3D Super-resolution Imaging Research Articles

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.

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.

An Omni-Mesoscope for multiscale high-throughput quantitative phase imaging of cellular dynamics and high-content molecular characterization.

The mesoscope has emerged as a powerful imaging tool in biomedical research, yet its high cost and low resolution have limited its broader application. Here, we introduce the Omni-Mesoscope, a cost-effective high-spatial-temporal, multimodal, and multiplex mesoscopic imaging platform built from cost-efficient off-the-shelf components. This system uniquely merges the capabilities of quantitative phase microscopy to capture live-cell dynamics over a large cell population with highly multiplexed fluorescence imaging for comprehensive molecular characterization. This integration facilitates simultaneous tracking of live-cell morphodynamics across thousands of cells, alongside high-content molecular analysis at the single-cell level. Furthermore, the Omni-Mesoscope offers a mesoscale field of view of approximately 5 mm 2 with a high spatial resolution down to 700 nm, enabling the capture of information-rich images with detailed sub-cellular features. We demonstrate such capability in delineating molecular characteristics underlying rare dynamic cellular phenomena, such as cancer cell responses to chemotherapy and the emergence of polyploidy in drug-resistant cells. Moreover, the cost-effectiveness and the simplicity of our Omni-Mesoscope democratizes mesoscopic imaging, making it accessible across diverse biomedical research fields. To further demonstrate its versatility, we integrate expansion microscopy to enhance 3D volumetric super-resolution imaging of thicker tissues, opening new avenues for biological exploration at unprecedented scales and resolutions.

Predicting the Prognosis of HIFU Ablation of Uterine Fibroids Using a Deep Learning-Based 3D Super-Resolution DWI Radiomics Model: A Multicenter Study

Rationale and ObjectivesTo assess the feasibility and efficacy of a deep learning based three-dimensional (3D) super-resolution diffusion-weighted imaging (DWI) radiomics model in predicting the prognosis of high-intensity focused ultrasound (HIFU) ablation of uterine fibroids. MethodsThis retrospective study included 360 patients with uterine fibroids who received HIFU treatment, including Center A (training set: N=240; internal testing set: N=60) and Center B (external testing set: N=60) and were classified as having a favorable or unfavorable prognosis based on the postoperative non-perfusion volume ratio. A deep transfer learning approach was used to construct super-resolution DWI (SR-DWI) based on conventional high-resolution DWI (HR-DWI), and 1198 radiomics features were extracted from manually segmented regions of interest in both image types. Following data preprocessing and feature selection, radiomics models were constructed for HR-DWI and SR-DWI using Support Vector Machine (SVM), Random Forest (RF), and Light Gradient Boosting Machine (LightGBM) algorithms, with performance evaluated using area under the curve (AUC) and decision curves. ResultAll DWI radiomics models demonstrated superior AUC in predicting HIFU ablated uterine fibroids prognosis compared to expert radiologists (AUC: 0.706, 95% CI: 0.647-0.748). When utilizing different machine learning algorithms, the HR-DWI model achieved AUC values of 0.805 (95% CI: 0.679-0.931) with SVM, 0.797 (95% CI: 0.672-0.921) with RF, and 0.770 (95% CI: 0.631-0.908) with LightGBM. Meanwhile, the SR-DWI model outperformed the HR-DWI model (P<0.05) across all algorithms, with AUC values of 0.868 (95% CI: 0.775-0.960) with SVM, 0.824 (95% CI: 0.715-0.934) with RF, and 0.821 (95% CI: 0.709-0.933) with LightGBM. And decision curve analysis further confirmed the good clinical value of the models. ConclusionDeep learning based 3D SR-DWI radiomics model demonstrated favorable feasibility and effectiveness in predicting the prognosis of HIFU ablated uterine fibroids, which was superior to HR-DWI model and assessment by expert radiologists.

3D super-resolution optical fluctuation imaging with temporal focusing two-photon excitation.

3D super-resolution fluorescence microscopy typically requires sophisticated setups, sample preparation, or long measurements. A notable exception, SOFI, only requires recording a sequence of frames and no hardware modifications whatsoever but being a wide-field method, it faces problems in thick, dense samples. We combine SOFI with temporal focusing two-photon excitation - the wide-field method that is capable of exciting a thin slice in 3D volume. Temporal focusing is simple to implement whenever the excitation path of the microscope can be accessed. The implementation of SOFI is straightforward. By merging these two methods, we obtain super-resolved 3D images of neurons stained with quantum dots. Our approach offers reduced bleaching of out-of-focus fluorescent probes and an improved signal-to-background ratio that can be used when robust resolution improvement is required in thick, dense samples.

3D drift correction for super-resolution imaging with a single laser light.

Single-molecule localization microscopy (SMLM) enables three-dimensional (3D) super-resolution imaging of nanoscale structures within biological samples. However, prolonged acquisition introduces a drift between the sample and the imaging system, resulting in artifacts in the reconstructed super-resolution image. Here, we present a novel, to our knowledge, 3D drift correction method that utilizes both the reflected and scattered light from the sample. Our method employs the reflected light of a near-infrared (NIR) laser for focus stabilization while synchronously capturing speckle images to estimate the lateral drift. This approach combines high-precision active compensation in the axial direction with lateral post-processing compensation, achieving the abilities of 3D drift correction with a single laser light. Compared to the popular localization events-based cross correlation method, our approach is much more robust, especially for datasets with sparse localization points.

Multimodal illumination platform for 3D single-molecule super-resolution imaging throughout mammalian cells.

Single-molecule super-resolution imaging is instrumental in investigating cellular architecture and organization at the nanoscale. Achieving precise 3D nanometric localization when imaging structures throughout mammalian cells, which can be multiple microns thick, requires careful selection of the illumination scheme in order to optimize the fluorescence signal to background ratio (SBR). Thus, an optical platform that combines different wide-field illumination schemes for target-specific SBR optimization would facilitate more precise 3D nanoscale studies of a wide range of cellular structures. Here, we demonstrate a versatile multimodal illumination platform that integrates the sectioning and background reduction capabilities of light sheet illumination with homogeneous, flat-field epi- and TIRF illumination. Using primarily commercially available parts, we combine the fast and convenient switching between illumination modalities with point spread function engineering to enable 3D single-molecule super-resolution imaging throughout mammalian cells. For targets directly at the coverslip, the homogenous intensity profile and excellent sectioning of our flat-field TIRF illumination scheme improves single-molecule data quality by providing low fluorescence background and uniform fluorophore blinking kinetics, fluorescence signal, and localization precision across the entire field of view. The increased contrast achieved with LS illumination, when compared with epi-illumination, makes this illumination modality an excellent alternative when imaging targets that extend throughout the cell. We validate our microscopy platform for improved 3D super-resolution imaging by two-color imaging of paxillin - a protein located in the focal adhesion complex - and actin in human osteosarcoma cells.

Pixel-reassigned line-scanning microscopy for fast volumetric super-resolution imaging.

Super-resolution microscopy has revolutionized the field of biophotonics by revealing detailed 3D biological structures. Nonetheless, the technique is still largely limited by the low throughput and hampered by increased background signals for dense or thick biological specimens. In this paper, we present a pixel-reassigned continuous line-scanning microscope for large-scale high-speed 3D super-resolution imaging, which achieves an imaging resolution of 0.41 µm in the lateral direction, i.e., a 2× resolution enhancement from the raw images. Specifically, the recorded line images are first reassigned to the line-excitation center at each scanning position to enhance the resolution. Next, a modified HiLo algorithm is applied to reduce the background signals. Parametric models have been developed to simulate the imaging results of randomly distributed fluorescent beads. Imaging experiments were designed and performed to verify the predicted performance on various biological samples, which demonstrated an imaging speed of 3400 pixels/ms on millimeter-scale specimens. These results confirm the pixel-reassigned line-scanning microscopy is a facile and powerful method to realize large-area super-resolution imaging on thick or dense biological samples.

Deciphering the sub-Golgi localization of glycosyltransferases via 3D super-resolution imaging.

The Golgi apparatus, a crucial organelle involved in protein processing, including glycosylation, exhibits complex sub-structures, i.e., cis-, medial, and trans-cisternae. This study investigated the distribution of glycosyltransferases within the Golgi apparatus of mammalian cells via 3D super-resolution imaging. Focusing on human glycosyltransferases involved in N-glycan modification, we found that even enzymes presumed to coexist in the same Golgi compartment exhibit nuanced variations in localization. By artificially making their N-terminal regions [composed of a cytoplasmic, transmembrane, and stem segment (CTS)] identical, it was possible to enhance the degree of their colocalization, suggesting the decisive role of this region in determining the sub-Golgi localization of enzymes. Ultimately, this study reveals the molecular codes within CTS regions as key determinants of glycosyltransferase localization, providing insights into precise control over the positioning of glycosyltransferases, and consequently, the interactions between glycosyltransferases and substrate glycoproteins as cargoes in the secretory pathway. This study advances our understanding of Golgi organization and opens avenues for programming the glycosylation of proteins for clinical applications.Key words: Golgi apparatus, glycosyltransferase, 3D super-resolution imaging, N-glycosylation.

3D Acoustic Wave Sparsely Activated Localization Microscopy With Phase Change Contrast Agents.

The aim of this study is to demonstrate 3-dimensional (3D) acoustic wave sparsely activated localization microscopy (AWSALM) of microvascular flow in vivo using phase change contrast agents (PCCAs). Three-dimensional AWSALM using acoustically activable PCCAs was evaluated on a crossed tube microflow phantom, the kidney of New Zealand White rabbits, and the brain of C57BL/6J mice through intact skull. A mixture of C 3 F 8 and C 4 F 10 low-boiling-point fluorocarbon gas was used to generate PCCAs with an appropriate activation pressure. A multiplexed 8-MHz matrix array connected to a 256-channel ultrasound research platform was used for transmitting activation and imaging ultrasound pulses and recording echoes. The in vitro and in vivo echo data were subsequently beamformed and processed using a set of customized algorithms for generating 3D super-resolution ultrasound images through localizing and tracking activated contrast agents. With 3D AWSALM, the acoustic activation of PCCAs can be controlled both spatially and temporally, enabling contrast on demand and capable of revealing 3D microvascular connectivity. The spatial resolution of the 3D AWSALM images measured using Fourier shell correlation is 64 μm, presenting a 9-time improvement compared with the point spread function and 1.5 times compared with half the wavelength. Compared with the microbubble-based approach, more signals were localized in the microvasculature at similar concentrations while retaining sparsity and longer tracks in larger vessels. Transcranial imaging was demonstrated as a proof of principle of PCCA activation in the mouse brain with 3D AWSALM. Three-dimensional AWSALM generates volumetric ultrasound super-resolution microvascular images in vivo with spatiotemporal selectivity and enhanced microvascular penetration.

Optimal sampling rate for 3D single molecule localization.

Resolution of single molecule localization microscopy (SMLM) depends on the localization accuracy, which can be improved by utilizing engineered point spread functions (PSF) with delicate shapes. However, the intrinsic pixelation effect of the detector sensor will deteriorate PSFs under different sampling rates. The influence of the pixelation effect to the achieved 3D localization accuracy for different PSF shapes under different signal to background ratio (SBR) and pixel dependent readout noise has not been investigated in detail so far. In this work, we proposed a framework to characterize the 3D localization accuracy of pixelated PSF at different sampling rates. Four different PSFs (astigmatic PSF, double helix (DH) PSF, Tetrapod PSF and 4Pi PSF) were evaluated and the pixel size with optimal 3D localization performance were derived. This work provides a theoretical guide for the optimal design of sampling rate for 3D super resolution imaging.

Nanoscale organization of the endogenous ASC speck

The NLRP3 inflammasome is a central component of the innate immune system. Its activation leads to formation of the ASC speck, a supramolecular assembly of the inflammasome adaptor protein ASC. Different models, based on ASC overexpression, have been proposed for the structure of the ASC speck. Using dual-color 3D super-resolution imaging (dSTORM and DNA-PAINT), we visualized the ASC speck structure following NLRP3 inflammasome activation using endogenous ASC expression. A complete structure was only obtainable by labeling with both anti-ASC antibodies and nanobodies. The complex varies in diameter between ∼800 and 1000nm, and is composed of a dense core with emerging filaments. Dual-color confocal fluorescence microscopy indicated that the ASC speck does not colocalize with the microtubule-organizing center at late time points after Nigericin stimulation. From super-resolution images of whole cells, the ASC specks were sorted into a pseudo-time sequence indicating that they become denser but not larger during formation.

SMITE: Single Molecule Imaging Toolbox Extraordinaire (MATLAB).

Fluorescence single molecule imaging comprises a variety of techniques that involve detecting individual fluorescent molecules. Many of these techniques involve localizing individual fluorescent molecules with precisions below the diffraction limit, which limits the spatial resolution of (visible) light-based microscopes. These methodologies are widely used to image biological structures at the nanometer scale by fluorescently tagging the structures of interest, elucidating details of the biological behavior observed. Two common techniques are single-molecule localization microscopy (SMLM), (Betzig et al., 2006; Fazel & Wester, 2022; Hell, 2007; Lidke et al., 2005; Rust et al., 2006; van de Linde et al., 2011) which is used to produce 2D or 3D super-resolution images of static or nearly static structures, and single-particle tracking (SPT) (Shen et al., 2017), which follows the time course of one or a very small number of moving tagged molecules. SMLM often involves distributions of particles at medium to high density, while SPT works in a very low density domain. These procedures all require intensive numerical computation, and the methods are tightly interwoven.

Exploring microstructure and petrophysical properties of microporous volcanic rocks through 3D multiscale and super-resolution imaging

Digital rock physics offers powerful perspectives to investigate Earth materials in 3D and non-destructively. However, it has been poorly applied to microporous volcanic rocks due to their challenging microstructures, although they are studied for numerous volcanological, geothermal and engineering applications. Their rapid origin, in fact, leads to complex textures, where pores are dispersed in fine, heterogeneous and lithified matrices. We propose a framework to optimize their investigation and face innovative 3D/4D imaging challenges. A 3D multiscale study of a tuff was performed through X-ray microtomography and image-based simulations, finding that accurate characterizations of microstructure and petrophysical properties require high-resolution scans (≤ 4 μm/px). However, high-resolution imaging of large samples may need long times and hard X-rays, covering small rock volumes. To deal with these limitations, we implemented 2D/3D convolutional neural network and generative adversarial network-based super-resolution approaches. They can improve the quality of low-resolution scans, learning mapping functions from low-resolution to high-resolution images. This is one of the first efforts to apply deep learning-based super-resolution to unconventional non-sedimentary digital rocks and real scans. Our findings suggest that these approaches, and mainly 2D U-Net and pix2pix networks trained on paired data, can strongly facilitate high-resolution imaging of large microporous (volcanic) rocks.

High-throughput single-molecule localization microscopy: Potential clinical applications.

High-throughput single-molecule localization microscopy: Potential clinical applications.

Field-dependent deep learning enables high-throughput whole-cell 3D super-resolution imaging.

Single-molecule localization microscopy in a typical wide-field setup has been widely used for investigating subcellular structures with super resolution; however, field-dependent aberrations restrict the field of view (FOV) to only tens of micrometers. Here, we present a deep-learning method for precise localization of spatially variant point emitters (FD-DeepLoc) over a large FOV covering the full chip of a modern sCMOS camera. Using a graphic processing unit-based vectorial point spread function (PSF) fitter, we can fast and accurately model the spatially variant PSF of a high numerical aperture objective in the entire FOV. Combined with deformable mirror-based optimal PSF engineering, we demonstrate high-accuracy three-dimensional single-molecule localization microscopy over a volume of ~180 × 180 × 5 μm3, allowing us to image mitochondria and nuclear pore complexes in entire cells in a single imaging cycle without hardware scanning; a 100-fold increase in throughput compared to the state of the art.

Construction and characterization of a versatile multimodal single-molecule super-resolution microscope for 3D whole cell studies.

The cellular environment is dense and complex, and imaging with 3D single-molecule super-resolution microscopy requires careful consideration of appropriate illumination methods to use in relation to the goals of the study. There is, however, typically a tradeoff between performance and simplicity when selecting illumination schemes. Epi-illumination is a common and simple technique, but it generates high unwanted background fluorescence and causes photodamage and photobleaching throughout the imaged cell. Total internal reflection fluorescence (TIRF) is another technique which provides excellent contrast, but it is limited to imaging a thin volume at the bottom of a sample and is thus incompatible with whole-cell imaging. Light sheet illumination is an alternative, whole-cell compatible approach where a thin sheet of light is directed orthogonally to the detection axis to optically section a cell and reduce the fluorescence background, photobleaching, and photodamage. In this work we demonstrate a versatile microscopy platform which integrates the sectioning and background reducing capability of light sheet illumination with homogeneous, flat-field epi- and TIRF illumination. A flat-field intensity profile improves the control of fluorophore blinking kinetics and the resolution across the entire field of view when compared to a standard Gaussian intensity profile. Additionally, this microscope is designed to allow very fast switching between illumination modalities and laser wavelengths, using primarily commercially available parts. When combined with point spread function engineering, our system conveniently enables 3D single-molecule super-resolution imaging and molecular counting throughout whole cells with optimized and adjustable illumination modalities.

Obtaining 3D super-resolution images by utilizing rotationally symmetric structures and 2D-to-3D transformation

Super-resolution imaging techniques have provided unprecedentedly detailed information by surpassing the diffraction-limited resolution of light microscopy. However, in order to derive high quality spatial resolution, many of these techniques require high laser power, extended imaging time, dedicated sample preparation, or some combination of the three. These constraints are particularly evident when considering three-dimensional (3D) super-resolution imaging. As a result, high-speed capture of 3D super-resolution information of structures and dynamic processes within live cells remains both desirable and challenging. Recently, a highly effective approach to obtain 3D super-resolution information was developed that can be employed in commonly available laboratory microscopes. This development makes it both scientifically possible and financially feasible to obtain super-resolution 3D information under certain conditions. This is accomplished by converting 2D single-molecule localization data captured at high speed within subcellular structures and rotationally symmetric organelles. Here, a high-speed 2D single-molecule tracking and post-localization technique, known as single-point edge-excitation sub-diffraction (SPEED) microcopy, along with its 2D-to-3D transformation algorithm is detailed with special emphasis on the mathematical principles and Monte Carlo simulation validation of the technique.

3D super-resolution live-cell imaging with radial symmetry and Fourier light-field microscopy.

Live-cell imaging reveals the phenotypes and mechanisms of cellular function and their dysfunction that underscore cell physiology, development, and pathology. Here, we report a 3D super-resolution live-cell microscopy method by integrating radiality analysis and Fourier light-field microscopy (rad-FLFM). We demonstrated the method using various live-cell specimens, including actins in Hela cells, microtubules in mammary organoid cells, and peroxisomes in COS-7 cells. Compared with conventional wide-field microscopy, rad-FLFM realizes scanning-free, volumetric 3D live-cell imaging with sub-diffraction-limited resolution of ∼150 nm (x-y) and 300 nm (z), milliseconds volume acquisition time, six-fold extended depth of focus of ∼6 µm, and low photodamage. The method provides a promising avenue to explore spatiotemporal-challenging subcellular processes in a wide range of cell biological research.

Technologies Enabling Single-Molecule Super-Resolution Imaging of mRNA.

The transient nature of RNA has rendered it one of the more difficult biological targets for imaging. This difficulty stems both from the physical properties of RNA as well as the temporal constraints associated therewith. These concerns are further complicated by the difficulty in imaging endogenous RNA within a cell that has been transfected with a target sequence. These concerns, combined with traditional concerns associated with super-resolution light microscopy has made the imaging of this critical target difficult. Recent advances have provided researchers the tools to image endogenous RNA in live cells at both the cellular and single-molecule level. Here, we review techniques used for labeling and imaging RNA with special emphases on various labeling methods and a virtual 3D super-resolution imaging technique.