Sensor less adaptive optics offers significant advantages over hardware-based wavefront sensing but faces persistent challenges: Its performance degrades when idealized models fail to capture system imperfections, it is largely restricted to spatially invariant aberrations, and it cannot accommodate dynamic biological samples due to static-object assumptions. Here we present graph-modeling and phase-diversity-based computational adaptive optics with self-calibration (GRAPHYCS), a differentiable graph-based modeling framework that addresses all three limitations. GRAPHYCS automatically self-calibrates to correct system-specific non-idealities, enables spatially variant wavefront sensing across extended fields of view by modeling local aberrations, and supports dynamic live-sample imaging where conventional computational methods fail. In simulations, GRAPHYCS achieves up to a 9-fold improvement in wavefront sensing accuracy compared to analytic phase diversity under system non-idealities. In real microscopy experiments, it consistently outperforms phase-diversity-based methods compared in this study. Furthermore, in live zebrafish brain imaging, GRAPHYCS enables simultaneous wavefront sensing and neuronal activity detection-an application beyond the reach of existing approaches without additional hardware complexity.

Event cameras, which asynchronously detect local brightness changes with sub-millisecond precision, offer high temporal resolution, wide dynamic range, and efficient data throughput. While these advantages have driven advances in dynamic vision and motion analysis, their application to functional biological imaging remains underexplored. Here we present a comprehensive event-based imaging framework that includes quantitative optical characterization of event cameras, multi-modal in vivo imaging including cortical blood flow and neuronal calcium dynamics, and a novel self-supervised reconstruction algorithm, Implicit Neural Factorization (INF), which converts sparse event streams into continuous activity signals. This framework opens new possibilities for high-resolution, data-efficient functional imaging in biology.

Voltage imaging has emerged as a powerful tool for recording membrane potential changes in living cells, offering a direct measurement of rapid neuronal events with high temporal precision. Since the brain is a three-dimensional circuit, it is essential to record signals across a volume. However, achieving effective three-dimensional voltage imaging over large neuronal populations remains challenging due to the need for high imaging speed, high signal-to-noise ratio, and extensive volume coverage. In this study, we demonstrate in vivo three-dimensional voltage imaging in larval zebrafish using oblique plane microscopy and QFDBD-QUAS-driven expression of the genetically encoded voltage indicator Ace-mNeon2-Kv2.1, achieving volumetric imaging rates of up to 200 volumes per second (VPS). This approach enables dye-free voltage imaging, simplifying experimental workflows and improving the reproducibility of in vivo voltage imaging experiments for investigating neuronal circuit dynamics in the living zebrafish animal model.

Analysis of biological samples often requires integrating diverse imaging modalities to gain a comprehensive understanding. While supervised biomedical image translation methods have shown success in synthesizing images across different modalities, they require paired data, which are often impractical to obtain due to challenges in data alignment and sample preparation. Unpaired methods, while not requiring paired data, struggle to preserve the precise spatial and quantitative information essential for accurate analysis. To address these challenges, we introduce STABLE (spatial and quantitative information preserving biomedical image translation), an unpaired image-to-image translation method that emphasizes the preservation of spatial and quantitative information by enforcing information consistency and employing dynamic, learnable upsampling operators to achieve pixel-level accuracy. We validate STABLE across various biomedical imaging tasks, including translating calcium imaging data from zebrafish brains and virtual histological staining, demonstrating its superior ability to preserve spatial details, signal intensities, and accurate alignment compared to existing methods.

Three-dimensional multiplexed fluorescence imaging is an indispensable technique in neuroscience. For two-dimensional multiplexed imaging, cyclic immunofluorescence, which involves repeating staining, imaging, and signal removal over multiple cycles, has been widely used. However, the application of cyclic immunofluorescence to three dimensions poses challenges, as a single staining process can take more than 12 hours for thick specimens, and repeating this process for multiple cycles can be prohibitively long. Here, we propose SEPARATE (Spatial Expression PAttern-guided paiRing And unmixing of proTEins), a method that reduces the number of cycles by half by imaging two proteins using a single fluorophore. This is achieved by labeling two proteins with the same fluorophores and unmixing their signals based on their three-dimensional spatial expression patterns, using a neural network. We employ a feature extraction network to quantify the spatial distinction between proteins, with these quantified values, termed feature-based distances, used to identify protein pairs. We then validate the feature extraction network with ten proteins, showing a high correlation between spatial pattern distinction and signal unmixing performance. We finally demonstrate the volumetric multiplexed imaging of six proteins using three fluorophores, pairing them based on feature-based distances and unmixing their signals through protein separation networks.

Nanoscale imaging of whole vertebrates is essential for the systematic understanding of human diseases, yet this goal has not yet been achieved. Expansion microscopy (ExM) is an attractive option for accomplishing this aim; however, the expansion of even mouse embryos at mid- and late-developmental stages, which have fewer calcified body parts than adult mice, is yet to be demonstrated due to the challenges of expanding calcified tissues. Here, we introduce a state-of-the-art ExM technique, termed whole-body ExM, that utilizes cyclic digestion. This technique allows for the super-resolution, volumetric imaging of anatomical structures, proteins, and endogenous fluorescent proteins (FPs) within embryonic and neonatal mice by expanding them 4-fold. The key feature of whole-body ExM is the alternating application of two enzyme compositions repeated multiple times. Through the simple repetition of this digestion process with an increasing number of cycles, mouse embryos of various stages up to E18.5, and even neonatal mice, which display a dramatic difference in the content of calcified tissues compared to embryos, are expanded without further laborious optimization. Furthermore, the whole-body ExM's ability to retain FP signals allows the visualization of various neuronal structures in transgenic mice. Whole-body ExM could facilitate studies of molecular changes in various vertebrates.

Recent advancements in genetically encoded calcium indicators, particularly those based on green fluorescent proteins, have optimized their performance for monitoring neuronal activities in a variety of model organisms. However, progress in developing red-shifted GECIs, despite their advantages over green indicators, has been slower, resulting in fewer options for end users. In this study, we explored topological inversion and soma-targeting strategies, which are complementary to conventional mutagenesis, to re-engineer a red genetically encoded calcium indicator, FRCaMP, for enhanced in vivo performance. The resulting sensors, FRCaMPi and soma-targeted FRCaMPi (SomaFRCaMPi), exhibit up to 2-fold higher dynamic range and peak ΔF/F0 per single AP compared to widely used jRGECO1a in neurons both in culture and in vivo. Compared to jRGECO1a and FRCaMPi, SomaFRCaMPi reduces erroneous correlation of neuronal activity in the brains of mice and zebrafish by two- to 4-fold due to diminished neuropil contamination without compromising the signal-to-noise ratio. Under wide-field imaging in primary somatosensory and visual cortices in mice with high labeling density (80-90%), SomaFRCaMPi exhibits up to 40% higher SNR and decreased artifactual correlation across neurons. Altogether, SomaFRCaMPi improves the accuracy and scale of neuronal activity imaging at single-neuron resolution in densely labeled brain tissues due to a 2-3-fold enhanced automated neuronal segmentation, 50% higher fraction of responsive cells, up to 2-fold higher SNR compared to jRGECO1a. Our findings highlight the potential of SomaFRCaMPi, comparable to the most sensitive soma-targeted GCaMP, for precise spatial recording of neuronal populations using popular imaging modalities in model organisms such as zebrafish and mice.

Recent advancements in genetically encoded calcium indicators, particularly those based on green fluorescent proteins, have optimized their performance for monitoring neuronal activities in a variety of model organisms. However, progress in developing red-shifted GECIs, despite their advantages over green indicators, has been slower, resulting in fewer options for end-users. In this study, we explored topological inversion and soma-targeting strategies, which are complementary to conventional mutagenesis, to re-engineer a red genetically encoded calcium indicator, FRCaMP, for enhanced in vivo performance. The resulting sensors, FRCaMPi and soma-targeted FRCaMPi (SomaFRCaMPi), exhibit up to 2-fold higher dynamic range and peak ΔF/F0 per single AP compared to widely used jRGECO1a in neurons in culture and in vivo. Compared to jRGECO1a and FRCaMPi, SomaFRCaMPi reduces erroneous correlation of neuronal activity in the brains of mice and zebrafish by two- to four-fold due to diminished neuropil contamination without compromising the signal-to-noise ratio. Under wide-field imaging in primary somatosensory and visual cortex in mice with high labeling density (80-90%), SomaFRCaMPi exhibits up to 40% higher SNR and decreased artifactual correlation across neurons. Altogether, SomaFRCaMPi improves the accuracy and scale of neuronal activity imaging at single-neuron resolution in densely labeled brain tissues due to a 2-3-fold enhanced automated neuronal segmentation, 50% higher fraction of responsive cells, up to 2-fold higher SNR compared to jRGECO1a. Our findings highlight the potential of SomaFRCaMPi, comparable to the most sensitive soma-targeted GCaMP, for precise spatial recording of neuronal populations using popular imaging modalities in model organisms such as zebrafish and mice.

Genetically encoded voltage indicators (GEVIs) are powerful tools for monitoring neuronal activity, but their application, particularly for long-term recordings in vivo , is often limited by photobleaching under the required high illumination intensities. This constraint restricts the total duration of continuous or trial-based experiments, crucial for studying processes like synaptic plasticity or circuit dynamics during behavior. Here, we introduce ElectraON and ElectraOFF, a pair of green fluorescent eFRET-based GEVIs engineered by incorporating a photostability-enhanced derivative of the bright monomeric fluorescent protein mBaoJin with Ace opsin variants. Critically, Electras demonstrate over 6-fold improved photostability compared to state-of-the-art eFRET GEVIs, pAce, and Ace-mNeon2, under one-photon illumination, while characterized by bright green fluorescence, millisecond kinetics, and good membrane localization. This enhanced stability translates to a 3-to >10-fold extension in functional recording duration, maintaining reliable spike detection in both cultured neurons in vitro and sparsely labeled neurons in the awake mouse cortex in vivo . We demonstrated sustained in vivo recordings exceeding 30 minutes, with instances surpassing one hour. Furthermore, Electras show functionality under scanless two-photon excitation in cultured cells. These highly photostable indicators significantly extend the temporal window for voltage imaging, broadening the scope of accessible biological questions. ### Competing Interest Statement The authors have declared no competing interest. National Natural Science Foundation of China, https://ror.org/01h0zpd94, 32171093, 323B200334

Established methods for imaging the living mammalian brain have, to date, taken optical properties of the tissue as fixed; we here demonstrate that it is possible to modify the optical properties of the brain itself to significantly enhance at-depth imaging while preserving native physiology. Using a small amount of any of several biocompatible materials to raise the refractive index of solutions superfusing the brain prior to imaging, we could increase several-fold the signals from the deepest cells normally visible and, under both one-photon and two-photon imaging, visualize cells previously too dim to see. The enhancement was observed for both anatomical and functional fluorescent reporters across a broad range of emission wavelengths. Importantly, visual tuning properties of cortical neurons in awake mice, and electrophysiological properties of neurons assessed ex vivo, were not altered by this procedure.

This review explores how artificial intelligence (AI) is transforming fluorescence microscopy, providing an overview of its fundamental principles and recent advancements. The roles of AI in improving image quality and introducing new imaging modalities are discussed, offering a comprehensive perspective on these changes. Additionally, a unified framework is introduced for comprehending AI‐driven microscopy methodologies and categorizing them into linear inverse problem‐solving, denoising, and nonlinear prediction. Furthermore, the potential of self‐supervised learning techniques that address the challenges associated with training the networks are explored, utilizing unlabeled microscopy data to enhance data quality and expand imaging capabilities. It is worth noting that while the specific examples and advancements discussed in this review focus on fluorescence microscopy, the general approaches and theories are directly applicable to other optical microscopy methods.

As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the cellular resolution. Here, we provide an optimized protocol for performing whole-brain imaging of larval zebrafish using three-dimensional fluorescence microscopy, including sample preparation and immobilization, sample embedding, image acquisition, and visualization after imaging. The current protocol enables in vivo imaging of the structure and neuronal activity of a larval zebrafish brain at a cellular resolution for over 1 h using confocal microscopy and custom-designed fluorescence microscopy. The critical steps in the protocol are also discussed, including sample mounting and positioning, preventing bubble formation and dust in the agarose gel, and avoiding motion in images caused by incomplete solidification of the agarose gel and paralyzation of the fish. The protocol has been validated and confirmed in multiple settings. This protocol can be easily adapted for imaging other organs of a larval zebrafish.

As a vertebrate model animal, larval zebrafish are widely used in neuroscience and provide a unique opportunity to monitor whole-brain activity at the cellular resolution. Here, we provide an optimized protocol for performing whole-brain imaging of larval zebrafish using three-dimensional fluorescence microscopy, including sample preparation and immobilization, sample embedding, image acquisition, and visualization after imaging. The current protocol enables in vivo imaging of the structure and neuronal activity of a larval zebrafish brain at a cellular resolution for over 1 h using confocal microscopy and custom-designed fluorescence microscopy. The critical steps in the protocol are also discussed, including sample mounting and positioning, preventing bubble formation and dust in the agarose gel, and avoiding motion in images caused by incomplete solidification of the agarose gel and paralyzation of the fish. The protocol has been validated and confirmed in multiple settings. This protocol can be easily adapted for imaging other organs of a larval zebrafish.

Here we report SUPPORT (statistically unbiased prediction utilizing spatiotemporal information in imaging data), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatiotemporal neighboring pixels, even when its temporally adjacent frames alone do not provide useful information for statistical prediction. Such dependency is captured and used by a convolutional neural network with a spatiotemporal blind spot to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulations and experiments, we show that SUPPORT enables precise denoising of voltage imaging data and other types of microscopy image while preserving the underlying dynamics within the scene.

Here we report SUPPORT (Statistically Unbiased Prediction utilizing sPatiOtempoRal information in imaging daTa), a self-supervised learning method for removing Poisson-Gaussian noise in voltage imaging data. SUPPORT is based on the insight that a pixel value in voltage imaging data is highly dependent on its spatially neighboring pixels in the same time frame, even when its temporally adjacent frames do not provide useful information for statistical prediction. Such spatiotemporal dependency is captured and utilized to accurately denoise voltage imaging data in which the existence of the action potential in a time frame cannot be inferred by the information in other frames. Through simulation and experiments, we show that SUPPORT enables precise denoising of voltage imaging data while preserving the underlying dynamics in the scene.

Nanoscale resolution imaging of whole vertebrates is required for a systematic understanding of human diseases, but this has yet to be realized. Expansion microscopy (ExM) is an attractive option for achieving this goal, but the expansion of whole vertebrates has not been demonstrated due to the difficulty of expanding hard body components. Here, we demonstrate whole-body ExM, which enables nanoscale resolution imaging of anatomical structures, proteins, and endogenous fluorescent proteins (FPs) of whole zebrafish larvae and mouse embryos by expanding them fourfold. We first show that post-digestion decalcification and digestion kinetics matching are critical steps in the expansion of whole vertebrates. Then, whole-body ExM is combined with the improved pan-protein labeling approach to demonstrate the three-dimensional super-resolution imaging of antibody- or FP-labeled structures and all major anatomical structures surrounding them. We also show that whole-body ExM enables visualization of the nanoscale details of neuronal structures across the entire body. ### Competing Interest Statement J.-B.C., J.S., C.E.P., I.C., and K.M. have applied for patents for whole-body ExM (KR patent application 10-2021-0024480, KR patent application 10-2022-0010077, and KR patent application 10-2022-0009372). J.-B.C., Y.-G.Y., J.S., C.E.P., I.C. have applied for patents for whole-body ExM (KR patent application 10-2021-0138718, KR patent application 10-2022-0010078, and US patent application 17675808).