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The hypothalamus plays a vital role in coordinating essential neuroendocrine, autonomic, and somatomotor responses for survival and reproduction. While previous studies have explored population-level projections of hypothalamic neurons, the specific innervation patterns of individual hypothalamic axons remain unclear. To understand the organization of hypothalamic axon projections, we conducted a comprehensive reconstruction of single-cell projectomes from 7,180 mouse hypothalamic neurons expressing specific neuropeptides. Our analysis identified 31 distinct subtypes based on projectome-defined characteristics, with many exhibiting long-range axon collateral projections to multiple brain regions. Notably, these subtypes selectively targeted specific subdomains within downstream areas, either unilaterally or bilaterally. Furthermore, we observed that individual peptidergic neuronal types encompassed multiple projectome-defined subtypes, explaining their diverse functional roles. Additionally, by examining intra-hypothalamic axon projections, we uncovered six modular subnetworks characterized by enriched intramodular connections and distinct preferences for downstream targets. This modular organization of the intra-hypothalamic network likely contributes to the coordinated organization of hypothalamic outputs. In summary, our comprehensive projectome analysis reveals the organizational principles governing hypothalamic axon projections, providing a framework for understanding the neural circuit mechanisms underlying the diverse and coordinated functions of the hypothalamus.

Line confocal (LC) microscopy is a fast 3D imaging technique, but its asymmetric detection slit limits resolution and optical sectioning. To address this, we propose the differential synthetic illumination (DSI) method based on multi-line detection to enhance the spatial resolution and optical sectioning capability of the LC system. The DSI method allows the imaging process to simultaneously accomplish on a single camera, which ensures the rapidity and stability of the imaging process. DSI-LC improves X- and Z-axis resolution by 1.28 and 1.26 times, respectively, and optical sectioning by 2.6 times compared to LC. Furthermore, the spatially resolved power and contrast are also demonstrated by imaging pollen, microtubule, and the fiber of the GFP fluorescence-labeled mouse brain. Finally, Video-rate imaging of zebrafish larval heart beating in a 665.6 × 332.8 µm2 field-of-view is achieved. DSI-LC provides a promising approach for 3D large-scale and functional imaging in vivo with improved resolution, contrast, and robustness.

Histamine is a conserved neuromodulator in mammalian brains and critically involved in many physiological functions. Understanding the precise structure of the histaminergic network is the cornerstone in elucidating its function. Herein, using histidine decarboxylase (HDC)-CreERT2 mice and genetic labeling strategies, we reconstructed a whole-brain three dimensional (3D) structure of histaminergic neurons and their outputs at 0.32 × 0.32 × 2 μm 3 pixel resolution with a cutting-edge fluorescence microoptical sectioning tomography system. We quantified the fluorescence density of all brain areas and found that histaminergic fiber density varied significantly among brain regions. The density of histaminergic fiber was positively correlated with the amount of histamine release induced by optogenetic stimulation or physiological aversive stimulation. Lastly, we reconstructed a fine morphological structure of 60 histaminergic neurons via sparse labeling and uncovered the largely heterogeneous projection pattern of individual histaminergic neurons. Collectively, this study reveals an unprecedented whole-brain quantitative analysis of histaminergic projections at the mesoscopic level, providing a foundation for future functional histaminergic study.

To decipher the organizational logic of complex brain circuits, it is important to chart long-distance pathways while preserving micron-level accuracy of local network. However, mapping the neuronal projections with individual-axon resolution in the large and complex primate brain is still challenging. Herein, we describe a highly efficient pipeline for three-dimensional mapping of the entire macaque brain with subcellular resolution. The pipeline includes a novel poly-N-acryloyl glycinamide (PNAGA)-based embedding method for long-term structure and fluorescence preservation, high-resolution and rapid whole-brain optical imaging, and image post-processing. The cytoarchitectonic information of the entire macaque brain was acquired with a voxel size of 0.32 μm × 0.32 μm × 10 μm, showing its anatomical structure with cell distribution, density, and shape. Furthermore, thanks to viral labeling, individual long-distance projection axons from the frontal cortex were for the first time reconstructed across the entire brain hemisphere with a voxel size of 0.65 μm × 0.65 μm × 3 μm. Our results show that individual cortical axons originating from the prefrontal cortex simultaneously target multiple brain regions, including the visual cortex, striatum, thalamus, and midbrain. This pipeline provides an efficient method for cellular and circuitry investigation of the whole macaque brain with individual-axon resolution, and can shed light on brain function and disorders.

Efficient storage and sharing of massive biomedical data would open up their wide accessibility to different institutions and disciplines. However, compressors tailored for natural photos/videos are rapidly limited for biomedical data, while emerging deep learning based methods demand huge training data and are difficult to generalize. Here we propose to conduct biomedical data compRession with Implicit nEural Function (BRIEF) by representing the original data with compact deep neural networks, which are data specific and thus have no generalization issues. Benefiting from the strong representation capability of implicit neural function, BRIEF achieves significantly higher-fidelity on diverse biomedical data than existing techniques. Besides, BRIEF is of consistent performance across the whole data volume, supports customized spatially-varying fidelity. BRIEF’s multi-fold advantageous features also serve reliable downstream tasks at low bandwidth. Our approach will facilitate biomedical data sharing at low bandwidth and maintenance costs, and promote collaboration and progress in the biomedical field.

An essential step toward understanding brain function is to establish a structural framework with cellular resolution on which multi-scale datasets spanning molecules, cells, circuits and systems can be integrated and interpreted1. Here, as part of the collaborative Brain Initiative Cell Census Network (BICCN), we derive a comprehensive cell type-based anatomical description of one exemplar brain structure, the mouse primary motor cortex, upper limb area (MOp-ul). Using genetic and viral labelling, barcoded anatomy resolved by sequencing, single-neuron reconstruction, whole-brain imaging and cloud-based neuroinformatics tools, we delineated the MOp-ul in 3D and refined its sublaminar organization. We defined around two dozen projection neuron types in the MOp-ul and derived an input–output wiring diagram, which will facilitate future analyses of motor control circuitry across molecular, cellular and system levels. This work provides a roadmap towards a comprehensive cellular-resolution description of mammalian brain architecture., Multi-modal analysis is used to generate a 3D atlas of the upper limb area of the mouse primary motor cortex, providing a framework for future studies of motor control circuitry.

The cortico–basal ganglia–thalamo–cortical loop is one of the fundamental network motifs in the brain. Revealing its structural and functional organization is critical to understanding cognition, sensorimotor behaviour, and the natural history of many neurological and neuropsychiatric disorders. Classically, this network is conceptualized to contain three information channels: motor, limbic and associative1–4. Yet this three-channel view cannot explain the myriad functions of the basal ganglia. We previously subdivided the dorsal striatum into 29 functional domains on the basis of the topography of inputs from the entire cortex5. Here we map the multi-synaptic output pathways of these striatal domains through the globus pallidus external part (GPe), substantia nigra reticular part (SNr), thalamic nuclei and cortex. Accordingly, we identify 14 SNr and 36 GPe domains and a direct cortico-SNr projection. The striatonigral direct pathway displays a greater convergence of striatal inputs than the more parallel striatopallidal indirect pathway, although direct and indirect pathways originating from the same striatal domain ultimately converge onto the same postsynaptic SNr neurons. Following the SNr outputs, we delineate six domains in the parafascicular and ventromedial thalamic nuclei. Subsequently, we identify six parallel cortico–basal ganglia–thalamic subnetworks that sequentially transduce specific subsets of cortical information through every elemental node of the cortico– basal ganglia–thalamic loop. Thalamic domains relay this output back to the originating corticostriatal neurons of each subnetwork in a bona fide closed loop., Mesoscale connectomic mapping of the cortico–basal ganglia–thalamic network reveals key architectural and information processing features.

Through synaptic connections, long-range circuits transmit information among neurons and connect different brain regions to form functional motifs and execute specific functions. Tracing the synaptic distribution of specific neurons requires submicron-level resolution information. However, it is a great challenge to map the synaptic terminals completely because these fine structures span multiple regions, even in the whole brain. Here, we develop a pipeline including viral tracing, sample embedding, fluorescent micro-optical sectional tomography, and big data processing. We mapped the whole-brain distribution and architecture of long projections of the parvalbumin neurons in the basal forebrain at the synaptic level. These neurons send massive projections to multiple downstream regions with subregional preference. With three-dimensional reconstruction in the targeted areas, we found that synaptic degeneration was inconsistent with the accumulation of amyloid-β plaques but was preferred in memory-related circuits, such as hippocampal formation and thalamus, but not in most hypothalamic nuclei in 8-month-old mice with five familial Alzheimer’s disease mutations. Our pipeline provides a platform for generating a whole-brain atlas of cell-type-specific synaptic terminals in the physiological and pathological brain, which can provide an important resource for the study of the organizational logic of specific neural circuits and the circuitry changes in pathological conditions.

The Nonword Repetition test measures the ability to maintain speech sounds in phonological memory. This ability is held to relate to learning new words (Baird, Slonims, Simonoff & Dworzynski, 2011; Gathercole & Baddeley, 1989; Gathercole, Willis, Emslie, & Baddeley, 1992, 1994; Nation & Hulme, 2010), and the task differentiates children with developmental language disorder (DLD) from typical-developing children across studies on different languages (Chiat & Polisenska, 2016). Currently, there are gaps in bilingual assessment for language disorders such as DLD. To help mitigate this gap, previous research aimed to develop nonword repetition tasks that would be sensitive across multiple languages, and also cross-linguistically (e.g., Chiat, 2015). The aim for the study described in this preregistered protocol is to investigate the psychometric properties of the Non-word Repetition task for Singapore. This task includes four versions based on the four official languages of Singapore (English, Mandarin Chinese, Bahasa Melayu ‘Malay’, and Tamil), along with a cross-linguistic ‘universal’ version. The English, cross-linguistic, and Chinese items were derived from other studies (Chiat, Polisenska & Szewczyk, 2012; Shiyun & Luo, 2019). The Malay and Tamil items were newly developed for a larger scale project, in collaboration with external experts. Similar to the earlier versions, item difficulty was manipulated with number and type of syllables (2, 3, 4; open, closed), as well as presence vs. absence of language specific features (nasals, diphthongs, short/long vowels, retroflex consonants or clusters), as a factorial arrangement across 24 items. Authors of the original task (S. Chiat & K. Polisenska), along with Malay language expert (R. Razak) and current authors (N.T. Yussof, P. Vijayakumar) deemed the item appropriateness and wordlikeness of the final lists of nonword items. This is a first step to develop a psychometrically sound standardized assessment tool to capture multilingual phonological short-term memory for Singapore. For the purpose of catering to bilingual children in Singapore, three versions of this task are administered to groups of Singaporean children from Nursery (N2) to Primary school (P3) in this study. Each participant completes versions that contain items based on phonotactic probability for (a) English (24 items), (b) their mother tongue language (Chinese, Malay or Tamil; 24 items) and (c) a universal set of probabilities (cross-linguistic; 16 items).

Phosphorylation of tau at Ser 396, 404 (p-tau

Tumor antigens (TAs)-induced humoral immune responses or TAs-specific antibodies have great application prospects for tumor therapy. However, more than half of TAs are intracellular antigens (intra-Ags) that are hardly recognized by antibodies. It is worthy to develop immunotherapeutic strategies for targeting intra-Ags.

The midbrain participates in complex neural information processing in the ascending and descending circuits, but their organization remains unclear due to the lack of comprehensive dissection of the characterization of individual neurons. Combining fluorescent micro-optical sectional tomography with sparse labeling, we acquired the whole-brain dataset with high resolution and reconstructed the detailed morphology of the pontine-tegmental cholinergic neurons (PTCNs). As the main cholinergic system of the midbrain, the individual PTCNs own abundant axons with length up to 60 cm and 5000 terminal branches and innervate multiple brain regions from the spinal cord to cortex in both hemispheres. According to various targeting regions in the ascending and descending circuits, individual PTCNs could be grouped into four types and the axonal fibers of cholinergic neurons in the pedunculopontine nucleus present more divergent while neurons in the laterodorsal tegmental nucleus contain richer axonal fibers and dendrites. In the axonal targeting nuclei, such as in the thalamus or cortex, the individual neurons innervate multiple sub-regions with separate pathways. These results provide the detailed organization characterization of the cholinergic neurons to understand the connection logic of the midbrain.

The stereotaxic brain atlas is a fundamental reference tool commonly used in the field of neuroscience. Here we provide a brief history of brain atlas development and clarify three key conceptual elements of stereotaxic brain atlasing: brain image, atlas, and stereotaxis. We also refine four technical indices for evaluating the construction of atlases: the quality of staining and labeling, the granularity of delineation, spatial resolution, and the precision of spatial location and orientation. Additionally, we discuss state-of-the-art technologies and their trends in the fields of image acquisition, stereotaxic coordinate construction, image processing, anatomical structure recognition, and publishing: the procedures of brain atlas illustration. We believe that the use of single-cell resolution and micron-level location precision will become a future trend in the study of the stereotaxic brain atlas, which will greatly benefit the development of neuroscience.

The microscopic visualization of large-scale three-dimensional (3D) samples by optical microscopy requires overcoming challenges in imaging quality and speed and in big data acquisition and management. We report a line-illumination modulation (LiMo) technique for imaging thick tissues with high throughput and low background. Combining LiMo with thin tissue sectioning, we further develop a high-definition fluorescent micro-optical sectioning tomography (HD-fMOST) method that features an average signal-to-noise ratio of 110, leading to substantial improvement in neuronal morphology reconstruction. We achieve a >30-fold lossless data compression at a voxel resolution of 0.32 × 0.32 × 1.00 μm3, enabling online data storage to a USB drive or in the cloud, and high-precision (95% accuracy) brain-wide 3D cell counting in real time. These results highlight the potential of HD-fMOST to facilitate large-scale acquisition and analysis of whole-brain high-resolution datasets. HD-fMOST is a microscopy technique for imaging large samples at high throughput and with high definition, which is achieved with a line-illumination modulation approach. The technology is illustrated by imaging fluorescently labeled neurons in whole mouse brains.

Supramolecular hydrogels attract extensive attention as waterborne biocompatible materials like biological tissues. Here we developed poly[N-(2-acetamidoethyl)acrylamide] (PAAE) supramolecular hydrogels with multiple hydrogen bonds, which exhibit ideal mechanical performances, such as strong resistance to compression and stretch, high stability in acidic or high ionic strength solution, excellent waterborne adhesiveness, self-healing and swelling behaviors. The small molecular precursor for PAAE hydrogels possesses great solubility up to 70 wt% in deionized water and superior infiltration capability to mouse brain tissues at 5 °C, and can be in situ polymerized at 30–45 °C and swollen in deionized water with a 1.75 times linear expansion factor. There is no noticeable loss of fluorescence intensity during the whole process from small molecular infiltration and polymerization to expansion. The mouse brain slices were embedded and used for expansion microscopy with enhanced optical resolution and preserved structure integrity.

In fluorescence diffuse optical tomography (fDOT), the quality of reconstruction is severely limited by mismodeling and ill-posedness of inverse problems. Although data-driven deep learning methods improve the quality of image reconstruction, the network architecture lacks interpretability and requires a lot of data for training. We propose an interpretable model-driven projected gradient descent network (MPGD-Net) to improve the quality of fDOT reconstruction using only a few training samples. MPGD-Net unfolds projected gradient descent into a novel deep network architecture that is naturally interpretable. Simulation and in vivo experiments show that MPGD-Net greatly improves the fDOT reconstruction quality with superior generalization ability.

Biomedical imaging is an emerging interdisciplinary area based on physics, mathematics, chemistry, information science, engineering science, life science, and medicine. It is a crucial tool supporting both fundamental research and clinical diagnosis and treatment in biomedical field, and has pushed numerous breakthroughs in life science and medicine. Biomedical imaging is not only one of the pillars of Health China construction, but also a main engine to implement the national strategies of Intelligent Manufacture and National Big Data. Well-developed biomedical imaging will contribute to build China’s strength in science, technology, and talents, and ensure national security and social stability. This paper starts with illustrating the development and trends of biomedical imaging, reveals the attention and inputs by China’s scientists, communities, and governments, summarizes the outputs and leading areas, presents a SWOT analysis, and finally proposes our prospects and suggestions on this fast-growing field.

The interdisciplinary of optical imaging and biomedical science dates back to the seventeenth century when the first observation of microorganisms with the microscope. Over the centuries, optical imaging has continuously expanded its connotation and extension. Due to its advantages of high resolution, multi-scale, multi-dimensional, low radiation, and easy to integrate, it has shown a steady stream of vitality in the field of biomedical science ranging from basic research to clinical diagnosis and treatments. It provides transformative scientific instruments and tools for the acquisition, processing, and visualization of bioscience information. It is the source of innovation for human beings from observing microscopic biological structures to understanding dynamic life processes. The review starts with the developments of biomedical optical imaging, focusing on several essential directions of imaging technologies, and expounds its progress and future trends.

Recent technological advancements have facilitated the imaging of specific neuronal populations at the single-axon level across the mouse brain. However, the digital reconstruction of neurons from a large dataset requires months of manual effort using the currently available software. In this study, we develop an open-source software called GTree (global tree reconstruction system) to overcome the above-mentioned problem. GTree offers an error-screening system for the fast localization of submicron errors in densely packed neurites and along with long projections across the whole brain, thus achieving reconstruction close to the ground truth. Moreover, GTree integrates a series of our previous algorithms to significantly reduce manual interference and achieve high-level automation. When applied to an entire mouse brain dataset, GTree is shown to be five times faster than widely used commercial software. Finally, using GTree, we demonstrate the reconstruction of 35 long-projection neurons around one injection site of a mouse brain. GTree is also applicable to large datasets (10 TB or higher) from various light microscopes.

The extreme complexity of mammalian brains requires a comprehensive deconstruction of neuroanatomical structures. Scientists normally use a brain stereotactic atlas to determine the locations of neurons and neuronal circuits. However, different brain images are normally not naturally aligned even when they are imaged with the same setup, let alone under the differing resolutions and dataset sizes used in mesoscopic imaging. As a result, it is difficult to achieve high-throughput automatic registration without manual intervention. Here, we propose a deep learning-based registration method called DeepMapi to predict a deformation field used to register mesoscopic optical images to an atlas. We use a self-feedback strategy to address the problem of imbalanced training sets (sampling at a fixed step size in nonuniform brains of structures and deformations) and use a dual-hierarchical network to capture the large and small deformations. By comparing DeepMapi with other registration methods, we demonstrate its superiority over a set of ground truth images, including both optical and MRI images. DeepMapi achieves fully automatic registration of mesoscopic micro-optical images, even macroscopic MRI datasets, in minutes, with an accuracy comparable to those of manual annotations by anatomists. Electronic supplementary material The online version of this article (10.1007/s12021-020-09483-7) contains supplementary material, which is available to authorized users.

The human brain is a biological organ, weighing about three pounds or 1.4 kg, that determines our behaviors, thoughts, emotions and consciousness. Although comprising only 2% of the total body weight, the brain consumes about 20% of the oxygen entering the body. With the expensive energy demand, the brain enables us to perceive and act upon the external world, as well as reflect on our internal thoughts and feelings. The brain is actually never at ‘rest’. Brain activities continue around the clock, ranging from functions enabling human–environment interactions to housekeeping during sleep, including processes such as synaptic homeostasis and memory formation. Whereas one could argue that sciences in the last century were dominated by physics and molecular biology, in the current century one of our major challenges is to elucidate how the brain works. A full understanding of brain functions and malfunctions is likely the most demanding task we will ever have.

Brain circuits comprise vast numbers of intricately interconnected neurons with diverse molecular, anatomical and physiological properties. To allow “user-defined” targeting of individual neurons for structural and functional studies, we created light-inducible site-specific DNA recombinases (SSRs) based on Cre, Dre and Flp (RecVs). RecVs can induce genomic modifications by one-photon or two-photon light induction in vivo. They can produce targeted, sparse and strong labeling of individual neurons by modifying multiple loci within mouse and zebrafish genomes. In combination with other genetic strategies, they allow intersectional targeting of different neuronal classes. In the mouse cortex they enable sparse labeling and whole-brain morphological reconstructions of individual neurons. Furthermore, these enzymes allow single-cell two-photon targeted genetic modifications and can be used in combination with functional optical indicators with minimal interference. In summary, RecVs enable spatiotemporally-precise optogenomic modifications that can facilitate detailed single-cell analysis of neural circuits by linking genetic identity, morphology, connectivity and function.

Renewable biobased porous aerogels with excellent biodegradability have versatile applications in oil/water separation, catalysis, and tissue engineering. However, processing of the porous matrix is challenging due to the high energy consumption and low efficiency from the fabrication procedures, such as freeze-drying or critical-drying of the hydrogel, which need to be improved. In the present study, natural amphiphilic oligomer shellac secreted by the lac Kerriar Lacca insect was employed to fabricate the porous template, which could self-assemble into a continuous rigid network with a hydrophobic core. Because of the hydrophobic core, the hydrated shellac network could be directly dried without collapse by the ambient air. The air-drying shellac aerogel presented a great mechanical property. The silane-coating treatment converted this shellac aerogel into a hydrophobic material that absorbed various organic solvents and oils. Also, this silane-coated shellac aerogel also could remove organic solvent or oil from the bottom or surface of the water. Notably, the saturable aerogel rapidly degraded in pH 14 and released the solvent absorbed by this matrix. This porous and hydrophobic matrix also could be applied as a filter that could connect with a vacuum pump to assemble a device for continuous collecting of oil from water. It also has great potential to be employed as a high-efficiency strategy to treat large scale oil spill issues. A new porous template composed of natural resin secreted by the insect was fabricated, and the whole fabrication process was green, low-cost, and energy saving. The surface of this template could be modified further to effectuate other processes, such as catalysis, heavy metal absorption, and tissue proliferation.

Fluorescence microscopy plays an irreplaceable role in biomedicine. However, limited depth of field (DoF) of fluorescence microscopy is always an obstacle of image quality, especially when the sample is with an uneven surface or distributed in different depths. In this manuscript, we combine deep learning with Fresnel incoherent correlation holography to describe a method to obtain significant large DoF fluorescence microscopy. Firstly, the hologram is restored by the Auto-ASP method from out-of-focus to in-focus in double-spherical wave Fresnel incoherent correlation holography. Then, we use a generative adversarial network to eliminate the artifacts introduced by Auto-ASP and output the high-quality image as a result. We use fluorescent beads, USAF target and mouse brain as samples to demonstrate the large DoF of more than 400µm, which is 13 times better than that of traditional wide-field microscopy. Moreover, our method is with a simple structure, which can be easily combined with many existing fluorescence microscopic imaging technology.

Background: Unraveling how new coronary arteries develop may provide critical information for establishing novel therapeutic approaches to treating ischemic cardiac diseases. There are 2 distinct coronary vascular populations derived from different origins in the developing heart. Understanding the formation of coronary arteries may provide insights into new ways of promoting coronary artery formation after myocardial infarction. Methods: To understand how intramyocardial coronary arteries are generated to connect these 2 coronary vascular populations, we combined genetic lineage tracing, light sheet microscopy, fluorescence micro-optical sectioning tomography, and tissue-specific gene knockout approaches to understand their cellular and molecular mechanisms. Results: We show that a subset of intramyocardial coronary arteries form by angiogenic extension of endocardium-derived vascular tunnels in the neonatal heart. Three-dimensional whole-mount fluorescence imaging showed that these endocardium-derived vascular tunnels or tubes adopt an arterial fate in neonates. Mechanistically, we implicate Mettl3 (methyltransferase-like protein 3) and Notch signaling in regulating endocardium-derived intramyocardial coronary artery formation. Functionally, these intramyocardial arteries persist into adulthood and play a protective role after myocardial infarction. Conclusions: A subset of intramyocardial coronary arteries form by extension of endocardium-derived vascular tunnels in the neonatal heart.

Fluorescence microscopy typically suffers from aberration induced by system and sample, which could be circumvented by image deconvolution. We proposed a novel, to the best of our knowledge, Richardson–Lucy (RL) model-driven deconvolution framework to improve reconstruction performance and speed. Two kinds of neural networks within this framework were devised, which are partially interpretable compared with previous deep learning methods. We first introduce RL into deep feature space, which has superior generalizability to the convolutional neural networks (CNN). We further accelerate it with an unmatched backprojector, providing a five times faster reconstruction speed than classic RL. Our deconvolution approaches outperform both CNN and traditional methods regarding image quality for blurred images caused by out-of-focus or imaging system aberration.

In traditional fluorescence microscopy, it is hard to achieve a large uniform imaging field with high resolution. In this manuscript, we developed a confocal fluorescence microscope combining the microlens array with spatial light modulator to address this issue. In our system, a multi-spot array generated by a spatial light modulator passes through the microlens array to form an optical probe array. Then multi-spot adaptive pixel-reassignment method for image scanning microscopy (MAPR-ISM) will be introduced in this parallelized imaging to improve spatial resolution. To generate a uniform image, we employ an optimized double weighted Gerchberg–Saxton algorithm (ODWGS) using signal feedback from the camera. We have built a prototype system with a FOV of 3.5 mm × 3.5 mm illuminated by 2500 confocal points. The system provides a lateral resolution of ∼0.82 µm with ∼1.6 times resolution enhancement after ISM processing. And the nonuniformity across the whole imaging field is 3%. Experimental results of fluorescent beads, mouse brain slices and melanoma slices are presented to validate the applicability and effectiveness of our system.

The imaging fidelity of mesoscopic fluorescence molecular tomography (MFMT) in reflective geometry suffers from spatial nonuniformity of measurement sensitivity and ill-posed reconstruction. In this study, we present a spatially adaptive split Bregman network (SSB-Net) to simultaneously overcome the spatial nonuniformity of measurement sensitivity and promote reconstruction sparsity. The SSB-Net is derived by unfolding the split Bregman algorithm. In each layer of the SSB-Net, residual block and 3D convolution neural networks (3D-CNNs) can adaptively learn spatially nonuniform error compensation, the spatially dependent proximal operator, and sparsity transformation. Simulations and experiments show that the proposed SSB-Net enables high-fidelity MFMT reconstruction of multifluorophores at different positions within a depth of a few millimeters. Our method paves the way for a practical reflection-mode diffuse optical imaging technique.

The liver contains a variety of vessels and participates in miscellaneous physiological functions. While past studies generally focused on certain hepatic vessels, we simultaneously obtained all the vessels and cytoarchitectural information of the intact mouse liver lobe at single-cell resolution. Here, taking structural discrepancies of various vessels into account, we reconstruct and visualize the portal vein, hepatic vein, hepatic artery, intrahepatic bile duct, intrahepatic lymph of an intact liver lobe and peribiliary plexus in its selected local areas, providing a technology roadmap for studying the fine hepatic vascular structures and their spatial relationship, which will help research into liver diseases and evaluation of medical efficacies in the future.

Fluorescence in situ hybridization (FISH) is a powerful tool to visualize transcripts in fixed cells and tissues. Despite the recent advances in FISH detection methods, it remains challenging to achieve high-level FISH imaging with a simple workflow. Here, we introduce a method to prepare long single-strand DNA concatemers (lssDNAc) through a controllable rolling-circle amplification (CRCA). Prepared lssDNAcs are used to develop AmpFISH workflows. In addition, we present its applications in different scenarios. AmpFISH shows the following advantages: 1) enhanced FISH signal-to-noise ratio (SNR) up to 160-fold compared with single-molecule FISH; 2) simultaneous detection of FISH signals and fluorescent proteins or immunofluorescence (IF) in tissues; 3) simple workflows; and 4) cost-efficiency. In brief, AmpFISH provides convenient and versatile tools for sensitive RNA/DNA detection and to gain useful information on cellular molecules using simple workflows., Cao et al. present a method to prepare long single-strand DNA concatemers (lssDNAc) through a controllable rolling-circle amplification (CRCA), used to develop AmpFISH workflows. Their method is suitable for labelling to both individual mRNA molecules and chromosomes and can also be multiplexed with immunofluorescence, allowing for sensitive RNA/DNA detection.

Optical visualization of complex microstructures in the entire organ is essential for biomedical research. However, the existing methods fail to accurately acquire the detailed microstructures of whole organs with good morphological and biochemical preservation. This study proposes a cryo-fluorescence micro-optical sectioning tomography (cryo-fMOST) to image whole organs in three dimensions (3D) with submicron resolution. The system comprises a line-illumination microscope module, cryo-microtome, three-stage refrigeration module, and heat insulation device. To demonstrate the imaging capacity and wide applicability of the system, we imaged and reconstructed various organs of mice in 3D, including the healthy tongue, kidney, and brain, as well as the infarcted heart. More importantly, imaged brain slices were performed sugar phosphates determination and fluorescence