super resolution is now public domain. However, despite its conceptual simplicity, localization microscopy remains prone to user errors. Using direct stochastic optical reconstruction microscopy, we investigate the impact of irradiation intensity, label density and photoswitching behavior on the distribution of membrane proteins in reconstructed super-resolution images. We demonstrate that high emitter densities in combination with inappropriate photoswitching rates give rise to the appearance of artificial membrane clusters. Especially, two-dimensional imaging of intrinsically three-dimensional membrane structures like microvilli, filopodia, overlapping membranes and vesicles with high local emitter densities is prone to generate artifacts. To judge the quality and reliability of super-resolution images, the single-molecule movies recorded to reconstruct the images have to be carefully investigated especially when investigating membrane organization and cluster analysis.

New innovations in single-molecule localization microscopy (SMLM) have revolutionized optical imaging, enabling the characterization of biological structures and interactions with unprecedented detail and resolution. However, multi-color or hyperspectral SMLM can pose particular challenges which affect image quality and data interpretation, such as unequal photophysical performance of fluorophores and non-linear image registration issues, which arise as two emission channels travel along different optical paths to reach the detector. In addition, using evanescent-wave based approaches (Total Internal Reflection Fluorescence: TIRF) where beam shape, decay depth, and power density are important, different illumination wavelengths can lead to unequal imaging depth across multiple channels on the same sample. A potential useful approach would be to use a single excitation wavelength to perform hyperspectral localization imaging. We report herein on the use of a variable angle tunable thin-film filter to spectrally isolate far-red emitting fluorophores. This solution was integrated into a commercial microscope platform using an open-source hardware design, enabling the rapid acquisition of SMLM images arising from fluorescence emission captured within ∼15 nm to 20 nm spectral windows (or detection bands). By characterizing intensity distributions, average intensities, and localization frequency through a range of spectral windows, we investigated several far-red emitting fluorophores and identified an optimal fluorophore pair for two-color SMLM using this method. Fluorophore crosstalk between the different spectral windows was assessed by examining the effect of varying the photon output thresholds on the localization frequency and fraction of data recovered. The utility of this approach was demonstrated by hyper-spectral super-resolution imaging of the interaction between the mitochondrial protein, TOM20, and the peroxisomal protein, PMP70.