Quantitative fibre analysis of single-molecule localization microscopy data

Quantitative Single-Molecule Localization Microscopy

Super-resolution fluorescence microscopy allows the investigation of cellular structures at nanoscale resolution using light. Current developments in super-resolution microscopy have focused on reliable quantification of the underlying biological data. In this review, we first describe the basic principles of super-resolution microscopy techniques such as stimulated emission depletion (STED) microscopy and single-molecule localization microscopy (SMLM), and then give a broad overview of methodological developments to quantify super-resolution data, particularly those geared toward SMLM data. We cover commonly used techniques such as spatial point pattern analysis, colocalization, and protein copy number quantification but also describe more advanced techniques such as structural modeling, single-particle tracking, and biosensing. Finally, we provide an outlook on exciting new research directions to which quantitative super-resolution microscopy might be applied.

Peptide-PAINT Using a Transfected-Docker Enables Live- and Fixed-Cell Super-Resolution Imaging

Point accumulation for imaging in nanoscale topography (PAINT) is a single-molecule technique for super-resolution microscopy, which uses exchangeable single stranded DNA oligos or peptide-pairs to create blinking phenomenon and achieves ≈5-25 nanometer resolution. Here, it is shown that by transfecting the protein-of-interest with a docker-coil, rather than by adding the docker externally-as is the norm when using DNA tethers or antibodies as dockers-similar localization can be achieved, ≈10 nm. However, using a transfected docker has several experimental advances and simplifications. Most importantly, it allows Peptide-PAINT to be applied to transfected live cells for imaging surface proteins in mammalian cells and neurons under physiological conditions. The enhanced resolution of Peptide-PAINT is also shown for organelles in fixed cells to unravel structural details including ≈40-nm and ≈60-nm axial repeats in vimentin filaments in the cytoplasm, and fiber shapes of sub-100-nm histone-rich regions in the nucleus.

The anatomy of transcriptionally active chromatin loops in Drosophila primary spermatocytes using super-resolution microscopy

While the biochemistry of gene transcription has been well studied, our understanding of how this process is organised in 3D within the intact nucleus is less well understood. Here we investigate the structure of actively transcribed chromatin and the architecture of its interaction with active RNA polymerase. For this analysis, we have used super-resolution microscopy to image the Drosophila melanogaster Y loops which represent huge, several megabases long, single transcription units. The Y loops provide a particularly amenable model system for transcriptionally active chromatin. We find that, although these transcribed loops are decondensed they are not organised as extended 10nm fibres, but rather they largely consist of chains of nucleosome clusters. The average width of each cluster is around 50nm. We find that foci of active RNA polymerase are generally located off the main fibre axis on the periphery of the nucleosome clusters. Foci of RNA polymerase and nascent transcripts are distributed around the Y loops rather than being clustered in individual transcription factories. However, as the RNA polymerase foci are considerably less prevalent than the nucleosome clusters, the organisation of this active chromatin into chains of nucleosome clusters is unlikely to be determined by the activity of the polymerases transcribing the Y loops. These results provide a foundation for understanding the topological relationship between chromatin and the process of gene transcription.

resPAINT: Accelerating Volumetric Super-Resolution Localisation Microscopy by Active Control of Probe Emission

Points for accumulation in nanoscale topography (PAINT) allows practically unlimited measurements in localisation microscopy but is limited by background fluorescence at high probe concentrations, especially in volumetric imaging. We present reservoir-PAINT (resPAINT), which combines PAINT and active control of probe photophysics. In resPAINT, an activatable probe "reservoir" accumulates on target, enabling a 50-fold increase in localisation rate versus conventional PAINT, without compromising contrast. By combining resPAINT with large depth-of-field microscopy, we demonstrate super-resolution imaging of entire cell surfaces. We generalise the approach by implementing various switching strategies and 3D imaging techniques. Finally, we use resPAINT with a Fab to image membrane proteins, extending the operating regime of PAINT to include a wider range of biological interactions.

resPAINT: Accelerating Volumetric Super-Resolution Localisation Microscopy by Active Control of Probe Emission

Points for accumulation in nanoscale topography (PAINT) allows practically unlimited measurements in localisation microscopy but is limited by background fluorescence at high probe concentrations, especially in volumetric imaging. We present reservoir-PAINT (resPAINT), which combines PAINT and active control of probe photophysics. In resPAINT, an activatable probe "reservoir" accumulates on target, enabling a 50-fold increase in localisation rate versus conventional PAINT, without compromising contrast. By combining resPAINT with large depth-of-field microscopy, we demonstrate super-resolution imaging of entire cell surfaces. We generalise the approach by implementing various switching strategies and 3D imaging techniques. Finally, we use resPAINT with a Fab to image membrane proteins, extending the operating regime of PAINT to include a wider range of biological interactions.

Simple methods for quantifying super-resolved cortical actin

Cortical actin plays a key role in cell movement and division, but has also been implicated in the organisation of cell surface receptors such as G protein-coupled receptors. The actin mesh proximal to the inner membrane forms small fenced regions, or 'corrals', in which receptors can be constrained. Quantification of the actin mesh at the nanoscale has largely been attempted in single molecule datasets and electron micrographs. This work describes the development and validation of workflows for analysis of super resolved fixed cortical actin images obtained by Super Resolved Radial Fluctuations (SRRF), Structured Illumination Microscopy (3D-SIM) and Expansion Microscopy (ExM). SRRF analysis was used to show a significant increase in corral area when treating cells with the actin disrupting agent cytochalasin D (increase of 0.31 µm² ± 0.04 SEM), and ExM analysis allowed for the quantitation of actin filament densities. Thus, this work allows complex actin networks to be quantified from super-resolved images and is amenable to both fixed and live cell imaging.

Protocol for multicolor three-dimensional dSTORM data analysis using MATLAB-based script package Grafeo

This protocol describes the step-by-step analysis of the multicolor (one-, two-, or three-color) two- and three-dimensional dSTORM (direct STochastic Optical Reconstruction Microscopy) data using MATLAB-based script package Grafeo. Grafeo primarily uses pointillist data visualization and analysis frameworks, including the nearest neighbors, approach, Voronoi tessellation, Delaunay triangulation, Ripley’s (K, L) and two-point correlation functions, and graph-based clustering.

For complete details on the use and execution of this protocol, please refer to Peaucelle et al. (2020), Haas et al., 2018, Haas et al. (2020b).

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Multicolor 3D dSTORM data visualization using scatter plots and Voronoi Diagrams

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Procedure for data filtering

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Spatial statistics, cluster, and colocalization analysis

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Graph-based cluster analysis with Delaunay triangulation

Super-Resolution Imaging Approaches for Quantifying F-Actin in Immune Cells

Immune cells comprise a diverse set of cells that undergo a complex array of biological processes that must be tightly regulated. A key component of cellular machinery that achieves this is the cytoskeleton. Therefore, imaging and quantitatively describing the architecture and dynamics of the cytoskeleton is an important research goal. Optical microscopy is well suited to this task. Here, we review the latest in the state-of-the-art methodology for labeling the cytoskeleton, fluorescence microscopy hardware suitable for such imaging and quantitative statistical analysis software applicable to describing cytoskeletal structures. We also highlight ongoing challenges and areas for future development.

Computational analyses reveal spatial relationships between nuclear pore complexes and specific lamins

Nuclear lamin isoforms form fibrous meshworks associated with nuclear pore complexes (NPCs). Using datasets prepared from subpixel and segmentation analyses of 3D-structured illumination microscopy images of WT and lamin isoform knockout mouse embryo fibroblasts, we determined with high precision the spatial association of NPCs with specific lamin isoform fibers. These relationships are retained in the enlarged lamin meshworks of Lmna-/- and Lmnb1-/- fibroblast nuclei. Cryo-ET observations reveal that the lamin filaments composing the fibers contact the nucleoplasmic ring of NPCs. Knockdown of the ring-associated nucleoporin ELYS induces NPC clusters that exclude lamin A/C fibers but include LB1 and LB2 fibers. Knockdown of the nucleoporin TPR or NUP153 alters the arrangement of lamin fibers and NPCs. Evidence that the number of NPCs is regulated by specific lamin isoforms is presented. Overall the results demonstrate that lamin isoforms and nucleoporins act together to maintain the normal organization of lamin meshworks and NPCs within the nuclear envelope.

Density Distribution Maps: A Novel Tool for Subcellular Distribution Analysis and Quantitative Biomedical Imaging

Subcellular spatial location is an essential descriptor of molecules biological function. Presently, super-resolution microscopy techniques enable quantification of subcellular objects distribution in fluorescence images, but they rely on instrumentation, tools and expertise not constituting a default for most of laboratories. We propose a method that allows resolving subcellular structures location by reinforcing each single pixel position with the information from surroundings. Although designed for entry-level laboratory equipment with common resolution powers, our method is independent from imaging device resolution, and thus can benefit also super-resolution microscopy. The approach permits to generate density distribution maps (DDMs) informative of both objects’ absolute location and self-relative displacement, thus practically reducing location uncertainty and increasing the accuracy of signal mapping. This work proves the capability of the DDMs to: (a) improve the informativeness of spatial distributions; (b) empower subcellular molecules distributions analysis; (c) extend their applicability beyond mere spatial object mapping. Finally, the possibility of enhancing or even disclosing latent distributions can concretely speed-up routine, large-scale and follow-up experiments, besides representing a benefit for all spatial distribution studies, independently of the image acquisition resolution. DDMaker, a Software endowed with a user-friendly Graphical User Interface (GUI), is also provided to support users in DDMs creation.

Single-molecule localization microscopy

Single-molecule localization microscopy (SMLM) describes a family of powerful imaging techniques that dramatically improve spatial resolution over standard, diffraction-limited microscopy techniques and can image biological structures at the molecular scale. In SMLM, individual fluorescent molecules are computationally localized from diffraction-limited image sequences and the localizations are used to generate a super-resolution image or a time course of super-resolution images, or to define molecular trajectories. In this Primer, we introduce the basic principles of SMLM techniques before describing the main experimental considerations when performing SMLM, including fluorescent labelling, sample preparation, hardware requirements and image acquisition in fixed and live cells. We then explain how low-resolution image sequences are computationally processed to reconstruct super-resolution images and/or extract quantitative information, and highlight a selection of biological discoveries enabled by SMLM and closely related methods. We discuss some of the main limitations and potential artefacts of SMLM, as well as ways to alleviate them. Finally, we present an outlook on advanced techniques and promising new developments in the fast-evolving field of SMLM. We hope that this Primer will be a useful reference for both newcomers and practitioners of SMLM.

Adaptive multiorientation resolution analysis of complex filamentous network images

MOTIVATION

Microscopy images of cytoskeletal, nucleoskeletal and other structures contain complex junctions of overlapping filaments with arbitrary geometry. Yet, state-of-the-art algorithms generally perform single orientation analysis to segment these structures, resulting in gaps near junctions, or assume particular junction geometries to detect them.

RESULTS

We developed a fully automated image analysis approach to address the challenge of determining the number of orientations and their values at each point in space to detect both lines and their junctions. Our approach does not assume any fixed number of orientations or any particular geometry in the case of multiple coincident orientations. It is based on analytically resolving coincident orientations revealed by steerable ridge filtering in an adaptive manner that balances orientation resolution and spatial localization. Combining this multiorientation resolution information with a generalization of the concept of non-maximum suppression allowed us to then identify the centers of lines and their junctions in an image. We validated our approach using a wide array of synthetic junctions and by comparison to manual segmentation. We also applied it to light microscopy images of cytoskeletal and nucleoskeletal networks.

AVAILABILITY AND IMPLEMENTATION

https://github.com/mkitti/AdaptiveResolutionOrientationSpace.

SUPPLEMENTARY INFORMATION

Supplementary information is available at Bioinformatics online.

A Review of Super-Resolution Single-Molecule Localization Microscopy Cluster Analysis and Quantification Methods

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 molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges.

Ultraprecise single-molecule localization microscopy enables in situ distance measurements in intact cells

Single-molecule localization microscopy (SMLM) has the potential to quantify the diversity in spatial arrangements of molecules in intact cells. However, this requires that the single-molecule emitters are localized with ultrahigh precision irrespective of the sample format and the length of the data acquisition. We advance SMLM to enable direct distance measurements between molecules in intact cells on the scale between 1 and 20 nm. Our actively stabilized microscope combines three-dimensional real-time drift corrections and achieves a stabilization of <1 nm and localization precision of ~1 nm. To demonstrate the biological applicability of the new microscope, we show a 4- to 7-nm difference in spatial separations between signaling T cell receptors and phosphatases (CD45) in active and resting T cells. In summary, by overcoming the major bottlenecks in SMLM imaging, it is possible to generate molecular images with nanometer accuracy and conduct distance measurements on the biological relevant length scales.

Quantitative Analysis of Nuclear Lamins Imaged by Super-Resolution Light Microscopy

The nuclear lamina consists of a dense fibrous meshwork of nuclear lamins, Type V intermediate filaments, and is ~14 nm thick according to recent cryo-electron tomography studies. Recent advances in light microscopy have extended the resolution to a scale allowing for the fine structure of the lamina to be imaged in the context of the whole nucleus. We review quantitative approaches to analyze the imaging data of the nuclear lamina as acquired by structured illumination microscopy (SIM) and single molecule localization microscopy (SMLM), as well as the requisite cell preparation techniques. In particular, we discuss the application of steerable filters and graph-based methods to segment the structure of the four mammalian lamin isoforms (A, C, B1, and B2) and extract quantitative information.