Search

Spectroscopic single‐molecule localization microscopy (SMLM) has revolutionized the visualization and analysis of molecular structures and dynamics at the nanoscale level. The technique of combining high spatial resolution of SMLM with spectral information, enables multicolor super‐resolution imaging and provides insights into the local chemical environment of individual molecules. However, spectroscopic SMLM faces significant challenges, including limited spectral resolution and compromised localization precision because of signal splitting and the difficulties in analyzing complex, multidimensional datasets, that limit its application in studying intricate biological systems and materials. The recent integration of artificial intelligence (AI) with spectroscopic SMLM has emerged as a powerful approach for addressing these challenges. Here, it is reviewed how AI‐based methods applied to spectroscopic SMLM enhance and expand the capabilities of these applications. Recent advancements in AI‐driven data analysis for spectroscopic SMLM, including improved spectral classification, localization precision, and extraction of rich spectral information from unmodified point‐spread functions are discussed, further examining their applications in biological studies, materials science, and single‐molecule reaction analysis, which highlight how AI provides new insights into molecular behavior and interactions. The AI‐empowered approach adds new dimensions of information and provides new opportunities and insights into the nanoscale world of rapidly evolving field of spectroscopic SMLM. [ABSTRACT FROM AUTHOR]

Single‐molecule localization super‐resolution fluorescence imaging relies on the fluorescence ON/OFF switching of fluorescent probes to break the diffraction limit. However, the unreacted or nonspecifically bound probes cause non‐targeted ON/OFF switching, resulting in substantial fluorescence background that significantly reduces localization precision and accuracy. Here, we report a blinkogenic probe HM‐DS655‐Halo that remains blinking OFF until it binds to HaloTag, thereby triggering its self‐blinking activity and enabling its application in direct SMLM imaging in living cells without wash‐out steps. We employed the ratio of the duty cycle before and after self‐blinking activation, termed as the parameter “RDC” to characterize blinkogenicity. The covalent binding to HaloTag induces HM‐DS655‐Halo to transition from a fluorescent OFF state to a fluorescence blinking state. This transition also leads to a change in the RDC value, which is calculated to be 12, ensuring super blinkogenicity to effectively suppress background signals in living cells. HM‐DS655‐Halo was successfully applied in dynamic SMLM imaging of diverse intracellular sub‐structures with minimal background noise, including mitochondrial fission and contact, cell migration, and pseudopod growth. [ABSTRACT FROM AUTHOR]

Single‐molecule localization microscopy (SMLM) has revolutionized optical microscopy by exceeding the diffraction limit and revealing previously unattainable nanoscale details of cellular structures and molecular dynamics. This super‐resolution imaging capability relies on fluorophore photoswitching, which is crucial for optimizing the imaging conditions and accurately determining the fluorophore positions. To understand the general on and off photoswitching mechanisms of single dye molecules, various photoswitching reagents were evaluated. Systematic measurement of the single‐molecule‐level fluorescence on and off rates (kon and koff) in the presence of various photoswitching reagents and theoretical calculation of the structure of the photoswitching reagent‐fluorophore pair indicated that the switch‐off mechanism is mainly determined by the nucleophilicity of the photoswitching reagent, and the switch‐on mechanism is a two‐photon‐induced dissociation process, which is related to the power of the illuminating laser and bond dissociation energy of this pair. This study contributes to a broader understanding of the molecular photoswitching mechanism in SMLM imaging and provides a basis for designing improved photoswitching reagents with potential applications extending to materials science and chemistry. [ABSTRACT FROM AUTHOR]

Single‐molecule localization microscopy (SMLM) based on temporal‐focusing multiphoton excitation (TFMPE) and single‐wavelength excitation is used to visualize the three‐dimensional (3D) distribution of spontaneously blinking fluorophore‐labeled subcellular structures in a thick specimen with a nanoscale‐level spatial resolution. To eliminate the photobleaching effect of unlocalized molecules in out‐of‐focus regions for improving the utilization rate of the photon budget in 3D SMLM imaging, SMLM with single‐wavelength TFMPE achieves wide‐field and axially confined two‐photon excitation (TPE) of spontaneously blinking fluorophores. TPE spectral measurement of blinking fluorophores is then conducted through TFMPE imaging at a tunable excitation wavelength, yielding the optimal TPE wavelength for increasing the number of detected photons from a single blinking event during SMLM. Subsequently, the TPE fluorescence of blinking fluorophores is recorded to obtain a two‐dimensional TFMPE‐SMLM image of the microtubules in cancer cells with a localization precision of 18±6 nm and an overall imaging resolution of approximately 51 nm, which is estimated based on the contribution of Nyquist resolution and localization precision. Combined with astigmatic imaging, the system is capable of 3D TFMPE‐SMLM imaging of brain tissue section of a 5XFAD transgenic mouse with the pathological features of Alzheimer's disease, revealing the distribution of neurotoxic amyloid‐beta peptide deposits. [ABSTRACT FROM AUTHOR]

Significance: Spectroscopic single-molecule localization microscopy (sSMLM) takes advantage of nanoscopy and spectroscopy, enabling sub-10 nm resolution as well as simultaneous multicolor imaging of multi-labeled samples. Reconstruction of raw sSMLM data using deep learning is a promising approach for visualizing the subcellular structures at the nanoscale. Aim: Develop a novel computational approach leveraging deep learning to reconstruct both label-free and fluorescence-labeled sSMLM imaging data. Approach: We developed a two-network-model based deep learning algorithm, termed DsSMLM, to reconstruct sSMLM data. The effectiveness of DsSMLM was assessed by conducting imaging experiments on diverse samples, including label-free single-stranded DNA (ssDNA) fiber, fluorescence-labeled histone markers on COS-7 and U2OS cells, and simultaneous multicolor imaging of synthetic DNA origami nanoruler. Results: For label-free imaging, a spatial resolution of 6.22 nm was achieved on ssDNA fiber; for fluorescence-labeled imaging, DsSMLM revealed the distribution of chromatin-rich and chromatin-poor regions defined by histone markers on the cell nucleus and also offered simultaneous multicolor imaging of nanoruler samples, distinguishing two dyes labeled in three emitting points with a separation distance of 40 nm. With DsSMLM, we observed enhanced spectral profiles with 8.8% higher localization detection for single-color imaging and up to 5.05% higher localization detection for simultaneous two-color imaging. Conclusions: We demonstrate the feasibility of deep learning-based reconstruction for sSMLM imaging applicable to label-free and fluorescence-labeled sSMLM imaging data. We anticipate our technique will be a valuable tool for high-quality super-resolution imaging for a deeper understanding of DNA molecules' photophysics and will facilitate the investigation of multiple nanoscopic cellular structures and their interactions. [ABSTRACT FROM AUTHOR]

The recent advances in super-resolution fluorescence microscopy, including single-molecule localization microscopy (SMLM), has enabled the study of previously inaccessible details, such as the organization of proteins within cellular compartments and even nanostructures in nonbiological nanomaterials, such as the polymers and semiconductors. With such developments, the need for the development of various computational nanostructure analysis methods for SMLM images is also increasing; however, this has been limited to protein cluster analysis. In this study, we developed an edge structure analysis method for pointillistic SMLM images based on the line edge roughness and power spectral density analyses. By investigating the effect of point properties in SMLM images, such as the size, density, and localization precision on the roughness measurement, we successfully demonstrated this analysis method for experimental SMLM images of actual samples, including the semiconductor line patterns, cytoskeletal elements, and cell membranes. This systematic investigation of the effect of each localization rendering parameter on edge roughness measurement provides a range for the optimal rendering parameters that preserve the relevant nanoscale structure of interest. These new methods are expected to expand our understanding of the targets by providing valuable insights into edge nanoscale structures that have not been previously obtained quantitatively.

Porous solids often contain complex pore networks with pores of various sizes. Tracking individual fluorescent probes as they diffuse through porous materials can be used to characterize pore networks at tens of nanometers resolution. However, understanding the motion behavior of fluorescent probes in confinement is crucial to reliably derive pore network properties. Here, we introduce well‐defined lithography‐made model pores developed to study probe behavior in confinement. We investigated the influence of probe‐host interactions on diffusion and trapping of confined single‐emitter quantum‐dot probes. Using the pH‐responsiveness of the probes, we were able to largely suppress trapping at the pore walls. This enabled us to define experimental conditions for mapping of the accessible pore space of a one‐dimensional pore array as well as a real‐life polymerization‐catalyst‐support particle. [ABSTRACT FROM AUTHOR]

In this work, we have developed an expansion microscopy (ExM) protocol that combines ExM with photoactivated localization microscopy (ExPALM) for yeast cell imaging, and report a robust protocol for single-molecule and expansion microscopy of fission yeast, abbreviated as SExY. Our optimized SExY protocol retains about 50% of the fluorescent protein signal, doubling the amount obtained compared to the original protein retention ExM (proExM) protocol. It allows for a fivefold, highly isotropic expansion of fission yeast cells, which we carefully controlled while optimizing protein yield. We demonstrate the SExY method on several exemplary molecular targets and explicitly introduce low-abundant protein targets (e.g. nuclear proteins such as cbp1 and mis16, and the centromere-specific histone protein cnp1). The SExY protocol optimizations increasing protein yield could be beneficial for many studies, when targeting low abundance proteins, or for studies that rely on genetic labelling for various reasons (e.g. for proteins that cannot be easily targeted by extrinsic staining or in case artefacts introduced by unspecific staining interfere with data quality).

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology. [ABSTRACT FROM AUTHOR]

Abstract Background Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions. Results We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software. Conclusions The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

Quantitative data analysis in single-molecule localization microscopy (SMLM) is crucial for studying cellular functions at the biomolecular level. In the past decade, several quantitative methods were developed for analyzing SMLM data; however, imaging artifacts in SMLM experiments reduce the accuracy of these methods, and these methods were seldom designed as user-friendly tools. Researchers are now trying to overcome these difficulties by developing easy-to-use SMLM data analysis software for certain image analysis tasks. But, this kind of software did not pay sufficient attention to the impact of imaging artifacts on the analysis accuracy, and usually contained only one type of analysis task. Therefore, users are still facing difficulties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs. In this paper, we report an ImageJ plug-in called DecodeSTORM, which not only has a simple GUI for human–computer interaction, but also combines artifact correction with several quantitative analysis methods. DecodeSTORM includes format conversion, channel registration, artifact correction (drift correction and localization filtering), quantitative analysis (segmentation and clustering, spatial distribution statistics and colocalization) and visualization. Importantly, these data analysis methods can be combined freely, thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods. We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in, which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.

Super‐resolution techniques like single‐molecule localisation microscopy (SMLM) and stimulated emission depletion (STED) microscopy have been extended by the use of non‐covalent, weak affinity‐based transient labelling systems. DNA‐based hybrid systems are a prominent example among these transient labelling systems, offering excellent opportunities for multi‐target fluorescence imaging. However, these techniques suffer from higher background relative to covalently bound fluorophores, originating from unbound fluorophore‐labelled single‐stranded oligonucleotides. Here, we introduce short‐distance self‐quenching in fluorophore dimers as an efficient mechanism to reduce background fluorescence signal, while at the same time increasing the photon budget in the bound state by almost 2‐fold. We characterise the optical and thermodynamic properties of fluorophore‐dimer single‐stranded DNA, and show super‐resolution imaging applications with STED and SMLM with increased spatial resolution and reduced background. [ABSTRACT FROM AUTHOR]

Single‐molecule localization microscopy (SMLM) has found extensive applications in various fields of biology and chemistry. As a vital component of SMLM, fluorophores play an essential role in obtaining super‐resolution fluorescence images. Recent research on spontaneously blinking fluorophores has greatly simplified the experimental setups and extended the imaging duration of SMLM. To support this crucial development, this review provides a comprehensive overview of the development of spontaneously blinking rhodamines from 2014 to 2023, as well as the key mechanistic aspects of intramolecular spirocyclization reactions. We hope that by offering insightful design guidelines, this review will contribute to accelerating the advancement of super‐resolution imaging technologies. [ABSTRACT FROM AUTHOR]

Abstract Single-molecule localization microscopy (SMLM) breaks the optical diffraction limit by numerically localizing sparse fluorescence emitters to achieve super-resolution imaging. Spectroscopic SMLM or sSMLM further allows simultaneous spectroscopy and super-resolution imaging of fluorescence molecules. Hence, sSMLM can extract spectral features with single-molecule sensitivity, higher precision, and higher multiplexity than traditional multicolor microscopy modalities. These new capabilities enabled advanced multiplexed and functional cellular imaging applications. While sSMLM suffers from reduced spatial precision compared to conventional SMLM due to splitting photons to form spatial and spectral images, several methods have been reported to mitigate these weaknesses through innovative optical design and image processing techniques. This review summarizes the recent progress in sSMLM, its applications, and our perspective on future work. Graphical Abstract

With the development of super-resolution imaging techniques, it is crucial to understand protein structure at the nanoscale in terms of clustering and organization in a cell. However, cluster analysis from single-molecule localization microscopy (SMLM) images remains challenging because the classical computational cluster analysis methods developed for conventional microscopy images do not apply to pointillism SMLM data, necessitating the development of distinct methods for cluster analysis from SMLM images. In this review, we discuss the development of computational cluster analysis methods for SMLM images by categorizing them into classical and machine-learning-based methods. Finally, we address possible future directions for machine learning-based cluster analysis methods for SMLM data.

Background: Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions. Results: We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software. Conclusions: The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at and (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux). [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Single-molecule localization microscopy (SMLM) breaks the optical diffraction limit by numerically localizing sparse fluorescence emitters to achieve super-resolution imaging. Spectroscopic SMLM or sSMLM further allows simultaneous spectroscopy and super-resolution imaging of fluorescence molecules. Hence, sSMLM can extract spectral features with single-molecule sensitivity, higher precision, and higher multiplexity than traditional multicolor microscopy modalities. These new capabilities enabled advanced multiplexed and functional cellular imaging applications. While sSMLM suffers from reduced spatial precision compared to conventional SMLM due to splitting photons to form spatial and spectral images, several methods have been reported to mitigate these weaknesses through innovative optical design and image processing techniques. This review summarizes the recent progress in sSMLM, its applications, and our perspective on future work. [ABSTRACT FROM AUTHOR]

Single-molecule localization microscopy (SMLM) is an optical super-resolution technique that circumvents the diffraction limit of light by localizing the center point of each fluorophore's recorded point spread function. SMLM requires the use of labeling strategies and specialized fluorophores and/or buffer conditions that enable most of the fluorophores in the sample to be "turned off" during the experiment so that a sparse subset of the fluorophores in the sample can be isolated from their nearest "on" neighbors and localized to build up an image of interest. Due to the nature of super-resolution single-molecule imaging, careful sample preparation, imaging buffer preparation, and optimized imaging conditions are fundamental requirements for obtaining quality data. This article discusses key factors to consider when preparing samples and acquiring data for SMLM to serve as a starting point for sample preparation and acquisition optimization. [ABSTRACT FROM AUTHOR]

By manipulating the spectral dispersion of detected photons, spectroscopic single-molecule localization microscopy (sSMLM) permits concurrent high-throughput single-molecular spectroscopic analysis and imaging. Despite its promising potential, using discrete optical components and managing the delicate balance between spectral dispersion and spatial localization compromise its performance, including nonuniform spectral dispersion, high transmission loss of grating, high optical alignment demands, and reduced precision. We designed a dual-wedge prism (DWP)-based monolithic imaging spectrometer to overcome these challenges. We optimized the DWP for spectrally dispersing focused beam without deviation and with minimal wavefront error. We integrated all components into a compact assembly, minimizing total transmission loss and significantly reducing optical alignment requirements. We show the feasibility of DWP using ray-tracing and numerical simulations. We validated our numerical simulations by experimentally imaging individual nanospheres and confirmed that DWP-sSMLM achieved much improved spatial and spectral precisions of grating-based sSMLM. We also demonstrated DWP-sSMLM in 3D multi-color imaging of cells.

Living erythrocyte (red blood cell, RBC) membranes present rich ultrastructural and dynamic details, which require synchronous super‐resolution imaging and single‐molecule tracking to be revealed. Yet, it poses a serious challenge to achieve these dual functions in a single probe, due to the rigid and conflicting photophysical demands of the different techniques. Herein, we rationally developed a far‐red boron dipyrromethene membrane probe with blinking capability and persistent single‐molecule emission, and constructed a microfluidic platform for noninvasive trapping and long‐term imaging of RBCs. By combining them, multi‐dimensional super‐resolution reconstructions and single‐molecule tracking were achieved at the molecular scale on living human RBC membranes in a high‐throughput manner. Our integrated system defines a quantitative method for analyzing RBC membranes under physiological and pathological conditions, improving precision and revealing new perspectives for future disease diagnostics. [ABSTRACT FROM AUTHOR]

Interactions between light and matter serve as the basis of many technologies, but the quality of these devices is inherently limited by the optical properties of their constituents. Plasmonic nanoparticles are a highly versatile and tunable platform for the enhancement of such optical properties. However, the near‐field nature of these effects has made thorough study and understanding of these mechanisms difficult. In this work, we introduce a fully confocal technique combining photoswitching super‐resolution microscopy with fluorescence lifetime imaging microscopy to study single‐molecule decay rate enhancement. We demonstrate that the technique combines a spatial resolution better than 20 nm, and a 16 ps temporal resolution. Simultaneously, an autocorrelation measurement is also performed to confirm that the data indeed originates from single molecules. This work provides insight into the various mechanisms of plasmon‐enhanced emission, and allows the study of the correlation between emission intensity and lifetime enhancement. This complicated relationship is shown to be dependent upon the relative influence of various radiative and nonradiative decay pathways. Here, we provide a platform for further study of emission mislocalization, the position‐dependent prominence of different decay pathways, and the direct super‐resolved measurement of the local density of states. [ABSTRACT FROM AUTHOR]

Baculovirus is one of the most complex viruses found in nature. Proteomic analysis of budded viruses (BVs) indicates that they are formed by at least 50 different structural proteins. The function of most of these structural proteins and their specific localization in individual virions remain unknown. In the present study, we have conducted single-molecule localization microscopy analysis of the spatial distribution of the nucleocapsid protein P24 and the envelope proteins GP64 and E25. Our results show that P24 and GP64 are polarized to one end of the baculovirus, while E25 distributes more homogenously along the viral envelope. This is the first study using optical microscopy to demonstrate the polarized distribution of structural proteins in individual baculoviruses.

Single-molecule localization super-resolution fluorescence imaging relies on the fluorescence ON/OFF switching of fluorescent probes to break the diffraction limit. However, the unreacted or nonspecifically bound probes cause non-targeted ON/OFF switching, resulting in substantial fluorescence background that significantly reduces localization precision and accuracy. Here, we report a blinkogenic probe HM-DS655-Halo that remains blinking OFF until it binds to HaloTag, thereby triggering its self-blinking activity and enabling its application in direct SMLM imaging in living cells without wash-out steps. We employed the ratio of the duty cycle before and after self-blinking activation, termed as the parameter "R DC " to characterize blinkogenicity. The covalent binding to HaloTag induces HM-DS655-Halo to transition from a fluorescent OFF state to a fluorescence blinking state. This transition also leads to a change in the R DC value, which is calculated to be 12, ensuring super blinkogenicity to effectively suppress background signals in living cells. HM-DS655-Halo was successfully applied in dynamic SMLM imaging of diverse intracellular sub-structures with minimal background noise, including mitochondrial fission and contact, cell migration, and pseudopod growth., (© 2024 Wiley-VCH GmbH.)

Current methods for determining equilibrium constants often operate in three-dimensional environments, which may not accurately reflect interactions with membrane-bound proteins. With our technique, based on single-molecule localization microscopy (SMLM), we directly determine protein-protein association ( K a ) and dissociation ( K d ) constants in cellular environments by quantifying associated and isolated molecules and their interaction area. We introduce Kernel Surface Density (ks-density,) a novel method for determining the accessible area for interacting molecules, eliminating the need for user-defined parameters. Simulation studies validate our method's accuracy across various density and affinity conditions. Applying this technique to T cell signaling proteins, we determine the 2D association constant of T cell receptors (TCRs) in resting cells and the pseudo-3D dissociation constant of pZAP70 molecules from phosphorylated intracellular tyrosine-based activation motifs on the TCR-CD3 complex. We address challenges of multiple detection and molecular labeling efficiency. This method enhances our understanding of protein interactions in cellular environments, advancing our knowledge of complex biological processes.

Nanoparticles (NPs) have proven their applicability in biosensing, drug delivery, and photothermal therapy, but their performance depends critically on the distribution and number of functional groups on their surface. When studying surface functionalization using super-resolution microscopy, the NP modifies the fluorophore's point-spread function (PSF). This leads to systematic mislocalizations in conventional analyses employing Gaussian PSFs. Here, we address this shortcoming by deriving the analytical PSF model for a fluorophore near a spherical NP. Its calculation is four orders of magnitude faster than numerical approaches and thus feasible for direct use in localization algorithms. We fit this model to individual 2D images from DNA-PAINT experiments on DNA-coated gold NPs and demonstrate extraction of the 3D positions of functional groups with <5 nm precision, revealing inhomogeneous surface coverage. Our method is exact, fast, accessible, and poised to become the standard in super-resolution imaging of NPs for biosensing and drug delivery applications.

Electrochemical techniques conventionally lack spatial resolution and average local information over an entire electrode. While advancements in spatial resolution have been made through scanning probe methods, monitoring dynamics over large areas is still challenging, and it would be beneficial to be able to decouple the probe from the electrode itself. In this work, we leverage single molecule microscopy to spatiotemporally monitor analyte surface concentrations over a wide area using unmodified hexagonal boron nitride (hBN) in organic solvents. Through a sensing scheme based on redox-active species interactions with fluorescent emitters at the surface of hBN, we observe a region of a linear decrease in the number of emitters against increasingly positive potentials applied to a nearby electrode. We find consistent trends in electrode reaction kinetics vs overpotentials between potentiostat-reported currents and optically read emitter dynamics, showing Tafel slopes greater than 290 mV·decade -1 . Finally, we draw on the capabilities of spectral single-molecule localization microscopy (SMLM) to monitor the fluorescent species' identity, enabling multiplexed readout. Overall, we show dynamic measurements of analyte concentration gradients on a micrometer-length scale with nanometer-scale depth and precision. Considering the many scalable options for engineering fluorescent emitters with two-dimensional (2D) materials, our method holds promise for optically detecting a range of interacting species with exceptional localization precision.

Probing light–matter interaction at the nanometer scale is one of the most fascinating topics of modern optics. Its importance is underlined by the large span of fields in which such accurate knowledge of light–matter interaction is needed, namely nanophotonics, quantum electrodynamics, atomic physics, biosensing, quantum computing and many more. Increasing innovations in the field of microscopy in the last decade have pushed the ability of observing such phenomena across multiple length scales, from micrometers to nanometers. In bioimaging, the advent of super-resolution single-molecule localization microscopy (SMLM) has opened a completely new perspective for the study and understanding of molecular mechanisms, with unprecedented resolution, which take place inside the cell. Since then, the field of SMLM has been continuously improving, shifting from an initial drive for pushing technological limitations to the acquisition of new knowledge. Interestingly, such developments have become also of great interest for the study of light–matter interaction in nanostructured materials, either dielectric, metallic, or hybrid metallic-dielectric. The purpose of this review is to summarize the recent advances in the field of nanophotonics that have leveraged SMLM, and conversely to show how some concepts commonly used in nanophotonics can benefit the development of new microscopy techniques for biophysics. To this aim, we will first introduce the basic concepts of SMLM and the observables that can be measured. Then, we will link them with their corresponding physical quantities of interest in biophysics and nanophotonics and we will describe state-of-the-art experiments that apply SMLM to nanophotonics. The problem of localization artifacts due to the interaction of the fluorescent emitter with a resonant medium and possible solutions will be also discussed. Then, we will show how the interaction of fluorescent emitters with plasmonic structures can be successfully employed in biology for cell profiling and membrane organization studies. We present an outlook on emerging research directions enabled by the synergy of localization microscopy and nanophotonics.

Single-molecule localization microscopy (SMLM) plays an irreplaceable role in biological studies, in which nanometer-sized biomolecules are hardly to be resolved due to diffraction limit unless being stochastically activated and accurately located by SMLM. For biological samples preimmobilized for SMLM, most biomolecules are cross-linked and constrained at their immobilizing sites but still expected to undergo confined stochastic motion in regard to their nanometer sizes. However, few lines of direct evidence have been reported about the detectability and influence of confined biomolecule stochastic motion on localization precision in SMLM. Here, we access the potential stochastic motion for each immobilized single biomolecule by calculating the displacements between any two of its localizations at different frames during sequential imaging of Alexa Fluor-647-conjugated oligonucleotides. For most molecules, localization displacements are remarkably larger at random frame intervals than at shortest intervals even after sample drift correction, increase with interval times and then saturate, showing that biomolecule stochastic motion is detected and confined around the immobilizing sizes in SMLM. Moreover, localization precision is inversely proportional to confined biomolecule stochastic motion, whereas it can be deteriorated or improved by enlarging the biomolecules or adding a post-crosslinking step, respectively. Consistently, post-crosslinking of cell samples sparsely stained for tubulin proteins results in a better localization precision. Overall, this study reveals that confined stochastic motion of immobilized biomolecules worsens localization precision in SMLM, and improved localization precision can be achieved via restricting such a motion.

A new method using a maximum likelihood estimator (MLE) is able to detect the location of soft interfaces, such as the oil–water interface, with high precision in single-molecule localization microscopy (SMLM) experiments. The method remains effective even if the interface contains, but is not saturated with, particles. [Display omitted] Knowing the exact location of soft interfaces, such as between water and oil, is essential to the study of nanoscale wetting phenomena. Recently, iPAINT was used to visualize soft interfaces in situ with minimal invasiveness, but computing the exact location of the interface remains challenging. We propose a new method to determine the interface with high accuracy. By modelling the localizations as points generated by two homogeneous Poisson processes, the exact location of the interface can be determined using a maximum likelihood estimator (MLE). An MLE was constructed to estimate the location of the interface based on the discontinuity in localization density at the interface. To test the MLE, we collected experimental data through iPAINT experiments of oil–water interfaces and generated simulated data using the Monte Carlo method. Simulations show that the interface given by the MLE rapidly converges to the true interface location. The error of the MLE drops below the experimental localization precision. Furthermore, we show that the MLE remains accurate even if the field-of-view is reduced or when one or more particles are on the interface within the field-of-view. This work provides a key step towards the in situ , sub-micron characterization of (nanoparticle-laden) interfaces with minimal invasiveness. [ABSTRACT FROM AUTHOR]

Recent developments in super-resolution fluorescence microscopic techniques (SRM) have allowed for nanoscale imaging that greatly facilitates our understanding of nanostructures. However, the performance of single-molecule localization microscopy (SMLM) is significantly restricted by the image analysis method, as the final super-resolution image is reconstructed from identified localizations through computational analysis. With recent advancements in deep learning, many researchers have employed deep learning-based algorithms to analyze SMLM image data. This review discusses recent developments in deep-learning-based SMLM image analysis, including the limitations of existing fitting algorithms and how the quality of SMLM images can be improved through deep learning. Finally, we address possible future applications of deep learning methods for SMLM imaging. [ABSTRACT FROM AUTHOR]

A-kinase anchoring protein 79-human/150-rodent (AKAP79/150) organizes signaling proteins to control synaptic plasticity. AKAP79/150 associates with the plasma membrane and endosomes through its N-terminal domain that contains three polybasic regions and two Cys residues that are reversibly palmitoylated. Mutations abolishing palmitoylation (AKAP79/150 CS) reduce its endosomal localization and association with the postsynaptic density (PSD). Here we combined advanced light and electron microscopy (EM) to characterize the effects of AKAP79/150 palmitoylation on its postsynaptic nanoscale organization, trafficking, and mobility in hippocampal neurons. Immunogold EM revealed prominent extrasynaptic membrane AKAP150 labeling with less labeling at the PSD. The label was at greater distances from the spine membrane for AKAP150 CS than WT in the PSD but not in extra-synaptic locations. Immunogold EM of GFP-tagged AKAP79 WT showed that AKAP79 adopts a vertical, extended conformation at the PSD with its N-terminus at the membrane, in contrast to extrasynaptic locations where it adopts a compact or open configurations of its N- and C-termini with parallel orientation to the membrane. In contrast, GFP-tagged AKAP79 CS was displaced from the PSD coincident with disruption of its vertical orientation, while proximity and orientation with respect to the extra-synaptic membrane was less impacted. Single-molecule localization microscopy (SMLM) revealed a heterogeneous distribution of AKAP150 with distinct high-density, nano-scale regions (HDRs) overlapping the PSD but more prominently located in the extrasynaptic membrane for WT and the CS mutant. Thick section scanning transmission electron microscopy (STEM) tomography revealed AKAP150 immunogold clusters similar in size to HDRs seen by SMLM and more AKAP150 labeled endosomes in spines for WT than for CS, consistent with the requirement for AKAP palmitoylation in endosomal trafficking. Hidden Markov modeling of single molecule tracking data revealed a bound/immobile fraction and two mobile fractions for AKAP79 in spines, with the CS mutant having shorter dwell times and faster transition rates between states than WT, suggesting that palmitoylation stabilizes individual AKAP molecules in various spine subpopulations. These data demonstrate that palmitoylation fine tunes the nanoscale localization, mobility, and trafficking of AKAP79/150 in dendritic spines, which might have profound effects on its regulation of synaptic plasticity.

Super-resolution microscopy techniques, and specifically single-molecule localization microscopy (SMLM), are approaching nanometer resolution inside cells and thus have great potential to complement structural biology techniques such as electron microscopy for structural cell biology. In this review, we introduce the different flavors of super-resolution microscopy, with a special emphasis on SMLM and MINFLUX (minimal photon flux). We summarize recent technical developments that pushed these localization-based techniques to structural scales and review the experimental conditions that are key to obtaining data of the highest quality. Furthermore, we give an overview of different analysis methods and highlight studies that used SMLM to gain structural insights into biologically relevant molecular machines. Ultimately, we give our perspective on what is needed to push the resolution of these techniques even further and to apply them to investigating dynamic structural rearrangements in living cells. [ABSTRACT FROM AUTHOR]

Structured illumination in single-molecule localization microscopy provides new information on the position of molecules and thus improves the localization precision compared to standard localization methods. Here, we used a time-shifted sinusoidal excitation pattern to modulate the fluorescence signal of the molecules whose position information is carried by the phase and recovered by synchronous demodulation. We designed two flexible fast demodulation systems located upstream of the camera, allowing us to overcome the limiting camera acquisition frequency and thus to maximize the collection of photons in the demodulation process. The temporally modulated fluorescence signal was then sampled synchronously on the same image, repeatedly during acquisition. This microscopy, called ModLoc, allows us to experimentally improve the localization precision by a factor of 2.4 in one direction, compared to classical Gaussian fitting methods. A temporal study and an experimental demonstration both show that the short lifetimes of the molecules in blinking regimes impose a modulation frequency in the kilohertz range, which is beyond the reach of current cameras. A demodulation system operating at these frequencies would thus be necessary to take full advantage of this new localization approach. [ABSTRACT FROM AUTHOR]

CRANAD‐2 is a fluorogenic curcumin derivative used for near‐infrared detection and imaging in vivo of amyloid aggregates, which are involved in neurodegenerative diseases. We explore the performance of CRANAD‐2 in two super‐resolution imaging techniques, namely stimulated emission depletion (STED) and single‐molecule localization microscopy (SMLM), with markedly different fluorophore requirements. By conveniently adapting the concentration of CRANAD‐2, which transiently binds to amyloid fibrils, we show that it performs well in both techniques, achieving a resolution in the range of 45–55 nm. Correlation of SMLM with atomic force microscopy (AFM) validates the resolution of fine features in the reconstructed super‐resolved image. The good performance and versatility of CRANAD‐2 provides a powerful tool for near‐infrared nanoscopic imaging of amyloids in vitro and in vivo. [ABSTRACT FROM AUTHOR]

ABSTRACT Mycoplasmas are the smallest free-living organisms. These bacteria are important models for both fundamental and synthetic biology, owing to their highly reduced genomes. They are also relevant in the medical and veterinary fields, as they are pathogenic to both humans and most livestock species. Mycoplasma cells have minute sizes, often in the 300- to 800-nm range. As these dimensions are close to the diffraction limit of visible light, fluorescence imaging in mycoplasmas is often poorly informative. Recently developed superresolution imaging techniques can break this diffraction limit, improving the imaging resolution by an order of magnitude and offering a new nanoscale vision of the organization of these bacteria. These techniques have, however, not been applied to mycoplasmas before. Here, we describe an efficient and reliable protocol to perform single-molecule localization microscopy (SMLM) imaging in mycoplasmas. We provide a polyvalent transposon-based system to express the photoconvertible fluorescent protein mEos3.2, enabling photo-activated localization microscopy (PALM) in most Mycoplasma species. We also describe the application of direct stochastic optical reconstruction microscopy (dSTORM). We showcase the potential of these techniques by studying the subcellular localization of two proteins of interest. Our work highlights the benefits of state-of-the-art microscopy techniques for mycoplasmology and provides an incentive to further the development of SMLM strategies to study these organisms in the future. IMPORTANCE Mycoplasmas are important models in biology, as well as highly problematic pathogens in the medical and veterinary fields. The very small sizes of these bacteria, well below a micron, limits the usefulness of traditional fluorescence imaging methods, as their resolution limit is similar to the dimensions of the cells. Here, to bypass this issue, we established a set of state-of-the-art superresolution microscopy techniques in a wide range of Mycoplasma species. We describe two strategies: PALM, based on the expression of a specific photoconvertible fluorescent protein, and dSTORM, based on fluorophore-coupled antibody labeling. With these methods, we successfully performed single-molecule imaging of proteins of interest at the surface of the cells and in the cytoplasm, at lateral resolutions well below 50 nm. Our work paves the way toward a better understanding of mycoplasma biology through imaging of subcellular structures at the nanometer scale.

DNA point accumulation in nanoscale topography (DNA-PAINT) is an easy-to-implement approach for localization-based super-resolution imaging. Conventional DNA-PAINT imaging typically requires tens of thousands of frames of raw data to reconstruct one super-resolution image, which prevents its potential application for live imaging. Here, we introduce a new DNA-PAINT labeling method that allows for imaging of microtubules with both DNA-PAINT and widefield illumination. We develop a U-Net-based neural network, namely, U-PAINT to accelerate DNA-PAINT imaging from a widefield fluorescent image and a sparse single-molecule localization image. Compared with the conventional method, U-PAINT only requires one-tenth of the original raw data, which permits fast imaging and reconstruction of super-resolution microtubules and can be adopted to analyze other SMLM datasets. We anticipate that this machine learning method enables faster and even live-cell DNA-PAINT imaging in the future.

The rise of super‐resolution fluorescence microscopy (SRM) has revolutionized our understanding of the nanoscale world. SRM has recently been correlated with spectroscopy techniques to obtain rich spectral information of individual molecules in addition to localization information. This technique ultimately provides multidimensional and functional information of single molecules, which is generally masked in the ensemble averages of previous bulk measurements and creates exciting new opportunities and challenges for single‐molecule spectroscopic imaging. We review the recent developments in single‐molecule spectroscopic imaging technique and how the challenges are addressed to effectively obtain single‐molecule spectroscopic information. Particular attention has been devoted to the applications of this method, including single‐molecule chemical kinetic studies and single‐molecule polarity sensing. Finally, we address the possible future enhancements and applications. Thus, we demonstrate how a single‐molecule spectroscopic imaging technique adds new dimensions of information to the SRM, providing new opportunities in a wide range of research fields. [ABSTRACT FROM AUTHOR]

Probing light–matter interaction at the nanometer scale is one of the most fascinating topics of modern optics. Its importance is underlined by the large span of fields in which such accurate knowledge of light–matter interaction is needed, namely nanophotonics, quantum electrodynamics, atomic physics, biosensing, quantum computing and many more. Increasing innovations in the field of microscopy in the last decade have pushed the ability of observing such phenomena across multiple length scales, from micrometers to nanometers. In bioimaging, the advent of super-resolution single-molecule localization microscopy (SMLM) has opened a completely new perspective for the study and understanding of molecular mechanisms, with unprecedented resolution, which take place inside the cell. Since then, the field of SMLM has been continuously improving, shifting from an initial drive for pushing technological limitations to the acquisition of new knowledge. Interestingly, such developments have become also of great interest for the study of light–matter interaction in nanostructured materials, either dielectric, metallic, or hybrid metallic-dielectric. The purpose of this review is to summarize the recent advances in the field of nanophotonics that have leveraged SMLM, and conversely to show how some concepts commonly used in nanophotonics can benefit the development of new microscopy techniques for biophysics. To this aim, we will first introduce the basic concepts of SMLM and the observables that can be measured. Then, we will link them with their corresponding physical quantities of interest in biophysics and nanophotonics and we will describe state-of-the-art experiments that apply SMLM to nanophotonics. The problem of localization artifacts due to the interaction of the fluorescent emitter with a resonant medium and possible solutions will be also discussed. Then, we will show how the interaction of fluorescent emitters with plasmonic structures can be successfully employed in biology for cell profiling and membrane organization studies. We present an outlook on emerging research directions enabled by the synergy of localization microscopy and nanophotonics. [ABSTRACT FROM AUTHOR]

Single-molecule localization microscopy (SMLM) plays an irreplaceable role in biological studies, in which nanometer-sized biomolecules are hardly to be resolved due to diffraction limit unless being stochastically activated and accurately located by SMLM. For biological samples preimmobilized for SMLM, most biomolecules are cross-linked and constrained at their immobilizing sites but still expected to undergo confined stochastic motion in regard to their nanometer sizes. However, few lines of direct evidence have been reported about the detectability and influence of confined biomolecule stochastic motion on localization precision in SMLM. Here, we access the potential stochastic motion for each immobilized single biomolecule by calculating the displacements between any two of its localizations at different frames during sequential imaging of Alexa Fluor-647-conjugated oligonucleotides. For most molecules, localization displacements are remarkably larger at random frame intervals than at shortest intervals even after sample drift correction, increase with interval times and then saturate, showing that biomolecule stochastic motion is detected and confined around the immobilizing sizes in SMLM. Moreover, localization precision is inversely proportional to confined biomolecule stochastic motion, whereas it can be deteriorated or improved by enlarging the biomolecules or adding a post-crosslinking step, respectively. Consistently, post-crosslinking of cell samples sparsely stained for tubulin proteins results in a better localization precision. Overall, this study reveals that confined stochastic motion of immobilized biomolecules worsens localization precision in SMLM, and improved localization precision can be achieved via restricting such a motion. [ABSTRACT FROM AUTHOR]

Single-molecule localization microscopy (SMLM) is a potent tool to examine biological systems with unprecedented resolution, enabling the investigation of increasingly smaller structures. At the forefront of these developments is DNA-based point accumulation for imaging in nanoscale topography (DNA-PAINT), which exploits the stochastic and transient binding of fluorescently labeled DNA probes. In its early stages the implementation of DNA-PAINT was burdened by low-throughput, excessive acquisition time, and difficult integration with live-cell imaging. However, recent advances are addressing these challenges and expanding the range of applications of DNA-PAINT. We review the current state of the art of DNA-PAINT in light of these advances and contemplate what further developments remain indispensable to realize live-cell imaging. In recent years the performance, utility, and ease-of-use of DNA-based point accumulation in nanoscale topography (DNA-PAINT) have greatly improved by increasing compatibility with existing technology and diversifying the usable probes. Multiplexing with DNA-PAINT has become possible, which allows users to probe many targets simultaneously and paves the way for high-throughput methods. Advances have alleviated the limiting factors that govern the binding frequency, thereby accelerating imaging acquisition and enabling DNA-PAINT imaging to be performed in several minutes. The complex environment in living cells creates numerous challenges for standard DNA-PAINT imaging. These challenges are being addressed through improved probe design such as using modified DNA nucleotides or amino acid-based backbones for the imager and docking probes. [ABSTRACT FROM AUTHOR]

Fresnel incoherent correlation holography (FINCH) was a milestone in incoherent holography. In this roadmap, two pathways, namely the development of FINCH and applications of FINCH explored by many prominent research groups, are discussed. The current state-of-the-art FINCH technology, challenges, and future perspectives of FINCH technology as recognized by a diverse group of researchers contributing to different facets of research in FINCH have been presented. [ABSTRACT FROM AUTHOR]

Single molecule localization microscopy has become a prominent technique to quantitatively study biological processes below the optical diffraction limit. By fitting the intensity profile of single sparsely activated fluorophores, which are often attached to a specific biomolecule within a cell, the locations of all imaged fluorophores are obtained with ∼20 nm resolution in the form of a coordinate table. While rendered super-resolution images reveal structural features of intracellular structures below the optical diffraction limit, the ability to further analyze the molecular coordinates presents opportunities to gain additional quantitative insights into the spatial distribution of a biomolecule of interest. For instance, pair-correlation or radial distribution functions are employed as a measure of clustering, and cross-correlation analysis reveals the colocalization of two biomolecules in two-color SMLM data. Here, we present an efficient filtering method for SMLM data sets based on pair- or cross-correlation to isolate localizations that are clustered or appear in proximity to a second set of localizations in two-color SMLM data. In this way, clustered or colocalized localizations can be separately rendered and analyzed to compare other molecular properties to the remaining localizations, such as their oligomeric state or mobility in live cell experiments. Current matrix-based cross-correlation analyses of large data sets quickly reach the limitations of computer memory due to the space complexity of constructing the distance matrices. Our approach leverages k-dimensional trees to efficiently perform range searches, which dramatically reduces memory needs and the time for the analysis. We demonstrate the versatile applications of this method with simulated data sets as well as examples of two-color SMLM data. The provided MATLAB code and its description can be integrated into existing localization analysis packages and provides a useful resource to analyze SMLM data with new detail.

[Display omitted] • DNA nanotechnology was correlated with multiplexed single-molecule super-resolution imaging. • A procedure for near-molecular visualization and analysis of membrane receptor networks was established. • This approach enabled quantitative microscopy of the FGFR1-4 receptor network at the native cellular expression level. • Information on receptor clustering and distance relationships was extracted. • The imaging and analysis pipeline can be easily adapted to other protein networks. Fibroblast growth factor receptors (FGFRs) are a subfamily of receptor tyrosine kinases and central players in health and disease. Following ligand binding and the formation of homo- and heteromeric complexes, FGFRs initiate a cellular response. Challenges in studying FGFR activation are inner-subfamily interactions and a complex heterogeneity of these in the cell membrane, which demand for observation techniques that can resolve individual protein complexes and that are compatible with endogenous protein levels. Here, we established an imaging and analysis pipeline for multiplexed single-molecule localization microscopy (SMLM) of the FGFR network at the plasma membrane. Using DNA-labeled primary antibodies, we visualize all four FGFRs in the same cell with near-molecular spatial resolution. From the super-resolution imaging data, we extract information on FGFR density, spatial distribution, and inner-subfamily colocalization. Our approach is straightforward and easily adaptable to other multiplexed SMLM data of membrane proteins. [ABSTRACT FROM AUTHOR]

Extracellular Vesicles (EVs) have been intensively explored for therapeutic delivery of proteins. However, methods to quantify cargo proteins loaded into engineered EVs are lacking. Here, we describe a workflow for EV analysis at the single‐vesicle and single‐molecule level to accurately quantify the efficiency of different EV‐sorting proteins in promoting cargo loading into EVs. Expi293F cells were engineered to express EV‐sorting proteins fused to green fluorescent protein (GFP). High levels of GFP loading into secreted EVs was confirmed by Western blotting for specific EV‐sorting domains, but quantitative single‐vesicle analysis by Nanoflow cytometry detected GFP in less than half of the particles analysed, reflecting EV heterogeneity. Anti‐tetraspanin EV immunostaining in ExoView confirmed a heterogeneous GFP distribution in distinct subpopulations of CD63+, CD81+, or CD9+ EVs. Loading of GFP into individual vesicles was quantified by Single‐Molecule Localization Microscopy. The combined results demonstrated TSPAN14, CD63 and CD63/CD81 fused to the PDGFRβ transmembrane domain as the most efficient EV‐sorting proteins, accumulating on average 50–170 single GFP molecules per vesicle. In conclusion, we validated a set of complementary techniques suitable for high‐resolution analysis of EV preparations that reliably capture their heterogeneity, and propose highly efficient EV‐sorting proteins to be used in EV engineering applications. [ABSTRACT FROM AUTHOR]

Super-resolution imaging has revealed that key synaptic proteins are dynamically organized within sub-synaptic domains (SSDs). To examine how different inhibitory receptors are regulated, we carried out dual-color direct stochastic optical reconstruction microscopy (dSTORM) of GlyRs and GABAARs at mixed inhibitory synapses in spinal cord neurons. We show that endogenous GlyRs and GABAARs as well as their common scaffold protein gephyrin form SSDs that align with pre-synaptic RIM1/2, thus creating trans-synaptic nanocolumns. Strikingly, GlyRs and GABAARs occupy different sub-synaptic spaces, exhibiting only a partial overlap at mixed inhibitory synapses. When network activity is increased by 4-aminopyridine treatment, the GABAAR copy numbers and the number of GABAAR SSDs are reduced, while GlyRs remain largely unchanged. This differential regulation is likely the result of changes in gephyrin phosphorylation that preferentially occurs outside of SSDs. The activity-dependent regulation of GABAARs versus GlyRs suggests that different signaling pathways control the receptors' sub-synaptic clustering. Taken together, our data reinforce the notion that the precise sub-synaptic organization of GlyRs, GABAARs, and gephyrin has functional consequences for the plasticity of mixed inhibitory synapses. [ABSTRACT FROM AUTHOR]

The development of super-resolution microscopy (SRM) has widened our understanding of biomolecular structure and function in biological materials. Imaging multiple targets within a single area would elucidate their spatial localization relative to the cell matrix and neighboring biomolecules, revealing multi-protein macromolecular structures and their functional co-dependencies. SRM methods are, however, limited to the number of suitable fluorophores that can be imaged during a single acquisition as well as the loss of antigens during antibody washing and restaining for organic dye multiplexing. We report the visualization of multiple protein targets within the pre- and postsynapse in 350–400 nm thick neuronal tissue sections using DNA-assisted single-molecule localization microscopy (SMLM). In a single labeling step, antibodies conjugated with short DNA oligonucleotides visualized multiple targets by sequential exchange of fluorophore-labeled complementary oligonucleotides present in the imaging buffer. This approach avoids potential effects on structural integrity when using multiple rounds of immunolabeling and eliminates chromatic aberration, because all targets are imaged using a single excitation laser wavelength. This method proved robust for multi-target imaging in semi-thin tissue sections with a lateral resolution better than 25 nm, paving the way toward structural cell biology with single-molecule SRM.