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Cao Z, Zhao Y, Sun H, Sun X, Zhang Y, Zhang S, Wang C, Xiong T, Naeem A, Zhang J, Yin X. Cross-scale tracing of nanoparticles and tumors at the single-cell level using the whole-lung atlas. SCIENCE ADVANCES 2023; 9:eadh7779. [PMID: 37531437 PMCID: PMC10396308 DOI: 10.1126/sciadv.adh7779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Accepted: 06/30/2023] [Indexed: 08/04/2023]
Abstract
Currently, the effectiveness of oncotherapy is limited by tumor heterogeneities, which presents a huge challenge for the development of nanotargeted drug delivery systems (DDSs). Therefore, it is important to resolve the spatiotemporal interactions between tumors and nanoparticles. However, targeting evaluation has been limited by particle visualization due to the gap between whole-organ scale and subcellular precision. Here, a high-precision three-dimensional (3D) visualization of tumor structure based on the micro-optical sectioning tomography (MOST) system and fluorescence MOST (fMOST) system is presented to clarify 3D spatial distribution of nanoparticles within the tumor. We demonstrate that through the MOST/fMOST system, it is possible to reveal multidimensional and cross-scale correlations between the tumor structure and nanoparticle distribution to remodel the tumor microenvironment and explore the structural parameters of vasculature. This visualization methodology provides an accurate assessment of the efficacy, distribution, and targeting efficiency of DDSs for oncotherapy compared to available approaches.
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Affiliation(s)
- Zeying Cao
- Center for Drug Delivery System, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yanli Zhao
- Lingang Laboratory, Shanghai 201602, China
| | - Hongyu Sun
- Center for Drug Delivery System, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Xian Sun
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201210, China
| | - Yu Zhang
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201210, China
| | - Shuo Zhang
- Lingang Laboratory, Shanghai 201602, China
| | - Caifen Wang
- Center for Drug Delivery System, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Ting Xiong
- Center for Drug Delivery System, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- College of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China
| | - Abid Naeem
- Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China
| | - Jiwen Zhang
- Center for Drug Delivery System, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- University of Chinese Academy of Sciences, Beijing 100049, China
- College of Pharmacy, Shenyang Pharmaceutical University, Shenyang 110016, China
- Key Laboratory of Modern Preparation of TCM, Ministry of Education, Jiangxi University of Traditional Chinese Medicine, Nanchang, 330004, China
- NMPA Key Laboratory for Quality Research and Evaluation of Pharmaceutical Excipients, National Institutes for Food and Drug Control, Beijing 100050, China
| | - Xianzhen Yin
- Lingang Laboratory, Shanghai 201602, China
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201210, China
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Sarmah M, Neelima A, Singh HR. Survey of methods and principles in three-dimensional reconstruction from two-dimensional medical images. Vis Comput Ind Biomed Art 2023; 6:15. [PMID: 37495817 PMCID: PMC10371974 DOI: 10.1186/s42492-023-00142-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 06/27/2023] [Indexed: 07/28/2023] Open
Abstract
Three-dimensional (3D) reconstruction of human organs has gained attention in recent years due to advances in the Internet and graphics processing units. In the coming years, most patient care will shift toward this new paradigm. However, development of fast and accurate 3D models from medical images or a set of medical scans remains a daunting task due to the number of pre-processing steps involved, most of which are dependent on human expertise. In this review, a survey of pre-processing steps was conducted, and reconstruction techniques for several organs in medical diagnosis were studied. Various methods and principles related to 3D reconstruction were highlighted. The usefulness of 3D reconstruction of organs in medical diagnosis was also highlighted.
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Affiliation(s)
- Mriganka Sarmah
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India.
| | - Arambam Neelima
- Department of Computer Science and Engineering, National Institute of Technology, Nagaland, 797103, India
| | - Heisnam Rohen Singh
- Department of Information Technology, Nagaland University, Nagaland, 797112, India
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Bisphenol-A (BPA) Impairs Hippocampal Neurogenesis via Inhibiting Regulation of the Ubiquitin Proteasomal System. Mol Neurobiol 2023; 60:3277-3298. [PMID: 36828952 DOI: 10.1007/s12035-023-03249-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 01/24/2023] [Indexed: 02/26/2023]
Abstract
The ubiquitin-proteasome system (UPS) controls protein homeostasis to maintain cell functionality and survival. Neurogenesis relies on proteasome function, and a defective proteasome system during brain development leads to neurological disorders. An endocrine-disrupting xenoestrogen bisphenol-A (BPA) used in plastic products adversely affects human health and causes neurotoxicity. Previously, we reported that BPA reduces neural stem cells (NSCs) proliferation and differentiation, impairs myelination and mitochondrial protein import, and causes excessive mitochondrial fragmentation leading to cognitive impairments in rats. Herein, we examined the effect(s) of prenatal BPA exposure on UPS functions during NSCs proliferation and differentiation in the hippocampus. Rats were orally treated with 40 µg/kg body weight BPA during day 6 gestation to day 21 postnatal. BPA significantly reduced proteasome activity in a cellular extract of NSCs. Immunocytochemistry exhibited a significant reduction of 20S proteasome/Nestin+ and PSMB5/Nestin+ cells in NSCs culture. BPA decreased 20S/Tuj1+ and PSMB5/Tuj1+ cells, indicating disrupted UPS during neuronal differentiation. BPA reduced the expression of UPS genes, 20S, and PSMB5 protein levels and proteasome activity in the hippocampus. It significantly reduced overall protein synthesis by the loss of Nissl substances in the hippocampus. Pharmacological activation of UPS by a bioactive triterpenoid 18α-glycyrrhetinic acid (18α GA) caused increased proteasome activities, significantly increased neurosphere size and number, and enhanced NSCs proliferation in BPA exposed culture, while proteasome inhibition by MG132 further aggravates BPA-mediated effects. In silico studies demonstrated that BPA strongly binds to catalytic sites of UPS genes (PSMB5, TRIM11, Parkin, and PSMD4) which may result in UPS inactivation. These results suggest that BPA significantly reduces NSCs proliferation by impairing UPS, and UPS activation by 18α GA could suppress BPA-mediated neurotoxicity and exerts neuroprotection.
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Ajayi AM, Melete J, Ben-Azu B, Umukoro S. Aggressive-like behaviour and neurocognitive impairment in alcohol herbal mixture-fed mice are associated with increased neuroinflammation and neuronal apoptosis in the prefrontal cortex. J Biochem Mol Toxicol 2023; 37:e23252. [PMID: 36281499 DOI: 10.1002/jbt.23252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 09/21/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022]
Abstract
Alcohol-induced aggression and related violence is a serious and common social problem globally. Alcohol use is increasingly found in the form of alcoholic herbal mixtures (AHM) with indiscriminate and unregulated alcohol content. This study investigated the effects of AHM on aggressive-like, neurocognitive impairment and brain biochemical alteration in mice. Thirty-two male resident mice were paired housed with female mice for 21 days in four groups (n = 8). Resident mice were treated orally with normal saline, AHM, ethanol and AHM + ethanol daily for 14 days. Aggressive-like behaviour was scored based on the latency and frequency of attacks by the resident mouse on the intruder. Neurocognitive impairment was determined using the Y-maze test (YMT) and novel object recognition test (NORT). Acetylcholinesterase, glutamic acid decarboxylase (GAD), pro-inflammatory and oxidative stress parameters were determined in the prefrontal cortex (PFC). Neuronal morphology, cytochrome c (Cyt-c) and nuclear factor-kappa B (NF-ĸB) expressions were determined. AHM and in combination with ethanol showed an increased index of aggression typified by frequency of attack and reduced latency to attack when compared to normal saline-treated animals. Co-administration of AHM and ethanol significantly reduced cognitive correct alternation (%) and discrimination index in the YMT and NORT, respectively. AHM and ethanol increased acetylcholinesterase, Pro-inflammatory cytokines and oxidative stress parameters while they reduced GAD. There were significantly reduced neuronal counts and increased expression of Cyt-c and NF-ĸB, respectively Alcoholic herbal mixture increased aggressiveness and caused neurocognitive impairment via increased oxido-inflammatory stress in the prefrontal cortex.
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Affiliation(s)
- Abayomi M Ajayi
- Neuropharmacology Unit, Department of Pharmacology and Therapeutics, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - John Melete
- Neuropharmacology Unit, Department of Pharmacology and Therapeutics, College of Medicine, University of Ibadan, Ibadan, Nigeria
| | - Benneth Ben-Azu
- Neuropharmacology Unit, Department of Pharmacology and Therapeutics, College of Medicine, University of Ibadan, Ibadan, Nigeria
- Department of Pharmacology, Faculty of Basic Medical Sciences, College of Health Sciences, Delta State University, Abraka, Nigeria
| | - Solomon Umukoro
- Neuropharmacology Unit, Department of Pharmacology and Therapeutics, College of Medicine, University of Ibadan, Ibadan, Nigeria
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5
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Liu Z, Zhu Y, Zhang L, Jiang W, Liu Y, Tang Q, Cai X, Li J, Wang L, Tao C, Yin X, Li X, Hou S, Jiang D, Liu K, Zhou X, Zhang H, Liu M, Fan C, Tian Y. Structural and functional imaging of brains. Sci China Chem 2022; 66:324-366. [PMID: 36536633 PMCID: PMC9753096 DOI: 10.1007/s11426-022-1408-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 09/28/2022] [Indexed: 12/23/2022]
Abstract
Analyzing the complex structures and functions of brain is the key issue to understanding the physiological and pathological processes. Although neuronal morphology and local distribution of neurons/blood vessels in the brain have been known, the subcellular structures of cells remain challenging, especially in the live brain. In addition, the complicated brain functions involve numerous functional molecules, but the concentrations, distributions and interactions of these molecules in the brain are still poorly understood. In this review, frontier techniques available for multiscale structure imaging from organelles to the whole brain are first overviewed, including magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), serial-section electron microscopy (ssEM), light microscopy (LM) and synchrotron-based X-ray microscopy (XRM). Specially, XRM for three-dimensional (3D) imaging of large-scale brain tissue with high resolution and fast imaging speed is highlighted. Additionally, the development of elegant methods for acquisition of brain functions from electrical/chemical signals in the brain is outlined. In particular, the new electrophysiology technologies for neural recordings at the single-neuron level and in the brain are also summarized. We also focus on the construction of electrochemical probes based on dual-recognition strategy and surface/interface chemistry for determination of chemical species in the brain with high selectivity and long-term stability, as well as electrochemophysiological microarray for simultaneously recording of electrochemical and electrophysiological signals in the brain. Moreover, the recent development of brain MRI probes with high contrast-to-noise ratio (CNR) and sensitivity based on hyperpolarized techniques and multi-nuclear chemistry is introduced. Furthermore, multiple optical probes and instruments, especially the optophysiological Raman probes and fiber Raman photometry, for imaging and biosensing in live brain are emphasized. Finally, a brief perspective on existing challenges and further research development is provided.
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Affiliation(s)
- Zhichao Liu
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241 China
| | - Ying Zhu
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Liming Zhang
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241 China
| | - Weiping Jiang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Chinese Academy of Sciences, Wuhan National Laboratory for Optoelectronics, Wuhan, 430071 China
| | - Yawei Liu
- State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022 China
| | - Qiaowei Tang
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Xiaoqing Cai
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Jiang Li
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Lihua Wang
- Interdisciplinary Research Center, Shanghai Synchrotron Radiation Facility, Zhangjiang Laboratory, Shanghai Advanced Research Institute, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, 201210 China
| | - Changlu Tao
- Interdisciplinary Center for Brain Information, Brain Cognition and Brain Disease Institute, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Faculty of Life and Health Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055 China
| | | | - Xiaowei Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Shangguo Hou
- Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen, 518055 China
| | - Dawei Jiang
- Department of Nuclear Medicine, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430022 China
| | - Kai Liu
- Department of Chemistry, Tsinghua University, Beijing, 100084 China
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Chinese Academy of Sciences, Wuhan National Laboratory for Optoelectronics, Wuhan, 430071 China
| | - Hongjie Zhang
- State Key Laboratory of Rare Earth Resource Utilization, Changchun Institute of Applied Chemistry, Chinese Academy of Sciences, Changchun, 130022 China
- Department of Chemistry, Tsinghua University, Beijing, 100084 China
| | - Maili Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Chinese Academy of Sciences, Wuhan National Laboratory for Optoelectronics, Wuhan, 430071 China
| | - Chunhai Fan
- School of Chemistry and Chemical Engineering, Frontiers Science Center for Transformative Molecules, Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, 200240 China
| | - Yang Tian
- Shanghai Key Laboratory of Green Chemistry and Chemical Processes, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai, 200241 China
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6
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Zhou W, Ke S, Li W, Yuan J, Li X, Jin R, Jia X, Jiang T, Dai Z, He G, Fang Z, Shi L, Zhang Q, Gong H, Luo Q, Sun W, Li A, Li P. Mapping the Function of Whole-Brain Projection at the Single Neuron Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2202553. [PMID: 36228099 PMCID: PMC9685445 DOI: 10.1002/advs.202202553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/18/2022] [Indexed: 06/16/2023]
Abstract
Axonal projection conveys neural information. The divergent and diverse projections of individual neurons imply the complexity of information flow. It is necessary to investigate the relationship between the projection and functional information at the single neuron level for understanding the rules of neural circuit assembly, but a gap remains due to a lack of methods to map the function to whole-brain projection. Here an approach is developed to bridge two-photon calcium imaging in vivo with high-resolution whole-brain imaging based on sparse labeling with the genetically encoded calcium indicator GCaMP6. Reliable whole-brain projections are captured by the high-definition fluorescent micro-optical sectioning tomography (HD-fMOST). A cross-modality cell matching is performed and the functional annotation of whole-brain projection at the single-neuron level (FAWPS) is obtained. Applying it to the layer 2/3 (L2/3) neurons in mouse visual cortex, the relationship is investigated between functional preferences and axonal projection features. The functional preference of projection motifs and the correlation between axonal length in MOs and neuronal orientation selectivity, suggest that projection motif-defined neurons form a functionally specific information flow, and the projection strength in specific targets relates to the information clarity. This pipeline provides a new way to understand the principle of neuronal information transmission.
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Affiliation(s)
- Wei Zhou
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Shanshan Ke
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Wenwei Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Jing Yuan
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Xiangning Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Rui Jin
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Xueyan Jia
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Tao Jiang
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Zimin Dai
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Guannan He
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Zhiwei Fang
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Liang Shi
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Qi Zhang
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
| | - Hui Gong
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Qingming Luo
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical EngineeringHainan UniversityHaikou570228China
| | - Wenzhi Sun
- Chinese Institute for Brain ResearchBeijing102206China
- School of Basic Medical SciencesCapital Medical UniversityBeijing100069China
| | - Anan Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Pengcheng Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
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7
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Yin X, Zhang X, Zhang J, Yang W, Sun X, Zhang H, Gao Z, Jiang H. High-Resolution Digital Panorama of Multiple Structures in Whole Brain of Alzheimer's Disease Mice. Front Neurosci 2022; 16:870520. [PMID: 35516801 PMCID: PMC9067162 DOI: 10.3389/fnins.2022.870520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2022] [Accepted: 03/11/2022] [Indexed: 11/24/2022] Open
Abstract
Simultaneously visualizing Amyloid-β (Aβ) plaque with its surrounding brain structures at the subcellular level in the intact brain is essential for understanding the complex pathology of Alzheimer's disease, but is still rarely achieved due to the technical limitations. Combining the micro-optical sectioning tomography (MOST) system, whole-brain Nissl staining, and customized image processing workflow, we generated a whole-brain panorama of Alzheimer's disease mice without specific labeling. The workflow employed the steps that include virtual channel splitting, feature enhancement, iso-surface rendering, direct volume rendering, and feature fusion to extract and reconstruct the different signals with distinct gray values and morphologies. Taking advantage of this workflow, we found that the denser-distribution areas of Aβ plaques appeared with relatively more somata and smaller vessels, but show a dissimilar distributing pattern with nerve tracts. In addition, the entorhinal cortex and adjacent subiculum regions present the highest density and biggest diameter of plaques. The neuronal processes in the vicinity of these Aβ plaques showed significant structural alternation such as bending or abrupt branch ending. The capillaries inside or adjacent to the plaques were observed with abundant distorted micro-vessels and abrupt ending. Depicting Aβ plaques, somata, nerve processes and tracts, and blood vessels simultaneously, this panorama enables us for the first time, to analyze how the Aβ plaques interact with capillaries, somata, and processes at a submicron resolution of 3D whole-brain scale, which reveals potential pathological effects of Aβ plaques from a new cross-scale view. Our approach opens a door to routine systematic studies of complex interactions among brain components in mouse models of Alzheimer's disease.
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Affiliation(s)
- Xianzhen Yin
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Lingang Laboratory, Shanghai, China
- *Correspondence: Xianzhen Yin
| | - Xiaochuan Zhang
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Jingjing Zhang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Weicheng Yang
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
| | - Xian Sun
- Center for MOST and Image Fusion Analysis, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
| | - Haiyan Zhang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Haiyan Zhang
| | - Zhaobing Gao
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Zhongshan Institute of Drug Discovery, Institution for Drug Discovery Innovation, Chinese Academy of Science, Zhongshan, China
- Zhaobing Gao
| | - Hualiang Jiang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, China
- Lingang Laboratory, Shanghai, China
- School of Chinese Materia Medica, Nanjing University of Chinese Medicine, Nanjing, China
- School of Life Science and Technology, Shanghai Institute for Advanced Immunochemical Studies, ShanghaiTech University, Shanghai, China
- Hualiang Jiang
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8
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Wei Z, Wu X, Tong W, Zhang S, Yang X, Tian J, Hui H. Elimination of stripe artifacts in light sheet fluorescence microscopy using an attention-based residual neural network. BIOMEDICAL OPTICS EXPRESS 2022; 13:1292-1311. [PMID: 35414974 PMCID: PMC8973169 DOI: 10.1364/boe.448838] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 01/15/2022] [Accepted: 01/28/2022] [Indexed: 06/14/2023]
Abstract
Stripe artifacts can deteriorate the quality of light sheet fluorescence microscopy (LSFM) images. Owing to the inhomogeneous, high-absorption, or scattering objects located in the excitation light path, stripe artifacts are generated in LSFM images in various directions and types, such as horizontal, anisotropic, or multidirectional anisotropic. These artifacts severely degrade the quality of LSFM images. To address this issue, we proposed a new deep-learning-based approach for the elimination of stripe artifacts. This method utilizes an encoder-decoder structure of UNet integrated with residual blocks and attention modules between successive convolutional layers. Our attention module was implemented in the residual blocks to learn useful features and suppress the residual features. The proposed network was trained and validated by generating three different degradation datasets with different types of stripe artifacts in LSFM images. Our method can effectively remove different stripes in generated and actual LSFM images distorted by stripe artifacts. Besides, quantitative analysis and extensive comparison results demonstrated that our method performs the best compared with classical image-based processing algorithms and other powerful deep-learning-based destriping methods for all three generated datasets. Thus, our method has tremendous application prospects to LSFM, and its use can be easily extended to images reconstructed by other modalities affected by the presence of stripe artifacts.
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Affiliation(s)
- Zechen Wei
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xiangjun Wu
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, School of Medicine and Engineering, Beihang University, Beijing 100083, China
| | - Wei Tong
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing 100853, China
| | - Suhui Zhang
- Senior Department of Cardiology, the Sixth Medical Center of PLA General Hospital, Beijing 100853, China
| | - Xin Yang
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Jie Tian
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- Zhuhai Precision Medical Center, Zhuhai People's Hospital, affiliated with Jinan University, Zhuhai 519000, China
| | - Hui Hui
- CAS Key Laboratory of Molecular Imaging, The State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Beijing 100190, China
- Beijing Key Laboratory of Molecular Imaging, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
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9
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Feng Q, An S, Wang R, Lin R, Li A, Gong H, Luo M. Whole-Brain Reconstruction of Neurons in the Ventral Pallidum Reveals Diverse Projection Patterns. Front Neuroanat 2022; 15:801354. [PMID: 34975422 PMCID: PMC8716739 DOI: 10.3389/fnana.2021.801354] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/22/2021] [Indexed: 11/15/2022] Open
Abstract
The ventral pallidum (VP) integrates reward signals to regulate cognitive, emotional, and motor processes associated with motivational salience. Previous studies have revealed that the VP projects axons to many cortical and subcortical structures. However, descriptions of the neuronal morphologies and projection patterns of the VP neurons at the single neuron level are lacking, thus hindering the understanding of the wiring diagram of the VP. In this study, we used recently developed progress in robust sparse labeling and fluorescence micro-optical sectioning tomography imaging system (fMOST) to label mediodorsal thalamus-projecting neurons in the VP and obtain high-resolution whole-brain imaging data. Based on these data, we reconstructed VP neurons and classified them into three types according to their fiber projection patterns. We systematically compared the axonal density in various downstream centers and analyzed the soma distribution and dendritic morphologies of the various subtypes at the single neuron level. Our study thus provides a detailed characterization of the morphological features of VP neurons, laying a foundation for exploring the neural circuit organization underlying the important behavioral functions of VP.
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Affiliation(s)
- Qiru Feng
- School of Life Science, Tsinghua University, Beijing, China.,Peking University - Tsinghua University-National Institute Biological Science (PTN) Joint Graduate Program, School of Life Science, Tsinghua University, Beijing, China.,National Institute of Biological Science, Beijing, China
| | - Sile An
- Wuhan National Laboratory for Optoelectronics, Ministry of Education Key Laboratory for Biomedical Photonics, Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
| | - Ruiyu Wang
- National Institute of Biological Science, Beijing, China.,School of Life Science, Peking University, Beijing, China
| | - Rui Lin
- National Institute of Biological Science, Beijing, China
| | - Anan Li
- Wuhan National Laboratory for Optoelectronics, Ministry of Education Key Laboratory for Biomedical Photonics, Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China.,Huazhong University of Science and Technology (HUST)-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute (JITRI), Suzhou, China
| | - Hui Gong
- Wuhan National Laboratory for Optoelectronics, Ministry of Education Key Laboratory for Biomedical Photonics, Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China.,Huazhong University of Science and Technology (HUST)-Suzhou Institute for Brainsmatics, Jiangsu Industrial Technology Research Institute (JITRI), Suzhou, China
| | - Minmin Luo
- National Institute of Biological Science, Beijing, China.,Tsinghua Institute of Multidisciplinary Biomedical Research, Beijing, China.,Chinese Institute for Brain Research, Beijing, China
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10
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Ricci P, Gavryusev V, Müllenbroich C, Turrini L, de Vito G, Silvestri L, Sancataldo G, Pavone FS. Removing striping artifacts in light-sheet fluorescence microscopy: a review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 168:52-65. [PMID: 34274370 DOI: 10.1016/j.pbiomolbio.2021.07.003] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/21/2021] [Accepted: 07/12/2021] [Indexed: 11/24/2022]
Abstract
In recent years, light-sheet fluorescence microscopy (LSFM) has found a broad application for imaging of diverse biological samples, ranging from sub-cellular structures to whole animals, both in-vivo and ex-vivo, owing to its many advantages relative to point-scanning methods. By providing the selective illumination of sample single planes, LSFM achieves an intrinsic optical sectioning and direct 2D image acquisition, with low out-of-focus fluorescence background, sample photo-damage and photo-bleaching. On the other hand, such an illumination scheme is prone to light absorption or scattering effects, which lead to uneven illumination and striping artifacts in the images, oriented along the light sheet propagation direction. Several methods have been developed to address this issue, ranging from fully optical solutions to entirely digital post-processing approaches. In this work, we present them, outlining their advantages, performance and limitations.
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Affiliation(s)
- Pietro Ricci
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, 50019, Italy; University of Florence, Department of Physics and Astronomy, Sesto Fiorentino, 50019, Italy
| | - Vladislav Gavryusev
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, 50019, Italy; University of Florence, Department of Physics and Astronomy, Sesto Fiorentino, 50019, Italy
| | | | - Lapo Turrini
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, 50019, Italy; University of Florence, Department of Physics and Astronomy, Sesto Fiorentino, 50019, Italy
| | - Giuseppe de Vito
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, 50019, Italy; University of Florence, Department of Neuroscience, Psychology, Drug Research and Child Health, Florence, 50139, Italy
| | - Ludovico Silvestri
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, 50019, Italy; University of Florence, Department of Physics and Astronomy, Sesto Fiorentino, 50019, Italy; National Institute of Optics, National Research Council, Sesto Fiorentino, 50019, Italy
| | - Giuseppe Sancataldo
- University of Palermo, Department of Physics and Chemistry, Palermo, 90128, Italy.
| | - Francesco Saverio Pavone
- European Laboratory for Non-Linear Spectroscopy, Sesto Fiorentino, 50019, Italy; University of Florence, Department of Physics and Astronomy, Sesto Fiorentino, 50019, Italy; National Institute of Optics, National Research Council, Sesto Fiorentino, 50019, Italy.
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11
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Constructing the rodent stereotaxic brain atlas: a survey. SCIENCE CHINA-LIFE SCIENCES 2021; 65:93-106. [PMID: 33860452 DOI: 10.1007/s11427-020-1911-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2020] [Accepted: 02/03/2021] [Indexed: 12/22/2022]
Abstract
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.
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12
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Sun X, Zhang X, Ren X, Sun H, Wu L, Wang C, Ye X, York P, Gao Z, Jiang H, Zhang J, Yin X. Multiscale Co-reconstruction of Lung Architectures and Inhalable Materials Spatial Distribution. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2021; 8:2003941. [PMID: 33898181 PMCID: PMC8061354 DOI: 10.1002/advs.202003941] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2020] [Revised: 12/09/2020] [Indexed: 06/12/2023]
Abstract
The effective pulmonary deposition of inhaled particulate carriers loaded with drugs is a prerequisite for therapeutic effects of drug delivery via inhalation route. Revealing the sophisticated lung scaffold and intrapulmonary distribution of particles at three-dimensional (3D), in-situ, and single-particle level remains a fundamental and critical challenge for dry powder inhalation in pre-clinical research. Here, taking advantage of the micro optical sectioning tomography system, the high-precision cross-scale visualization of entire lung anatomy is obtained. Then, co-localized lung-wide datasets of both cyto-architectures and fluorescent particles are collected at full scale with the resolution down to individual particles. The precise spatial distribution pattern reveals the region-specific distribution and structure-associated deposition of the inhalable particles in lungs, which is undetected by previous methods. Overall, this research delivers comprehensive and high-resolution 3D detection of pulmonary drug delivery vectors and provides a novel strategy to evaluate materials distribution for drug delivery.
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Affiliation(s)
- Xian Sun
- Center for MOST and Image Fusion AnalysisShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
- Center for Drug Delivery SystemsShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
- University of Chinese Academy of SciencesBeijing100049China
| | - Xiaochuan Zhang
- School of PharmacyEast China University of Science and TechnologyShanghai200237China
- CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201203China
| | - Xiaohong Ren
- Center for Drug Delivery SystemsShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
| | - Hongyu Sun
- Center for Drug Delivery SystemsShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
| | - Li Wu
- Center for Drug Delivery SystemsShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
| | - Caifen Wang
- Center for Drug Delivery SystemsShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
| | - Xiaohui Ye
- School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
- Shanghai Institute for Advanced Immunochemical StudiesSchool of Life Science and TechnologyShanghaiTech UniversityShanghai200031China
| | - Peter York
- School of PharmacyUniversity of BradfordBradfordBD71DPUK
| | - Zhaobing Gao
- CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201203China
| | - Hualiang Jiang
- School of PharmacyEast China University of Science and TechnologyShanghai200237China
- CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201203China
- School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefei230027China
- Shanghai Institute for Advanced Immunochemical StudiesSchool of Life Science and TechnologyShanghaiTech UniversityShanghai200031China
| | - Jiwen Zhang
- Center for Drug Delivery SystemsShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
- University of Chinese Academy of SciencesBeijing100049China
- NMPA Key Laboratory for Quality Research and Evaluation of Pharmaceutical ExcipientsNational Institutes for Food and Drug ControlBeijing100050China
| | - Xianzhen Yin
- Center for MOST and Image Fusion AnalysisShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201210China
- CAS Key Laboratory of Receptor ResearchShanghai Institute of Materia MedicaChinese Academy of SciencesShanghai201203China
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13
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Pollatou A. An automated method for removal of striping artifacts in fluorescent whole-slide microscopy. J Neurosci Methods 2020; 341:108781. [DOI: 10.1016/j.jneumeth.2020.108781] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2019] [Revised: 03/31/2020] [Accepted: 05/11/2020] [Indexed: 11/25/2022]
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14
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Anatomically revealed morphological patterns of pyramidal neurons in layer 5 of the motor cortex. Sci Rep 2020; 10:7916. [PMID: 32405029 PMCID: PMC7220918 DOI: 10.1038/s41598-020-64665-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Accepted: 04/17/2020] [Indexed: 11/24/2022] Open
Abstract
Neuronal cell types are essential to the comprehensive understanding of the neuronal function and neuron can be categorized by their anatomical property. However, complete morphology data for neurons with a whole brain projection, for example the pyramidal neurons in the cortex, are sparse because it is difficult to trace the neuronal fibers across the whole brain and acquire the neuron morphology at the single axon resolution. Thus the cell types of pyramidal neurons have yet to be studied at the single axon resolution thoroughly. In this work, we acquire images for a Thy1 H-line mouse brain using a fluorescence micro-optical sectioning tomography system. Then we sample 42 pyramidal neurons whose somata are in the layer 5 of the motor cortex and reconstruct their morphology across the whole brain. Based on the reconstructed neuronal anatomy, we analyze the axonal and dendritic fibers of the neurons in addition to the soma spatial distributions, and identify two axonal projection pattern of pyramidal tract neurons and two dendritic spreading patterns of intratelencephalic neurons. The raw image data are available upon request as an additional asset to the community. The morphological patterns identified in this work can be a typical representation of neuron subtypes and reveal the possible input-output function of a single pyramidal neuron.
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15
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Long B, Jiang T, Zhang J, Chen S, Jia X, Xu X, Luo Q, Gong H, Li A, Li X. Mapping the Architecture of Ferret Brains at Single-Cell Resolution. Front Neurosci 2020; 14:322. [PMID: 32351352 PMCID: PMC7174703 DOI: 10.3389/fnins.2020.00322] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 03/19/2020] [Indexed: 12/11/2022] Open
Abstract
Mapping the cytoarchitecture of the whole brain can reveal the organizational logic of neural systems. However, this remains a significant challenge, especially for gyrencephalic brains with a large volume. Here we propose an integrated pipeline for generating a cytoarchitectonic atlas with single-cell resolution of the whole brain. To analyze a large-volume brain, we used a modified en-bloc Nissl staining protocol to achieve uniform staining of large-scale brain specimens from ferret (Mustela putorius furo). By combining whole-brain imaging and big data processing, we established strategies for parsing cytoarchitectural information at a voxel resolution of 0.33 μm × 0.33 μm × 1 μm and terabyte-scale data analysis. Using the cytoarchitectonic datasets for adult ferret brain, we identified giant pyramidal neurons in ferret brains and provide the first report of their morphological diversity, neurochemical phenotype, and distribution patterns in the whole brain in three dimensions. This pipeline will facilitate studies on the organization and development of the mammalian brains, from that of rodents to the gyrencephalic brains of ferret and even primates.
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Affiliation(s)
- Ben Long
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Jiang
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Jianmin Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Siqi Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xueyan Jia
- HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Xiaofeng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
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16
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A Robust Image Registration Interface for Large Volume Brain Atlas. Sci Rep 2020; 10:2139. [PMID: 32034219 PMCID: PMC7005806 DOI: 10.1038/s41598-020-59042-y] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2019] [Accepted: 01/23/2020] [Indexed: 11/12/2022] Open
Abstract
Accurately mapping brain structures in three-dimensions is critical for an in-depth understanding of brain functions. Using the brain atlas as a hub, mapping detected datasets into a standard brain space enables efficient use of various datasets. However, because of the heterogeneous and nonuniform brain structure characteristics at the cellular level introduced by recently developed high-resolution whole-brain microscopy techniques, it is difficult to apply a single standard to robust registration of various large-volume datasets. In this study, we propose a robust Brain Spatial Mapping Interface (BrainsMapi) to address the registration of large-volume datasets by introducing extracted anatomically invariant regional features and a large-volume data transformation method. By performing validation on model data and biological images, BrainsMapi achieves accurate registration on intramodal, individual, and multimodality datasets and can also complete the registration of large-volume datasets (approximately 20 TB) within 1 day. In addition, it can register and integrate unregistered vectorized datasets into a common brain space. BrainsMapi will facilitate the comparison, reuse and integration of a variety of brain datasets.
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17
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Zhang Y, Xiao Z, He Z, Chen J, Wang X, Jiang L. Dendritic complexity change in the triple transgenic mouse model of Alzheimer's disease. PeerJ 2020; 8:e8178. [PMID: 31942251 PMCID: PMC6955100 DOI: 10.7717/peerj.8178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 11/08/2019] [Indexed: 11/20/2022] Open
Abstract
Alzheimer’s disease (AD) is an irreversible, neurodegenerative disease that is characterized by memory impairment and executive dysfunction. However, the change of fine structure of neuronal morphology remains unclear in the AD model mouse. In this study, high-resolution mouse brain sectional images were scanned by Micro-Optical Sectioning Tomography (MOST) technology and reconstructed three-dimensionally to obtain the pyramidal neurons. The method of Sholl analysis was performed to analyze the neurons in the brains of 6- and 12-month-old AD mice. The results showed that dendritic complexity was not affected in the entorhinal cortex between 6-month-old mice and 12-month-old mice. The dendritic complexity had increased in the primary motor cortex and CA1 region of hippocampus of 12- month-old mice compared with 6-month-old mice. On the contrary, dendritic complexity in the prefrontal cortex was decreased significantly between 6-month-old and 12-month-old mice. To our knowledge, this is the first study to provide high-resolution brain images of triple transgenic AD mice for statistically analyzing neuronal dendrite complexity by MOST technology to reveal the morphological changes of neurons during AD progression.
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Affiliation(s)
- Yu Zhang
- Shenzhen University, Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen, China
| | - Zhenlong Xiao
- Harbin Institute of Technology (Shenzhen), Department of Mechanical and Automation Engineering, Shenzhen, China
| | - Zhijun He
- Shenzhen University, Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen, China
| | - Junyu Chen
- Shenzhen University, Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen, China
| | - Xin Wang
- Harbin Institute of Technology (Shenzhen), Department of Mechanical and Automation Engineering, Shenzhen, China
| | - Liang Jiang
- Shenzhen University, Shenzhen Key Laboratory of Marine Bioresources and Ecology, Brain Disease and Big Data Research Institute, College of Life Sciences and Oceanography, Shenzhen, China
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18
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Zhang Y, Jiang S, Xu Z, Gong H, Li A, Luo Q, Ren M, Li X, Wu H, Yuan J, Chen S. Pinpointing Morphology and Projection of Excitatory Neurons in Mouse Visual Cortex. Front Neurosci 2019; 13:912. [PMID: 31555081 PMCID: PMC6727359 DOI: 10.3389/fnins.2019.00912] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 08/16/2019] [Indexed: 01/10/2023] Open
Abstract
The excitatory neurons in the visual cortex are of great significance for us in understanding brain functions. However, the diverse neuron types and their morphological properties have not been fully deciphered. In this paper, we applied the brain-wide positioning system (BPS) to image the entire brain of two Thy1-eYFP H-line male mice at 0.2 μm × 0.2 μm × 1 μm voxel resolution. A total of 103 neurons were reconstructed in layers 5 and 6 of the visual cortex with single-axon-level resolution. Based on the complete topology of neurons and the inherent positioning function of the imaging method, we classified the observed neurons into six types according to their apical dendrites and somata location: star pyramidal cells in layer 5 (L5-sp), slender-tufted pyramidal cells in layer 5 (L5-st), tufted pyramidal cells in layer 5 (L5-tt), spiny stellate-like cells in layer 6 (L6-ss), star pyramidal cells in layer 6 (L6-sp), and slender-tufted pyramidal cells in layer 6 (L6-st). By examining the axonal projection patterns of individual neurons, they can be categorized into three modes: ipsilateral circuit connection neurons, callosal projection neurons and corticofugal projection neurons. Correlating the two types of classifications, we have found that there are at least two projection modes comprised in the former defined neuron types except for L5-tt. On the other hand, each projection mode may consist of four dendritic types defined in this study. The axon projection mode only partially correlates with the apical dendrite feature. This work has demonstrated a paradigm for resolving the visual cortex through single-neuron-level quantification and has shown potential to be extended to reveal the connectome of other defined sensory and motor systems.
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Affiliation(s)
- Yalun Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Siqi Jiang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Zhengchao Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Miao Ren
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hao Wu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
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19
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Zhang X, Yin X, Zhang J, Li A, Gong H, Luo Q, Zhang H, Gao Z, Jiang H. High-resolution mapping of brain vasculature and its impairment in the hippocampus of Alzheimer's disease mice. Natl Sci Rev 2019; 6:1223-1238. [PMID: 34692000 PMCID: PMC8291402 DOI: 10.1093/nsr/nwz124] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Revised: 07/31/2019] [Accepted: 08/05/2019] [Indexed: 01/24/2023] Open
Abstract
Accumulating evidence indicates the critical importance of cerebrovascular dysfunction in the pathogenesis of Alzheimer's disease (AD). However, systematic comparative studies on the precise brain vasculature of wild-type and AD model mice are still rare. Using an image-optimization method for analysing Micro-Optical Sectioning Tomography (MOST) data, we generated cross-scale whole-brain 3D atlases that cover the entire vascular system from large vessels down to smallest capillaries at submicron resolution, for both wild-type mice and a transgenic (APP/PS1) mouse model of AD. In addition to distinct vascular patterns in different brain regions, we found that the main vessels of the molecular layer of the hippocampal dentate gyrus (DG-ml) undergo abrupt changes in both diameter and branch angle, spreading a unique comb-like pattern of capillaries. By using a quantitative analysis workflow, we identified in the hippocampus of AD mice an overall reduction of the mean vascular diameter, volume fraction and branch angle, with most significant impairment in the DG-ml. In addition, virtual endoscopy revealed irregular morphological features in the vessel lumen of the AD mice, potentially contributing to the impairment of blood flow. Our results demonstrate the capability of high-resolution cross-scale evaluation of brain vasculature and underscore the importance of studying hippocampal microcirculation for understanding AD pathogenesis.
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Affiliation(s)
- Xiaochuan Zhang
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Xianzhen Yin
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Jingjing Zhang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Haiyan Zhang
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Zhaobing Gao
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
| | - Hualiang Jiang
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
- CAS Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China
- Shanghai Institute for Advanced Immunochemical Studies, and School of Life Science and Technology, ShanghaiTech University, Shanghai 200031, China
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20
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Lin R, Wang R, Yuan J, Feng Q, Zhou Y, Zeng S, Ren M, Jiang S, Ni H, Zhou C, Gong H, Luo M. Cell-type-specific and projection-specific brain-wide reconstruction of single neurons. Nat Methods 2018; 15:1033-1036. [PMID: 30455464 DOI: 10.1038/s41592-018-0184-y] [Citation(s) in RCA: 74] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2018] [Accepted: 08/31/2018] [Indexed: 11/09/2022]
Abstract
We developed a dual-adeno-associated-virus expression system that enables strong and sparse labeling of individual neurons with cell-type and projection specificity. We demonstrated its utility for whole-brain reconstruction of midbrain dopamine neurons and striatum-projecting cortical neurons. We further extended the labeling method for rapid reconstruction in cleared thick brain sections and simultaneous dual-color labeling. This labeling system may facilitate the process of generating mesoscale single-neuron projectomes of mammalian brains.
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Affiliation(s)
- Rui Lin
- School of Life Sciences, Peking University, Beijing, China.,National Institute of Biological Sciences, Beijing, China
| | - Ruiyu Wang
- School of Life Sciences, Peking University, Beijing, China.,National Institute of Biological Sciences, Beijing, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China
| | - Qiru Feng
- National Institute of Biological Sciences, Beijing, China.,School of Life Sciences, Tsinghua University, Beijing, China
| | - Youtong Zhou
- National Institute of Biological Sciences, Beijing, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Miao Ren
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Siqi Jiang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hong Ni
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Can Zhou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China. .,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China. .,HUST-Suzhou Institute for Brainsmatics, JITRI Institute for Brainsmatics, Suzhou, China.
| | - Minmin Luo
- National Institute of Biological Sciences, Beijing, China. .,School of Life Sciences, Tsinghua University, Beijing, China. .,Chinese Institute for Brain Research, Beijing, China.
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21
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Salili SM, Harrington M, Durian DJ. Note: Eliminating stripe artifacts in light-sheet fluorescence imaging. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2018; 89:036107. [PMID: 29604752 DOI: 10.1063/1.5016546] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
We report two techniques to mitigate stripe artifacts in light-sheet fluorescence imaging. The first uses an image processing algorithm called the multidirectional stripe remover method to filter stripes from an existing image. The second uses an elliptical holographic diffuser with strong scattering anisotropy to prevent stripe formation during image acquisition. These techniques facilitate accurate interpretation of image data, especially in denser samples. They are also facile and cost-effective.
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Affiliation(s)
- S M Salili
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - M Harrington
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
| | - D J Durian
- Department of Physics and Astronomy, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA
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22
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Xiong B, Li A, Lou Y, Chen S, Long B, Peng J, Yang Z, Xu T, Yang X, Li X, Jiang T, Luo Q, Gong H. Precise Cerebral Vascular Atlas in Stereotaxic Coordinates of Whole Mouse Brain. Front Neuroanat 2017; 11:128. [PMID: 29311856 PMCID: PMC5742197 DOI: 10.3389/fnana.2017.00128] [Citation(s) in RCA: 118] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2017] [Accepted: 12/11/2017] [Indexed: 12/27/2022] Open
Abstract
Understanding amazingly complex brain functions and pathologies requires a complete cerebral vascular atlas in stereotaxic coordinates. Making a precise atlas for cerebral arteries and veins has been a century-old objective in neuroscience and neuropathology. Using micro-optical sectioning tomography (MOST) with a modified Nissl staining method, we acquired five mouse brain data sets containing arteries, veins, and microvessels. Based on the brain-wide vascular spatial structures and brain regions indicated by cytoarchitecture in one and the same mouse brain, we reconstructed and annotated the vascular system atlas of both arteries and veins of the whole mouse brain for the first time. The distributing patterns of the vascular system within the brain regions were acquired and our results show that the patterns of individual vessels are different from each other. Reconstruction and statistical analysis of the microvascular network, including derivation of quantitative vascular densities, indicate significant differences mainly in vessels with diameters less than 8 μm and large than 20 μm across different brain regions. Our precise cerebral vascular atlas provides an important resource and approach for quantitative studies of brain functions and diseases.
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Affiliation(s)
- Benyi Xiong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Yang Lou
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Ben Long
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Jie Peng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Zhongqin Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tonghui Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xiaoquan Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Tao Jiang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China.,MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, China
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23
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Guo C, Peng J, Zhang Y, Li A, Li Y, Yuan J, Xu X, Ren M, Gong H, Chen S. Single-axon level morphological analysis of corticofugal projection neurons in mouse barrel field. Sci Rep 2017; 7:2846. [PMID: 28588276 PMCID: PMC5460143 DOI: 10.1038/s41598-017-03000-8] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2017] [Accepted: 04/20/2017] [Indexed: 12/20/2022] Open
Abstract
Corticofugal projection neurons are key components in connecting the neocortex and the subcortical regions. In the barrel field, these neurons have various projection targets and play crucial roles in the rodent whisker sensorimotor system. However, the projection features of corticofugal projection neurons at the single-axon level are far from comprehensive elucidation. Based on a brain-wide positioning system with high-resolution imaging for Thy1-GFP M-line mice brains, we reconstructed and analyzed more than one hundred corticofugal projection neurons in both layer V and VI of barrel cortex. The dual-color imaging made it possible to locate the neurons’ somata, trace their corresponding dendrites and axons and then distinguish the neurons as L5 type I/II or L6 type. The corticofugal projection pattern showed significant diversity across individual neurons. Usually, the L5 type I neurons have greater multi-region projection potential. The thalamus and the midbrain are the most frequent projection targets among the investigated multidirectional projection neurons, and the hypothalamus is particularly unique in that it only appears in multidirectional projection situations. Statistically, the average branch length of apical dendrites in multi-region projection groups is larger than that of single-region projection groups. This study demonstrated a single-axon-level analysis for barrel corticofugal projection neurons, which could provide a micro-anatomical basis for interpreting whisker sensorimotor circuit function.
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Affiliation(s)
- Congdi Guo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jie Peng
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yalun Zhang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Yuxin Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Xiaofeng Xu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Miao Ren
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China.,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, 430074, China. .,Key Laboratory for Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
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24
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Guo C, Long B, Hu Y, Yuan J, Gong H, Li X. Early-stage reduction of the dendritic complexity in basolateral amygdala of a transgenic mouse model of Alzheimer's disease. Biochem Biophys Res Commun 2017; 486:679-685. [PMID: 28336433 DOI: 10.1016/j.bbrc.2017.03.094] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 03/19/2017] [Indexed: 12/11/2022]
Abstract
Alzheimer's disease is a representative age-related neurodegenerative disease that could result in loss of memory and cognitive deficiency. However, the precise onset time of Alzheimer's disease affecting neuronal circuits and the mechanisms underlying the changes are not clearly known. To address the neuroanatomical changes during the early pathologic developing process, we acquired the neuronal morphological characterization of AD in APP/PS1 double-transgenic mice using the Micro-Optical Sectioning Tomography system. We reconstructed the neurons in 3D datasets with a resolution of 0.32 × 0.32 × 1 μm and used the Sholl method to analyze the anatomical characterization of the dendritic branches. The results showed that, similar to the progressive change in amyloid plaques, the number of dendritic branches were significantly decreased in 9-month-old mice. In addition, a distinct reduction of dendritic complexity occurred in third and fourth-order dendritic branches of 9-month-old mice, while no significant changes were identified in these parameters in 6-month-old mice. At the branch-level, the density distribution of dendritic arbors in the radial direction decreased in the range of 40-90 μm from the neuron soma in 6-month-old mice. These changes in the dendritic complexity suggest that these reductions contribute to the progressive cognitive impairment seen in APP/PS1 mice. This work may yield insights into the early changes in dendritic abnormality and its relevance to dysfunctional mechanisms of learning, memory and emotion in Alzheimer's disease.
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Affiliation(s)
- Congdi Guo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ben Long
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yarong Hu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Jing Yuan
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China.
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25
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High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level. Nat Commun 2016; 7:12142. [PMID: 27374071 PMCID: PMC4932192 DOI: 10.1038/ncomms12142] [Citation(s) in RCA: 188] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2015] [Accepted: 06/06/2016] [Indexed: 01/19/2023] Open
Abstract
The precise annotation and accurate identification of neural structures are prerequisites for studying mammalian brain function. The orientation of neurons and neural circuits is usually determined by mapping brain images to coarse axial-sampling planar reference atlases. However, individual differences at the cellular level likely lead to position errors and an inability to orient neural projections at single-cell resolution. Here, we present a high-throughput precision imaging method that can acquire a co-localized brain-wide data set of both fluorescent-labelled neurons and counterstained cell bodies at a voxel size of 0.32 × 0.32 × 2.0 μm in 3 days for a single mouse brain. We acquire mouse whole-brain imaging data sets of multiple types of neurons and projections with anatomical annotation at single-neuron resolution. The results show that the simultaneous acquisition of labelled neural structures and cytoarchitecture reference in the same brain greatly facilitates precise tracing of long-range projections and accurate locating of nuclei. High-throughput imaging methods for brain-wide connectome mapping with precise location reference have been lacking. Here authors report a method that allows simultaneous acquisition of fluorescently labelled neurons and cytoarchitectural landmarks in the same mouse brain at the single-cell resolution.
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26
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Li J, Quan T, Li S, Zhou H, Luo Q, Gong H, Zeng S. Reconstruction of micron resolution mouse brain surface from large-scale imaging dataset using resampling-based variational model. Sci Rep 2015; 5:12782. [PMID: 26245266 PMCID: PMC4526862 DOI: 10.1038/srep12782] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 06/08/2015] [Indexed: 11/09/2022] Open
Abstract
Brain surface profile is essential for brain studies, including registration, segmentation of brain structure and drawing neuronal circuits. Recent advances in high-throughput imaging techniques enable imaging whole mouse brain at micron spatial resolution and provide a basis for more fine quantitative studies in neuroscience. However, reconstructing micron resolution brain surface from newly produced neuronal dataset still faces challenges. Most current methods apply global analysis, which are neither applicable to a large imaging dataset nor to a brain surface with an inhomogeneous signal intensity. Here, we proposed a resampling-based variational model for this purpose. In this model, the movement directions of the initial boundary elements are fixed, the final positions of the initial boundary elements that form the brain surface are determined by the local signal intensity. These features assure an effective reconstruction of the brain surface from a new brain dataset. Compared with conventional typical methods, such as level set based method and active contour method, our method significantly increases the recall and precision rates above 97% and is approximately hundreds-fold faster. We demonstrated a fast reconstruction at micron level of the whole brain surface from a large dataset of hundreds of GB in size within 6 hours.
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Affiliation(s)
- Jing Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Mathematics and Statistics, Hubei University of Education, Wuhan 430205, China
| | - Shiwei Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- Key Laboratory of Biomedical Photonics of Ministry of Education, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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27
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iCut: an Integrative Cut Algorithm Enables Accurate Segmentation of Touching Cells. Sci Rep 2015; 5:12089. [PMID: 26168908 PMCID: PMC4501004 DOI: 10.1038/srep12089] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2014] [Accepted: 06/17/2015] [Indexed: 11/09/2022] Open
Abstract
Individual cells play essential roles in the biological processes of the brain. The number of neurons changes during both normal development and disease progression. High-resolution imaging has made it possible to directly count cells. However, the automatic and precise segmentation of touching cells continues to be a major challenge for massive and highly complex datasets. Thus, an integrative cut (iCut) algorithm, which combines information regarding spatial location and intervening and concave contours with the established normalized cut, has been developed. iCut involves two key steps: (1) a weighting matrix is first constructed with the abovementioned information regarding the touching cells and (2) a normalized cut algorithm that uses the weighting matrix is implemented to separate the touching cells into isolated cells. This novel algorithm was evaluated using two types of data: the open SIMCEP benchmark dataset and our micro-optical imaging dataset from a Nissl-stained mouse brain. It has achieved a promising recall/precision of 91.2 ± 2.1%/94.1 ± 1.8% and 86.8 ± 4.1%/87.5 ± 5.7%, respectively, for the two datasets. As quantified using the harmonic mean of recall and precision, the accuracy of iCut is higher than that of some state-of-the-art algorithms. The better performance of this fully automated algorithm can benefit studies of brain cytoarchitecture.
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28
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He Y, Meng Y, Gong H, Chen S, Zhang B, Ding W, Luo Q, Li A. An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm. PLoS One 2014; 9:e104437. [PMID: 25111442 PMCID: PMC4128780 DOI: 10.1371/journal.pone.0104437] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2013] [Accepted: 07/14/2014] [Indexed: 11/25/2022] Open
Abstract
Characterizing cytoarchitecture is crucial for understanding brain functions and neural diseases. In neuroanatomy, it is an important task to accurately extract cell populations' centroids and contours. Recent advances have permitted imaging at single cell resolution for an entire mouse brain using the Nissl staining method. However, it is difficult to precisely segment numerous cells, especially those cells touching each other. As presented herein, we have developed an automated three-dimensional detection and segmentation method applied to the Nissl staining data, with the following two key steps: 1) concave points clustering to determine the seed points of touching cells; and 2) random walker segmentation to obtain cell contours. Also, we have evaluated the performance of our proposed method with several mouse brain datasets, which were captured with the micro-optical sectioning tomography imaging system, and the datasets include closely touching cells. Comparing with traditional detection and segmentation methods, our approach shows promising detection accuracy and high robustness.
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Affiliation(s)
- Yong He
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yunlong Meng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Bin Zhang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Wenxiang Ding
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology-Wuhan National Laboratory for Optoelectronics, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, Hubei, China
- * E-mail:
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29
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Wu J, Guo C, Chen S, Jiang T, He Y, Ding W, Yang Z, Luo Q, Gong H. Direct 3D Analyses Reveal Barrel-Specific Vascular Distribution and Cross-Barrel Branching in the Mouse Barrel Cortex. Cereb Cortex 2014; 26:23-31. [PMID: 25085882 DOI: 10.1093/cercor/bhu166] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022] Open
Abstract
Whether vascular distribution is spatially specific among cortical columns is a fundamental yet controversial question. Here, we have obtained 1-μm resolution 3D datasets that cover the whole mouse barrel cortex by combining Nissl staining with micro-optical sectioning tomography to simultaneously visualize individual cells and blood vessels, including capillaries. Pinpointing layer IV of the posteromedial barrel subfield, direct 3D reconstruction and quantitative analysis showed that (1) penetrating vessels preferentially locate in the interbarrel septa/barrel wall (75.1%) rather than the barrel hollows, (2) the branches of 70% penetrating vessels only reach the neighboring but not always all the neighboring barrels and the other 30% extend beyond the neighboring barrels and may provide cross-barrel blood supply or drainage, (3) the branches of 59.6% penetrating vessels reach all the neighboring barrels, while the rest only reach part of them, and (4) the length density of microvessels in the interbarrel septa/barrel wall is lower than that in the barrel hollows with a ratio of 0.92. These results reveal that the penetrating vessels and microvessels exhibit a barrel-specific organization, whereas the branches of penetrating vessels do not, which suggests a much more complex vascular distribution pattern among cortical columns than previously thought.
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Affiliation(s)
- Jingpeng Wu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Congdi Guo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Tao Jiang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yong He
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wenxiang Ding
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhongqin Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics.,MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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30
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Quan T, Li J, Zhou H, Li S, Zheng T, Yang Z, Luo Q, Gong H, Zeng S. Digital reconstruction of the cell body in dense neural circuits using a spherical-coordinated variational model. Sci Rep 2014; 4:4970. [PMID: 24829141 PMCID: PMC4021323 DOI: 10.1038/srep04970] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2013] [Accepted: 04/09/2014] [Indexed: 02/03/2023] Open
Abstract
Mapping the neuronal circuits is essential to understand brain function. Recent technological advancements have made it possible to acquire the brain atlas at single cell resolution. Digital reconstruction of the neural circuits down to this level across the whole brain would significantly facilitate brain studies. However, automatic reconstruction of the dense neural connections from microscopic image still remains a challenge. Here we developed a spherical-coordinate based variational model to reconstruct the shape of the cell body i.e. soma, as one of the procedures for this purpose. When intuitively processing the volumetric images in the spherical coordinate system, the reconstruction of somas with variational model is no longer sensitive to the interference of the complicated neuronal morphology, and could automatically and robustly achieve accurate soma shape regardless of the dense spatial distribution, and diversity in cell size, and morphology. We believe this method would speed drawing the neural circuits and boost brain studies.
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Affiliation(s)
- Tingwei Quan
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- School of Mathematics and Statistics, Hubei University of Education, Wuhan 430205, China
- These authors contributed equally to this work
| | - Jing Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
- These authors contributed equally to this work
| | - Hang Zhou
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shiwei Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Ting Zheng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhongqing Yang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shaoqun Zeng
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology- Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China
- MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
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31
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Wu J, He Y, Yang Z, Guo C, Luo Q, Zhou W, Chen S, Li A, Xiong B, Jiang T, Gong H. 3D BrainCV: simultaneous visualization and analysis of cells and capillaries in a whole mouse brain with one-micron voxel resolution. Neuroimage 2013; 87:199-208. [PMID: 24185025 DOI: 10.1016/j.neuroimage.2013.10.036] [Citation(s) in RCA: 59] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2013] [Revised: 10/17/2013] [Accepted: 10/21/2013] [Indexed: 01/07/2023] Open
Abstract
Systematic cellular and vascular configurations are essential for understanding fundamental brain anatomy and metabolism. We demonstrated a 3D brainwide cellular and vascular (called 3D BrainCV) visualization and quantitative protocol for a whole mouse brain. We developed a modified Nissl staining method that quickly labeled the cells and blood vessels simultaneously in an entire mouse brain. Terabytes 3D datasets of the whole mouse brains, with unprecedented details of both individual cells and blood vessels, including capillaries, were simultaneously imaged at 1-μm voxel resolution using micro-optical sectioning tomography (MOST). For quantitative analysis, we proposed an automatic image-processing pipeline to perform brainwide vectorization and analysis of cells and blood vessels. Six representative brain regions from the cortex to the deep, including FrA, M1, PMBSF, V1, striatum, and amygdala, and six parameters, including cell number density, vascular length density, fractional vascular volume, distance from the cells to the nearest microvessel, microvascular length density, and fractional microvascular volume, had been quantitatively analyzed. The results showed that the proximity of cells to blood vessels was linearly correlated with vascular length density, rather than the cell number density. The 3D BrainCV made overall snapshots of the detailed picture of the whole brain architecture, which could be beneficial for the state comparison of the developing and diseased brain.
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Affiliation(s)
- Jingpeng Wu
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Yong He
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Zhongqin Yang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Congdi Guo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Qingming Luo
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Wei Zhou
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Shangbin Chen
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Benyi Xiong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Tao Jiang
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Huazhong University of Science and Technology - Wuhan National Laboratory for Optoelectronics, Wuhan 430074, China; MoE Key Laboratory for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan 430074, China.
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