Single-molecule Localization Research Articles

Spontaneously Blinkogenic Probe for Wash-Free Single-Molecule Localization-Based Super-Resolution Imaging in Living Cells.

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 superblinkogenicity 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.

Mapping membrane biophysical nano-environments

The mammalian plasma membrane is known to contain domains with varying lipid composition and biophysical properties. However, studying these membrane lipid domains presents challenges due to their predicted morphological similarity to the bulk membrane and their scale being below the classical resolution limit of optical microscopy. To address this, we combine the solvatochromic probe di-4-ANEPPDHQ, which reports on its biophysical environment through changes in its fluorescence emission, with spectrally resolved single-molecule localisation microscopy. The resulting data comprises nanometre-precision localisation coordinates and a generalised polarisation value related to the probe’s environment – a marked point pattern. We introduce quantification algorithms based on topological data analysis (PLASMA) to detect and map nano-domains in this marked data, demonstrating their effectiveness in both artificial membranes and live cells. By leveraging environmentally sensitive fluorophores, multi-modal single molecule localisation microscopy, and advanced analysis methods, we achieve nanometre scale mapping of membrane properties and assess changes in response to external perturbation with methyl-β-cyclodextrin. This integrated methodology represents an integrated toolset for investigating marked point pattern data at nanometre spatial scales.

Fast and universal single-molecule localization using multi-dimensional point spread functions

The recent development of single-molecule imaging techniques has enabled not only high accuracy spatial resolution imaging but also information rich functional imaging. Abundant information about single molecules can be encoded in its diffraction pattern and be extracted precisely (e.g. 3D position, wavelength, dipole orientation). However, sophisticated high dimensional point spread function (PSF) modeling and analyzing methods have greatly impeded the broad accessibility of these techniques. Here, we present a graphics processing unit (GPU) -based B-spline PSF modeling method that could flexibly model high dimensional PSFs with arbitrary shape without greatly increasing the model parameters. Our B-spline fitter achieves 100 times speed improvement and minimal uncertainty for each dimension, enabling efficient high dimensional single-molecule analysis. We demonstrated, both in simulations and experiments, the universality and flexibility of our B-spline fitter to accurately extract the abundant information from different types of high dimensional single-molecule data, including multicolor PSF (3D + color), multi-channel four-dimensional 4Pi-PSF (3D + interference phase) and five-dimensional vortex PSF (3D + dipole orientation).

Long-Axial-Range Double-Helix Point Spread Functions for 3D Volumetric Super-Resolution Imaging.

Single-molecule localization microscopy (SMLM) is a powerful tool for observing structures beyond the diffraction limit of light. Combining SMLM with engineered point spread functions (PSFs) enables 3D imaging over an extended axial range, as has been demonstrated for super-resolution imaging of various cellular structures. However, super-resolving structures in 3D in thick samples, such as whole mammalian cells, remains challenging as it typically requires acquisition and postprocessing stitching of multiple slices to cover the entire sample volume or more complex analysis of the data. Here, we demonstrate how the imaging and analysis workflows can be simplified by 3D single-molecule super-resolution imaging with long-axial-range double-helix (DH)-PSFs. First, we experimentally benchmark the localization precisions of short- and long-axial-range DH-PSFs at different signal-to-background ratios by imaging fluorescent beads. The performance of the DH-PSFs in terms of achievable resolution and imaging speed was then quantified for 3D single-molecule super-resolution imaging of mammalian cells by DNA-PAINT imaging of nuclear lamina protein lamin B1 in U-2 OS cells. Furthermore, we demonstrate how the use of a deep-learning-based algorithm allows the localization of dense emitters, drastically improving the achievable imaging speed and resolution. Our data demonstrate that using long-axial-range DH-PSFs offers stitching-free, 3D super-resolution imaging of whole mammalian cells, simplifying the experimental and analysis procedures for obtaining volumetric nanoscale structural information.

Microfluidics-based automatic immunofluorescence staining for single-molecule localization microscopy

Microfluidics-based automatic immunofluorescence staining for single-molecule localization microscopy

Super resolution reconstruction of fluorescence microscopy images by a convolutional network with physical priors

Super-solution fluorescence microscopy, such as single-molecule localization microscopy (SMLM), is effective in observing subcellular structures and achieving excellent enhancement in spatial resolution in contrast to traditional fluorescence microscopy. Recently, deep learning has demonstrated excellent performance in SMLM in solving the trade-offs between spatiotemporal resolution, phototoxicity, and signal intensity. However, most of these researches rely on sufficient and high-quality datasets. Here, we propose a physical priors-based convolutional super-resolution network (PCSR), which incorporates a physical-based loss term and an initial optimization process based on the Wiener filter to create excellent super-resolution images directly using low-resolution images. The experimental results demonstrate that PCSR enables the achievement of a fast reconstruction time of 100 ms and a high spatial resolution of 10 nm by training on a limited dataset, allowing subcellular research with high spatiotemporal resolution, low cell phototoxic illumination, and high accessibility. In addition, the generalizability of PCSR to different live cell structures makes it a practical instrument for diverse cell research.

Vortex light field microscopy: 3D spectral single-molecule imaging with a twist

3D single-molecule imaging reveals nanoscale structures in cell volumes but is limited by the need for spectrally distinct fluorophores. We address this limitation with vortex light field microscopy (VLFM), a 3D spectral single-molecule localization technique with 25 nm spatial and 3 nm spectral precision over a 4 µm depth of field. By modifying our previous single-molecule light field microscope with an azimuthally oriented prism array, we generated spectral disparity orthogonal to axial disparity, enabling simultaneous spatial and spectral localization on a single detector. We demonstrate VLFM with four-color 3D single-particle tracking and two-color 3D dSTORM imaging in fixed cells, successfully identifying dyes with spectral peaks just 15 nm apart. This shows VLFM’s potential for enhancing spatial biology workflows requiring highly multiplexed imaging.

Activity-dependent synapse clustering underlies eye-specific competition in the developing retinogeniculate system.

Co-active synaptic connections are often spatially clustered to facilitate local dendritic computations underlying learning, memory, and basic sensory processing. In the mammalian visual system, retinal ganglion cell (RGC) axons converge to form clustered synaptic inputs that enable local signal integration in the dorsal lateral geniculate nucleus (dLGN) of the thalamus. While visual experience promotes retinogeniculate synapse clustering after eye-opening, the earliest events in cluster formation prior to visual experience are unknown. Here, using volumetric super-resolution single-molecule localization microscopy and eye-specific labeling of developing retinogeniculate synapses in mice, we show that synaptic clustering is eye-specific and activity-dependent during retinogeniculate refinement in the first postnatal week. We identified a subset of retinogeniculate synapses with multiple active zones that are surrounded by like-eye synapses and depleted of synapse clustering from the opposite eye. In mutant mice with disrupted spontaneous retinal wave activity, synapses with multiple active zones still form, but do not exhibit the synaptic clustering seen in controls. These results highlight a role for spontaneous retinal activity in regulating eye-specific synaptic clustering in circuits essential for visual perception and behavior.

Early Steps of Individual Multireceptor Viral Interactions Dissected by High-Density, Multicolor Quantum Dot Mapping in Living Cells.

Viral capture and entry to target cells are the first crucial steps that ultimately lead to viral infection. Understanding these events is essential toward the design and development of suitable antiviral drugs and/or vaccines. Viral capture involves dynamic interactions of the virus with specific receptors in the plasma membrane of the target cells. In the last years, single virus tracking has emerged as a powerful approach to assess real time dynamics of viral processes in living cells and their engagement with specific cellular components. However, direct visualization of the early steps of multireceptor viral interactions at the single level has been largely impeded by the technical challenges associated with imaging individual multimolecular systems at relevant spatial (nanometer) and temporal (millisecond) scales. Here, we present a four-color, high-density quantum dot spatiotemporal mapping methodology to capture real-time interactions between individual virus-like-particles (VLPs) and three different viral (co-) receptors on the membrane of primary living immune cells derived from healthy donors. Together with quantitative tools, our approach revealed the existence of a coordinated spatiotemporal diffusion of the three different (co)receptors prior to viral engagement. By varying the temporal-windows of cumulated single-molecule localizations, we discovered that such a concerted diffusion impacts on the residence time of HIV-1 and SARS-CoV-2 VLPs on the host membrane and potential viral infectivity. Overall, our methodology offers the possibility for systematic analysis of the initial steps of viral-host interactions and could be easily implemented for the investigation of other multimolecular systems at the single-molecule level.

Quantitative Analysis of Protein-Protein Equilibrium Constants in Cellular Environments Using Single-Molecule Localization Microscopy.

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 (Ka) and dissociation (Kd) 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.

Enhancing the data processing speed of a deep-learning-based three-dimensional single molecule localization algorithm (FD-DeepLoc) with a combination of feature compression and pipeline programming

Three-dimensional (3D) single molecule localization microscopy (SMLM) plays an important role in biomedical applications, but its data processing is very complicated. Deep learning is a potential tool to solve this problem. As the state of art 3D super-resolution localization algorithm based on deep learning, FD-DeepLoc algorithm reported recently still has a gap with the expected goal of online image processing, even though it has greatly improved the data processing throughput. In this paper, a new algorithm Lite-FD-DeepLoc is developed on the basis of FD-DeepLoc algorithm to meet the online image processing requirements of 3D SMLM. This new algorithm uses the feature compression method to reduce the parameters of the model, and combines it with pipeline programming to accelerate the inference process of the deep learning model. The simulated data processing results show that the image processing speed of Lite-FD-DeepLoc is about twice as fast as that of FD-DeepLoc with a slight decrease in localization accuracy, which can realize real-time processing of [Formula: see text] pixels size images. The results of biological experimental data processing imply that Lite-FD-DeepLoc can successfully analyze the data based on astigmatism and saddle point engineering, and the global resolution of the reconstructed image is equivalent to or even better than FD-DeepLoc algorithm.

Accurate 3D single-molecule localization via vectorial in situ point spread function retrieval and aberration assessment

Single-molecule localization microscopy (SMLM) gradually plays an important role in deep tissue imaging. However, current SMLM methods primarily rely on fiducial marks, neglecting aberrations introduced by thick samples, thereby resulting in decreased image quality in deep tissues. Here, we introduce vectorial in situ point spread function (PSF) retrieval (VISPR), a method that retrieves a precise PSF model considering both system- and sample-induced aberrations under SMLM conditions. By employing the theory of vectorial PSF model and maximum likelihood estimation (MLE) phase retrieval, VISPR is capable of reconstructing an accurate in situ 3D PSF model achieving the theoretically minimum uncertainty and accurately reflecting three-dimensional information of single molecules. This capability enables accurate 3D super-resolution reconstruction in deep regions away from the coverslips. Additionally, VISPR demonstrates applicability in low signal-to-noise ratio scenarios and compatibility with various SMLM microscope modalities. From both simulations and experiments, we verified the superiority and effectiveness of VISPR. We anticipate that VISPR will become a pivotal tool for advancing deep tissue SMLM imaging.

Using polarization sensitive SMLM to infer the interaction strength of dye-plasmonic nanosphere systems

Single-molecule microscopy is an effective tool for the characterization of fluorophore–plasmonic structure interaction. However, sophisticated evaluation is required as specific information is hidden in the emission. Here, we theoretically and experimentally investigated the emission polarization of rotationally mobile fluorophores near plasmonic Au–Ag alloy nanospheres. We developed an elaborate calculation based on the Mie theory, which considers the rotational mobility of dyes near spherical plasmonic nanoparticles of any size. Furthermore, we have created a simplified model that describes the emission’s degree of polarization within 10% accuracy in the case of small nanoparticles, when dipole approximation is valid. Our results indicate the close relation of the polarization degree of the emission, which can reach up to be ∼0.8 in the resonant case, to fluorescence scattering. DNA-PAINT single-molecule localization microscopy experiments conducted using AF488 and Atto647N dyes revealed that the measured polarization degree distribution followed the tendency predicted by calculations.

Single-molecule tracking technology based on symmetric power exponential phases.

We present a design approach for a symmetric power exponential phase, resulting in a phase-modulated point-spread function with two symmetrical mainlobes that can be steered toward the optical axis opposite the x axis. This design offers nanoscale 3D localization capabilities suitable for 3D single-molecule tracking and localization imaging. The axial probing depth of this symmetric power exponential point-spread function can be adjusted as needed by manipulating specific parameters. Optimization of the symmetric power exponential phase involves truncation filtering to reduce sidelobes and the utilization of a phase inversion-based optimization algorithm to enhance transfer function efficiency and localization precision. A successful multi-molecule 3D tracking experiment was conducted at a 10 µm axial depth using a numerical aperture of 1.4 to validate the efficacy of the proposed design methodology.

Targeted Photoactivatable Green-Emitting BODIPY Based on Directed Photooxidation-Induced Activation and its Application to Live Dynamic Super-Resolution Microscopy.

Photoactivatable fluorescent probes are valuable tools in bioimaging for tracking cells down to single molecules and for single molecule localization microscopy. For the latter application, green emitting dyes are in demand. We herein developed an efficient green-emitting photoactivatable furanyl-BODIPY (PFB) and we established a new mechanism of photoactivation called Directed Photooxidation Induced Activation (DPIA) where the furan is photo-oxidized in a directed manner by the singlet oxygen produced by the probe. The efficient photoconverter (93-fold fluorescence enhancement at 510 nm, 49% yield conversion) is functionalizable and allowed targeting of several subcellular structures and organelles, which were photoactivated in live cells. Finally, we demonstrated the potential of PFB in super-resolution imaging by performing PhotoActivated Localization Microscopy (PALM) in live cells.

Neural network-assisted localization of clustered point spread functions in single-molecule localization microscopy.

Single-molecule localization microscopy (SMLM), which has revolutionized nanoscale imaging, faces challenges in densely labelled samples due to fluorophore clustering, leading to compromised localization accuracy. In this paper, we propose a novel convolutional neural network (CNN)-assisted approach to address the issue of locating the clustered fluorophores. Our CNN is trained on a diverse data set of simulated SMLM images where it learns to predict point spread function (PSF) locations by generating Gaussian blobs as output. Through rigorous evaluation, we demonstrate significant improvements in PSF localization accuracy, especially in densely labelled samples where traditional methods struggle. In addition, we employ blob detection as a post-processing technique to refine the predicted PSF locations and enhance localization precision. Our study underscores the efficacy of CNN in addressing clustering challenges in SMLM, thereby advancing spatial resolution and enabling deeper insights into complex biologicalstructures.

POLCAM: instant molecular orientation microscopy for the life sciences

Current methods for single-molecule orientation localization microscopy (SMOLM) require optical setups and algorithms that can be prohibitively slow and complex, limiting widespread adoption for biological applications. We present POLCAM, a simplified SMOLM method based on polarized detection using a polarization camera, which can be easily implemented on any wide-field fluorescence microscope. To make polarization cameras compatible with single-molecule detection, we developed theory to minimize field-of-view errors, used simulations to optimize experimental design and developed a fast algorithm based on Stokes parameter estimation that can operate over 1,000-fold faster than the state of the art, enabling near-instant determination of molecular anisotropy. To aid in the adoption of POLCAM, we developed open-source image analysis software and a website detailing hardware installation and software use. To illustrate the potential of POLCAM in the life sciences, we applied our method to study α-synuclein fibrils, the actin cytoskeleton of mammalian cells, fibroblast-like cells and the plasma membrane of live human T cells.

Monitoring Electrochemical Dynamics through Single-Molecule Imaging of hBN Surface Emitters in Organic Solvents.

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.

High Refractive Index Imaging Buffer for Dual-Color 3D SMLM Imaging of Thick Samples.

The current limitations of single-molecule localization microscopy (SMLM) in deep tissue imaging, primarily due to depth-dependent aberrations caused by refractive index (RI) mismatch, present a significant challenge in achieving high-resolution images at greater depths. To extend the imaging depth, we optimized the imaging buffer of SMLM with the RI matched to that of the objective immersion medium and systematically evaluated five different RI-matched buffers, focusing on their impact on the blinking behavior of red-absorbing dyes and the quality of reconstructed super-resolution images. Particularly, we found that clear unobstructed brain imaging cocktails-based imaging buffer could match the RI of oil and was able to clear the tissue samples. With the help of the RI-matched imaging buffers, we showed high-quality dual-color 3D SMLM images with imaging depths ranging from a few micrometers to tens of micrometers in both cultured cells and sectioned tissue samples. This advancement offers a practical and accessible method for high-resolution imaging at greater depths without the need for specialized optical equipment or expertise.

Self-interference digital holography with computational aberration correction

Self-interference digital holography (SIDH) enables 3D imaging of incoherently emitting objects over a large axial range using only three 2D images. Our previous research demonstrated that point-like sources emitting as few as 4,200 photons can be reconstructed over a 10 µm axial range. Combining SIDH with single-molecule localization microscopy (SMLM) has the potential to achieve 3D super-resolution imaging across a large axial range without mechanical refocusing of the sample. However, optical aberrations affect the localization performance of SIDH and must be corrected, especially for large-volume 3D imaging. In this paper, we propose a fast, guide-star-free computational aberration correction method for SIDH. Our method can correct optical aberrations in low signal light conditions over the entire imaging axial range without any additional hardware. We use a sensorless-AO method in a virtual pupil plane to optimize the wavefront based on a frequency-space metric. Using this method, we demonstrate an improvement in both the Strehl ratio up to ∼0.98 and the SIDH localization precision to near the ideal case.