High-fidelity 3D live-cell nanoscopy through data-driven enhanced super-resolution radial fluctuation

Protocol for the isolation and characterization of murine hematopoietic stem and progenitor cell-derived extracellular vesicles

Hematopoietic stem cells (HSCs) maintain their self-renewal capacity in an autocrine manner through hematopoietic stem and progenitor cell (HSPC)-derived extracellular vesicles (EVs). Here, we present a protocol for the isolation and characterization of EVs from HSPCs starting from an in vivo murine model. We describe steps for murine bone marrow isolation, HSPC staining and sorting, HSPC-derived EV isolation, size and concentration characterization, and EV visualization and marker description. For complete details on the use and execution of this protocol, please refer to Bonora et al.1.

Imaging 3D cell cultures with optical microscopy

Three-dimensional (3D) cell cultures have gained popularity in recent years due to their ability to represent complex tissues or organs more faithfully than conventional two-dimensional (2D) cell culture. This article reviews the application of both 2D and 3D microscopy approaches for monitoring and studying 3D cell cultures. We first summarize the most popular optical microscopy methods that have been used with 3D cell cultures. We then discuss the general advantages and disadvantages of various microscopy techniques for several broad categories of investigation involving 3D cell cultures. Finally, we provide perspectives on key areas of technical need in which there are clear opportunities for innovation. Our goal is to guide microscope engineers and biomedical end users toward optimal imaging methods for specific investigational scenarios and to identify use cases in which additional innovations in high-resolution imaging could be helpful.

Super-resolution algorithms for imaging FCS enhancement: A comparative study

Understanding the structure and dynamics of biological systems is often limited by the trade-off between spatial and temporal resolution. Imaging fluorescence correlation spectroscopy (ImFCS) is a powerful technique for capturing molecular dynamics with high temporal precision but remains diffraction limited. This constraint poses challenges for quantifying dynamics of subcellular structures like membrane-proximal cortical actin fibers. Computational super-resolution microscopy (CSRM) presents an accessible strategy for enhancing spatial resolution without specialized instrumentation, enabling compatibility with ImFCS. In this study, we evaluated various CSRM techniques, including super-resolution radial fluctuations, mean-shift super-resolution, and multiple signal classification imaging, using total internal reflection fluorescence datasets of actin fibers labeled with F-tractin-mApple. By combining structural masks from total internal reflection fluorescence and CSRM, we distinguished off-fiber, mixed, and on-fiber regions for region-specific diffusion analyses. Although all CSRM algorithms improve ImFCS data analysis, super-resolution radial fluctuations demonstrated superior performance in identifying cortical actin fibers, showing minimal variance in on-fiber diffusion coefficients. These findings establish a framework for integrating CSRM with ImFCS to achieve high-resolution spatial and dynamic characterization of subcellular structures from single measurements.

Crossing the Dimensional Divide with Optoelectronic Tweezers: Multicomponent Light-Driven Micromachines with Motion Transfer in Three Dimensions

Micromachines capable of performing diverse mechanical tasks in complex and constrained microenvironments are of great interest. Despite important milestones in this pursuit, until now, micromachines are confined to actuation within a single 2D plane due to the challenges of transferring motion across different planes in limited space. Here, a breakthrough method is presented to overcome this limitation: multi-component micromachines that facilitate 3D motion transfer across different planes. These light-driven 3D micromachines, fabricated using standard photolithography combined with direct laser writing, are assembled and actuated via programmable light patterns within an optoelectronic tweezers system. Utilizing charge-induced repulsion and dielectrophoretic levitation effects, the micromachines enable highly efficient mechanical rotation and effective inter-component motion transfer. Through this work, fascinating patterns of similarities are unveiled for the new microscale 3D systems when compared with the macro-scale world in which they live, paving the way for the development of micromechanical devices and microsystems with ever increasing functionality and versatility.

Super-resolution microscopy as a drug discovery tool

At the turn of the century a fundamental resolution barrier in fluorescence microscopy known as the diffraction limit was broken, giving rise to the field of super-resolution microscopy. Subsequent nanoscopic investigation with visible light revolutionised our understanding of how previously unknown molecular features give rise to the emergent behaviour of cells. It transpires that the devil is in these fine molecular details, and essential nanoscale processes were found everywhere researchers chose to look. Now, after nearly two decades, super-resolution microscopy has begun to address previously unmet challenges in the study of human disease and is poised to become a pivotal tool in drug discovery.

Efficiently accelerated bioimage analysis with NanoPyx, a Liquid Engine-powered Python framework

The expanding scale and complexity of microscopy image datasets require accelerated analytical workflows. NanoPyx meets this need through an adaptive framework enhanced for high-speed analysis. At the core of NanoPyx, the Liquid Engine dynamically generates optimized central processing unit and graphics processing unit code variations, learning and predicting the fastest based on input data and hardware. This data-driven optimization achieves considerably faster processing, becoming broadly relevant to reactive microscopy and computing fields requiring efficiency.

Single Extracellular VEsicle Nanoscopy-Universal Protocol (SEVEN-UP): Accessible Imaging Platform for Quantitative Characterization of Single Extracellular Vesicles

Extracellular vesicles (EVs), membrane-encapsulated nanoparticles shed from all cells, are tightly involved in critical cellular functions. Moreover, EVs have recently emerged as exciting therapeutic modalities, delivery vectors, and biomarker sources. However, EVs are difficult to characterize, because they are typically small and heterogeneous in size, origin, and molecular content. Recent advances in single EV methods have addressed some of these challenges by providing sensitive tools for assessing individual vesicles; one example is our recently developed Single Extracellular VEsicle Nanoscopy (SEVEN) approach. However, these tools are typically not universally available to the general research community, as they require highly specialized equipment. Here, we show how single EV studies may be democratized via a novel method that employs super-resolution radial fluctuations (SRRF) microscopy and advanced data analysis. SRRF is compatible with a wide range of microscopes and fluorophores. We herein quantified individual EVs by combining affinity isolation (analytical protocol based on SEVEN) with SRRF microscopy and new analysis algorithms supported by machine learning-based EV assessment. Using SEVEN, we first optimized the workflow and validated the data obtained on wide-field and total internal reflection fluorescence microscopes. We further demonstrated that our approach, which we call the SEVEN-Universal Protocol (SEVEN-UP), can robustly assess the number, size, and content of plasma and recombinant EVs. Finally, we used the platform to assess RNA in EVs from conditioned cell culture media. Using SYTO RNASelect dye, we found that 18% of EVs from HEK 293T cells appear to contain RNA; these EVs were significantly larger compared with the general EV population. Altogether, we developed an economical, multiparametric, single EV characterization approach for the research community.

A generative benchmark for evaluating the performance of fluorescent cell image segmentation

Fluorescent cell imaging technology is fundamental in life science research, offering a rich source of image data crucial for understanding cell spatial positioning, differentiation, and decision-making mechanisms. As the volume of this data expands, precise image analysis becomes increasingly critical. Cell segmentation, a key analysis step, significantly influences quantitative analysis outcomes. However, selecting the most effective segmentation method is challenging, hindered by existing evaluation methods' inaccuracies, lack of graded evaluation, and narrow assessment scope. Addressing this, we developed a novel framework with two modules: StyleGAN2-based contour generation and Pix2PixHD-based image rendering, producing diverse, graded-density cell images. Using this dataset, we evaluated three leading cell segmentation methods: DeepCell, CellProfiler, and CellPose. Our comprehensive comparison revealed CellProfiler's superior accuracy in segmenting cytoplasm and nuclei. Our framework diversifies cell image data generation and systematically addresses evaluation challenges in cell segmentation technologies, establishing a solid foundation for advancing research and applications in cell image analysis.

Neural space-time model for dynamic multi-shot imaging

Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space-time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.

Obtaining super-resolved images at the mesoscale through super-resolution radial fluctuations

Significance

Current super-resolution imaging techniques allow for a greater understanding of cellular structures; however, they are often complex or only have the ability to image a few cells at once. This small field of view (FOV) may not represent the behavior across the entire sample, and manual selection of regions of interest (ROIs) may introduce bias. It is possible to stitch and tile many small ROIs; however, this can result in artifacts across an image.

Aim

The aim is to achieve accurate super-resolved images across a large FOV ( 4.4 × 3.0    mm ).

Approach

We have applied super-resolution radial fluctuations processing in conjunction with the Mesolens, which has the unusual combination of a low-magnification and high numerical aperture, to obtain super-resolved images.

Results

We demonstrate it is possible to achieve images with a resolution of 446.3 ± 10.9    nm , providing a ∼ 1.6 -fold improvement in spatial resolution, over an FOV of 4.4 × 3.0    mm , with minimal error, and consistent structural agreement.

Conclusions

We provide a simple method for obtaining accurate super-resolution images over a large FOV, allowing for a simultaneous understanding of both subcellular structures and their large-scale interactions.

Morphology Determination of Luminescent Carbon Nanotubes by Analytical Super-Resolution Microscopy Approaches

The ability to determine the precise structure of nano-objects is essential for a multitude of applications. This is particularly true of single-walled carbon nanotubes (SWCNTs), which are produced as heterogeneous samples. Current techniques used for their characterization require sophisticated instrumentation, such as atomic force microscopy (AFM), or compromise on accuracy. In this paper, we propose to use super-resolution microscopy (SRM) to accurately determine the morphology (orientation, length, and shape) of individual luminescent SWCNTs. We generate super-resolved images using three recently published SRM analytical software packages (DPR, eSRRF, and MSSR) and metrologically compare their performances to determine the morphological properties of SWCNTs. For this, ground-truth information on nanotube morphologies was obtained using polarization measurements and AFM to directly correlate the results from SRM at the single particle level. We show a more than 4-fold improvement in resolution over standard photoluminescence imaging, revealing hidden morphologies as efficiently as AFM. We finally demonstrate that DPR, and eventually eSRRF, can effectively assess SWCNT length distribution in a much faster and more accessible way than AFM. We believe that this approach can be generalized to other types of luminescent nanostructures and thus become a standard for rapid and accurate characterization of samples.

One-step nanoscale expansion microscopy reveals individual protein shapes

The attainable resolution of fluorescence microscopy has reached the subnanometer range, but this technique still fails to image the morphology of single proteins or small molecular complexes. Here, we expand the specimens at least tenfold, label them with conventional fluorophores and image them with conventional light microscopes, acquiring videos in which we analyze fluorescence fluctuations. One-step nanoscale expansion (ONE) microscopy enables the visualization of the shapes of individual membrane and soluble proteins, achieving around 1-nm resolution. We show that conformational changes are readily observable, such as those undergone by the ~17-kDa protein calmodulin upon Ca2+ binding. ONE is also applied to clinical samples, analyzing the morphology of protein aggregates in cerebrospinal fluid from persons with Parkinson disease, potentially aiding disease diagnosis. This technology bridges the gap between high-resolution structural biology techniques and light microscopy, providing new avenues for discoveries in biology and medicine.

Fluorescence super-resolution microscopy via fluctuation-based multi-route synergy

Fluorescence fluctuation-based super-resolution microscopy (FF-SRM) is an economical and widely applicable technique that significantly enhances the spatial resolution of fluorescence imaging by capitalizing on fluorescence intermittency. However, each variant of FF-SRM imaging has inherent limitations. This study proposes a super-resolution reconstruction strategy (synSRM) by synergizing multiple variants of the FF-SRM approach to address the limitations and achieve high-quality and high-resolution imaging. The simulation and experimental results demonstrate that, compared to images reconstructed using single FF-SRM algorithms, by selecting suitable synSRM routes according to various imaging conditions, further improvements of the spatial resolution and image reconstruction quality can be obtained for super-resolution fluorescence imaging.

Guidelines for naming and studying plasma membrane domains in plants

Biological membranes play a crucial role in actively hosting, modulating and coordinating a wide range of molecular events essential for cellular function. Membranes are organized into diverse domains giving rise to dynamic molecular patchworks. However, the very definition of membrane domains has been the subject of continuous debate. For example, in the plant field, membrane domains are often referred to as nanodomains, nanoclusters, microdomains, lipid rafts, membrane rafts, signalling platforms, foci or liquid-ordered membranes without any clear rationale. In the context of plant-microbe interactions, microdomains have sometimes been used to refer to the large area at the plant-microbe interface. Some of these terms have partially overlapping meanings at best, but they are often used interchangeably in the literature. This situation generates much confusion and limits conceptual progress. There is thus an urgent need for us as a scientific community to resolve these semantic and conceptual controversies by defining an unambiguous nomenclature of membrane domains. In this Review, experts in the field get together to provide explicit definitions of plasma membrane domains in plant systems and experimental guidelines for their study. We propose that plasma membrane domains should not be considered on the basis of their size alone but rather according to the biological system being considered, such as the local membrane environment or the entire cell.

Tool to Resolve Distortions in Elemental and Isotopic Imaging

Nanoscale secondary ion mass spectrometry (NanoSIMS) makes it possible to visualize elements and isotopes in a wide range of samples at a high resolution. However, the fidelity and quality of NanoSIMS images often suffer from distortions because of a requirement to acquire and integrate multiple image frames. We developed an optical flow-based algorithm tool, NanoSIMS Stabilizer, for all-channel postacquisition registration of images. The NanoSIMS Stabilizer effectively deals with the distortions and artifacts, resulting in a high-resolution visualization of isotope and element distribution. It is open source with an easy-to-use ImageJ plugin and is accompanied by a Python version with GPU acceleration.

Deep SBP+ 2.0: a physics-driven generation capability enhanced framework to reconstruct a space-bandwidth product expanded image from two image shots

The space-bandwidth product (SBP) limitation makes it difficult to obtain an image with both a high spatial resolution and a large field of view (FoV) through commonly used optical imaging systems. Although FoV and spectrum stitch provide solutions for SBP expansion, they rely on spatial and spectral scanning, which lead to massive image captures and a low processing speed. To solve the problem, we previously reported a physics-driven deep SBP-expanded framework (Deep SBP+) [J. Opt. Soc. Am. A40, 833 (2023)JOAOD60740-323210.1364/JOSAA.480920]. Deep SBP+ can reconstruct an image with both high spatial resolution and a large FoV from a low-spatial-resolution image in a large FoV and several high-spatial-resolution images in sub-FoVs. In physics, Deep SBP+ reconstructs the convolution kernel between the low- and high-spatial-resolution images and improves the spatial resolution through deconvolution. But Deep SBP+ needs multiple high-spatial-resolution images in different sub-FoVs, inevitably complicating the operations. To further reduce the image captures, we report an updated version of Deep SBP+ 2.0, which can reconstruct an SBP expanded image from a low-spatial-resolution image in a large FoV and another high-spatial-resolution image in a sub-FoV. Different from Deep SBP+, the assumption that the convolution kernel is a Gaussian distribution is added to Deep SBP+ 2.0 to make the kernel calculation simple and in line with physics. Moreover, improved deep neural networks have been developed to enhance the generation capability. Proven by simulations and experiments, the receptive field is analyzed to prove that a high-spatial-resolution image in the sub-FoV can also guide the generation of the entire FoV. Furthermore, we also discuss the requirement of the sub-FoV image to obtain an SBP-expanded image of high quality. Considering its SBP expansion capability and convenient operation, the updated Deep SBP+ 2.0 can be a useful tool to pursue images with both high spatial resolution and a large FoV.

hydroSIM: super-resolution speckle illumination microscopy with a hydrogel diffuser

Super-resolution microscopy has emerged as an indispensable methodology for probing the intricacies of cellular biology. Structured illumination microscopy (SIM), in particular, offers an advantageous balance of spatial and temporal resolution, allowing for visualizing cellular processes with minimal disruption to biological specimens. However, the broader adoption of SIM remains hampered by the complexity of instrumentation and alignment. Here, we introduce speckle-illumination super-resolution microscopy using hydrogel diffusers (hydroSIM). The study utilizes the high scattering and optical transmissive properties of hydrogel materials and realizes a remarkably simplified approach to plug-in super-resolution imaging via a common epi-fluorescence platform. We demonstrate the hydroSIM system using various phantom and biological samples, and the results exhibited effective 3D resolution doubling, optical sectioning, and high contrast. We foresee hydroSIM, a cost-effective, biocompatible, and user-accessible super-resolution methodology, to significantly advance a wide range of biomedical imaging and applications.

Zero-shot learning enables instant denoising and super-resolution in optical fluorescence microscopy

Computational super-resolution methods, including conventional analytical algorithms and deep learning models, have substantially improved optical microscopy. Among them, supervised deep neural networks have demonstrated outstanding performance, however, demanding abundant high-quality training data, which are laborious and even impractical to acquire due to the high dynamics of living cells. Here, we develop zero-shot deconvolution networks (ZS-DeconvNet) that instantly enhance the resolution of microscope images by more than 1.5-fold over the diffraction limit with 10-fold lower fluorescence than ordinary super-resolution imaging conditions, in an unsupervised manner without the need for either ground truths or additional data acquisition. We demonstrate the versatile applicability of ZS-DeconvNet on multiple imaging modalities, including total internal reflection fluorescence microscopy, three-dimensional wide-field microscopy, confocal microscopy, two-photon microscopy, lattice light-sheet microscopy, and multimodal structured illumination microscopy, which enables multi-color, long-term, super-resolution 2D/3D imaging of subcellular bioprocesses from mitotic single cells to multicellular embryos of mouse and C. elegans.

RASP: Optimal Single Puncta Detection in Complex Cellular Backgrounds

Super-resolution and single-molecule microscopies have been increasingly applied to complex biological systems. A major challenge of these approaches is that fluorescent puncta must be detected in the low signal, high noise, heterogeneous background environments of cells and tissue. We present RASP, Radiality Analysis of Single Puncta, a bioimaging-segmentation method that solves this problem. RASP removes false-positive puncta that other analysis methods detect and detects features over a broad range of spatial scales: from single proteins to complex cell phenotypes. RASP outperforms the state-of-the-art methods in precision and speed using image gradients to separate Gaussian-shaped objects from the background. We demonstrate RASP's power by showing that it can extract spatial correlations between microglia, neurons, and α-synuclein oligomers in the human brain. This sensitive, computationally efficient approach enables fluorescent puncta and cellular features to be distinguished in cellular and tissue environments, with sensitivity down to the level of the single protein. Python and MATLAB codes, enabling users to perform this RASP analysis on their own data, are provided as Supporting Information and links to third-party repositories.

Harnessing artificial intelligence to reduce phototoxicity in live imaging

Fluorescence microscopy is essential for studying living cells, tissues and organisms. However, the fluorescent light that switches on fluorescent molecules also harms the samples, jeopardizing the validity of results - particularly in techniques such as super-resolution microscopy, which demands extended illumination. Artificial intelligence (AI)-enabled software capable of denoising, image restoration, temporal interpolation or cross-modal style transfer has great potential to rescue live imaging data and limit photodamage. Yet we believe the focus should be on maintaining light-induced damage at levels that preserve natural cell behaviour. In this Opinion piece, we argue that a shift in role for AIs is needed - AI should be used to extract rich insights from gentle imaging rather than recover compromised data from harsh illumination. Although AI can enhance imaging, our ultimate goal should be to uncover biological truths, not just retrieve data. It is essential to prioritize minimizing photodamage over merely pushing technical limits. Our approach is aimed towards gentle acquisition and observation of undisturbed living systems, aligning with the essence of live-cell fluorescence microscopy.