Observing single cells in whole organs with optical imaging

Cells are the basic unit of human organs that are not fully understood.The revolutionary advancements of optical imaging alowed us to observe single cells in whole organs,revealing the complicated composition of cells with spatial information.Therefore,in this review,we revisit the principles of optical contrast related to those biomolecules and the optical techniques that transform optical contrast into detectable optical signals.Then,we describe optical imaging to achieve threedimensional spatial discrimination for biological tisutes.Due to the milky appearance of tissues,the spatial information burred deep in the whole organ.Fortunately,strategies developed in the last decade could circumvent this issue and lead us into a new era of investigation of the cells with their original spatial information.

Ultrasensitive and point-of-care detection of plasma phosphorylated tau in Alzheimer’s disease using colorimetric and surface-enhanced Raman scattering dual-readout lateral flow assay

Phosphorylation of tau at Ser(396,404)(p-tau^(396,404))is one of the earliest phosphorylation events,plasma p-tau^(396,404) level appears to be a potentially promising biomarker of Alzheimer’s disease(AD).The low abundance and easy degradation of p-tau in the plasma make the lateral flow assay(LFA)a suitable choice for point-of-care detection of plasma p-tau^(396,404) levels.Herein,based on our screening of a pair of p-tau^(396,404)-specific antibodies,we developed a colorimetric and surface-enhanced Raman scattering(SERS)dual-readout LFA for the rapid,highly sensitive,robust detection of plasma p-tau^(396,404) levels.This LFA realized a detection limit of 60 pg/mL by the naked eye or 3.8 pg/mL by SERS without cross-reacting with other tau species.More importantly,LFA rapidly and accurately differentiated AD patients from healthy controls,suggesting that it has the potential for clinical point-of-care application in AD diagnosis.This dual-readout LFA has the advantages of simple operation,rapid,ultra-sensitive detection,providing a new way for early AD diagnosis and intervention,especially in primary and community AD screening.

Deep self-learning enables fast,high-fidelity isotropic resolution restoration for volumetric fluorescence microscopy

One intrinsic yet critical issue that troubles the field of fluorescence microscopy ever since its introduction is the unmatched resolution in the lateral and axial directions(i.e.,resolution anisotropy),which severely deteriorates the quality,reconstruction,and analysis of 3D volume images.By leveraging the natural anisotropy,we present a deep self-learning method termed Self-Net that significantly improves the resolution of axial images by using the lateral images from the same raw dataset as rational targets.By incorporating unsupervised learning for realistic anisotropic degradation and supervised learning for high-fidelity isotropic recovery,our method can effectively suppress the hallucination with substantially enhanced image quality compared to previously reported methods.In the experiments,we show that Self-Net can reconstruct high-fidelity isotropic 3D images from organelle to tissue levels via raw images from various microscopy platforms,e.g.,wide-field,laser-scanning,or super-resolution microscopy.For the first time,Self-Net enables isotropic whole-brain imaging at a voxel resolution of 0.2×0.2×0.2μm^(3),which addresses the last-mile problem of data quality in single-neuron morphology visualization and reconstruction with minimal effort and cost.Overall,Self-Net is a promising approach to overcoming the inherent resolution anisotropy for all classes of 3D fluorescence microscopy.

Whole-Brain Connectome of GABAergic Neurons in the Mouse Zona Incerta

The zona incerta(ZI)is involved in various functions and may serve as an integrative node of the circuits for global behavioral modulation.However,the long-range connectivity of different sectors in the mouse ZI has not been comprehensively mapped.Here,we obtained whole-brain images of the input and output connections via fluorescence micro-optical sectioning tomography and viral tracing.The principal regions in the input-output circuits of ZI GABAergic neurons were topologically organized.The 3D distribution of cortical inputs showed rostro-caudal correspondence with different ZI sectors,while the projection fibers from ZI sectors were longitudinally organized in the superior colliculus.Clustering results show that the medial and lateral ZI are two different major functional compartments,and they can be further divided into more subdomains based on projection and input connectivity.This study provides a comprehensive anatomical foundation for understanding how the ZI is involved in integrating different information,conveying motivational states,and modulating global behaviors.

Challenges of Processing and Analyzing Big Data in Mesoscopic Whole-brain Imaging

The brain is recognized as the most complex organ on earth.The complex neuronal network in the human brain consists of approximately 86 billion neurons and more than one hundred trillion connections[1].Even the mouse brain,which is commonly used as a model in neuroscience studies,contains more than 71 million neurons.Despite the great efforts made by the neuroscientists,our knowledge about the brain is still limited and studies on small model animals have only provided preliminary results[2–4].To improve the situation,brainsmatics studies try to accelerate neuroscience research by providing a standard whole-brain spatial coordinate system and standardized labeling tools,imaging the brain,as well as discovering new neuronal cell types,neuronal circuits,and brain vascular structures.

完整猕猴脑连续亚微米分辨的三维成像

解剖整个脑回路结构对于揭示神经系统的组织结构逻辑至关重要.然而,在庞大而复杂的灵长类大脑中绘制亚微米分辨率的神经元投影仍然具有挑战性.本文开发了高效的技术方案,以亚微米分辨率绘制整个猕猴大脑的神经元投射.该方案包括一种可长期保存组织结构和荧光信号的新型包埋方法,高分辨率快速全脑光学成像方法和图像后处理方法.利用该方案获取完整猕猴脑的0.32μm×0.32μm×10.00μm体素分辨率数据集展示了单细胞定位、密度、形状等解剖构筑信息.结合病毒标记,追踪了猕猴半脑从前额皮质到丘脑的长距离投射神经纤维,结果显示,额叶皮层的轴突可投射到视皮层、纹状体、丘脑和中脑等多个脑区.该方案为整个猕猴大脑的亚微米级三维细胞构筑和神经元环路研究提供了有效的方法,有助于揭示大脑功能和疾病基础.

Flexible,video-rate,and aberration-compensated axial dual-line scanning imaging with field-of-view jointing and stepped remote focusing

Parallel dual-plane imaging with a large axial interval enables the simultaneous observation of biological structures and activities in different views of interest.However,the inflexibility in adjusting the field-of-view(FOV)positions in three dimensions and optical sectioning effects,as well as the relatively small effective axial range limited by spherical aberration,have hindered the application of parallel dual-plane imaging.Herein,we propose a flexible,video-rate,and defocus-aberration-compensated axial dual-line scanning imaging method.We used a stepped mirror to remotely generate and detect dual axial lines with compensation for spherical aberration and FOV-jointing to rearrange into a head-to-head line for high-speed optical sectioning acquisition.The lateral and axial positions of the two FOVs could be flexibly adjusted before and during imaging,respectively.The method also allows the adjustment of optical sectioning effects according to specific experimental requirements.We experimentally verified the consistent imaging performance over an axial range of 300μm.We demonstrated high throughput by simultaneously imaging Brownian motions in two 250μm×250μm FOVs with axial and lateral intervals of 150μm and 240μm,respectively,at 24.5 Hz.We also showed potential application in functional imaging by simultaneously acquiring neural activities in the optic tectum and hindbrain of a zebrafish brain.The proposed method is,thus,advantageous compared to existing parallel dual-plane imaging and potentially facilitates intravital biological study in large axial range.

Whole-Brain Direct Inputs to and Axonal Projections from Excitatory and Inhibitory Neurons in the Mouse Primary Auditory Area

Neurons in the primary auditory area(AUDp)innervate multiple brain regions with long-range projections while receiving informative inputs for diverse functions.However,the brain-wide connections of these neurons have not been comprehensively investigated.Here,we simultaneously applied virus-based anterograde and retrograde tracing,labeled the connections of excitatory and inhibitory neurons in the mouse AUDp,and acquired whole-brain information using a dual-channel fuorescence micro-optical sectioning tomography system.Quantified results showed that the two types of neurons received inputs with similar patterns but sent heterogeneous projections to downstream regions.In the isocortex,functionally different areas consistently sent feedback-dominated projections to these neurons,with concomitant laterallydominated projections from the sensory and limbic cortices to inhibitory neurons.In subcortical regions,the dorsal and medial parts of the non-lemniscal auditory thalamus(AT)were reciprocally connected to the AUDp,while the ventral part contained the most fibers of passage from the excitatory neurons and barely sent projections back,indicating the regional heterogeneity of the AUDp-AT circuit.Our results reveal details of the whole-brain network and provide new insights for further physiological and functional studies of the AUDp.

Constructing the rodent stereotaxic brain atlas:a survey

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.

Deep learning-based large-scale named entity recognition for anatomical region of mammalian brain

Background:Images of anatomical regions and neuron type distribution,as well as their related literature are valuable assets for neuroscience research.They are vital evidence and vehicles in discovering new phenomena and knowledge refinement through image and text big data.The knowledge acquired from image data generally echoes with the literature accumulated over the years.The knowledge within the literature can provide a comprehensive context for a deeper understanding of the image data.However,it is quite a challenge to manually identify the related literature and summarize the neuroscience knowledge in the large-scale corpus.Thus,neuroscientists are in dire need of an automated method to extract neuroscience knowledge from large-scale literature.Methods:A proposed deep learning model named BioBERT-CRF extracts brain region entities from the WhiteText dataset.This model takes advantage of BioBERT and CRF to predict entity labels while training.Results:The proposed deep learning model demonstrated comparable performance against or even outperforms the previous models on the WhiteText dataset.The BioBERT-CRF model has achieved the best average precision,recall,and F1 score of 81.3%,84.0%,and 82.6%,respectively.We used the BioBERT-CRF model to predict brain region entities in a large-scale PubMed abstract dataset and used a rule-based method to normalize all brain region entities to three neuroscience dictionaries.Conclusions:Our work shows that the BioBERT-CRF model can be well-suited for brain region entity extraction.The rankings of different brain region entities by their appearance in the large-scale corpus indicate the anatomical regions that researchers are most concerned about.