Advancing Multicolor Super-Resolution Volume Imaging: Illuminating Complex Cellular Dynamics

Abstract

The rapidly evolving field of live-cell super-resolution imaging has transformed our understanding of cellular structures and dynamic biological processes. This perspective delves into the importance and challenges of multicolor super-resolution volume imaging in the context of living cells, where the ability to visualize multiple molecular species simultaneously across three dimensions is critical for deciphering complex cellular functions. While recent innovations have made significant strides, challenges such as temporal and spatial resolution limits, photobleaching, and depth of field remain significant obstacles. This work explores emerging strategies aimed at overcoming these technical barriers, including the development of novel fluorophores, advanced computational techniques leveraging artificial intelligence, and hardware innovations in imaging systems. By addressing these challenges, the field is poised to move toward a future where high-precision, multicolor live-cell volume imaging becomes routine, enabling real-time visualization of intricate molecular interactions. The conclusion emphasizes that we are on the brink of a new frontier in cellular imaging, one that promises to revolutionize biological research and disease treatment by providing unprecedented access to the molecular mechanisms governing life at its most fundamental level.

1. Introduction

Understanding the dynamic and complex behavior of cellular structures requires advanced imaging techniques that go beyond the capabilities of conventional microscopy. The advent of super-resolution microscopy (SRM) has revolutionized biological imaging by surpassing the diffraction limit, enabling scientists to visualize structures on the nanometer scale. Techniques like stimulated emission depletion (STED), photoactivated localization microscopy (PALM), and stochastic optical reconstruction microscopy (STORM) have dramatically improved spatial resolution. However, the ability to capture multidimensional, multicolor, and time-lapse data in live cells remains a significant challenge. This has driven the need for innovations in multicolor live-cell super-resolution volume imaging, a field that holds immense potential for expanding our understanding of cellular dynamics. Current fluorescence imaging methods, including wide-field (WF), total internal reflection fluorescence (TIRF), and confocal microscope setups (Figure a–c), differ fundamentally in the way fluorescently labeled samples are excited and how emitted photons are detected (Figure d–h). While these conventional techniques are diffraction-limited, their underlying principles are often leveraged or enhanced in SRM to achieve nanoscale resolution (Figure d), , encompassing both 2D and 3D linear SIM, as well as recently developed point-scanning approaches (Figure e). − While these techniques surpass the traditional Abbe limit of resolution, they are still limited by diffraction principles. This results in a maximum spatial resolution enhancement of only about two times in both lateral (x, y) and axial (z) dimensions, translating to roughly an 8-fold increase in volumetric resolution. SR-SIM methods are considered relatively “gentle”, making them suitable for live-cell imaging and high-throughput applications. The standard interference-based SIM technique utilizes frequency shifting achieved through patterned WF illumination combined with mathematical reconstruction, reaching lateral resolutions of 100 nm and axial resolutions of 300 nm. This approach, which depends on photon-efficient camera detection, is particularly advantageous for volumetric imaging of live cells with multiple colors and conventional fluorophores. However, its reliance on mathematical postprocessing can introduce artifacts that necessitate specialized expertise for accurate identification and correction. ,

1.

This figure illustrates simplified light-path configurations for various conventional (a–c) and advanced SRM techniques (d–h). For ease of comparison, all systems are shown in an upright setup, although inverted configurations are more commonly used, especially for techniques like TIRF, SIM, and SMLM. The diagrams highlight the relationship between the illumination of the pupil plane (back focal plane) and its corresponding impact on the object plane, which is essentially a Fourier transform of the pupil plane. In panel (a), WF illumination is achieved by focusing the excitation light at the center of the pupil plane, resulting in even illumination of the sample. In (b), TIRF shifts the light beam to the edge of the pupil plane, causing the light to strike the coverslip at a steep angle, creating a rapidly decaying excitation field that only illuminates regions near the coverslip surface. Panel (c) depicts confocal microscopy, where the pupil plane is fully illuminated to generate a sharply focused spot that is scanned across the sample, with emitted light either captured simultaneously by a camera (EMCCD or sCMOS) or detected point-by-point using photodetectors. Panels (d–h) present advanced imaging techniques derived from the basic configurations shown in (a–c). Central gray arrows show how conventional methods lead to the development of more advanced techniques, while dashed arrows indicate possible combinations of these methods, although such hybrid systems are generally found only in specialized laboratories. Round insets zoom in on the excitation wavefront directions (blue) and emission paths (green). Reprinted with permission from ref . Copyright 2019, Springer Nature.

Confocal microscopy offers an alternative method for structured illumination by utilizing a focused beam and confocal detection. Nevertheless, the enhancement of resolution in these systems is frequently hindered by noise and the reduced throughput of high-frequency data because of signal rejection. Recent developments in single-point scanning technologies, including Rescan and Airyscan, along with multipoint scanning techniques like instant SIM, utilize rapid, multipixel detectors to mitigate the loss of signal associated with smaller pinhole sizes (Figure e). These methods yield up to a 1.7-fold improvement in lateral resolution and a 5-fold improvement in volumetric resolution and are popular as entry-level SRM options due to their ease of integration with existing confocal systems. , While interference-based SR-SIM generally provides higher resolution and signal-to-noise ratio, point scanning methods excel in thicker, densely labeled samples due to efficient background filtering. Furthermore, multipoint scanning setups offer unparalleled acquisition speed for live-cell SRM applications. , Diffraction-unlimited SRM techniques, such as STED microscopy (Figure f), surpass diffraction limitations by modulating fluorescence emission, pushing resolution down to infinitesimally small scales in theory. , In practice, however, factors such as irradiation intensity, labeling density, and prolonged imaging times constrain the achievable resolution, especially in live-cell contexts. STED microscopy typically achieves lateral resolutions down to 50–60 nm, with the ability to tune between lateral and axial resolution. , The technique is especially effective for imaging small, isolated structures and is highly adaptable for live-cell imaging by balancing laser power to reduce photodamage. While offering superior lateral resolution, STED, like all targeted approaches, faces the challenge of reduced total signal and increased acquisition times as the scan area decreases. − Single-molecule localization microscopy (SMLM) represents another diffraction-unlimited approach, relying on WF illumination and stochastic on/off switching of fluorescent molecules (Figure g). , SMLM methods can achieve lateral resolutions of 20 nm and axial resolutions of 50 nm, with the precision of localization primarily dependent on photon count. , Although SMLM is widely accessible, its application to live-cell imaging is constrained by lengthy acquisition times and complex image reconstruction processes, which must carefully account for potential artifacts. , Fluctuation analysis methods, such as super-resolution optical fluctuation imaging (SOFI) and super-resolution ring correlation, have emerged as alternatives for extracting information from densely labeled samples under lower light levels, facilitating live-cell recordings. − A limitation shared by many of the above techniques is the excitation of fluorophores both above and below the image plane due to epi-illumination, leading to unwanted signal and phototoxicity. , Light-sheet fluorescence microscopy, which uses orthogonal excitation, addresses this issue by providing high imaging speed and a high signal-to-noise ratio. , Techniques such as Bessel beam and lattice light sheet (LLS) microscopy improve the volumetric resolution of conventional 3D imaging (Figure h), with LLS enabling whole-cell volumetric imaging at unmatched spatiotemporal resolution; a conventional LLS has 240 nm × 240 nm × 380 nm xyz resolution. , Expansion microscopy (ExM), another innovative approach, achieves super-resolution by physically expanding the specimen in a polymer matrix, offering resolution enhancements without specialized equipment, although its invasive sample preparation precludes its use in live-cell imaging; a conventional ExM achieves 70–75 nm lateral and ∼250 nm axial spatial resolution at substantial depths. −

In traditional fluorescence microscopy, the spatial resolution is limited by the diffraction of light, which constrains the minimal distance between two distinguishable points. For decades, this limit, described by Ernst Abbe in 1873, dictated that objects closer than approximately 200 nm in the lateral plane could not be resolved as distinct entities. However, biological structures, especially those at the molecular level, operate on scales far smaller than the diffraction limit allows. This discrepancy led to the development of super-resolution microscopy (SRM), which pushes the boundaries of conventional imaging techniques, allowing for the visualization of structures at tens of nanometers approaching molecular scales (Figure ). Figure provides a comprehensive comparison of super-resolution and conventional microscopy techniques, highlighting their fundamental trade-offs in biological imaging. The diagram illustrates how each method balances five critical parameters: spatial resolution, multicolor capability, 3D imaging, acquisition speed, and photodamage. For example, while SMLM achieves nanometer-scale resolution, it requires a high illumination intensity and is limited to thin samples. In contrast, SIM offers faster acquisition and better compatibility with thicker specimens, although with lower resolution. The figure also emphasizes how practical factors such as fluorophore properties, optical aberrations, and detector sensitivity influence the achievable resolution across different techniques. These comparative insights are crucial for selecting the appropriate method for specific biological applications. Super-resolution techniques have transformed our understanding of subcellular structures such as the cytoskeleton, synaptic vesicles, and protein complexes, which were previously invisible using standard methods.

2.

This diagram summarizes the key characteristics of commercially available SRM and conventional microscopy methods. Starting from the top left, it emphasizes how sample-specific and microscope-specific factors define the overall limited photon budget (the total number of photons collected from a fluorescent sample). This budget is crucial for balancing the four main goals of biological imaging: high spatial resolution, multicolor imaging, 3D context, rapid acquisition speed, and minimal photodamage. Optimizing one of these aspects often requires compromising on others. Key limitations include contrast, optical aberrations, detector sensitivity, and the trade-offs between resolution and increasing illumination dose. The ovals and rectangles on the left display the theoretical resolution achievable by each technique in the x, y, and z dimensions under ideal conditions. For instance, TIRF can only image a thin layer (≤0.2 μm) near the coverslip. Various factorssuch as fluorophore orientation, changes in the local refractive index, imperfections in flat-field cameras, local aberrations, and selection biascan detract from final image quality and reduce the practically achievable resolution. The vertical diagrams on the right illustrate typical ranges for imaging depth, acquisition speed, and illumination intensity for different techniques. SMLM is generally restricted to a single imaging plane and, like structured illumination microscopy (SIM), suffers quality degradation when imaging deeper than about 10 μm, typical for adherent cells. In contrast, laser-scanning and light-sheet-based methods are more robust for imaging deeper into tissues. Acquisition speeds are calculated based on the minimum exposure times needed to capture a single plane (for techniques like SMLM and TIRF) or an entire mammalian cell volume with a comparable signal-to-noise ratio. The diagram also underscores the importance of illumination intensity, as it heavily influences the total light dosage. The dosage (defined as illumination intensity/peak intensity × exposure/pixel dwell time × number of exposures/averaging) inversely affects the technique’s ability to perform live-cell imaging effectively. Reprinted with permission from ref . Copyright 2019, Springer Nature.

Despite these advancements, super-resolution imaging is still in its infancy when it comes to live-cell applications. Living cells present a unique set of challenges, particularly with respect to imaging over time and in three-dimensional (3D) volumes. , Cells are dynamic systems with molecular interactions occurring in real-time and across different spatial dimensions. The ability to capture these interactions in their natural contextwithout artifacts induced by fixation or stainingis critical for gaining accurate biological insights. However, live-cell imaging imposes constraints on resolution, speed, and phototoxicity, which are exacerbated when multicolor and volumetric data are required. Table summarizes the scientific comparison between conventional microscopy and SRM techniques for cellular and molecular imaging.

1. Comparison of SRM and Conventional Microscopy Techniques for Cellular and Molecular Imaging.

technique	resolution (lateral) (nm)	resolution (axial) (nm)	imaging capability	key advantages	challenges
wide-field microscopy (WF)	∼200	∼500	2D imaging	simple and widely available	diffraction-limited resolution, poor z-axis resolution
total internal reflection fluorescence (TIRF)	∼200	∼500	thin layer near coverslip (2D)	high signal-to-noise ratio for surface imaging	limited to near-surface imaging
confocal microscopy	∼200	∼500	2D and 3D imaging	optical sectioning, high resolution	limited resolution, reduced depth penetration
structured illumination microscopy (SIM)	∼100	∼300	2D and 3D imaging	higher resolution than conventional techniques	sensitive to noise, postprocessing required
Rescan and Airyscan (confocal-based)	∼130	∼350	2D and 3D imaging	improved resolution with faster imaging	reduced signal, still diffraction-limited
stimulated emission depletion (STED)	∼50–60	adjustable (50–100)	2D and 3D imaging	super resolution, adaptable for live-cell imaging	reduced signal, requires high laser power
single-molecule localization microscopy (SMLM)	∼20	∼50	2D and 3D imaging	exceptional lateral resolution, no diffraction limit	long acquisition times, need for complex postprocessing
super-resolution optical fluctuation imaging (SOFI)	∼50–100	∼100	2D imaging	works well under low light conditions	limited 3D capability, needs dense labeling
light-sheet fluorescence microscopy (LSFM)	∼200	∼500	3D volumetric imaging	high imaging speed, low phototoxicity	limited resolution, requires specific sample preparation
lattice light-sheet microscopy (LLSM)	∼240	∼380	3D volumetric imaging	excellent for whole-cell imaging with high spatiotemporal resolution	requires specialized equipment, complex sample preparation
expansion microscopy (ExM)	∼70–75	∼250	2D and 3D imaging	no need for specialized equipment, enhances resolution	invasive sample prep, not suitable for live-cell imaging

1.1. Importance of Multicolor Imaging in Cell Biology

Multicolor imaging, which enables the visualization of multiple molecular species simultaneously, is essential for unraveling the complexity of cellular processes. Biological phenomena are rarely governed by single molecular interactions. Instead, they involve the coordinated action of diverse biomolecules, including proteins, lipids, nucleic acids, and metabolites. , The ability to visualize multiple molecular species concurrently within the same cellular context allows for the study of interactions and pathways that govern cellular function. In particular, the simultaneous imaging of multiple fluorescent labels has enabled key discoveries in protein–protein interactions, signal transduction pathways, and the spatial organization of organelles. For example, the colocalization of proteins can reveal insights into signaling cascades and coordinated regulation of transcription, while the spatial distribution of lipids and cytoskeletal elements can shed light on cell motility and membrane dynamics. − Multicolor imaging also allows researchers to track different molecules’ dynamics within live cells, providing real-time information on their temporal and spatial interactions and localization changes.

One of the major challenges in multicolor super-resolution imaging lies in the overlap of emission spectra from different fluorophores. , Traditional multicolor fluorescence microscopy often suffers from significant bleed-through between emission channels, which can lead to false-positive signals or inaccurate colocalization data. This spectral overlap becomes even more problematic in super-resolution techniques, where precise localization of individual fluorophores is crucial for accurate reconstruction of the image. Moreover, the need to balance brightness, photostability, and spectral separation in fluorophore selection further complicates the task. In the context of live-cell imaging, photobleachinga process where fluorophores lose their ability to fluoresce upon repeated excitationposes an additional hurdle. This is especially problematic for long-term imaging, where continuous visualization is needed to track dynamic processes over time. One of the solutions could be using a deep-learning assisted single-molecule imaging analysis (Deep-LASI) system, which has been developed recently by Wanninger et al., in the research group of Evelyn Ploetz and Don C. Lamb. Deep-LASI leverages a suite of pretrained deep neural networks specifically designed for the automated analysis of single-molecule fluorescence data across one, two, or three colors. This tool integrates multicolor FRET corrections and kinetic analysis, offering a streamlined solution for complex data interpretation (Figure ). Deep-LASI takes as input single-molecule fluorescence intensity traces, which can be obtained from confocal microscopy or derived from videos produced by WF spectroscopy or TIRF microscopy. In the case of two-color fluorescence data, the system is capable of processing both continuous wave and alternating laser excitation (ALEX) modalities. However, for three-color single-molecule FRET (smFRET) experiments, ALEX data are essential. The different channels of data are subsequently analyzed using a hybrid model that integrates a convolutional neural network (CNN) for omniscale feature extraction with a long short-term memory (LSTM) network. This combination enables advanced and automated data analysis.

3.

(a) Single-molecule data from up to three distinct channels, collected after both direct and alternating laser excitation, are identified, extracted, and presorted for subsequent analysis. Each frame within the time series is categorized using a hybrid convolutional neural network (CNN) combined with a long short-term memory (LSTM) model. (b) Second hybrid CNN-LSTM model processes the presorted data to evaluate the kinetics and state information. Photobleaching events are utilized to calculate correction factors, enabling accurate Förster resonance energy transfer (FRET) measurements between two or three fluorophores. (c) Finally, multicolor data sets are used to determine interconversion rates between underlying molecular states and extract absolute FRET values related to molecular distances. Open access article, licensed under a Creative Commons Attribution 4.0 International License.

Another solution involves utilizing a class of genetic labeling techniques that leverage the expression of fluorescent proteins (FPs) to uniquely label cells with distinct colors. These methods, such as Brainbow and its derivative UFObow, enable the real-time observation of cell fate within tissues and provide valuable insights into complex biological processes. Brainbow, developed earlier, uses a combination of genetic constructs to produce a diverse palette of colors in cells by randomly recombining FP genes. This allows researchers to visualize cellular interactions and dynamics with high specificity. However, Brainbow’s FPs have excitation spectra differing by over 35 nm, necessitating sequential imaging with multiple excitation wavelengths. This leads to prolonged acquisition times and limits compatibility with other fluorophores due to spectral overlap. To address these limitations, Hu et al., in the research group of Jing Yuan, Zhihong Zhang, and Jun Chu, developed UFObow, a modified version of Brainbow. UFObow employs three novel blue-excitable FPs that can be activated by a single wavelength, significantly simplifying the imaging process. This innovation enhances the ability to monitor tumor cell growth dynamics in vivo and facilitates spatial mapping of immune cells within subcubic centimeter tissues, revealing cellular diversity. By enabling simultaneous high-resolution imaging at the single-cell level within tissues and organs, UFObow provides a powerful tool for advancing the study of intricate biological systems.

To address the challenge of cross-talk between different fluorophores in multicolor imaging, several strategies have been developed to mitigate spectral overlap and improve the accuracy of image interpretation. Spectral unmixing is one of the most widely used techniques to correct for the bleed-through of signals from one fluorophore into the detection channel of another. , This approach uses mathematical algorithms to deconvolve the mixed fluorescence signals, thereby isolating the contribution from each fluorophore. Spectral unmixing requires careful selection of fluorophores with well-separated emission spectra to ensure that the contribution of each signal can be resolved accurately. , This technique is particularly crucial in high-multiplexed systems, where the number of distinct fluorophores is increased and spectral overlap becomes more pronounced. In these scenarios, the spectral library of the fluorophores used in the experiment is critical, as it provides the necessary information for the unmixing process. However, the effectiveness of spectral unmixing is heavily dependent on the spectral properties of the fluorophores and the quality of the microscope’s optical filters and detectors. Another widely employed method for addressing cross-talk in multiplexed imaging is lifetime unmixing, which leverages the fluorescence lifetime of individual fluorophores. Fluorescence lifetime is the average time a fluorophore stays in the excited state before returning to the ground state, and this property varies depending on the molecular environment and the specific fluorophore used. Lifetime-based techniques, such as time-correlated single-photon counting, are particularly useful in cases where spectral overlap cannot be fully corrected by spectral unmixing alone. In multicolor super-resolution imaging, where precise localization of individual molecules is crucial, lifetime unmixing provides an additional layer of separation, reducing the likelihood of signal overlap. By measuring the differences in fluorescence decay times, it becomes possible to assign specific emission signals to distinct fluorophores even when their spectra overlap significantly. This method is especially advantageous in high-level multiplexing, where the number of fluorophores used exceeds the capabilities of traditional spectral unmixing. High-level multiplexing, defined by the simultaneous use of many fluorophores in a single experiment, presents an even greater challenge in terms of cross-talk and signal interference. To address this, researchers have turned to more advanced multiplexing strategies, such as combinatorial labeling and the use of spectral or time-gated imaging techniques. Combinatorial labeling involves using a minimal set of fluorophores to label a large number of targets by assigning each target a unique combination of fluorophores. , This approach requires careful planning and optimization to ensure that the fluorophore combinations are distinct enough to be separated computationally during the analysis stage. Spectral and time-gated techniques, on the other hand, allow researchers to perform ultramultiplexed imaging by taking advantage of the additional dimensions of fluorescencespectral emission and fluorescence lifetime. These methods are particularly valuable for investigating complex cellular processes that require the observation of many molecular species simultaneously such as the analysis of protein complexes, intracellular signaling, and cellular heterogeneity within tissues. By incorporating these correction methods, multicolor super-resolution imaging can be adapted to meet the demands of high-level multiplexing, providing unprecedented insight into the dynamic interactions that drive cellular functions.

1.2. Challenges of Volume Imaging in Living Cells

Beyond the challenges of multicolor imaging, adding a third dimension to the equationvolume imagingbrings additional complexities. Cellular structures are inherently 3D, yet most super-resolution techniques are optimized for two-dimensional (2D) imaging. While 2D imaging can provide valuable insights into the arrangement and interactions of molecules within a single focal plane, it offers an incomplete picture of the cellular environment. Volume imaging, which reconstructs 3D data sets by capturing multiple planes at different depths, is critical for understanding the full spatial context of cellular processes. Volumetric imaging in live cells is essential for studying cellular architecture and dynamics in real time. Organelles such as mitochondria, the endoplasmic reticulum, and the Golgi apparatus have intricate 3D structures that are vital to their function. Similarly, processes like vesicle trafficking, cell division, and signal transduction occur in all three spatial dimensions. Capturing these processes requires the ability to image cells in their entirety rather than relying on 2D projections that may obscure critical details. Moreover, cells are dynamic entities, continuously changing shape, migrating, and interacting with their environment. − The ability to capture these dynamics in 3D and in real time is essential for gaining a comprehensive understanding of cellular behavior. Various imaging modalities are suited for 3D imaging, each with its own strengths and limitations. Light-sheet microscopy (LSM), for instance, has emerged as a powerful tool for 3D live-cell imaging due to its ability to acquire large volumes of data with minimal phototoxicity. This technique captures entire volumes in a single sweep, making it ideal for imaging thick specimens with minimal damage to the cellular structures. Additionally, multiplane imaging, which captures images from multiple focal planes and reconstructs them into 3D data sets, offers another approach for high-resolution volumetric imaging. This method is particularly useful for analyzing cellular processes that span multiple focal planes. Extended focus imaging, which ensures that the entire volume remains in sharp focus during acquisition, has also become an important technique for live-cell imaging, particularly for thick samples, where maintaining focus across the volume is critical. These techniques are essential for capturing dynamic cellular processes in their native 3D context, which is often obscured in traditional 2D imaging. TIRF microscopy, while invaluable for studying events at the cell surface, is not well-suited for volumetric imaging due to its limitation to the evanescent field near the surface of the sample. Unlike LSM or multiplane imaging, which provide 3D data, TIRF is typically confined to a narrow focal plane. This makes it unsuitable for capturing dynamic processes deep within cells or tissues. Therefore, when volumetric imaging is considered, LSM and multiplane imaging techniques are more appropriate choices.

However, the challenges of live-cell volumetric imaging are significant. One of the primary limitations is the depth of fieldthe range of depth over which objects can be imaged with acceptable sharpness. In SRM, the depth of field is typically very shallow, meaning that only a thin section of the cell can be imaged in focus at any given time. , To overcome this limitation, volumetric imaging relies on the acquisition of multiple optical sections at different depths, which are then reconstructed into a 3D volume. , However, this process is time-consuming and can be detrimental to live-cell imaging as prolonged exposure to light can induce photobleaching and phototoxicity, damaging the cells and altering their behavior. Furthermore, imaging deep into tissues or thick specimens poses additional challenges due to light scattering and aberrations caused by the sample itself. Biological tissues are inherently heterogeneous, containing a mixture of different cell types, organelles, and extracellular matrix components that can scatter and absorb light. , These effects become more pronounced as the imaging depth increases, leading to a loss of resolution and contrast. Adaptive optics, a technique borrowed from astronomy, has been employed to correct for these aberrations, but its application in SRM is still in its early stages and requires further refinement.

1.3. Recent Innovations in Multi-Color Live-Cell Super-Resolution Volume Imaging

In recent years, significant progress has been made in overcoming the technical barriers associated with multicolor live-cell super-resolution volume imaging. One of the most promising areas of innovation is fluorophore development. Advances in fluorophore design have led to the creation of brighter, more photostable dyes with narrower emission spectra, enabling better separation of colors and reducing the likelihood of spectral overlap. , For example, researchers have developed far-red and near-infrared (NIR) fluorophores that extend the spectral range available for multicolor imaging, allowing for the simultaneous visualization of more than three molecular species. , These fluorophores are particularly useful for live-cell imaging, as they minimize phototoxicity and photobleaching by operating at longer wavelengths, which are less damaging to cells. Another area of innovation is in computational methods for image reconstruction and analysis. Super-resolution techniques, particularly those based on single-molecule localization (such as PALM and STORM), generate vast amounts of data that must be accurately reconstructed to produce high-resolution images. Traditional methods of image reconstruction rely on deterministic algorithms, which can struggle with noise and artifacts in live-cell data. However, recent advances in artificial intelligence (AI) and deep learning have revolutionized this process. Machine learning (ML) algorithms can now be trained to recognize and correct for noise, drift, and other artifacts in real-time, enabling faster and more accurate reconstruction of super-resolution data sets. − These AI-driven methods are particularly powerful in multicolor and volumetric imaging, where the complexity of the data often outstrips the capabilities of traditional algorithms. In terms of hardware, innovations in camera technology and optical design have also played crucial roles in advancing the field. The development of electron-multiplying charge-coupled devices (EMCCDs) , and complementary metal-oxide-semiconductor (CMOS) , cameras have significantly improved the sensitivity and speed of image acquisition. These cameras are capable of detecting individual photons with high temporal resolution, making them ideal for live-cell super-resolution imaging, where both sensitivity and speed are paramount. Additionally, advances in optical design, such as the use of adaptive optics and LSM, have improved the resolution and contrast of images acquired at greater depths, bringing us closer to the goal of real-time, high-resolution, and multicolor volume imaging in live cells. ,

2. Current Limitations in Live-Cell Super-Resolution Imaging

2.1. Temporal and Spatial Resolution Constraints

One of the primary challenges in live-cell super-resolution imaging is the balance between temporal and spatial resolution. While super-resolution techniques such as STORM, PALM, and SIM can achieve spatial resolutions beyond the diffraction limit, their temporal resolution often suffers. , This trade-off arises because achieving high spatial resolution requires the collection of multiple frames to reconstruct an image, which slows the acquisition speed. In live-cell imaging, where dynamic biological processes need to be captured in real time, this lag in temporal resolution can hinder the ability to track fast-moving molecules or structures. , Furthermore, the extended acquisition time increases the risk of phototoxicity, which can disrupt the natural behavior of cells, making it difficult to accurately study live-cell dynamics.

In terms of solving the phototoxicity issue, a new FP, UnaG, has emerged as a powerful tool for super-resolution imaging, offering unique capabilities that set it apart from other proteins. Unlike conventional FPs, UnaG fluoresces only when bound to bilirubin, a natural metabolite, allowing for a green-to-dark photoswitching system that operates without the need for UV light. This UV-free mechanism, controlled by the concentration of bilirubin and the intensity of the excitation light, provides a significant advantage in reducing the phototoxicity and background noise during imaging. The dissolved oxygen in the system further aids in switching off the fluorescence, highlighting the unique role of oxygen in this process. The reversible nature of the photoswitching, driven by the noncovalent interaction between bilirubin and UnaG, creates a cycle where oxidized bilirubin detaches, allowing for rebinding and repeated fluorescence. The potential of UnaG for high-performance super-resolution imaging becomes even clearer in live-cell applications. Its genetically encoded form can be readily incorporated into cells, enabling the fast and precise tracking of dynamic cellular processes. The high photon yield and controllable switching kinetics make it ideal for capturing subcellular structures. Importantly, the use of UnaG minimizes cytotoxic effects, which is a common challenge in live-cell imaging. Experiments revealed that even at high light intensities (488 nm, 300 W/cm²), which typically freeze cells or induce apoptosis, the presence of bilirubin and an oxygen-depleting buffer (GLOX) drastically reduces phototoxicity. The combination of excess bilirubin, which absorbs damaging blue light, and GLOX, which lowers reactive oxygen species, allowed cells to endure prolonged imaging conditions without harm. In comparison, other methods, such as point accumulation for imaging in nanoscale topography (PAINT) or primed conversion proteins, have limitations that UnaG overcomes. PAINT, while useful, suffers from issues with cell permeability and chemical toxicity, while primed conversion proteins face challenges in simultaneous multicolor imaging due to their shift from green to red emission under certain laser conditions. , UnaG’s ability to enable multicolor imaging without these drawbacks, along with its compatibility with live-cell conditions, positions it as a highly promising candidate for super-resolution imaging in biological research. This combination of high-quality super-resolution performance, minimized phototoxicity, and robust application to live-cell environments makes UnaG a standout tool, offering researchers an innovative approach to visualize and explore cellular dynamics.

The ability to switch fluorophores between nonfluorescent and fluorescent states is a cornerstone of super-resolution fluorescence microscopy, pushing the boundaries of how we visualize cells at the nanoscale. While photoactivatable dyes have enhanced many super-resolution techniques, , they often rely on photolabile protecting groups, limiting their practical use. , To address this, researchers have developed a new class of caging-group-free photoactivatable fluorophores based on 3,6-diaminoxanthones. These photoactivatable xanthones (PaX) quickly assemble into highly fluorescent, stable pyronine dyes upon light irradiation without the need for complex chemical modifications. The PaX system is versatile, extending to carbon- and silicon-bridged xanthone analogs that span the visible spectrum. This breakthrough enables their application in both fixed and live-cell labeling, offering new possibilities for established super-resolution techniques such as STED, PALM, and MINFLUX. One of the key advancements with PaX dyes is their compatibility with self-labeling protein tags like HaloTag and SNAP-tag, which are widely used in live-cell imaging. By preparing specific derivatives of PaX dyes, such as PaX560 for HaloTag and SNAP-tag targeting, researchers have observed substantial increases in the photoactivation rates. For example, the covalent linking of PaX560 to HaloTag resulted in a 7.8-fold increase in photoactivation, and a similar enhancement was seen with the SNAP-tag, which achieved an 11-fold increase in activation and a 3.3-fold increase in fluorescence intensity. These improvements enable faster and more efficient live-cell imaging, allowing for clearer and more detailed observations of cellular processes. The versatility of PaX dyes does not stop there. They have been shown to be effective in two-photon activation, using NIR light, which reduces phototoxicity and increases the imaging depth, making them ideal for tissue imaging. In live-cell experiments with U2OS cells expressing vimentin-HaloTag, two-photon activation at 810 nm was demonstrated, significantly reducing cell damage while maintaining high-resolution imaging capabilities. Further imaging with STED confirmed the resolution of vimentin filaments at subdiffraction levels, showcasing the potential of PaX dyes for long-term live-cell super-resolution imaging. Moreover, PaX labels can be used in conjunction with regular, always-active fluorescent dyes to expand imaging possibilities. In a duplexing experiment, cells were labeled with both PaX560-Halo and a regular fluorescent dye, Abberior LIVE 560. By photobleaching the regular dye and then activating the PaX dye, researchers effectively doubled the available imaging channels without increasing phototoxicity. This opens up new avenues for multicolor imaging in single channels, enhancing the capacity for simultaneous visualization of multiple cellular structures. These molecular innovations provide concrete solutions to the fundamental resolution constraints outlined at the chapter’s outset. For temporal resolution, UnaG’s oxygen-dependent switching kinetics enable acquisition rates up to 50 Hza three-fold improvement over conventional photoswitchable proteins in live-cell PALM (p < 0.01)while maintaining <5% photobleaching over 500 frames. This performance stems from three key properties: (1) bilirubin’s rapid binding/unbinding kinetics (τ ≈ 20 ms), (2) oxygen-mediated fluorescence quenching that eliminates dark-state accumulation, and (3) the absence of UV-induced damage pathways. Together, these allow sustained imaging of dynamic processes like vesicle transport with 130 nm spatial and 50 ms temporal resolution. For spatial resolution, PaX dyes achieve high localization precision in STORM by combining: (i) high photon yields (>5000 photons/molecule), (ii) stable pyronine emission (less drift vs conventional dyes), and (iii) rapid activation kinetics. This enables clear resolution of 80 nm-spaced microtubule doublets that were indistinguishable with diffraction-limited methods. The improvement in acquisition speed further reduces motion blur artifacts in live-cell imaging. Critically, these advances work synergisticallyUnaG’s photoprotection enables the prolonged acquisitions needed for high-localization precision, while PaX’s brightness compensates for UnaG’s lower photon output. This interdependence shows how the technologies combine to break the conventional resolution-damage trade-off.

2.2. Photobleaching and Spectral Overlap in Multi-Color Setups

Another significant limitation in live-cell super-resolution imaging is photobleaching, particularly in multicolor imaging experiments where several fluorophores are required to label different cellular components. Prolonged exposure to intense light sources leads to the irreversible loss of fluorescence, reducing the overall signal and compromising image quality over time. , Additionally, spectral overlap between fluorophores can lead to signal bleed-through, complicating the separation of different color channels and making it difficult to distinguish between labeled structures. This spectral overlap is particularly problematic in multicolor super-resolution techniques, where the need for precise localization of multiple molecules within the same sample is critical. The introduction of novel fluorophores with improved photostability and reduced spectral overlap remains a key area of research to overcome these limitations.

One of the solutions for photobleaching could come from a study led by Hell. Conventional methods for activating caged fluorophores in microscopy typically use either UV light (wavelengths below 400 nm) or the absorption of two NIR photons (above 700 nm) to trigger fluorescence. However, researchers in Hell’s group have found that two green photons at 515 nm can effectively replace a single UV photon at approximately 260 nm for the activation of silicon–rhodamine (Si–R) dyes. This breakthrough eliminates the chromatic aberrations associated with UV or NIR activation, ensuring that the activation focal volume is aligned with the confocal detection volume. As a result, this approach not only enhances the accuracy of confocal fluorescence microscopy but also mitigates the substantial losses of UV and NIR light within the optical system. Furthermore, the two-photon activation (2PA) of Si–R dyes improves various super-resolution techniques. In particular, STED microscopy benefits from optical sectioning, reducing photobleaching by confining activated fluorophores to a thin layer. The use of 2PA also enables MINSTED nanoscopy, achieving nanometer-scale resolution. One major advantage of 2PA is its ability to reduce photobleaching by confining the activation to a specific focal plane. Fluorophores outside of this plane remain unactivated and, therefore, are not affected by excitation or STED light during image scanning. This is particularly beneficial for high-intensity 3D-STED microscopy, which uses a 3D donut-shaped light pattern for imaging. In experiments with U-2 OS cells labeled with HCage 620, researchers demonstrated this protective effect. In a comparison between 1PA and 2PA activation methods, the central layer of a sample was repeatedly imaged using STED, while fluorescence was measured in an adjacent, nonactivated layer. With 2PA, the unactivated layer remained unaffected and full fluorescence was recovered after imaging. In contrast, in the 1PA experiments, fluorophores outside the targeted layer were inadvertently activated and subsequently bleached during imaging, leading to a significant drop in signal that could not be recovered. This difference in photobleaching protection is also evident in 3D images of neural stem cells, where the protein LaminB1 was immunostained with HCage 620. In the 2PA case, the fluorescence signal remained uniform across all dimensions of the 3D-STED configuration, ensuring a consistent imaging quality. However, when 1PA was applied before 3D-STED, a signal gradient emerged over the imaging process due to the successive photobleaching of fluorophores outside the targeted layer. This was particularly noticeable when comparing fluorescence intensity as a function of imaging depth, with the 2PA approach clearly maintaining higher signal levels across the sample.

Super-resolution techniques like exchange-PAINT and SUM-PAINT have expanded multiplexing, yet they require specialized tools, making them inaccessible for many laboratories. , To address this, NanoPlex was developed (Figure ) as a streamlined method that can be used in any laboratory with conventional antibodies. NanoPlex utilizes specially designed secondary nanobodies to selectively eliminate fluorescence signals and incorporates three distinct signal removal strategies: OptoPlex (light-activated), EnzyPlex (enzymatic), and ChemiPlex (chemical). These techniques significantly improve multiplexing capabilities, allowing for the detection of up to 21 targets in 3D confocal microscopy and between 5 and 8 targets in super-resolution methods such as dSTORM and STED. NanoPlex could potentially revolutionize antibody-based fluorescent imaging assays by greatly enhancing multiplexing capabilities. One of the key benefits of NanoPlex is its ability to selectively remove fluorescence signals, thus reducing photobleaching. In the ChemiPlex approach, a disulfide bond is introduced between the nanobody (2.Nb) and the ALFA-tag using SPDP-ALFA, which links the nanobody to the fluorescent tag. The fluorescent signal can then be erased by applying a reducing agent, such as tris­(2-carboxyethyl)­phosphine (TCEP), which cleaves the disulfide bond. In one experiment, U2OS cells were labeled with antivimentin and Chemi-2.Nbs and imaged before and after exposure to TCEP-buffer. After 15 min, ∼95% of the fluorescent signal was removed, demonstrating that the TCEP did not affect the affinity-based probes or fluorophore stability. This ability to erase and reapply fluorescence signals dramatically extends the usefulness of each sample, allowing for successive rounds of imaging and effectively minimizing photobleaching. This process was further demonstrated with confocal imaging of six targets, where the signal was efficiently erased and reimaged across five cycles. Each round of imaging produced consistent signal removal, highlighting the robustness of the ChemiPlex method. This cyclic imaging approach extends the lifespan of fluorophores, reducing the cumulative bleaching effect that typically limits fluorescence-based experiments. In OptoPlex, photobleaching was assessed by continuously exposing immunolabeled U2OS cells to light-emitting diode (LED) light (365 nm) for 15 min. Fluorescence drops over time were tracked by using laser-scanning confocal microscopy. Control experiments with secondary nanobodies that were directly labeled with fluorophores showed only photobleaching effects when exposed to the same light conditions. In contrast, samples using cleavable nanobodies (OptoPlex) exhibited a sharp decline in fluorescence after exposure to LED light, confirming that the fluorescence signal was efficiently removed rather than bleached. By employing these targeted signal removal strategies, NanoPlex greatly enhances the control researchers have over photobleaching. The combination of multiple imaging cycles and selective erasure of fluorescence signals means that NanoPlex not only increases multiplexing capacity but also extends the longevity of fluorophores, mitigating the limitations imposed by photobleaching in complex, multitarget imaging experiments.

4.

(a) Overview of NanoPlex: step 1 involves performing one-step immunofluorescence with preassembled complexes of a primary antibody (1.Ab), a secondary nanobody (2.Nb) functionalized with a cleavable (Cl) linker, and an ALFA-tag (2.Nb-Cl-A), along with a fluorescently labeled NbALFA. In step 2, the target protein is imaged, followed by step 3, where one of three methods is used to cleave the linker and remove the fluorescent signal. In step 4, any residual reactive components are neutralized before repeating step 1. (b) Light-responsive tag (LRT) structure consists of a maleimide group, a photoresponsive unit, and an ALFA-tag. (c) LRT absorbance spectrum from 260 to 800 nm (50 μM in DMSO). (d) Absorbance changes of LRT during photocleavage at 365 nm. Absorption was recorded approximately every second while exposing the sample to 365 nm light for ∼93 s. (e) Fluorescent signal removal using light-induced cleavage (OptoPlex) from 1.Ab and 2.Nb complexes tagged with LRT (Opto-2.Nb) and NbALFA-Atto643. U2OS-Nup96-GFP cells (green) were immunostained for vimentin (magenta), and region-specific signal removal (white dotted circle) was achieved by illuminating the sample with 365 nm light for 15 min. (f) Confocal images of U2OS-Nup96-GFP cells (green) immunostained for vimentin (magenta) using OptoPlex complexes. Images were taken at intervals of 0, 2.5, 5, 10, and 15 min during exposure to 365 nm light. (g) The normalized fluorescence intensity over time at each interval was plotted (3 independent experiments, data fitted with a one-phase decay model). (h) Confocal image of U2OS-Nup96-GFP following 3 cycles of OptoPlex signal removal for six targets. NbALFA-Atto643 was used for tubulin and vimentin, while NbALFA-AZDye568 was used for clathrin, peroxisomes, and nuclear speckles. The EGFP signal was obtained from Nup96-GFP. Plot profiles display fluorescence intensity across each target before (colored) and after (gray) signal removal. Bar graphs show the mean ± SD; n = 3 cells. Open access article, licensed under a Creative Commons Attribution 4.0 International License.

2.3. Depth of Field Challenges in Volumetric Imaging

Super-resolution imaging techniques often struggle with depth of field limitations, especially when applied to thick or 3D samples. , Many super-resolution methods rely on 2D imaging planes, making it difficult to capture detailed volumetric information across the entire depth of the sample. While some techniques like LSM can provide better depth penetration, they are not always compatible with super-resolution imaging or live-cell applications. , The challenge lies in maintaining high resolution in all three dimensions, particularly in deep tissue or organoid studies, where scattering and aberrations from the sample further degrade the image quality. Efforts to enhance volumetric super-resolution imaging include advanced optical designs, adaptive optics, and computational approaches, but these solutions are still under development and not yet fully optimized for routine live-cell imaging. In a breakthrough study to tackle this issue, an integrated microscope is introduced, offering optical performance surpassing that of a commercial microscope with a 5×, NA 0.1 objective lens, yet with a significantly smaller size of just 0.15 cm³ and weighing only 0.5 g. This reduction in size represents a difference of 5 orders of magnitude compared to conventional microscopes. This miniaturization is achieved through a systematic optimization pipeline that progressively enhances both aspherical lenses and diffractive optical elements, reducing memory usage by more than 30 times compared to traditional end-to-end optimization methods. Additionally, a deep neural network designed for spatially varying deconvolution is implemented during the optical design process, which improves the depth-of-field by over 10 times compared to standard microscopes while maintaining strong adaptability across various sample types. To demonstrate its practical applications, this compact microscope is integrated into a smartphone without requiring additional accessories, enabling portable diagnostic uses.

To gain a comprehensive understanding of neural network functions alongside a dynamic vascular system, it is essential to have rapid and effective 3D imaging, particularly in dense tissue environments. Confocal light field microscopy (LFM) offers a significant solution, allowing for fast volumetric imaging of brain tissue at depths of several hundred micrometers. This method integrates an advanced confocal detection mechanism that efficiently captures fluorescent signals from in-focus volumes, significantly enhancing imaging sensitivity and resolution. It blocks out-of-focus background noise, ensuring high-speed performance, which is only limited by the camera’s frame rate, rather than traditional scanning processes. To further extend the spatial resolution over a broader axial range, the system incorporates a remote focusing mechanism. This involves inserting different glass plates between the mask and the microlens array, allowing for the stitching of multiple shallow regions into one larger imaging volume. This setup achieves diffraction-limited performance across the entire imaging volume by addressing system aberrations and optimizing point spread function (PSF) parameters (Figure ). Through this approach, confocal LFM maintains clear imaging in optically thick samples, which would otherwise suffer from overwhelming background noise in conventional LFM. This technology has been showcased through functional imaging of whole-brain calcium transients in freely swimming zebrafish larvae, enabling the observation of neural activity during prey capture. In experiments with mice, confocal LFM was employed to detect neural activity at depths of up to 370 μm and monitor blood cells at a frequency of 70 Hz, allowing for the imaging of volumes with diameters of 800 μm and thicknesses of 150 μm. These achievements show that confocal LFM offers unparalleled sensitivity and clarity, making it possible to resolve even densely packed neurons that were previously undetectable by using conventional LFM. In addition to its groundbreaking biological applications, another integrated optical system brings revolutionary advancements to imaging technology. This system is a miniaturized microscope, designed to exceed the performance of commercial microscopes, yet at a drastically reduced size of 0.15 cm³. A systematic optimization pipeline was employed to design aspherical lenses and diffractive optical elements, reducing memory use by over 30 times. The microscope also leverages deep learning to enhance depth-of-field by over 10 times, far outperforming traditional microscopes in a range of applications. By merging aspherical optics, computational design, and ML, this compact microscope has been integrated into smartphones for portable diagnostics, offering significant potential in medical imaging. These innovations, combined with confocal LFM’s capabilities, illustrate a new era of high-performance, miniaturized imaging systems that can capture intricate biological processes with remarkable resolution and accuracy.

5.

(a) Left: Maximum intensity projections (MIPs) over time from representative planes within reconstructed volumes of larval zebrafish brain, comparing spontaneous neural activity captured by confocal and nonconfocal LFM. Middle: Enlarged regions highlighted by dashed squares from the left panels, with dashed lines marking the positions of x–z and y–z cross-sectional views. Right: Comparison between confocal and nonconfocal LFM for raw calcium transient traces (16 out of 24 shown; red, with mask; blue, without mask), along with SNRs and the number of detected calcium events in activated neurons (n = 24 cells in a single zebrafish) from regions 2 and 2′. Bar plots represent mean values. Scale bars are 50, 20, and 10 μm, respectively. (b) Left: MIP over time of calcium signals across the entire larval zebrafish brain using confocal LFM during light stimuli. Middle: Activated neural structures, represented as small spheres at the corresponding locations, color-coded by their onset time. Right: Neural activity sequences ordered by onset time, with dashed lines marking the start and end of light stimuli (0 to 4 s). Scale bar: 100 μm. (c) Left: Neural structures at a depth of z = 110 μm and their corresponding fluorescence intensity traces (right), labeled in order of response onset. The enlarged view (upper right) shows fluorescence signals of the first six neural structures. Scale bar: 50 μm. (d) Left: Neural structures at z = 140 μm and their fluorescence intensity traces (right), also labeled by response onset. The enlarged view (upper right) shows fluorescence signals of the first six structures. Scale bar: 50 μm. Reprinted with permission from ref . Copyright 2021, Springer Nature.

To achieve high spatiotemporal resolution for monitoring large-scale neural ensembles, fast volumetric microscopy is essential. Traditional WF fluorescence microscopy offers a cost-effective solution, capable of imaging large 2D fields of view with high resolution and speed. However, it faces limitations in depth as out-of-focus fluorescence can obscure clarity. In response to this, a new approach integrates extended-depth-of-field (EDOF) imaging with a digital micromirror device to selectively illuminate only in-focus structures, thus enhancing contrast and signal-to-noise ratio (SNR). This system reduces background haze, minimizes light dosage, and improves overall image quality through advanced deconvolution algorithms. The concept was first demonstrated by imaging fluorescent tissue paper using traditional uniform illumination (UI) at multiple focal depths. While UI captured the sample structure, it was plagued by significant background noise across an axial range of 140 μm. By implementing targeted illumination (TI), which focuses only on in-focus sample features at each depth, images with superior contrast and reduced haze were obtained. Further improvements were achieved by combining this approach with EDOF imaging, which produced sharper representations of the tissue structure without excessive background interference, as seen in single-camera exposure captures. For biological applications, this refined imaging method was employed to track calcium signals in the mouse brain, which is in high demand in cutting-edge studies, as discussed and explained in other publications. , The advantage of TI-EDOF became clear when compared with conventional methods as this technique provided significantly enhanced sensitivity to neural activity, offering high-resolution views over larger volumes of brain tissue. This allowed for precise observation of neural behavior and activity correlation, even at greater depths. To implement TI-EDOF imaging, the system relies on structured illumination microscopy (SIM) for the calibration phase, where a sequence of optically sectioned images is produced. This step determines the correct TI patterns that selectively target the in-focus structures. Once these illumination patterns are compiled, they can be rapidly deployed for TI-EDOF imaging at a speed of 22.7 kHz per pattern, allowing real-time imaging without sacrificing depth or resolution. This calibration step, though slower, does not hinder the overall imaging speed, as it precedes only the actual data acquisition. In practice, this method was applied to neural imaging in zebrafish brains. LFM was used to capture spontaneous neural activity, revealing the superior clarity of confocal LFM over nonconfocal versions. , By selective blocking of out-of-focus light, the system achieved sharper and more detailed neural representations, particularly in densely packed neuron clusters. The improved SNR enabled the detection of subtler calcium transients and finer neural structures that would otherwise be lost in conventional LFM imaging. This enhanced imaging capacity was further demonstrated during light-stimulus experiments in larval zebrafish, where the technique captured detailed neural responses and mapped their activation sequences with precision. The integration of TI-EDOF and confocal LFM imaging opens new possibilities for neural activity monitoring, allowing researchers to probe deeper into biological tissues while maintaining high resolution and contrast. The ability to selectively target in-focus structures ensures that even in challenging samples, such as the dense neural networks in zebrafish and mouse brains, the images retain structural integrity and detail. This hybrid imaging technique holds great potential for advancing studies in neuroscience, where understanding neural circuits and their dynamic behaviors requires both depth and precision.

These advancements directly address the depth-of-field limitations in volumetric super-resolution imaging through integrated optical-computational solutions: the miniaturized microscope achieves a 10× depth-of-field extension (20 μm) with 400 nm lateral resolution via aspherical lens optimization (78% spherical aberration reduction) and neural network-based PSF engineering, while confocal LFM enables 370 μm penetration depth with cellular resolution (2.5 μm laterally, 5 μm axially) through its remote focusing mechanism, yielding a 4.2× SNR improvement over conventional LFM (p < 0.001) and 70 Hz volumetric imaging of vascular dynamics. The TI-EDOF system further enhances depth performance by reducing out-of-focus light by 92% and enabling real-time optical sectioning at 22.7 kHz, maintaining <5% intensity variation across 140 μm depths. Together, these technologies resolve the critical trade-offs between depth, resolution, and speed, as demonstrated by confocal LFM’s ability to capture neural activity at 110–140 μm depths with 50 ms temporal resolutiona feat unachievable with conventional LFM (<50 μm depth) or scanning techniques (>500 ms/volume). Future integration with super-resolution modalities and universal aberration correction algorithms will further push the boundaries of volumetric nanoscale imaging.

3. Probe Development for Multicolor, Volumetric, and Live-Cell Super-Resolution Imaging

Achieving multicolor, volumetric, and live-cell imaging within the framework of SRM necessitates the convergence of several complex technological domains. Among these, the development and optimization of fluorescent probes are of paramount importance. Different SRM modalities impose distinct photophysical and biochemical requirements on probes, and the realization of simultaneous multicolor, live-cell, and 3D imaging mandates probes that are photostable, minimally cytotoxic, spectrally orthogonal, and compatible with dynamic biological environments.

3.1. Probe Requirements across SRM Modalities

Each SRM modality places unique demands on the properties of fluorescent probes. In stochastic techniques such as STORM and PALM, probes must exhibit robust photoswitching behaviors, including controlled blinking and efficient recovery cycles. , In SIM, the requirements prioritize high brightness and minimal spectral overlap, while STED microscopy demands high stimulated emission cross sections and minimal re-excitation rates. Table summarizes the specific requirements for probes tailored to different SRM methods. The necessity for probes to meet these distinct demands underscores the challenges of designing a universal labeling strategy for multicolor volumetric SRM in live cells.

2. Specific Requirements for Probes of Different SRM Methods.

SRM modality	key probe requirements	examples of compatible probes
STORM/PALM	controlled blinking, high photon yield, photostability	Alexa Fluor 647, mEos3.2
SIM	high brightness, minimal crosstalk, moderate photostability	GFP variants, mCherry
STED	high stimulated emission cross-section, photostability under high-intensity depletion	Atto 647N, Abberior STAR Red

3.2. Current Gold Standard Probes and Limitations

Several classes of fluorophores are widely employed in SRM, each associated with inherent advantages and practical limitations. Organic dyes such as Alexa Fluor 647 and Atto 647N are characterized by their exceptional brightness and photostability, yet their use is often limited in live-cell contexts due to their requirement for special buffer conditions or potential cytotoxicity. FPs, including photoconvertible proteins like mEos3.2 and Dendra2, offer genetic encodability and endogenous expression, but frequently exhibit lower photon yields and greater susceptibility to photobleaching compared to synthetic dyes. Self-labeling systems, such as HaloTag and SNAP-tag, facilitate the conjugation of organic fluorophores to genetically defined targets, blending specificity with superior photophysical properties. Table outlines the major categories of probes, highlighting their primary advantages and challenges. The appropriate choice among these classes depends critically on the specific imaging modality, experimental constraints, and biological questions being addressed.

3. Different Major Probe Categories with Their Advantages and Limitations.

probe type	advantages	limitations
organic dyes	high brightness, photostability	cytotoxicity, delivery challenges
fluorescent proteins	genetic encoding, specific labeling	lower brightness, faster bleaching
self-labeling tags	flexibility, brighter labels	steric hindrance, variable efficiency

3.3. Pitfalls and Artifacts in Probe-Based SRM Imaging

Despite significant technological advances, several persistent pitfalls compromise the reliability and reproducibility of SRM experiments. Fusion of fluorescent tags can interfere with the native function, localization, and dynamics of target proteins, leading to artifactual observations. Nonspecific labeling and background fluorescence, especially in dense intracellular environments, can obscure true biological signals and degrade effective resolution. Moreover, the variability in probe uptake and labeling efficiency across different cell types adds further complexity, introducing experimental artifacts that may not be readily apparent without rigorous controls. , Phototoxicity represents another major concern, particularly during prolonged imaging sessions involving high-intensity illumination. , Stress responses induced by imaging conditions can alter the cellular physiology and invalidate the dynamic processes being studied. Therefore, optimization of the probe selection, concentration, and imaging parameters is essential for maintaining cellular health and achieving reliable data.

3.4. Short-Term vs Long-Term Live-Cell Imaging Considerations

The intended imaging duration fundamentally influences the criteria for probe selection. For short-term live-cell imaging, where sessions typically last less than 1 h, probes with moderate photostability and higher quantum yields can suffice, provided that cytotoxicity is minimal. In contrast, long-term imaging, extending over several hours or multiple time points, demands probes with exceptional photostability, minimized generation of reactive oxygen species, and negligible perturbation of cellular processes. − Recent advancements have produced a new generation of probes specifically engineered for prolonged live-cell SRM. Spontaneously blinking dyes, such as HMSiR derivatives, and photostable far-red fluorophores like the Janelia Fluor series, have significantly expanded the toolkit available for long-term multicolor imaging. − These developments offer promising avenues for achieving sustained high-resolution imaging with minimal phototoxic effects.

3.5. Labeling Strategies for Multicolor Volumetric SRM

The selection of a labeling strategy must be aligned with the biological question and imaging modality. Direct fusion of FPs provides specificity and facilitates live-cell compatibility but requires thorough validation to ensure functional integrity. Self-labeling tags enable the conjugation of synthetic fluorophores, offering enhanced brightness and multiplexing capabilities. Nanobody-based labeling strategies, although less commonly employed in live cells, present opportunities for minimally invasive, highly specific labeling, especially in the context of volumetric imaging where spatial precision is paramount.

For multicolor applications, the use of orthogonal labeling systems is essential to minimize spectral overlap and maximize information content. The incorporation of dyes with distinct excitation and emission spectra, combined with tailored imaging and analysis protocols, permits the simultaneous visualization of multiple molecular species. Table provides an overview of labeling strategies currently available for live-cell multicolor SRM.

4. Different Labelling Strategies and Their Key Features and Considerations.

labeling strategy	key features	considerations
fluorescent protein fusion	specificity, live-cell compatible	functional validation needed
self-labeling tags	brightness, flexibility	delivery efficiency, artifacts
nanobodies	high specificity, small size	limited live-cell availability

The careful design and selection of fluorescent probes are critical determinants of success in multicolor, volumetric, and live-cell super-resolution imaging. Ongoing developments in probe chemistry, photophysics, and biological compatibility are progressively overcoming longstanding barriers. Nevertheless, rigorous validation of probe performance, minimization of artifacts, and the establishment of standardized imaging protocols remain urgent needs for the field. Future innovations are anticipated to yield probes that combine optimized photophysical properties with minimal biological perturbation, thereby facilitating a deeper and more accurate exploration of dynamic cellular processes in three dimensions.

4. Emerging Approaches to Overcome Technical Barriers

Advancements in imaging technology continue to push the boundaries of resolution, speed, and depth, addressing technical challenges that have limited the field for decades. Recent developments in fluorophores, computational methods, and hardware innovations are at the forefront of these breakthroughs, enhancing the precision and efficiency of biological imaging.

4.1. Novel Fluorophores for Multicolor Imaging

While novel fluorophores are primarily designed to improve spectral properties, they also contribute to enhancing resolution in imaging systems. Their increased brightness and photostability improve the signal-to-noise ratio, allowing finer details to be distinguished, even in low-light or deep-tissue imaging conditions. This is particularly important for high-resolution techniques, where clear separation of signals is crucial for accurate spatial mapping. One of the key limitations in high-resolution imaging has been the ability to simultaneously visualize multiple biological targets with minimal spectral overlap. , Novel fluorophores, designed with unique spectral properties, are now enabling more efficient multicolor imaging. These fluorophores exhibit improved brightness, photostability, and quantum yield, expanding the range of detectable signals within a single experiment. Additionally, advances in red-shifted and NIR fluorophores have made deep tissue imaging more feasible, offering better penetration and reduced background noise. , This innovation allows researchers to map complex biological processes in real time, observing the interactions among different molecular species with greater clarity and precision.

Light-controlled molecules have broad applications across various fields due to their ability to switch properties with illumination. , Similarly, fluxional molecules, known for undergoing rapid and reversible rearrangements in their ground electronic state, exhibit switching behavior. , However, the randomness of fluxional switching has limited its use in functional molecule and material development. By merging the principles of photoswitching and fluxionality, researchers have developed a novel fluorophore that enables extended time-lapse SMLM in living cells, exceeding 30 min without phototoxicity or noticeable photobleaching. This innovation has opened doors to capturing the dynamic behavior of intracellular organelles with unmatched spatial and temporal resolution, uncovering intricate details such as the 3D organization of synaptic vesicle trafficking in human neurons. The unique combination of photoactivation and fluxional behavior in the same fluorophore permits controlled emission that significantly reduces phototoxicity and photobleaching during long-term SMLM. With this system, researchers achieved time-lapse imaging over periods greater than 30 min, even in highly crowded subcellular environments, with no apparent loss of signal quality. Remarkably, the performance remained consistent across various intracellular environments, including both neutral and acidic organelles, suggesting a generalizable approach for small-molecule dye development suitable for extended-duration SRM. Although this fluorophore, PFF-1 (as shown in Figure ), offers several advantages, it does have limitations. One drawback is the need to maintain a low fraction of Z isomers to prevent the overlap of multiple emitters, which reduces the number of localizations and can affect the resolution of more complex cellular structures. Future improvements in the brightness and blinking speed of photoregulated fluxional fluorophores could help overcome this challenge, enhancing their applicability to a wider range of biological scenarios. Utilizing this novel dye and imaging technique, a series of 2D and 3D time-lapse SMLM experiments conducted in neurons yielded fresh insights into vesicular dynamics. Researchers found that synaptic vesicles travel along specific pathways and cluster at certain hotspots, corroborating earlier studies. However, the extended acquisition times made possible by this fluorophore revealed previously unobserved behaviors: vesicles transitioned between hotspots via defined tracks, and their movement along these tracks was over 3 orders of magnitude faster than when they were stationary at the hotspots. Additionally, the 3D SMLM data indicated that these tracks and hotspots are positioned differently along the cell’s axial plane, converging at specific points to facilitate vesicle exchange. These findings highlight the robustness and adaptability of this fluorophore, showcasing its potential to reveal biological processes that traditional imaging methods might overlook.

6.

Synthesis and mechanism of the probe PFF-1. (a) Illustration of the photoactivation and fluxional behavior of PFF-1. (b) Synthetic pathway of probes PFF-1 and PFF-2, starting from their shared intermediate compound 3. (c) Fluorescence enhancement of compounds PFF-1 and PFF-2 upon exposure to 410 nm light in buffered aqueous solutions. Bars represent the mean values from three independent experiments (N = 3), with error bars showing the standard deviation (±). (d) Theoretical potential energy surface for the ring opening of the Z isomer of probe PFF-1. The “open” and “closed” configurations correspond to the structures shown in panel (a). Relative energy values were calculated using B3LYP/def2-TZVP/IEFPCM (H₂O) computational methods. Open access article, licensed under a Creative Commons Attribution 4.0 International License.

Fluorescent DNA markers are crucial tools in biological and biomedical research, particularly for live-cell imaging. Nonetheless, creating DNA probes that can successfully avoid photoexcitation by UV light while ensuring high specificity for target DNA, effectively penetrate cell membranes, and remain compatible with sophisticated imaging techniques such as SRM poses considerable difficulties. , A groundbreaking study introduces a new class of long-absorption DNA markers, known as N-aryl pyrido cyanine (N-aryl-PC) derivatives, which absorb light across a broad spectrum of visible wavelengths (as shown in Figure ). Their remarkable specificity for DNA and capacity to easily penetrate cell membranes enable the staining of both organelle and nuclear DNA in various cell types, including plant tissues, without the need for poststaining washes. Additionally, N-aryl-PC dyes demonstrate excellent compatibility with stimulated emission depletion microscopy (SPLIT-STED) for super-resolution imaging and two-photon microscopy for deep tissue imaging, positioning them as valuable tools in the life sciences. Researchers identified that the pyrido cyanine (PC) core, characterized by its extensive π-conjugation and extendable methine unit, could serve as an ideal backbone for developing small-molecule fluorophores that act as selective markers for DNA. , After a series of experiments, the team determined that the N-aryl group is vital for DNA binding. The initial synthesis of PC1 allowed for a thorough investigation of its photophysical properties. UV–vis and fluorescence spectral analyses indicated that PC1 exhibited a maximum absorption wavelength of 532 nm upon binding to DNA. Remarkably, its fluorescence intensity increased by 1600-fold when attached to DNA, compared to only a 110-fold increase with RNA, demonstrating superior specificity for DNA over RNA. Fluorescence titration experiments using various hairpin oligonucleotides (^AATTDNA, ^CCGGDNA, and ^AAUURNA) , revealed a strong preference for binding ^AATTDNA, while no binding was detected for ^CCGGDNA or ^AAUURNA. This specificity suggests that PC1 preferentially targets the AT base pairs in nucleic acids. Additional competitive binding experiments confirmed that PC1 and Hoechst share the same binding site on the ^AATTDNA sequence. Circular dichroism (CD) spectroscopy was employed to assess how PC1 interacts with double-stranded DNA (dsDNA), revealing a positive Cotton effect characteristic of minor-groove binders, akin to Hoechst and 4′,6-diamidino-2-phenylindole (DAPI). This suggests that PC1 interacts with double-stranded DNA in a manner akin to Hoechst, thereby increasing its selectivity for DNA over RNA. To gain deeper insights into the enhanced selectivity of PC dyes (PC1–4) compared to Hoechst, researchers synthesized a modified dye, PC9, which includes a (methyl)­piperazine group by substituting one of the diethylamino groups in PC4. This modification led to a remarkable increase in fluorescence intensity250-fold upon binding to RNAsignificantly surpassing PC4, which exhibited only a 53-fold increase. These results indicate that the presence of the (methyl)­piperazine group reduces DNA selectivity when interacting with RNA, offering valuable information for the development of more effective fluorophores tailored for specific applications in live-cell imaging.

7.

(a) Molecular structure and key components of PC1. (b) Comparison of the selectivity for DNA versus RNA of PC1, Hoechst 33342, and PG. (c) Titration curve shows the response of 100 nM PC1 to varying concentrations of hairpin oligonucleotides, with fluorescence intensity presented in arbitrary units (arb. u.). Error bars represent the mean ± standard deviation (s.d.) from three independent experiments. (d) General structure of PC dyes, including their substituent patterns and corresponding compound names; the lengths of methylene units and N-aryl groups are indicated as “n” and “R,” respectively. (e) Normalized absorption spectra and (f) fluorescence spectra of all PC dyes when bound to calf thymus double-stranded DNA (dsDNA) in Tris–EDTA buffer solution (pH = 8.0). Open access article, licensed under a Creative Commons Attribution 4.0 International License.

Lysosomes, traditionally recognized for their acidic interiors and efficient breakdown of cellular waste, are now understood to play a more complex role in cellular signaling and interaction with other organelles. However, their small size, rapid dynamics, and acidic environments pose significant challenges for fluorescence imaging. To address these issues, researchers have introduced a novel far-red small molecule, HMSiR680-Me, which fluoresces specifically in acidic conditions, allowing for targeted labeling of acidic organelles within live cells. HMSiR680-Me stands out for its compatibility with other far-red dyes in multicolor imaging experiments and also has the potential to be used in multicolor volumetric super-resolution imaging. Unlike existing lysosomal probes, this compound exhibits enhanced photostability, while preserving cell viability and lysosomal motility. The dye’s effectiveness is demonstrated through overnight time-lapse experiments, as well as SRM at a frame rate of 1.5 frames per second for at least 1000 frames. Moreover, HMSiR680-Me can be used in conjunction with silicon rhodamine dyes, , facilitating the visualization of mitochondrial and lysosomal interactions using a single excitation laser and simultaneous depletion, thereby enabling more intricate studies of lysosomal functions in cellular dynamics and disease. In comparative toxicity tests, HMSiR680-Me proved to be less harmful to cell health even at higher concentrations, in stark contrast to another dye, LTDR, which caused significant off-target labeling and cell stress. Cells labeled with 500 nM HMSiR680-Me maintained their health throughout the imaging process, demonstrating no adverse effects on lysosome motility. In fact, while cells labeled with LTDR showed decreased lysosomal movement over time, those labeled with HMSiR680-Me did not exhibit a similar impairment. This stability was confirmed through various imaging conditions, indicating that HMSiR680-Me allows for extensive confocal imaging without detrimental effects on organelle function. Furthermore, HMSiR680-Me’s robustness makes it suitable for long-term imaging applications. Researchers successfully monitored HeLa cells labeled with HMSiR680-Me for 16 h with excellent signal retention and continued cell division. By utilizing a 775 nm depletion laser, they achieved super-resolution imaging of lysosomes at high frame rates, collecting detailed images over extended periods. The capability to visualize lysosomes alongside other organelles opens avenues for investigating organelle interactions below the diffraction limit. This is particularly relevant given the role of lysosomes in nutrient transfer and cellular function, especially in the context of neurodegenerative diseases and cancer, where impaired interactions between lysosomes and mitochondria are often observed. By employing a two-dye approach, researchers utilized HMSiR680-Me alongside a newly developed mitochondrial probe, MAO-SiR, to capture super-resolution images of lysosome–mitochondria interactions. The two dyes were excited with a single laser, significantly enhancing the imaging efficiency and temporal resolution while minimizing photobleaching.

Mitochondria are critical organelles that perform a variety of metabolic functions within eukaryotic cells. There has been an increasing interest in the imaging, targeting, and examination of mitochondrial mechanisms related to cell death. , Small-molecule fluorescent probes have become valuable instruments in utilizing light to enhance the study of mitochondrial biology. A breakthrough investigation presents a rational design approach for developing cationic Nile blue probes, which are characterized by a permanent positive charge and are intended for these specific applications. These cationic probes display remarkable permeability across mitochondrial membranes, exhibit unique solvatochromic properties, and show significant resistance to oxidative processes. The findings revealed that these probes exhibited reduced fluorescence in aqueous solutions compared to lipophilic solvents, which effectively minimized background fluorescence in the cytoplasm. Additionally, the researchers successfully achieved photoredox switching of the cationic Nile blue probes under mild conditions, marking a groundbreaking application of these probes in SMLM focused on mitochondria. This advancement allowed for the detailed observation of mitochondrial fission and fusion events. In contrast to conventional cyanine fluorophores, this new class of probes demonstrated greater resistance to photobleaching, likely attributable to their antioxidative properties. Moreover, the application of cationic Nile blue probes was extended to enable the targeted delivery of taxanes to mitochondria. This targeted approach facilitated investigations into the direct interactions between these chemotherapeutic agents and the organelles. Such a novel strategy for inducing cell death, independent of microtubule binding, offers valuable insights into the realms of anticancer drug development and the mechanisms of drug resistance. Mitochondria play a crucial role in initiating apoptotic pathways by regulating the transport of pro-apoptotic proteins from the intermembrane space into the cytosol, emphasizing their importance in cellular processes and therapeutic strategies. Taxanes, such as paclitaxel and docetaxel, are a class of antitumor agents believed to induce cell death by directly engaging with mitochondria. These drugs are thought to interact with mitochondrial components, leading to alterations in cellular function and ultimately contributing to the apoptotic process. , This hypothesis is bolstered by findings showing that high concentrations of paclitaxel lead to the release of cytochrome c (cyt c) when isolated mitochondria are subjected to the drug. Nonetheless, demonstrating the significance of this phenomenon in intact cells has proven difficult because of paclitaxel’s inevitable interactions with the abundant microtubules within the cytoskeleton. Earlier efforts to target taxanes specifically to mitochondria using liposomes adorned with delocalized lipophilic cations faced challenges concerning subcellular trafficking and the dynamics of release, hindering successful molecular targeting. Consequently, it was essential to re-examine the direct interactions between taxanes and mitochondria without considering their impact on microtubules. To achieve this, cationic Nile blue was employed as a carrier capable of permeating mitochondria for the precise delivery of taxanes. Some research teams have reported the conjugation of taxanes with TPP and rhodamine through esterification at the C2′ hydroxyl group; − however, this method significantly diminishes the binding affinity of taxanes for microtubules. As a result, a new approach was implemented that involved esterifying the hydroxyl group at the C7 position. This modification was intended to retain the biological activity of the original drug, now referred to as CNB-PTX (Figure ). For the sake of comparison, Nile red was utilized instead of CNB, achieved by substituting the pyrrolidine group with an oxygen atom, leading to the compound designated as NR-PTX. Since the modification occurs at a location distant from the taxane structure, it is expected that both derivatives will exhibit similar affinities for tubulin while displaying distinct subcellular accumulation behaviors. In the pursuit of developing cationic Nile blue dyes for targeted mitochondrial applications, prior research has primarily focused on alterations to the three substituents attached to the 5/9-amino groups of Nile blue, which are referred to as N,N′-trisubstituted Nile blue. , Nonetheless, these modifications often lead to deprotonation and an accompanying blueshift in absorption, which constrains their practical use. Additionally, these dyes typically diffuse into lysosomes and various cellular compartments through passive mechanisms, making their application more challenging. , In contrast, N,N′-tetrasubstituted Nile blue fluorophores, which function as delocalized cations ideal for imaging mitochondria, have been largely overlooked in the current literature. The characterization of this class of dyes has predominantly focused on assessing their melting points and mass, lacking a deeper exploration in biological settings. Attempts to create N,N′-tetrasubstituted Nile blue dyes highlighted their susceptibility to nucleophilic attacks, which not only complicated their chemical synthesis but also led to instability within living cells. To enhance the stability of the cationic Nile blue fluorophores, several modifications were introduced. The initial modification involved replacing the 5-amino group of Nile blue A with a pyrrolidine ring. This alteration was strategically chosen because the pyrrolidine ring possesses lower polarity and greater lipophilicity compared to the NH₂ group. Unlike tetraethyl Nile blue (TENB), the modified cationic Nile blue featuring a pyrrolidine ring avoids significant steric hindrance between the CH group at position C4 and the CH₂ group of the pyrrolidine. By introduction of these conformational constraints, the pyrrolidine ring minimizes steric interactions and encourages a more optimal atomic arrangement within the molecule. Furthermore, unlike trisubstituted Nile blue, which retains an extra proton, the modified structure is less sensitive to fluctuations in environmental pH. The addition of a phenyl ring at position C1 provides additional protection for the unique nitrenium, making it less prone to nucleophilic attacks. This phenyl ring also serves as a potential site for attaching binding agents or drugs intended for mitochondrial delivery. A comprehensive synthesis approach for cationic Nile blue (CNB) and its derivatives was established. This efficient procedure, requiring only 3 to 5 steps, allows for rapid access to these dyes. A crucial transformation in this synthesis involves the acetic-acid-catalyzed condensation of amino naphthalene and aromatic nitroso compounds, which can be easily sourced from commercially available materials. Attempts to further modify the pyrrolidine ring of CNB to create azetidine or aziridine derivatives were not successful, likely due to increased ring strain and the resulting instability. Notably, cationic Nile blue exhibits distinctive solvatochromic behavior that sets it apart from those of typical oxazine dyes. It shows an over 8-fold increase in fluorescence intensity in lipophilic media, such as octanol, compared to aqueous environments. In aqueous buffers, CNB demonstrates a larger Stokes shift (ranging from 40 to 50 nm) and a red-shifted emission peak around 700 nm, while this effect is less prominent in lipophilic solvents like octanol. In contrast, the absorption and emission characteristics of MitoTracker deep red (MTDR), a specific cyanine derivative, remain relatively stable in both PBS and octanol. Additionally, the stability of cationic Nile blue probes against bioanalytes and their quantum yields were also evaluated.

8.

Assessment of the pro-apoptotic effects of taxane derivatives on HeLa cells. (A) Chemical structures of taxane derivatives. (B,C) Nuclear fragmentation is caused by paclitaxel (8 nM, 24 h). (D,E) Nuclear fragmentation induced by NR-PTX (250 nM, 24 h). Visualization of cytochrome c distribution in cells treated with 250 nM (F) or 500 nM (G) NR-PTX for 24 h using cytochrome c monoclonal antibody-Alexa Fluor 488 conjugates. (H) Cells treated with CNB-PTX (2 μM, 24 h) display normal nuclear morphology but show fragmented mitochondria. Cytochrome c distribution in cells treated with 2 μM (I) or 500 nM (J) CNB-PTX for 24 h. Black arrows indicate nearly complete release of cytochrome c into the cytosol, while white arrows signify partial release. (K) Cytochrome c distribution in cells treated with DMSO. (L) Cytochrome c distribution in cells treated with 500 nM CNB for 24 h. (M) Fluorescence calibration curve for CNB and CNB-PTX in 95% ethanol. (N) Fluorescence analysis of the cellular uptake of CNB and CNB-PTX following 6 h of incubation. Excitation wavelengths: 633 nm for CNB, CNB-Cl, CNB-PTX, and MTDR; 561 nm for MTR and NR-PTX; 488 nm for cytochrome c monoclonal antibody-Alexa Fluor 488 conjugates, LTG, and MTG; and 405 nm for Hoechst. Scale bars: (C,E), 5 μm; (G,H), 10 μm; others, 20 μm.

The extensive development of dye chemistry has produced a range of fluorophores with absorption wavelengths that extend from UV to NIR. Contemporary imaging predominantly relies on a small selection of dye scaffolds, and an analysis of representative dyes demonstrates shared characteristics within each category. This section emphasizes widely accessible fluorescent labels with well-defined chemical structures. Furthermore, it is acknowledged that green fluorescent protein (GFP) and other FPs have emerged as preferred markers for cellular imaging, largely due to their broad spectral range and user-friendliness. Consequently, a comparison is made between selected FPs and small-molecule dyes across different wavelength regions (see Figure ). Many natural products are capable of absorbing UV light, with several exhibiting fluorescence, including tryptophan, NADH, and the small-molecule fluorophore quinine. However, the majority of fluorescent dyes that respond to UV and violet light are categorized into three primary classes. The first and most prevalent class comprises coumarins, which include well-known fluorophores like 4-methylumbelliferone, along with commercial options such as Alexa Fluor 350 and Alexa Fluor 430. These Alexa Fluor dyes are distinguished by their significant sulfonation, with Alexa Fluor 430 exhibiting structural rigidity, a characteristic that is prevalent among various modern fluorophore families. The second primary group comprises fluorogenic DNA stains, including DAPI, which was initially created as an antiparasitic agent, and Hoechst 33342, which shares a similar structure. − The attachment of various fluorophores to Hoechst 33342 illustrates the creation of dye-ligand conjugates intended for cellular staining applications. This novel strategy subsequently resulted in the development of fluorogenic Si–rhodamine–Hoechst conjugates for visualizing DNA, and further improvements produced a variety of cell-permeable stains. , The third class of UV and violet-excited fluorophores is based on sulfonated pyrene molecules, such as Alexa Fluor 405. In comparison, the FP mTagBFP2 exhibits greater molecular brightness than many small-molecule dyes due to its larger extinction coefficient. Fluorophores that are excited by UV and violet light play an essential role in multicolor imaging and are frequently utilized as reference markers for various cellular components, including nuclei. Nonetheless, the use of short-wavelength light poses challenges, as it can be phototoxic to cells and lead to considerable autofluorescence from naturally occurring fluorophores. This issue highlights the advantage of employing dyes with longer wavelengths for experiments that necessitate prolonged exposure times or aim to minimize background fluorescence. The blue excitation range, typically around 488 nm, has gained widespread acceptance, primarily due to the historical importance of fluorescein labels, the introduction of the argon-ion laser, and the groundbreaking discovery of GFP. There are several primary categories of small-molecule dyes that are responsive to blue light. A prominent category includes fluorescein, which, although it has limitations such as low photostability, sensitivity to pH changes, and inadequate permeability across cell membranes, continues to be a valuable labeling agent. Another significant group consists of rhodamine 110 derivatives, which are commonly featured in various commercial fluorophore collections. Additionally, BODIPY dyes, such as BODIPY FL, are widely utilized fluorescent labels recognized for their excellent extinction coefficients and high quantum yields. However, they tend to have small Stokes shifts and are relatively lipophilic in nature. Additionally, fluorinated GFP chromophore analogs, such as (Z)-5-(3,5-difluoro-4-hydroxybenzylidene)-2,3-dimethyl-3,5-dihydro-4H-imidazole-4-one, and related compounds like 4-hydroxy-3-methylbenzylidene rhodanine, have been employed in noncovalent labeling strategies, binding to evolved RNA aptamers or small proteins to enhance fluorescence significantly. The FP mNeonGreen demonstrates brightness comparable to that of fluorescein, rhodamine, and BODIPY dyes, surpassing many fluorogen systems. Green excitation light, particularly in the vicinity of 560 nm, is commonly employed because it is sufficiently differentiated from the 488 nm wavelength, making it suitable for two-color imaging. By substituting the oxygen atom in xanthene compounds, like rhodamines and fluoresceins, with a quaternary carbon, researchers have achieved a significant shift of 60 nm in both absorption and emission wavelengths, exemplified by the compound carbofluorescein. − The work of Arden-Jacob et al. describes carborhodamine-based ATTO dyes as another significant category of green-excited fluorophores. The N-alkylated rhodamine derivatives, including tetramethylrhodamine and the brighter analog JF₅₄₉, represent an important subgroup. Additional dialkylrhodamine derivatives, such as ATTO 550 and Alexa Fluor 546, can be fine-tuned for the green excitation spectrum by implementing modifications, such as rigidifying the rhodamine framework. Another significant group of green-excited dyes comprises indocyanine derivatives, which include Cy310 and its enhanced variants like Alexa Fluor 555 and Cy3B. , The FP mScarlet-i is situated within this spectral range and demonstrates molecular brightness comparable to that of chemical fluorophores found in this spectrum. Additional alterations to the rhodamine framework facilitate shifts in both absorption and emission wavelengths toward longer ranges, resulting in effective excitation within the spectral window centered at around 594 nm. Sulforhodamine 101 exemplifies this, employing julolidine rings and sulfonation to produce a dye that is sensitive to orange light. Furthermore, amine-reactive sulfonyl chloride derivatives of this dye are available commercially under the brand name Texas Red. A more intricate illustration is provided by Alexa Fluor 594, which resembles Alexa Fluor 546. However, it distinguishes itself by featuring an enhanced N-alkylation and an extended conjugation system. These modifications allow Alexa Fluor 594 to achieve longer-wavelength absorption capabilities, enhancing its utility in various imaging applications. , ATTO 594 features a core structure that closely resembles that of Alexa Fluor 594, and it shares a carboxamide attachment site with ATTO 550. By implementing the oxygen-to-quaternary-carbon modification strategy, researchers can achieve precise adjustments to the properties of carborhodamines. This approach has facilitated the development of orange-emitting dyes, such as Janelia Fluor 585, which are tailored for specific imaging applications. , In addition to rhodamine derivatives, phenoxazine resorufin provides a flexible framework for developing a range of probes, acting as a red counterpart to fluorescein, which is well-suited for creating enzyme substrates and redox sensors. The genetically encoded FP mNeptune2 is also found within this spectral range, featuring a Stokes shift greater than 50 nm. However, it exhibits a lower quantum yield when compared to many chemical dyes that emit in the yellow-orange spectrum. , The spectral range exceeding 620 nm highlights the notable benefits of small-molecule fluorophores. While numerous protein-based fluorophores are categorized as “red fluorescent proteins” (RFPs), the majority are activated by yellow-orange light, absorbing wavelengths between 560 and 620 nm, and only emit light that overlaps with the red spectrum. This characteristic complicates the creation of FPs that can be directly excited by red light. In contrast, there is a diverse array of small-molecule fluorophore frameworks present in this spectral range. Notably, cyanine dyes, including Cy5 and its enhanced derivatives like Alexa Fluor 647 and Cy5B, constitute a prominent group of red-excited fluorescent compounds. , Red excitation represents the upper boundary of the traditional rhodamine framework, which contains oxygen, with the extensively modified Alexa Fluor 633 barely entering this spectral area. Carborhodamines provide a pathway to red-excited fluorescent dyes, with ATTO 647N emerging as a particularly useful fluorophore for a wide range of applications, including single-molecule imaging. , Recently introduced Si-rhodamines can be transitioned into the red spectrum by replacing the oxygen atom in the xanthene structure with a dimethylsilicon group. This modification leads to significant alterations in both absorption and emission wavelengths, allowing for their integration into fluorogenic labels and stains. A prominent example of this is JF635. − Extensively modified phenoxazines, such as ATTO 655, constitute another important class of red-excited dyes. The difficulties associated with broadening the emission wavelengths of traditional FPs have prompted investigations of alternative protein frameworks. One such example is smURFP, a red-excited fluorophore developed from a phycobiliprotein that has the ability to bind endogenous biliverdin, a byproduct of heme metabolism. As research continues to advance into the NIR region (>700 nm), small-molecule probe development remains an emerging frontier. Si-rhodamines can be adapted to this range, exemplified by the rigidified SiR 700. Significant shifts in absorption wavelengths can also be accomplished with rhodamine dyes by incorporating heteroatom moieties such as phosphinate, phosphine oxide, or sulfone; notable examples include Nebraska Red and Janelia Fluor 722. , By lengthening the polymethine chains in cyanine dyes and fine-tuning their substituents, researchers can produce Cy7 derivatives, including FNIR-tag, which are particularly well-suited for labeling antibodies. Additional modifications to the cyanine dye framework that incorporate flavylium substituents led to the development of polymethine dyes like Flav7, which are capable of absorbing light at longer wavelengths. This characteristic makes them suitable for in vivo imaging in freely moving mice. These intravital imaging studies underscore the benefits of using small-molecule fluorophore labels. In contrast, the range of protein-based fluorophores that can be engineered for NIR excitation is quite limited; one example is miRFP720, which has an absorption peak at 702 nm but possesses a relatively low quantum yield of 0.06. ,,

9.

Comparison of common small-molecule and protein-based fluorophores organized by their excitation wavelengths. The spectral characteristics are provided beneath each molecule, presented as λ_abs (nm) ■ ε (M^–1 cm^–1) ■ λ_em (nm) ■ Φ_f, with the primary chromophore system or protein structure highlighted in colors corresponding to λ_em; nr indicates that the data are not reported. Some fluorophores are represented as their carboxylic acid derivatives. The structures of FPs are derived from the following PDB entries: 3M24, 5LTR, 3V3D, 5LK4, 6FZN, and 4R6L. Reprinted with permission from ref . Copyright 2022, Springer Nature.

4.2. Computational Methods (AI, Deep Learning) for Improved Image Reconstruction

As imaging systems generate increasingly complex data sets, computational methods like AI and deep learning are emerging as powerful tools for improving image reconstruction. Deep learning algorithms can rapidly process large volumes of imaging data, identifying patterns and structures that are otherwise obscured by noise or limited resolution. AI-driven reconstruction techniques can enhance image quality by optimizing contrast, eliminating artifacts, and improving temporal resolution without increasing light exposure to the sample. These methods are especially valuable in techniques such as super-resolution imaging, where fine details are critical for understanding molecular and cellular dynamics. Furthermore, AI-based approaches can automate segmentation, classification, and analysis, making it easier to interpret large-scale data sets and accelerating discovery in the biological sciences.

Optical tomography is an advanced imaging technique that enables noninvasive, 3D reconstruction of biological specimens by acquiring projection images from multiple angles. While it offers depth-resolved structural information, its spatial resolution is often limited by diffraction and incomplete angular sampling. To overcome these limitations, recent developments have integrated super-resolution strategies into optical tomography workflows. These approaches, which include advanced illumination schemes and deep learning-based reconstructions, allow for enhanced visualization of subcellular features beyond the classical resolution limits. The fusion of super-resolution with optical tomography has opened new avenues for high-resolution imaging of live cells and tissues, significantly advancing our ability to study dynamic biological processes at the microscale. It has become increasingly significant as a noninvasive imaging method, enabling the 3D visualization of subcellular structures. This advancement allows for a deeper comprehension of cellular functions, interactions, and processes, thereby enriching the overall understanding of cellular biology. Conventional imaging techniques encounter challenges stemming from a limited illumination scanning range, leading to anisotropic resolution and partial imaging of cellular structures. To overcome this constraint, an innovative study has presented a compact multicore fiber-optic cell rotator system. This system facilitates accurate optical manipulation of cells within a microfluidic chip, thereby enabling full-angle projection tomography with isotropic resolution. Moreover, a novel AI-enhanced workflow for tomographic reconstruction was introduced, marking a significant transition from traditional computational techniques that require extensive manual input to a completely automated system. The effectiveness of this innovative cell rotation tomography approach has been validated through 3D reconstructions of both cell phantoms and HL60 human cancer cells. This AI-driven reconstruction method is versatile and has the potential for broad applications in various tomographic imaging techniques such as flow cytometry tomography and acoustic rotation tomography. As a result, this methodology could lead to considerable progress in the field of cell biology, aid in the creation of new therapeutics, and enhance the early cancer diagnostic processes. In a related context, organoid models have gained recognition as a valuable resource for investigating the fundamental mechanisms governing organ development and function. Nevertheless, the intricate 3D architecture of organoids, coupled with the time-consuming process of immunofluorescent staining, presents significant hurdles for image-based phenotypic quantification. To tackle these challenges, a recent study introduced an innovative virtual painting system termed PhaseFIT (phase-fluorescent image transformation). This system utilizes specially designed, morphologically complex 2.5D intestinal organoids, enabling more efficient analysis and visualization of phenotypic characteristics. PhaseFIT is an innovative system that creates virtual fluorescent images for phenotypic quantification using straightforward and economical phase images. This approach incorporates a cutting-edge segmentation-informed deep generative model, which is adept at recognizing overlaps and spatial relationships among various objects. As a result, it enables a seamless, annotation-free digital conversion of phase-contrast images into multichannel fluorescent images. When applied to nuclei, markers of secretory cells, and stem cells, PhaseFIT demonstrated superior performance compared with existing deep learning models for stain transformation, yielding exceptionally detailed visual outputs. The system’s precision and effectiveness were further corroborated through experiments assessing the impact of three different compounds on crypt formation, cell populations, and stemness. Notably, PhaseFIT is the first deep learning-based virtual painting system specifically designed for live organoids, paving the way for large-scale, insightful, and efficient phenotypic quantification. This system has significant potential for application in high-throughput drug screening using organoids.

The architecture and behavior of cellular structures, particularly protein assemblies, play crucial roles in both beneficial and detrimental cellular processes. Advanced imaging methods such as SMLM offer the spatial resolution required to study these assemblies. However, conventional analytical tools struggle to efficiently quantify and interpret the resulting super-resolution data. To address these limitations, a comprehensive ML framework called SEMORE has been developed. SEMORE is a universal and semiautomated platform designed for the system- and input-independent analysis of SRM data, effectively tackling challenges in structure quantification. SEMORE’s framework consists of two main components: a clustering module and a morphology fingerprinting module. These modules enable the analysis of protein assemblies, such as those observed in techniques like STORM, PALM, PAINT, and DNA-PAINT, by processing the spatial data (x, y coordinates) and, when available, temporal information (x, y, t coordinates) (Figure ). ,, This time-aware feature enhances its ability to analyze dynamic biological systems, thus facilitating the investigation of evolving assemblies across time. The clustering module of SEMORE operates by inspecting high-density regions in a standardized 3D Euclidean space, which is essential for handling the inherently heterogeneous nature of biological assemblies. This module employs data-driven models, such as HDBSCAN and DBSCAN, to differentiate high-density regions (representing protein clusters or aggregates) from low-density noise. The temporal refinement feature, particularly beneficial for dynamic systems, aids in segmenting overlapping structures in space and time, further refining the results by applying density-based filters that eliminate inaccurate predictions. Meanwhile, the morphology fingerprinting module quantifies various geometric- and kinetics-based descriptors, enabling comprehensive classification and characterization of assemblies. This combination of modules allows SEMORE to extract detailed insights into protein assembly behavior, as demonstrated on diverse data sets including insulin aggregates, nuclear pore complexes, and fibroblast growth factor receptors. The system’s ability to process time-resolved data significantly enhances its potential, making it adaptable for applications requiring four-dimensional analysis in SRM. , Simultaneously, the investigation of cellular structures has progressed significantly through optical tomography, a noninvasive imaging technique that offers 3D perspectives on subcellular configurations. Traditional optical tomography methods have encountered challenges due to restricted scanning ranges, resulting in anisotropic resolution and partial imaging of cellular components. To address these limitations, researchers have developed a compact multicore fiber-optic cell rotator system that is integrated with a microfluidic chip. This innovative approach facilitates full-angle projection tomography while achieving isotropic resolution, significantly enhancing the quality and completeness of the imaging process. By combination of this with AI-driven tomographic reconstruction, the limitations of manual processing are removed, enabling a fully automated workflow that accelerates the analysis of cellular structures. The AI-driven tomographic approach, validated on both cell phantoms and human cancer cells, delivers detailed 3D reconstructions with an isotropic resolution. This advancement, coupled with SEMORE’s innovative machine-learning capabilities, highlights a paradigm shift in cellular analysis. The integration of AI with advanced imaging techniques such as SEMORE and optical tomography paves the way for broader applications in fields such as drug discovery, cancer diagnostics, and the study of dynamic cellular processes. These cutting-edge technologies are poised to revolutionize the study of biological assemblies, offering researchers a robust, time-efficient, and scalable method to gain unprecedented insights into cellular behavior, interactions, and therapeutic opportunities.

10.

(a) SEMORE processes input data consisting of x, y coordinates from SMLM methods such as PALM or STORM, or x, y, t coordinates from time-resolved SMLM (TR-SMLM), like the REPLOM approach, representing individual localization or aggregation events. The example shown here is insulin aggregation, imaged using the REPLOM method on a TIRF microscope. (b) First step involves clustering the data based on a density-based algorithm, taking into account spatial coordinates (x, y) and time (t). Different colors represent distinct clusters, and the scale bar is 10 μm. (c) Second step refines the clusters temporally, using time-based clustering across frames to identify and separate underlying subclusters. (d) Result of this temporal refinement is a set of spatially distinct structures, now distinguishable even when close to other aggregations. (e) Each identified cluster undergoes morphology fingerprinting, where four categories of descriptive features are computed: morphology circularity, internal graph network, symmetry, and geometric structure. These groups together form a unique morphological fingerprint comprising over 40 distinct features. (f) Computed morphology fingerprints are saved for each protein assembly, enabling full quantification and providing insights into the distribution of diverse morphologies or aggregation pathways. Open access article, licensed under a Creative Commons Attribution 4.0 International License.

Recent breakthroughs in physics-informed neural networks have demonstrated remarkable success in overcoming specific limitations of SRM. For instance, Fourier neural operators have achieved 85% fidelity in reconstructing 3D-SIM data sets from just 30% of conventional raw frames, effectively addressing the temporal resolution constraints. Similarly, diffusion models have been shown to reduce STORM imaging time by 4× while maintaining <8 nm localization precision through learned prior distributions of cellular structures. These approaches directly tackle the photon budget limitations inherent to live-cell super-resolution imaging by incorporating physical constraints (e.g., PSF models, fluorophore blinking kinetics) as regularizers during network training. Also, emerging neural architectures now specifically target SRM’s unique requirements. The DEEP-STORM3D network combines 3D residual learning with adaptive nonlocal attention to simultaneously enhance axial resolution (up to 40 nm) and depth penetration (300 μm in tissue). For dynamic imaging, recurrent vision transformers have achieved 50 ms temporal resolution in PALM imaginga 10× improvement over conventional analysisby learning spatiotemporal correlations across frames. These specialized networks outperform general-purpose models by incorporating microscope-specific physical priors, with demonstrated applications ranging from nuclear pore complex dynamics to whole-organelle tracking in developing embryos.

Optical aberrations pose a significant challenge in fluorescence microscopy, especially when imaging thick tissue samples. These aberrations compromise key image features, such as signal strength, contrast, and resolution, making it difficult to capture accurate visual data. To address this, a novel deep learning-based approach is introduced, designed to correct these aberrations without compromising image acquisition speed, without increasing the exposure dose, and without requiring additional optical equipment. This technique works by artificially introducing aberrations to images from the surface layers of a sample, mimicking the distortions observed deeper in the sample. A neural network is then trained to reverse these distortions, effectively “deaberrating” the images. Through both simulations and experimental testing, the approach outperforms traditional aberration correction methods, offering results comparable to those achieved using adaptive optics. When applied to diverse microscopy techniquesincluding confocal, light-sheet, multiphoton, and SRMthe method enhances image quality, aiding both qualitative image evaluation and more accurate image quantification. Specifically, it supports tasks like analyzing blood vessel orientation in mouse tissue and improving membrane and nuclear segmentation in Caenorhabditis elegans embryos. In further tests, this technique, termed DeAbe, was applied to larger, more complex samplesabout 10,000 times greater in volume than those previously tested. E11.5 mouse embryos, cleared using the iDISCO method, were immunostained to highlight neurons and blood vessels and imaged with low-magnification confocal microscopy (Figure ). While tissue clearing typically minimizes refractive index variations throughout the sample, significant degradation was still observed across the depth of the embryo, with issues such as photobleaching and resolution loss becoming more pronounced toward the deeper sections. By digitally compensating for photobleaching, applying DeAbe, and then performing deconvolution, substantial restoration of the image quality was achieved. The application of DeAbe prior to deconvolution provided superior results compared with direct deconvolution of raw data. This improvement was particularly evident in axial views, where the restored images clearly enhanced the visualization of fibrillar structures, such as the vagus nerve and its roots, across the entire sample volume. Quantitative analysis further demonstrated the effectiveness of the DeAbe approach. Automated tools were employed to assess the 3D orientation and directional variance of blood vessels within the sample. The DeAbe-corrected images exhibited cleaner vessel separations, enabling more precise quantification in areas with dense vessel networks. In deeper sections of the volume, the angular distribution of vessel orientations was noticeably tighter in DeAbe-processed data compared with the raw images. This was also reflected in the analysis of directional variance, where significant improvements were observed, particularly in regions with varying vessel alignment, such as the aortic arches and the diencephalon. Overall, these quantitative improvements highlighted the value of DeAbe in both enhancing image quality and enabling a more accurate analysis of complex biological structures.

11.

(a) E11.5 mouse embryos, fixed and cleared using iDISCO, were immunostained for neurons (TuJ1, cyan) and blood vessels (CD31, magenta), then imaged with confocal microscopy and processed using a trained DeAbe model. (b) Axial view of the region marked by the dotted rectangle in a, comparing raw data with depth-compensated, deaberrated, and deconvolved data (DeAbe+). (c) Higher magnification lateral view at the axial depth of 1689 μm, indicated by orange arrowheads in b. (d) Close-up views of the white dotted region in c, showing a comparison between raw (left) and DeAbe+ processed images for neuronal (top) and blood vessel (bottom) staining. (e) Analysis of vessel orientation (θ, transverse angle) in the DeAbe+ data, shown on a single lateral plane at the specified axial depth. (f) Detailed lateral view of the white dotted region in e (note the axial plane difference), comparing intensity (left) and orientation (right) between raw data (top) and DeAbe+ results (middle). Right insets show higher magnification of the vessel and surrounding area marked by dotted lines. The bottom row shows a histogram of all orientations within the vessel, with the full-width-at-half-maximum of the peak region displayed. (g) Directional variance analysis of blood vessel staining within the indicated plane, with higher magnification views of regions of interest shown on the right. A histogram of directional variance in both regions is also provided. Scale bars: 500 μm (a,b,c,e); 100 μm (d), 50 μm inset; 300 μm (f), 50 μm inset; 300 μm (g), 50 μm inset. Data represent samples from N = 3 experiments for (a–d) and N = 1 for (e–g). Open access article, licensed under a Creative Commons Attribution 4.0 International License.

One critical consideration when applying AI models, particularly in the context of image reconstruction and analysis, is their reliability across different data sets and imaging conditions. AI models, particularly deep learning algorithms, are often trained on highly specific data sets, which can result in overfitting to the particular features and noise profiles of those data. This means that a model trained on one data set may not perform optimally when applied to new, unseen data, especially if the imaging conditions (such as sample type, resolution, or noise levels) differ significantly. To improve the reliability of these models, rigorous validation and testing across multiple data sets are essential. Cross-validation techniques, such as k-fold cross-validation, can be employed to assess model robustness, while external validation on independent data sets ensures that the model generalizes well across various conditions. Moreover, incorporating diverse training data sets that represent a wide range of biological and imaging conditions can help improve the model’s robustness and reduce the risk of overfitting. The transferability of AI models or the ability to apply a trained model to different research settings and imaging systems is also a significant challenge. Models trained on one type of super-resolution microscope or imaging modality may not perform as well when transferred to another due to differences in imaging physics, data characteristics, or noise patterns. Techniques such as domain adaptation and transfer learning are often used to address this issue, allowing a model trained on one data set to be fine-tuned for use with new data sets or imaging modalities. Transfer learning, in particular, can be leveraged to improve model performance when there are limited data available for the new domain. By leveraging pretrained models on large, generalized data sets, the model can be adapted to the specific needs of the new research context, improving transferability. As the field progresses, more standardized AI models and frameworks will likely emerge, enabling better transferability and reducing the need for extensive retraining across different experimental setups.

4.3. New Hardware Innovations: Faster Cameras and Enhanced Optics

In tandem with computational advancements, innovations in hardware are revolutionizing imaging capabilities. Faster, high-sensitivity cameras equipped with more efficient detectors now allow for real-time imaging of dynamic biological processes at unprecedented frame rates. These cameras, often paired with advanced CMOS sensors, offer high quantum efficiency and low noise, crucial for capturing weak signals in low-light conditions. ,− Enhanced optics, such as adaptive lenses and wavefront correction systems, further improve image quality by compensating for aberrations and distortions in deep-tissue imaging. − Coupled with faster data acquisition systems, these innovations enable researchers to capture rapid biological events with high spatial and temporal resolution, pushing the limits of what can be visualized in living organisms. ,, Current state-of-the-art super-resolution techniques have achieved spatial resolutions as fine as 5 nm and localization precisions of around 1 nm in certain in vitro setups. However, these high resolutions are difficult to replicate in cellular environments, and achieving Ångström resolution in such contexts has remained elusive. To address this, a novel method termed resolution enhancement by sequential imaging (RESI) has been developed, which enables Ångström-scale resolution in fluorescence microscopy. By sequentially imaging small, sparse subsets of targets at moderate resolutions of >15 nm, the method achieves single-protein level resolution for biomolecules within whole, intact cells. Additionally, RESI was successfully applied to resolve the distances between individual DNA bases in DNA origami structures with Ångström precision. In a proof-of-concept experiment, RESI was used to map the molecular arrangement of the immunotherapy target CD20 in situ, in both untreated cells and those subjected to drug treatment. This application highlights the potential of RESI to explore molecular mechanisms in targeted immunotherapies. By enabling high-resolution imaging of biomolecules under natural ambient conditions in intact cells, RESI bridges the gap between SRM and structural biology, providing critical insights into complex biological processes. Also in another attempt, a new type of image sensor has been developed where each pixel can be individually programmed with customizable sampling speed and phase. This allows for the simultaneous sampling of high-speed events while maintaining an enhanced signal-to-noise ratio. In experiments focused on high-speed voltage imaging, this advanced image sensor demonstrated a significant improvement in SNRapproximately 2 to 3 times highercompared to low-noise scientific CMOS cameras. This enhancement in SNR made it possible to detect weak neuronal action potentials and subthreshold activities that standard CMOS cameras typically miss. Additionally, the camera’s ability to configure pixel exposure flexibly offers a range of sampling strategies, improving signal quality across different experimental conditions. From another perspective, a rolling Fourier ring correlation (rFRC) method has been introduced, allowing for precise evaluation of reconstruction uncertainties down to the super-resolution scale. Additionally, by combining a filtered rFRC with a modified resolution-scaled error map, this approach visually highlights areas of lower reliability, providing a clear and detailed map for further analysis. The effectiveness of these methods has been demonstrated across various super-resolution imaging techniques, with the resulting quantitative maps facilitating the integration of better-quality super-resolution images from multiple reconstructions. This new framework is expected to become a valuable tool for biologists in evaluating image data sets and could inspire further progress in the evolving field of computational imaging.

5. Future Prospects in Live-Cell Super-Resolution Volume Imaging

The future of live-cell super-resolution volume imaging holds immense promise, particularly as technological advancements continue to push the boundaries of spatiotemporal resolution and imaging depth. One of the key areas of focus will likely be the development of more sophisticated optical and computational techniques to overcome current limitations such as phototoxicity, limited temporal resolution, and the challenges associated with imaging thick, dynamic biological tissues. A major future direction is the integration of advanced LSM with super-resolution techniques. This approach offers the potential to achieve rapid, high-resolution imaging of living cells in 3D, with minimal photodamage. In addition, the continued improvement of adaptive optics could play a significant role in mitigating aberrations caused by biological specimens, ensuring higher image fidelity in complex tissue environments. AI and ML are expected to further revolutionize live-cell super-resolution volume imaging. AI-driven image reconstruction algorithms can enhance resolution and speed by accurately predicting high-resolution details from lower-quality data, reducing the need for extensive data acquisition. Moreover, the application of AI in real-time data analysis could streamline the processing of large volumetric data sets, enabling more efficient, real-time visualization of dynamic cellular processes. Another exciting prospect is the advancement of novel fluorescent probes that are less prone to photobleaching and can sustain long-term imaging. These probes, in combination with new imaging techniques such as MINFLUX or multicolor live-cell imaging, will enable researchers to explore cellular processes in unprecedented detail, revealing interactions and structures previously obscured in conventional imaging modalities. The convergence of super-resolution imaging with other modalities, such as cryo-electron microscopy and mass spectrometry, may open new avenues for multiscale investigations. This integrated approach could bridge the gap between molecular-level insights and cellular-scale dynamics, offering a more comprehensive understanding of biological systems in their native environments.

Advances in live-cell super-resolution volume imaging hold immense potential for unraveling the complexities of transcriptional regulation. This intricate process involves the interplay of transcription factors, epigenetic modulators, and chromatin architecture, with each contributing to the precise control of gene expression. Multicolor live-cell imaging, particularly at the single-molecule level, can provide unprecedented insights into the spatiotemporal dynamics of transcription initiation. By enabling the simultaneous visualization of multiple molecular players within their native cellular environment, this approach can help elucidate how these components interact in real time, revealing critical regulatory mechanisms. For instance, tracking transcription factors as they bind to DNA, observing chromatin remodeling events, or mapping the recruitment of epigenetic modifiers could uncover new dimensions of transcriptional control that remain elusive with static or bulk-cell analyses. To further enhance the utility of live-cell imaging in this context, strategies to improve the resolution and sensitivity must be prioritized. One promising avenue is the development of adaptive optics systems that correct for sample-induced aberrations, ensuring sharper images deep within live tissues. Additionally, leveraging novel fluorophores with higher brightness, photostability, and minimal spectral overlap can improve both the spatial and temporal resolution of multicolor imaging. Combining these advances with machine-learning-based image reconstruction techniques could further push the boundaries of resolution, enabling detailed visualization of transcriptional processes at the nanometer scale. While achieving these improvements in live-cell settings remains technically challenging, ongoing innovations in optics, computational methods, and fluorophore chemistry provide a strong foundation for addressing these limitations. By integration of these approaches, future imaging platforms can offer a more comprehensive understanding of transcriptional regulation, bridging the gap between molecular dynamics and cellular function.

6. Conclusion: Toward a New Frontier in Cellular Imaging

As cellular imaging advances into a new era, the convergence of super-resolution techniques, computational innovation, and cutting-edge optics has redefined the limits of what is possible in biological research. Live-cell super-resolution volume imaging stands at the forefront of this transformation, offering unprecedented insights into the intricate dynamics and interactions of living systems. The integration of AI, ML, and real-time data analysis has significantly enhanced the accuracy, speed, and accessibility of high-resolution imaging. These developments, paired with the emergence of novel fluorescent probes and adaptive optical systems, are driving the exploration of cellular structures and processes with unparalleled precision and depth. However, this is only the beginning. Future innovations in imaging technology, from faster and less invasive microscopy techniques to more intelligent computational frameworks, promise to unlock the hidden complexities of the cellular world. The potential to visualize molecular mechanisms in real time and in their native environments will accelerate breakthroughs in fields as diverse as cancer research, neuroscience, and regenerative medicine. As the boundaries of cellular imaging continue to expand, we are moving toward a new frontierone where the once invisible becomes visible and the unknown becomes knowable. The implications for scientific discovery are profound, offering the potential to not only understand life at its most fundamental level but also revolutionize the diagnosis and treatment of diseases. In this rapidly evolving landscape, live-cell super-resolution imaging will undoubtedly play a critical role in shaping the future of biological research and medical innovation.

CRediT: Navid Rabiee conceptualization, investigation, project administration, supervision, writing - original draft, writing - review & editing; Xun Lan conceptualization, project administration, supervision, writing - original draft, writing - review & editing.

The authors declare no competing financial interest.