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Khilkevich A, Lohse M, Low R, Orsolic I, Bozic T, Windmill P, Mrsic-Flogel TD. Brain-wide dynamics linking sensation to action during decision-making. Nature 2024:10.1038/s41586-024-07908-w. [PMID: 39261727 DOI: 10.1038/s41586-024-07908-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 08/05/2024] [Indexed: 09/13/2024]
Abstract
Perceptual decisions rely on learned associations between sensory evidence and appropriate actions, involving the filtering and integration of relevant inputs to prepare and execute timely responses1,2. Despite the distributed nature of task-relevant representations3-10, it remains unclear how transformations between sensory input, evidence integration, motor planning and execution are orchestrated across brain areas and dimensions of neural activity. Here we addressed this question by recording brain-wide neural activity in mice learning to report changes in ambiguous visual input. After learning, evidence integration emerged across most brain areas in sparse neural populations that drive movement-preparatory activity. Visual responses evolved from transient activations in sensory areas to sustained representations in frontal-motor cortex, thalamus, basal ganglia, midbrain and cerebellum, enabling parallel evidence accumulation. In areas that accumulate evidence, shared population activity patterns encode visual evidence and movement preparation, distinct from movement-execution dynamics. Activity in movement-preparatory subspace is driven by neurons integrating evidence, which collapses at movement onset, allowing the integration process to reset. Across premotor regions, evidence-integration timescales were independent of intrinsic regional dynamics, and thus depended on task experience. In summary, learning aligns evidence accumulation to action preparation in activity dynamics across dozens of brain regions. This leads to highly distributed and parallelized sensorimotor transformations during decision-making. Our work unifies concepts from decision-making and motor control fields into a brain-wide framework for understanding how sensory evidence controls actions.
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Affiliation(s)
- Andrei Khilkevich
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK.
| | - Michael Lohse
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK.
| | - Ryan Low
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Ivana Orsolic
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Tadej Bozic
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Paige Windmill
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK
| | - Thomas D Mrsic-Flogel
- Sainsbury Wellcome Centre for Neural Circuits and Behaviour, University College London, London, UK.
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2
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Timonidis N, Rubio-Teves M, Alonso-Martínez C, Bakker R, García-Amado M, Tiesinga P, Clascá F. Analyzing Thalamocortical Tract-Tracing Experiments in a Common Reference Space. Neuroinformatics 2024; 22:23-43. [PMID: 37864741 PMCID: PMC10917831 DOI: 10.1007/s12021-023-09644-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/09/2023] [Indexed: 10/23/2023]
Abstract
Current mesoscale connectivity atlases provide limited information about the organization of thalamocortical projections in the mouse brain. Labeling the projections of spatially restricted neuron populations in thalamus can provide a functionally relevant level of connectomic analysis, but these need to be integrated within the same common reference space. Here, we present a pipeline for the segmentation, registration, integration and analysis of multiple tract-tracing experiments. The key difference with other workflows is that the data is transformed to fit the reference template. As a test-case, we investigated the axonal projections and intranuclear arrangement of seven neuronal populations of the ventral posteromedial nucleus of the thalamus (VPM), which we labeled with an anterograde tracer. Their soma positions corresponded, from dorsal to ventral, to cortical representations of the whiskers, nose and mouth. They strongly targeted layer 4, with the majority exclusively targeting one cortical area and the ones in ventrolateral VPM branching to multiple somatosensory areas. We found that our experiments were more topographically precise than similar experiments from the Allen Institute and projections to the primary somatosensory area were in agreement with single-neuron morphological reconstructions from publicly available databases. This pilot study sets the basis for a shared virtual connectivity atlas that could be enriched with additional data for studying the topographical organization of different thalamic nuclei. The pipeline is accessible with only minimal programming skills via a Jupyter Notebook, and offers multiple visualization tools such as cortical flatmaps, subcortical plots and 3D renderings and can be used with custom anatomical delineations.
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Affiliation(s)
- Nestor Timonidis
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands.
| | - Mario Rubio-Teves
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, C. Arzobispo Morcillo 4, 28029, Madrid, Spain
| | - Carmen Alonso-Martínez
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, C. Arzobispo Morcillo 4, 28029, Madrid, Spain
| | - Rembrandt Bakker
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands
- Inst. of Neuroscience and Medicine (INM-6) and Inst. for Advanced Simulation (IAS-6) and JARA BRAIN Inst. I, Jülich Research Centre, Wilhelm-Johnen-Strasse, 52425, Jülich, Germany
| | - María García-Amado
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, C. Arzobispo Morcillo 4, 28029, Madrid, Spain
| | - Paul Tiesinga
- Neuroinformatics Department, Donders Centre for Neuroscience, Radboud University Nijmegen, Heyendaalseweg 135, 6525 AJ, Nijmegen, The Netherlands
| | - Francisco Clascá
- Department of Anatomy and Neuroscience, School of Medicine, Autónoma de Madrid University, C. Arzobispo Morcillo 4, 28029, Madrid, Spain
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3
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Ma C, Xia D, Huang S, Du Q, Liu J, Zhang B, Zhu Q, Bi G, Wang H, Xu RX. High precision vibration sectioning for 3D imaging of the whole central nervous system. J Neurosci Methods 2023; 399:109966. [PMID: 37666283 DOI: 10.1016/j.jneumeth.2023.109966] [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: 03/26/2023] [Revised: 06/21/2023] [Accepted: 08/30/2023] [Indexed: 09/06/2023]
Abstract
BACKGROUND Imaging and reconstruction of the morphology of neurons within the entire central nervous system (CNS) is important for deciphering the neural circuitry and related brain functions. With combination of tissue clearing and light sheet microscopy, previous studies have imaged the mouse CNS at cellular resolution, while remaining single axons unresolvable due to the tradeoff between sample size and imaging resolution. This could be improved by sectioning the sample into thick slices and imaged with high resolution light sheet microscopy as described in our previous study. However, the achievable quality for 3D imaging of serial thick slices is often hindered by surface undulation and other artifacts introduced by sectioning and handling limitations. NEW METHODS In order to improve the imaging quality for mouse CNS, we develop a high-performance vibratome system for sample sectioning and handling automation. The sectioning mechanism of the system was modeled theoretically and verified experimentally. The effects of process parameters and sample properties on sectioning accuracy were studied to optimize the sectioning outcome. The resultant imaging outcome was demonstrated on mouse samples. RESULTS Our theoretical model of vibratome effectively depicts the relationship between the sample surface undulation errors and the sectioning parameters. With the guidance of the theoretical model, the vibratome is able to achieve a local surface undulation error of ±0.5 µm and a surface arithmetic mean deviation (Sa) of 220 nm for 300-μm-thick tissue slices. Imaging results of mouse CNS show the continuous sectioning capability of the vibratome. COMPARISON WITH EXISTING METHOD Our automatic sectioning and handling system is able to process serial thick slices for 3D imaging of the whole CNS at a single-axon resolution, superior to the commercially available vibratome devices. CONCLUSION Our automatic sectioning and handling system can be optimized to prepare thick sample slices with minimal surface undulation and manual manipulation in support of 3D brain mapping with high-throughput and high-accuracy.
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Affiliation(s)
- Canzhen Ma
- School of Engineering Science, University of Science and Technology of China, Hefei 230027, China; Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; School of Biomedical Engineering, University of Science and Technology of China, Suzhou 215123, China
| | - Debin Xia
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China, Hefei 230027, China
| | - Shichang Huang
- Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Qing Du
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Jiajun Liu
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China, Hefei 230027, China
| | - Bo Zhang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China, Hefei 230027, China
| | - Qingyuan Zhu
- Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Guoqiang Bi
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; Hefei National Laboratory for Physical Sciences at the Microscale, and School of Life Sciences, University of Science and Technology of China, Hefei, Anhui 230027, China
| | - Hao Wang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China; National Engineering Laboratory for Brain-inspired Intelligence Technology and Application, University of Science and Technology of China, Hefei 230027, China.
| | - Ronald X Xu
- School of Engineering Science, University of Science and Technology of China, Hefei 230027, China; School of Biomedical Engineering, University of Science and Technology of China, Suzhou 215123, China.
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4
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Carey H, Pegios M, Martin L, Saleeba C, Turner AJ, Everett NA, Bjerke IE, Puchades MA, Bjaalie JG, McMullan S. DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas. Nat Commun 2023; 14:5884. [PMID: 37735467 PMCID: PMC10514056 DOI: 10.1038/s41467-023-41645-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2023] [Accepted: 09/12/2023] [Indexed: 09/23/2023] Open
Abstract
Registration of data to a common frame of reference is an essential step in the analysis and integration of diverse neuroscientific data. To this end, volumetric brain atlases enable histological datasets to be spatially registered and analyzed, yet accurate registration remains expertise-dependent and slow. In order to address this limitation, we have trained a neural network, DeepSlice, to register mouse brain histological images to the Allen Brain Common Coordinate Framework, retaining registration accuracy while improving speed by >1000 fold.
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Affiliation(s)
- Harry Carey
- Macquarie Medical School, Faculty of Medicine, Health & Human Sciences, Macquarie University, Marsfield, NSW, Australia
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Michael Pegios
- Macquarie Medical School, Faculty of Medicine, Health & Human Sciences, Macquarie University, Marsfield, NSW, Australia
| | | | - Chris Saleeba
- Macquarie Medical School, Faculty of Medicine, Health & Human Sciences, Macquarie University, Marsfield, NSW, Australia
| | - Anita J Turner
- Macquarie Medical School, Faculty of Medicine, Health & Human Sciences, Macquarie University, Marsfield, NSW, Australia
| | | | - Ingvild E Bjerke
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Maja A Puchades
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan G Bjaalie
- Department of Molecular Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Simon McMullan
- Macquarie Medical School, Faculty of Medicine, Health & Human Sciences, Macquarie University, Marsfield, NSW, Australia.
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5
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Bjerke IE, Yates SC, Carey H, Bjaalie JG, Leergaard TB. Scaling up cell-counting efforts in neuroscience through semi-automated methods. iScience 2023; 26:107562. [PMID: 37636060 PMCID: PMC10457595 DOI: 10.1016/j.isci.2023.107562] [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] [Indexed: 08/29/2023] Open
Abstract
Quantifying how the cellular composition of brain regions vary across development, aging, sex, and disease, is crucial in experimental neuroscience, and the accuracy of different counting methods is continuously debated. Due to the tedious nature of most counting procedures, studies are often restricted to one or a few brain regions. Recently, there have been considerable methodological advances in combining semi-automated feature extraction with brain atlases for cell quantification. Such methods hold great promise for scaling up cell-counting efforts. However, little focus has been paid to how these methods should be implemented and reported to support reproducibility. Here, we provide an overview of practices for conducting and reporting cell counting in mouse and rat brains, showing that critical details for interpretation are typically lacking. We go on to discuss how novel methods may increase efficiency and reproducibility of cell counting studies. Lastly, we provide practical recommendations for researchers planning cell counting.
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Affiliation(s)
- Ingvild Elise Bjerke
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Sharon Christine Yates
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Harry Carey
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Jan Gunnar Bjaalie
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Trygve Brauns Leergaard
- Neural Systems Laboratory, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
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6
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Kasaragod DK, Aizawa H. Deep ultraviolet fluorescence microscopy of three-dimensional structures in the mouse brain. Sci Rep 2023; 13:8553. [PMID: 37237102 DOI: 10.1038/s41598-023-35650-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 05/22/2023] [Indexed: 05/28/2023] Open
Abstract
Three-dimensional (3D) imaging at cellular resolution improves our understanding of the brain architecture and is crucial for structural and functional integration as well as for the understanding of normal and pathological conditions in the brain. We developed a wide-field fluorescent microscope for 3D imaging of the brain structures using deep ultraviolet (DUV) light. This microscope allowed fluorescence imaging with optical sectioning due to the large absorption at the surface of the tissue and hence low tissue penetration of DUV light. Multiple channels of fluorophore signals were detected using single or a combination of dyes emitting fluorescence in the visible range of spectrum upon DUV excitation. Combination of this DUV microscope with microcontroller-based motorized stage enabled wide-field imaging of a coronal section of the cerebral hemisphere in mouse for deciphering cytoarchitecture of each substructure in detail. We extended this by integrating vibrating microtome which allowed serial block-face imaging of the brain structure such as the habenula in mouse. Acquired images were with resolution high enough for quantification of the cell numbers and density in the mouse habenula. Upon block-face imaging of the tissues covering entire extent of the cerebral hemisphere of the mouse brain, acquired data were registered and segmented for quantification of cell number in each brain regions. Results in the current analysis indicated that this novel microscope could be a convenient tool for large-scale 3D analysis of the brain in mice.
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Affiliation(s)
- Deepa Kamath Kasaragod
- Department of Neurobiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8553, Japan.
| | - Hidenori Aizawa
- Department of Neurobiology, Graduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, 734-8553, Japan.
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7
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Arias A, Manubens-Gil L, Dierssen M. Fluorescent transgenic mouse models for whole-brain imaging in health and disease. Front Mol Neurosci 2022; 15:958222. [PMID: 36211979 PMCID: PMC9538927 DOI: 10.3389/fnmol.2022.958222] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2022] [Accepted: 08/08/2022] [Indexed: 11/25/2022] Open
Abstract
A paradigm shift is occurring in neuroscience and in general in life sciences converting biomedical research from a descriptive discipline into a quantitative, predictive, actionable science. Living systems are becoming amenable to quantitative description, with profound consequences for our ability to predict biological phenomena. New experimental tools such as tissue clearing, whole-brain imaging, and genetic engineering technologies have opened the opportunity to embrace this new paradigm, allowing to extract anatomical features such as cell number, their full morphology, and even their structural connectivity. These tools will also allow the exploration of new features such as their geometrical arrangement, within and across brain regions. This would be especially important to better characterize brain function and pathological alterations in neurological, neurodevelopmental, and neurodegenerative disorders. New animal models for mapping fluorescent protein-expressing neurons and axon pathways in adult mice are key to this aim. As a result of both developments, relevant cell populations with endogenous fluorescence signals can be comprehensively and quantitatively mapped to whole-brain images acquired at submicron resolution. However, they present intrinsic limitations: weak fluorescent signals, unequal signal strength across the same cell type, lack of specificity of fluorescent labels, overlapping signals in cell types with dense labeling, or undetectable signal at distal parts of the neurons, among others. In this review, we discuss the recent advances in the development of fluorescent transgenic mouse models that overcome to some extent the technical and conceptual limitations and tradeoffs between different strategies. We also discuss the potential use of these strains for understanding disease.
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Affiliation(s)
- Adrian Arias
- Department of System Biology, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
| | - Linus Manubens-Gil
- Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Mara Dierssen
- Department of System Biology, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain
- Department of Experimental and Health Sciences, University Pompeu Fabra, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), Barcelona, Spain
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8
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Yan M, Yu W, Lv Q, Lv Q, Bo T, Chen X, Liu Y, Zhan Y, Yan S, Shen X, Yang B, Hu Q, Yu J, Qiu Z, Feng Y, Zhang XY, Wang H, Xu F, Wang Z. Mapping brain-wide excitatory projectome of primate prefrontal cortex at submicron resolution and comparison with diffusion tractography. eLife 2022; 11:72534. [PMID: 35593765 PMCID: PMC9122499 DOI: 10.7554/elife.72534] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 04/07/2022] [Indexed: 11/13/2022] Open
Abstract
Resolving trajectories of axonal pathways in the primate prefrontal cortex remains crucial to gain insights into higher-order processes of cognition and emotion, which requires a comprehensive map of axonal projections linking demarcated subdivisions of prefrontal cortex and the rest of brain. Here, we report a mesoscale excitatory projectome issued from the ventrolateral prefrontal cortex (vlPFC) to the entire macaque brain by using viral-based genetic axonal tracing in tandem with high-throughput serial two-photon tomography, which demonstrated prominent monosynaptic projections to other prefrontal areas, temporal, limbic, and subcortical areas, relatively weak projections to parietal and insular regions but no projections directly to the occipital lobe. In a common 3D space, we quantitatively validated an atlas of diffusion tractography-derived vlPFC connections with correlative green fluorescent protein-labeled axonal tracing, and observed generally good agreement except a major difference in the posterior projections of inferior fronto-occipital fasciculus. These findings raise an intriguing question as to how neural information passes along long-range association fiber bundles in macaque brains, and call for the caution of using diffusion tractography to map the wiring diagram of brain circuits. In the brain is a web of interconnected nerve cells that send messages to one another via spindly projections called axons. These axons join together at junctions called synapses to create circuits of nerve cells which connect neighboring or distant brain regions. Notably, long-range neural connections underpin higher-order cognitive skills (such as planning and emotion regulation) which make humans distinct from our primate relatives. Only by untangling these far-reaching networks can researchers begin to delineate what sets the human brain apart from other species. Researchers deploy a range of imaging techniques to map neural networks: scanning entire brains using MRI machines, or imaging thin slices of fluorescently labelled brain tissue using powerful microscopes. However, tracing long-range axons at a high resolution is challenging, and has stirred up debate about whether some neural tracts, such as the inferior fronto-occipital fasciculus, are present in all primates or only humans. To address these discrepancies, Yan, Yu et al. employed a two-pronged approach to map neural circuits in the brains of macaques. First, two techniques – called viral tracing and two-photon microscopy – were used to create a three-dimensional, fine-grain map showing how the ventrolateral prefrontal cortex (vlPFC), which regulates complex behaviors, connects to the rest of the brain. This revealed prominent axons from the vlPFC projecting via a single synapse to distant brain regions involved in higher-order functions, such as encoding memories and processing emotion. However, there were no direct, monosynaptic connections between the vlPFC and the occipital lobe, the brain’s visual processing center at the back of the head. Next, Yan, Yu et al. used a specialized MRI scanner to create an atlas of neural circuits connected to the vlPFC, and compared these results to a technique tracing axons stained with a fluorescent dye. In general, there was good agreement between the two methods, except for major differences in the rear-end projections that typically form the inferior fronto-occipital fasciculus. This suggests that this long-range neural pathway exists in monkeys, but it connects via multiple synapses instead of a single junction as was previously thought. The findings of Yan, Yu et al. provide new insights on the far-reaching neural pathways connecting distant parts of the macaque brain. It also suggests that atlases of neural circuits from whole brain scans should be taken with caution and validated using neural tracing experiments.
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Affiliation(s)
- Mingchao Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Wenwen Yu
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Qian Lv
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - Qiming Lv
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Tingting Bo
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiaoyu Chen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yilin Liu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yafeng Zhan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Shengyao Yan
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Xiangyu Shen
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Baofeng Yang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Qiming Hu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jiangli Yu
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Zilong Qiu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiao-Yong Zhang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - He Wang
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai, China
| | - Fuqiang Xu
- Shenzhen Key Lab of Neuropsychiatric Modulation and Collaborative Innovation Center for Brain Science, Guangdong Provincial Key Laboratory of Brain Connectome and Behavior, CAS Key Laboratory of Brain Connectome and Manipulation, Brain Cognition and Brain Disease Institute (BCBDI), Shenzhen Institutes of Advanced Technology, Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, China
| | - Zheng Wang
- School of Psychological and Cognitive Sciences; Beijing Key Laboratory of Behavior and Mental Health; IDG/McGovern Institute for Brain Research; Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
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9
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Bennett HC, Kim Y. Advances in studying whole mouse brain vasculature using high-resolution 3D light microscopy imaging. NEUROPHOTONICS 2022; 9:021902. [PMID: 35402638 PMCID: PMC8983067 DOI: 10.1117/1.nph.9.2.021902] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 03/11/2022] [Indexed: 06/14/2023]
Abstract
Significance: The cerebrovasculature has become increasingly recognized as a major player in overall brain health and many brain disorders. Although there have been several landmark studies to understand details of these crucially important structures in an anatomically defined area, brain-wide examination of the whole cerebrovasculature, including microvessels, has been challenging. However, emerging techniques, including tissue processing and three-dimensional (3D) microscopy imaging, enable neuroscientists to examine the total vasculature in the entire mouse brain. Aim: Here, we aim to highlight advances in these high-resolution 3D mapping methods including block-face imaging and light sheet fluorescent microscopy. Approach: We summarize latest mapping tools to understand detailed anatomical arrangement of the cerebrovascular network and the organizing principles of the neurovascular unit (NVU) as a whole. Results: We discuss biological insights gained from studies using these imaging methods and how these tools can be used to advance our understanding of the cerebrovascular network and related cell types in the entire brain. Conclusions: This review article will help to understand recent advance in high-resolution NVU mapping in mice and provide perspective on future studies.
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Affiliation(s)
- Hannah C. Bennett
- The Pennsylvania State University, Department of Neural and Behavioral Sciences, Hershey, Pennsylvania, United States
| | - Yongsoo Kim
- The Pennsylvania State University, Department of Neural and Behavioral Sciences, Hershey, Pennsylvania, United States
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10
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Zeng C, Chen Z, Yang H, Fan Y, Fei L, Chen X, Zhang M. Advanced high resolution three-dimensional imaging to visualize the cerebral neurovascular network in stroke. Int J Biol Sci 2022; 18:552-571. [PMID: 35002509 PMCID: PMC8741851 DOI: 10.7150/ijbs.64373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2021] [Accepted: 10/28/2021] [Indexed: 11/05/2022] Open
Abstract
As an important method to accurately and timely diagnose stroke and study physiological characteristics and pathological mechanism in it, imaging technology has gone through more than a century of iteration. The interaction of cells densely packed in the brain is three-dimensional (3D), but the flat images brought by traditional visualization methods show only a few cells and ignore connections outside the slices. The increased resolution allows for a more microscopic and underlying view. Today's intuitive 3D imagings of micron or even nanometer scale are showing its essentiality in stroke. In recent years, 3D imaging technology has gained rapid development. With the overhaul of imaging mediums and the innovation of imaging mode, the resolution has been significantly improved, endowing researchers with the capability of holistic observation of a large volume, real-time monitoring of tiny voxels, and quantitative measurement of spatial parameters. In this review, we will summarize the current methods of high-resolution 3D imaging applied in stroke.
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Affiliation(s)
- Chudai Zeng
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Zhuohui Chen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Haojun Yang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Yishu Fan
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Lujing Fei
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Xinghang Chen
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
| | - Mengqi Zhang
- Department of Neurology, Xiangya Hospital of Central South University, Changsha, Hunan, China, 410008.,National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China, 410008
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11
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Automated computation of nerve fibre inclinations from 3D polarised light imaging measurements of brain tissue. Sci Rep 2022; 12:4328. [PMID: 35288611 PMCID: PMC8921329 DOI: 10.1038/s41598-022-08140-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 03/01/2022] [Indexed: 02/06/2023] Open
Abstract
The method 3D polarised light imaging (3D-PLI) measures the birefringence of histological brain sections to determine the spatial course of nerve fibres (myelinated axons). While the in-plane fibre directions can be determined with high accuracy, the computation of the out-of-plane fibre inclinations is more challenging because they are derived from the amplitude of the birefringence signals, which depends e.g. on the amount of nerve fibres. One possibility to improve the accuracy is to consider the average transmitted light intensity (transmittance weighting). The current procedure requires effortful manual adjustment of parameters and anatomical knowledge. Here, we introduce an automated, optimised computation of the fibre inclinations, allowing for a much faster, reproducible determination of fibre orientations in 3D-PLI. Depending on the degree of myelination, the algorithm uses different models (transmittance-weighted, unweighted, or a linear combination), allowing to account for regionally specific behaviour. As the algorithm is parallelised and GPU optimised, it can be applied to large data sets. Moreover, it only uses images from standard 3D-PLI measurements without tilting, and can therefore be applied to existing data sets from previous measurements. The functionality is demonstrated on unstained coronal and sagittal histological sections of vervet monkey and rat brains.
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12
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Newmaster KT, Kronman FA, Wu YT, Kim Y. Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain. Front Neuroanat 2022; 15:787601. [PMID: 35095432 PMCID: PMC8794814 DOI: 10.3389/fnana.2021.787601] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/02/2021] [Indexed: 12/14/2022] Open
Abstract
The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.
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Affiliation(s)
- Kyra T Newmaster
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Fae A Kronman
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yuan-Ting Wu
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
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13
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Chen S, Liu Z, Li A, Gong H, Long B, Li X. High-Throughput Strategy for Profiling Sequential Section With Multiplex Staining of Mouse Brain. Front Neuroanat 2022; 15:771229. [PMID: 35002637 PMCID: PMC8732995 DOI: 10.3389/fnana.2021.771229] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/29/2021] [Indexed: 12/04/2022] Open
Abstract
The brain modulates specific functions in its various regions. Understanding the organization of different cells in the whole brain is crucial for investigating brain functions. Previous studies have focused on several regions and have had difficulty analyzing serial tissue samples. In this study, we introduced a pipeline to acquire anatomical and histological information quickly and efficiently from serial sections. First, we developed a serial brain-slice-staining method to stain serial sections and obtained more than 98.5% of slices with high integrity. Subsequently, using the self-developed analysis software, we registered and quantified the signals of imaged sections to the Allen Mouse Brain Common Coordinate Framework, which is compatible with multimodal images and slant section planes. Finally, we validated the pipeline with immunostaining by analyzing the activity variance in the whole brain during acute stress in aging and young mice. By removing the problems resulting from repeated manual operations, this pipeline is widely applicable to serial brain slices from multiple samples in a rapid and convenient manner, which benefits to facilitate research in life sciences.
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Affiliation(s)
- Siqi Chen
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China
| | - Zhixiang Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, 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.,HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Ben Long
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Xiangning Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.,HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
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14
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Li Y, Ding Z, Deng L, Fan G, Zhang Q, Gong H, Li A, Yuan J, Chen J. Precision vibratome for high-speed ultrathin biotissue cutting and organ-wide imaging. iScience 2021; 24:103016. [PMID: 34522859 PMCID: PMC8426277 DOI: 10.1016/j.isci.2021.103016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 06/25/2021] [Accepted: 08/18/2021] [Indexed: 10/31/2022] Open
Abstract
Cutting tissues into ultrathin slices is highly desired in sectioning-based organ-wide imaging. However, it is difficult to perform tissue cutting at a high speed with excellent quality. Here, we design a precision vibratome based on a paired double parallelogram flexure, which enables a vibrating blade to move strictly along a straight line. Meanwhile, we develop a high-speed cutting method that does not compromise cutting quality, which the vibratome operated at a high frequency mode. The characterized parasitic motion errors of a 180-Hz vibratome were less than 300 nm. It achieved a cutting speed six times that of an 85-Hz vibratome with acceptable quality. The capacity of the vibratome was investigated by organ-wide imaging, and the results revealed that it can be adapted in different tissues, such as the mouse brain and liver. This new vibratome shows great potential in speeding up organ-wide imaging applications especially for large volume biotissues.
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Affiliation(s)
- Yafeng 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, Hubei 430074, China.,Innovation Institute, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Zhangheng Ding
- 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, Hubei 430074, China
| | - Lei Deng
- 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, Hubei 430074, China
| | - Guoqing Fan
- 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, Hubei 430074, China
| | - Qi Zhang
- 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, Hubei 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, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, 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, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
| | - Jing Yuan
- 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, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
| | - Jianwei Chen
- 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, Hubei 430074, China.,HUST-Suzhou Institute for Brainsmatics, Suzhou 215125, China
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15
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Taranda J, Turcan S. 3D Whole-Brain Imaging Approaches to Study Brain Tumors. Cancers (Basel) 2021; 13:cancers13081897. [PMID: 33920839 PMCID: PMC8071100 DOI: 10.3390/cancers13081897] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 04/05/2021] [Accepted: 04/09/2021] [Indexed: 02/06/2023] Open
Abstract
Simple Summary Brain tumors integrate into the brain and consist of tumor cells with different molecular alterations. During brain tumor pathogenesis, a variety of cell types surround the tumors to either inhibit or promote tumor growth. These cells are collectively referred to as the tumor microenvironment. Three-dimensional and/or longitudinal visualization approaches are needed to understand the growth of these tumors in time and space. In this review, we present three imaging modalities that are suitable or that can be adapted to study the volumetric distribution of malignant or tumor-associated cells in the brain. In addition, we highlight the potential clinical utility of some of the microscopy approaches for brain tumors using exemplars from solid tumors. Abstract Although our understanding of the two-dimensional state of brain tumors has greatly expanded, relatively little is known about their spatial structures. The interactions between tumor cells and the tumor microenvironment (TME) occur in a three-dimensional (3D) space. This volumetric distribution is important for elucidating tumor biology and predicting and monitoring response to therapy. While static 2D imaging modalities have been critical to our understanding of these tumors, studies using 3D imaging modalities are needed to understand how malignant cells co-opt the host brain. Here we summarize the preclinical utility of in vivo imaging using two-photon microscopy in brain tumors and present ex vivo approaches (light-sheet fluorescence microscopy and serial two-photon tomography) and highlight their current and potential utility in neuro-oncology using data from solid tumors or pathological brain as examples.
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16
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Xu YKT, Call CL, Sulam J, Bergles DE. Automated in vivo Tracking of Cortical Oligodendrocytes. Front Cell Neurosci 2021; 15:667595. [PMID: 33912017 PMCID: PMC8072161 DOI: 10.3389/fncel.2021.667595] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 03/19/2021] [Indexed: 11/18/2022] Open
Abstract
Oligodendrocytes exert a profound influence on neural circuits by accelerating action potential conduction, altering excitability, and providing metabolic support. As oligodendrogenesis continues in the adult brain and is essential for myelin repair, uncovering the factors that control their dynamics is necessary to understand the consequences of adaptive myelination and develop new strategies to enhance remyelination in diseases such as multiple sclerosis. Unfortunately, few methods exist for analysis of oligodendrocyte dynamics, and even fewer are suitable for in vivo investigation. Here, we describe the development of a fully automated cell tracking pipeline using convolutional neural networks (Oligo-Track) that provides rapid volumetric segmentation and tracking of thousands of cells over weeks in vivo. This system reliably replicated human analysis, outperformed traditional analytic approaches, and extracted injury and repair dynamics at multiple cortical depths, establishing that oligodendrogenesis after cuprizone-mediated demyelination is suppressed in deeper cortical layers. Volumetric data provided by this analysis revealed that oligodendrocyte soma size progressively decreases after their generation, and declines further prior to death, providing a means to predict cell age and eventual cell death from individual time points. This new CNN-based analysis pipeline offers a rapid, robust method to quantitatively analyze oligodendrocyte dynamics in vivo, which will aid in understanding how changes in these myelinating cells influence circuit function and recovery from injury and disease.
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Affiliation(s)
- Yu Kang T. Xu
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, United States
- Kavli Neuroscience Discovery Institute, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Cody L. Call
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, United States
| | - Jeremias Sulam
- Kavli Neuroscience Discovery Institute, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Dwight E. Bergles
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University, Baltimore, MD, United States
- Kavli Neuroscience Discovery Institute, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD, United States
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17
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Zhan Y, Wu H, Liu L, Lin J, Zhang S. Organic solvent-based tissue clearing techniques and their applications. JOURNAL OF BIOPHOTONICS 2021; 14:e202000413. [PMID: 33715302 DOI: 10.1002/jbio.202000413] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 03/05/2021] [Accepted: 03/05/2021] [Indexed: 02/05/2023]
Abstract
Revealing the true structure of tissues and organs with tissue slicing technology is difficult since images reconstructed in three dimensions are easily distorted. To address the limitations in tissue slicing technology, tissue clearing has been invented and has recently achieved significant progress in three-dimensional imaging. Currently, this technology can mainly be divided into two types: aqueous clearing methods and solvent-based clearing methods. As one of the important parts of this technology, organic solvent-based tissue clearing techniques have been widely applied because of their efficient clearing speed and high clearing intensity. This review introduces the primary organic solvent-based tissue clearing techniques and their applications.
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Affiliation(s)
- Yanjing Zhan
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Haoyan Wu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Linfeng Liu
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Jie Lin
- Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Shiwen Zhang
- State Key Laboratory of Oral Diseases & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.,Department of Oral Implantology, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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18
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Conte D, Borisyuk R, Hull M, Roberts A. A simple method defines 3D morphology and axon projections of filled neurons in a small CNS volume: Steps toward understanding functional network circuitry. J Neurosci Methods 2020; 351:109062. [PMID: 33383055 DOI: 10.1016/j.jneumeth.2020.109062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 12/11/2020] [Accepted: 12/22/2020] [Indexed: 10/22/2022]
Abstract
BACKGROUND Fundamental to understanding neuronal network function is defining neuron morphology, location, properties, and synaptic connectivity in the nervous system. A significant challenge is to reconstruct individual neuron morphology and connections at a whole CNS scale and bring together functional and anatomical data to understand the whole network. NEW METHOD We used a PC controlled micropositioner to hold a fixed whole mount of Xenopus tadpole CNS and replace the stage on a standard microscope. This allowed direct recording in 3D coordinates of features and axon projections of one or two neurons dye-filled during whole-cell recording to study synaptic connections. Neuron reconstructions were normalised relative to the ventral longitudinal axis of the nervous system. Coordinate data were stored as simple text files. RESULTS Reconstructions were at 1 μm resolution, capturing axon lengths in mm. The output files were converted to SWC format and visualised in 3D reconstruction software NeuRomantic. Coordinate data are tractable, allowing correction for histological artefacts. Through normalisation across multiple specimens we could infer features of network connectivity of mapped neurons of different types. COMPARISON WITH EXISTING METHODS Unlike other methods using fluorescent markers and utilising large-scale imaging, our method allows direct acquisition of 3D data on neurons whose properties and synaptic connections have been studied using whole-cell recording. CONCLUSIONS This method can be used to reconstruct neuron 3D morphology and follow axon projections in the CNS. After normalisation to a common CNS framework, inferences on network connectivity at a whole nervous system scale contribute to network modelling to understand CNS function.
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Affiliation(s)
- Deborah Conte
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
| | - Roman Borisyuk
- College of Engineering, Mathematics and Physical Sciences, University of Exeter, Harrison Building, North Park Road, Exeter, EX4 4QF, United Kingdom; Institute of Mathematical Problems of Biology, the Branch of Keldysh Institute of Applied Mathematics, Russian Academy of Sciences, Pushchino, 142290, Russia; School of Computing, Engineering and Mathematics, University of Plymouth, PL4 8AA, United Kingdom.
| | - Mike Hull
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom; Institute for Adaptive and Neural Computation, University of Edinburgh, Edinburgh, EH8 9AB, United Kingdom.
| | - Alan Roberts
- School of Biological Sciences, University of Bristol, 24 Tyndall Avenue, Bristol, BS8 1TQ, United Kingdom.
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19
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Winfree S, Weiler C, Bledsoe SB, Gardner T, Sommer AJ, Evan AP, Lingeman JE, Krambeck AE, Worcester EM, El-Achkar TM, Williams JC. Multimodal imaging reveals a unique autofluorescence signature of Randall's plaque. Urolithiasis 2020; 49:123-135. [PMID: 33026465 DOI: 10.1007/s00240-020-01216-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Accepted: 09/25/2020] [Indexed: 12/01/2022]
Abstract
Kidney stones frequently develop as an overgrowth on Randall's plaque (RP) which is formed in the papillary interstitium. The organic composition of RP is distinct from stone matrix in that RP contains fibrillar collagen; RP in tissue has also been shown to have two proteins that are also found in stones, but otherwise the molecular constituents of RP are unstudied. We hypothesized that RP contains unique organic molecules that can be differentiated from the stone overgrowth by fluorescence. To test this, we used micro-CT-guided polishing to expose the interior of kidney stones for multimodal imaging with multiphoton, confocal and infrared microscopy. We detected a blue autofluorescence signature unique to RP, the specificity of which was also confirmed in papillary tissue from patients with stone disease. High-resolution mineral mapping of the stone also showed a transition from the apatite within RP to the calcium oxalate in the overgrowth, demonstrating the molecular and spatial transition from the tissue to the urine. This work provides a systematic and practical approach to uncover specific fluorescence signatures which correlate with mineral type, verifies previous observations regarding mineral overgrowth onto RP and identifies a novel autofluorescence signature of RP demonstrating RP's unique molecular composition.
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Affiliation(s)
- Seth Winfree
- Division of Nephrology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Courtney Weiler
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sharon B Bledsoe
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Tony Gardner
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - André J Sommer
- Molecular Microspectroscopy Laboratory, Department of Chemistry and Biochemistry, Miami University, Oxford, OH, USA
| | - Andrew P Evan
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - James E Lingeman
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Amy E Krambeck
- Department of Urology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Elaine M Worcester
- Division of Nephrology, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Tarek M El-Achkar
- Division of Nephrology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN, USA.,Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - James C Williams
- Department of Anatomy, Cell Biology and Physiology, Indiana University School of Medicine, Indianapolis, IN, USA.
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20
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Li LZ, Masek M, Wang T, Xu HN, Nioka S, Baur JA, Ragan TM. Two-Photon Autofluorescence Imaging of Fixed Tissues: Feasibility and Potential Values for Biomedical Applications. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1232:375-381. [PMID: 31893434 DOI: 10.1007/978-3-030-34461-0_48] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The value of optical redox imaging (ORI) of cells/tissues based on the intrinsic fluorescences of NADH (nicotinamide adenine dinucleotide) and oxidized flavoproteins (containing flavin adenine dinucleotide, i.e., FAD) has been demonstrated for potential biomedical applications including diagnosis, prognosis, and determining treatment response. However, the Chance redox scanner (a 3D cryogenic tissue imager) is limited by spatial resolution (~50 μm), and tissue ORI using fluorescence microscopy (single or multi-photon) is limited by the light penetration depth. Furthermore, viable or snap-frozen tissues are usually required. In this project, we aimed to study whether ORI may be achieved for unstained fixed tissue using a state-of-the-art modern Serial Two-Photon (STP) Tomography scanner that can rapidly acquire multi-plane images at micron resolution. Tissue specimens of mouse muscle, liver, and tumor xenografts were harvested and fixed in 4% paraformaldehyde (PFA) for 24 h. Tissue blocks were scanned by STP Tomography under room temperature to acquire the autofluorescence signals (NADH channel: excitation 750 nm, blue emission filter; FAD channel: excitation 860 nm, green emission filter). We observed remarkable signals with significant intra-tissue heterogeneity in images of NADH, FAD and redox ratio (FAD/(NADH+FAD)), which are worthy of further investigation for extracting biological information.
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Affiliation(s)
- Lin Z Li
- Department of Radiology & Britton Chance Laboratory of Redox Imaging, Johnson Research Foundation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. .,Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA. .,Institute of Translational Medicine and Therapeutics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | | | - Ting Wang
- Department of Radiology & Britton Chance Laboratory of Redox Imaging, Johnson Research Foundation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - He N Xu
- Department of Radiology & Britton Chance Laboratory of Redox Imaging, Johnson Research Foundation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shoko Nioka
- Department of Radiology & Britton Chance Laboratory of Redox Imaging, Johnson Research Foundation, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Joseph A Baur
- Department of Physiology, Institute for Diabetes, Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
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21
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Quintana DD, Lewis SE, Anantula Y, Garcia JA, Sarkar SN, Cavendish JZ, Brown CM, Simpkins JW. The cerebral angiome: High resolution MicroCT imaging of the whole brain cerebrovasculature in female and male mice. Neuroimage 2019; 202:116109. [PMID: 31446129 PMCID: PMC6942880 DOI: 10.1016/j.neuroimage.2019.116109] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2019] [Revised: 08/15/2019] [Accepted: 08/17/2019] [Indexed: 01/09/2023] Open
Abstract
The cerebrovascular system provides crucial functions that maintain metabolic and homeostatic states of the brain. Despite its integral role of supporting cerebral viability, the topological organization of these networks remains largely uncharacterized. This void in our knowledge surmises entirely from current technological limitations that prevent the capturing of data through the entire depth of the brain. We report high-resolution reconstruction and analysis of the complete vascular network of the entire brain at the capillary level in adult female and male mice using a vascular corrosion cast procedure. Vascular network analysis of the whole brain revealed sex-related differences of vessel hierarchy. In addition, region-specific network analysis demonstrated different patterns of angioarchitecture between brain subregions and sex. Furthermore, our group is the first to provide a three-dimensional analysis of the angioarchitecture and network organization in a single reconstructed tomographic data set that encompasses all hierarchy of vessels in the brain of the adult mouse.
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Affiliation(s)
- D D Quintana
- Department of Physiology and Pharmacology, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA
| | - S E Lewis
- Department of Physiology and Pharmacology, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA
| | - Y Anantula
- Department of Neuroscience, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA
| | - J A Garcia
- Department of Neuroscience, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA
| | - S N Sarkar
- Department of Physiology and Pharmacology, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA
| | - J Z Cavendish
- Department of Physiology and Pharmacology, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA
| | - C M Brown
- Department of Neuroscience, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA
| | - J W Simpkins
- Department of Physiology and Pharmacology, Center for Basic Translational and Stroke Research, West Virginia University, Morgantown, WV, 26506, USA.
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22
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Acute and chronic stage adaptations of vascular architecture and cerebral blood flow in a mouse model of TBI. Neuroimage 2019; 202:116101. [DOI: 10.1016/j.neuroimage.2019.116101] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 08/12/2019] [Accepted: 08/14/2019] [Indexed: 11/18/2022] Open
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23
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Poinsatte K, Betz D, Torres VO, Ajay AD, Mirza S, Selvaraj UM, Plautz EJ, Kong X, Gokhale S, Meeks JP, Ramirez DMO, Goldberg MP, Stowe AM. Visualization and Quantification of Post-stroke Neural Connectivity and Neuroinflammation Using Serial Two-Photon Tomography in the Whole Mouse Brain. Front Neurosci 2019; 13:1055. [PMID: 31636534 PMCID: PMC6787288 DOI: 10.3389/fnins.2019.01055] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Accepted: 09/19/2019] [Indexed: 01/14/2023] Open
Abstract
Whole-brain volumetric microscopy techniques such as serial two-photon tomography (STPT) can provide detailed information on the roles of neuroinflammation and neuroplasticity throughout the whole brain post-stroke. STPT automatically generates high-resolution images of coronal sections of the entire mouse brain that can be readily visualized in three dimensions. We developed a pipeline for whole brain image analysis that includes supervised machine learning (pixel-wise random forest models via the "ilastik" software package) followed by registration to a standardized 3-D atlas of the adult mouse brain (Common Coordinate Framework v3.0; Allen Institute for Brain Science). These procedures allow the detection of cellular fluorescent signals throughout the brain in an unbiased manner. To illustrate our imaging techniques and automated image quantification, we examined long-term post-stroke motor circuit connectivity in mice that received a motor cortex photothrombotic stroke. Two weeks post-stroke, mice received intramuscular injections of pseudorabies virus (PRV-152), a trans-synaptic retrograde herpes virus driving expression of green fluorescent protein (GFP), into the affected contralesional forelimb to label neurons in descending tracts to the forelimb musculature. Mice were sacrificed 3 weeks post-stroke. We also quantified sub-acute neuroinflammation in the post-stroke brain in a separate cohort of mice following a 60 min transient middle cerebral artery occlusion (tMCAo). Naive e450+-labeled splenic CD8+ cytotoxic T cells were intravenously injected at 7, 24, 48, and 72 h post-tMCAo. Mice were sacrificed 4 days after stroke. Detailed quantification of post-stroke neural connectivity and neuroinflammation indicates a role for remote brain regions in stroke pathology and recovery. The workflow described herein, incorporating STPT and automated quantification of fluorescently labeled features of interest, provides a framework by which one can objectively evaluate labeled neuronal or lymphocyte populations in healthy and injured brains. The results provide region-specific quantification of neural connectivity and neuroinflammation, which could be a critical tool for investigating mechanisms of not only stroke recovery, but also a wide variety of brain injuries or diseases.
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Affiliation(s)
- Katherine Poinsatte
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Dene Betz
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Vanessa O Torres
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Apoorva D Ajay
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Shazia Mirza
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Uma M Selvaraj
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Erik J Plautz
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Xiangmei Kong
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Sankalp Gokhale
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Julian P Meeks
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States.,Department of Neuroscience, UT Southwestern Medical Center, Dallas, TX, United States
| | - Denise M O Ramirez
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Mark P Goldberg
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States
| | - Ann M Stowe
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Peter O'Donnell Jr. Brain Institute, Dallas, TX, United States.,Department of Neurology, University of Kentucky, Lexington, KY, United States
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24
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Li T, Liu CJ, Akkin T. Contrast-enhanced serial optical coherence scanner with deep learning network reveals vasculature and white matter organization of mouse brain. NEUROPHOTONICS 2019; 6:035004. [PMID: 31338386 PMCID: PMC6646884 DOI: 10.1117/1.nph.6.3.035004] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2019] [Accepted: 07/02/2019] [Indexed: 06/01/2023]
Abstract
Optical coherence tomography provides volumetric reconstruction of brain structure with micrometer resolution. Gray matter and white matter can be highlighted using conventional and polarization-based contrasts; however, vasculature in ex-vivo fixed brain has not been investigated at large scale due to lack of intrinsic contrast. We present contrast enhancement to visualize the vasculature by perfusing titanium dioxide particles transcardially into the mouse vascular system. The brain, after dissection and fixation, is imaged by a serial optical coherence scanner. Accumulation of particles in blood vessels generates distinguishable optical signals. Among these, the cross-polarization images reveal the vasculature organization remarkably well. The conventional and polarization-based contrasts are still available for probing the gray matter and white matter structures. The segmentation and reconstruction of the vasculature are presented by using a deep learning algorithm. Axonal fiber pathways in the mouse brain are delineated by utilizing the retardance and optic axis orientation contrasts. This is a low-cost method that can be further developed to study neurovascular diseases and brain injury in animal models.
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Affiliation(s)
- Tianqi Li
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Chao J. Liu
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Taner Akkin
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
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25
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Kim YY, Chao JR, Kim C, Jung H, Kim B, Kang TC, Chang J, Park HS, Suh JG, Lee JH. Comparing the Superficial Vasculature of the Central Nervous System in Six Laboratory Animals: A Hypothesis About the Role of the "Circle of Willis". Anat Rec (Hoboken) 2019; 302:2049-2061. [PMID: 31087813 DOI: 10.1002/ar.24146] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2018] [Revised: 03/05/2019] [Accepted: 03/23/2019] [Indexed: 11/08/2022]
Abstract
We provide images of the entire central nervous system vasculature, and compare the anatomical findings in six different laboratory animals. A detailed understanding of the specific anatomy for each is important in the design of experimental modeling and for understanding the specific function of each target organ. Six different types of animals, the Korean wild mouse, C57BL/6J mouse, F344 rat, mongolian gerbil, Syrian hamsters, and guinea pigs, were included. To stain the blood vessels in each of the animals, Alcian blue reagent was used to perfuse each species. The bifurcation and anastomotic patterns of the anterior cerebral arteries differed in each species. The vascular supply to the olfactory nerve was visualized as a single artery supplying both olfactory nerves, and arteries supplying the lateral portion of the olfactory nerves originating from the olfactory bulb area. The posterior communicating arteries of the six animals demonstrated unique morphologies. The shape of the hypophyseal portal system varied by species. Most animals used in this study had a hexagonal Circle of Willis, except for the Korean wild mouse. Using this approach, we successfully mapped the brain vascular system in six different species of animals. This information and the images created can guide other researchers as they design research studies and create experimental models for new surgical procedures and approaches. Anat Rec, 2019. © 2019 Wiley Periodicals, Inc. Anat Rec, 302:2049-2061, 2019. © 2019 American Association for Anatomy.
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Affiliation(s)
- Yoo Yeon Kim
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Janet Ren Chao
- Department of Surgery, Division of Otolaryngology, Yale School of Medicine, New Haven, Connecticut
| | - Chulho Kim
- Department of Neurology, Chuncheon Sacred Heart Hospital, Hallym University, Chuncheon, Republic of Korea
| | - Harry Jung
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Boyoung Kim
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Tae-Cheon Kang
- Department of Anatomy and Neurobiology, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Jiwon Chang
- Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Hae Sang Park
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Jun-Gyo Suh
- Department of Medical Genetics, College of Medicine, Hallym University, Chuncheon, Republic of Korea
| | - Jun Ho Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Department of Otorhinolaryngology-Head and Neck Surgery, College of Medicine, Hallym University, Chuncheon, Republic of Korea
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26
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Sepehrband F, Cabeen RP, Choupan J, Barisano G, Law M, Toga AW. Perivascular space fluid contributes to diffusion tensor imaging changes in white matter. Neuroimage 2019; 197:243-254. [PMID: 31051291 DOI: 10.1016/j.neuroimage.2019.04.070] [Citation(s) in RCA: 53] [Impact Index Per Article: 10.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2019] [Revised: 04/16/2019] [Accepted: 04/26/2019] [Indexed: 10/26/2022] Open
Abstract
Diffusion tensor imaging (DTI) has been extensively used to map changes in brain tissue related to neurological disorders. Among the most widespread DTI findings are increased mean diffusivity and decreased fractional anisotropy of white matter tissue in neurodegenerative diseases. Here we utilize multi-shell diffusion imaging to separate diffusion signal of the brain parenchyma from non-parenchymal fluid within the white matter. We show that unincorporated anisotropic water in perivascular space (PVS) significantly, and systematically, biases DTI measures, casting new light on the biological validity of many previously reported findings. Despite the challenge this poses for interpreting these past findings, our results suggest that multi-shell diffusion MRI provides a new opportunity for incorporating the PVS contribution, ultimately strengthening the clinical and scientific value of diffusion MRI.
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Affiliation(s)
- Farshid Sepehrband
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA.
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
| | - Jeiran Choupan
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Department of Psychology, University of Southern California, Los Angeles, USA
| | - Giuseppe Barisano
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Neuroscience Graduate Program, University of Southern California, Los Angeles, USA
| | - Meng Law
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA; Radiology and Nuclear Medicine, Alfred Health, Melbourne, Australia
| | - Arthur W Toga
- Laboratory of Neuro Imaging, USC Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, USA
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27
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Hess A, Hinz R, Keliris GA, Boehm-Sturm P. On the Usage of Brain Atlases in Neuroimaging Research. Mol Imaging Biol 2019; 20:742-749. [PMID: 30094652 DOI: 10.1007/s11307-018-1259-y] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
Brain atlases play a key role in modern neuroimaging analysis of brain structure and function. We review available atlas databases for humans and animals and illustrate common state-of-the-art workflows in neuroimaging research based on image registration. Advances in noninvasive imaging methods, 3D ex vivo microscopy, and image processing are summarized which will eventually close the current resolution gap between brain atlases based on conventional 2D histology and those based on 3D in vivo imaging.
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Affiliation(s)
- Andreas Hess
- Institute for Experimental Pharmacology, Friedrich Alexander University Erlangen Nuremberg, Fahrstraße 17, 91054, Erlangen, Germany.
| | - Rukun Hinz
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
| | | | - Philipp Boehm-Sturm
- Department of Experimental Neurology and Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany. .,NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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28
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Whitesell JD, Buckley AR, Knox JE, Kuan L, Graddis N, Pelos A, Mukora A, Wakeman W, Bohn P, Ho A, Hirokawa KE, Harris JA. Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer's disease. J Comp Neurol 2018; 527:2122-2145. [PMID: 30311654 DOI: 10.1002/cne.24555] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2018] [Accepted: 09/13/2018] [Indexed: 01/08/2023]
Abstract
A variety of Alzheimer's disease (AD) mouse models overexpress mutant forms of human amyloid precursor protein (APP), producing high levels of amyloid β (Aβ) and forming plaques. However, the degree to which these models mimic spatiotemporal patterns of Aβ deposition in brains of AD patients is unknown. Here, we mapped the spatial distribution of Aβ plaques across age in three APP-overexpression mouse lines (APP/PS1, Tg2576, and hAPP-J20) using in vivo labeling with methoxy-X04, high throughput whole brain imaging, and an automated informatics pipeline. Images were acquired with high resolution serial two-photon tomography and labeled plaques were detected using custom-built segmentation algorithms. Image series were registered to the Allen Mouse Brain Common Coordinate Framework, a 3D reference atlas, enabling automated brain-wide quantification of plaque density, number, and location. In both APP/PS1 and Tg2576 mice, plaques were identified first in isocortex, followed by olfactory, hippocampal, and cortical subplate areas. In hAPP-J20 mice, plaque density was highest in hippocampal areas, followed by isocortex, with little to no involvement of olfactory or cortical subplate areas. Within the major brain divisions, distinct regions were identified with high (or low) plaque accumulation; for example, the lateral visual area within the isocortex of APP/PS1 mice had relatively higher plaque density compared with other cortical areas, while in hAPP-J20 mice, plaques were densest in the ventral retrosplenial cortex. In summary, we show how whole brain imaging of amyloid pathology in mice reveals the extent to which a given model recapitulates the regional Aβ deposition patterns described in AD.
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Affiliation(s)
| | | | - Joseph E Knox
- Allen Institute for Brain Science, Seattle, Washington
| | - Leonard Kuan
- Allen Institute for Brain Science, Seattle, Washington
| | - Nile Graddis
- Allen Institute for Brain Science, Seattle, Washington
| | - Andrew Pelos
- Allen Institute for Brain Science, Seattle, Washington.,Department of Neuroscience, Pomona College, Claremont, California
| | - Alice Mukora
- Allen Institute for Brain Science, Seattle, Washington
| | - Wayne Wakeman
- Allen Institute for Brain Science, Seattle, Washington
| | - Phillip Bohn
- Allen Institute for Brain Science, Seattle, Washington
| | - Anh Ho
- Allen Institute for Brain Science, Seattle, Washington
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29
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Li Y, Xu J, Wan P, Yu T, Zhu D. Optimization of GFP Fluorescence Preservation by a Modified uDISCO Clearing Protocol. Front Neuroanat 2018; 12:67. [PMID: 30158858 PMCID: PMC6104128 DOI: 10.3389/fnana.2018.00067] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Accepted: 07/25/2018] [Indexed: 12/12/2022] Open
Abstract
Tissue optical clearing techniques provide alternative approaches for imaging large-volume specimens. uDISCO, an organic-solvent-based method, stands out from the enormous array of available optical clearing methods by achieving whole-brain imaging with high transparency, size reduction and fluorescence preservation. In this study, we aimed to modify the uDISCO protocol to achieve better fluorescence preservation and to thereby further improve its optical imaging quality. First, we determined the optimal pH value for optimized uDISCO, termed “a-uDISCO” (alkaline pH-based uDISCO). Then, we compared fluorescence preservation between a-uDISCO and uDISCO. In addition, we validated the clearing performance of the optimized method according to several parameters, including tissue transparency, size changes, and the maintenance of cell morphology. Finally, we demonstrated that a-uDISCO enabled the high-quality brain-wide visualization of neuronal structures. This method potentially provides a better alternative for high-throughput imaging of samples with low-level fluorescence protein expression or for archiving and repetitive revisiting of rare samples.
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Affiliation(s)
- Yusha 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
| | - Jianyi 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
| | - Peng Wan
- 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
| | - Tingting Yu
- 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
| | - Dan Zhu
- 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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30
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Yang J, Hu W, Li H, Hou H, Tu Y, Liu B. Facile synthesis of a two-photon fluorescent probe based on pyrimidine 2-isothiocyanate and its application in bioimaging. Photochem Photobiol Sci 2018; 17:474-481. [PMID: 29582875 DOI: 10.1039/c8pp00071a] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Two-photon microscopy imaging has been widely applied in biological imaging, but the development of two-photon absorption probes is obviously lagging behind in the development of imaging technology. In this paper, a two-photon fluorescent probe (1) based on pyrimidine 2-isothiocyanate has been designed and synthesized through a simple method for two-photon biological imaging. Probe 1 was able to couple effectively with the amino groups on biomolecules. To verify the reactivity of the isothiocyanate group on probe 1 and the amine groups on the biomolecules, d-glucosamine was chosen as a model biomolecule to conjugate with probe 1. The result showed that probe 1 could effectively conjugate with d-glucosamine to synthesize probe 2, and the yield of probe 2 was 83%. After conjugating with d-glucosamine, linear absorption spectra, single-photon fluorescence spectra, and two-photon fluorescence spectra of probes 1 and 2 did not present significant changes. Probes 1 and 2 exhibited high fluorescence quantum yields (0.71-0.79) in toluene and chloroform. They also exhibited different photo-physical properties in solvents with different polarities. The two-photon absorption cross-section of probe 1 was 953 GM in toluene. In addition, probe 1 could be effectively conjugated with transferrin, and the conjugated probe (Tf-1) could be transported into Hep G2 cells through a receptor-mediated process for biological imaging. These results demonstrate that such probes are expected to have great potential applications in two-photon fluorescence bioimaging.
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Affiliation(s)
- Jie Yang
- College of Life Science and Chemistry, Wuhan Donghu University, Wuhan 430212, P. R. China.
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31
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Cohen-Adad J. Microstructural imaging in the spinal cord and validation strategies. Neuroimage 2018; 182:169-183. [PMID: 29635029 DOI: 10.1016/j.neuroimage.2018.04.009] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 03/02/2018] [Accepted: 04/06/2018] [Indexed: 12/13/2022] Open
Abstract
In vivo histology using magnetic resonance imaging (MRI) is a newly emerging research field that aims to non-invasively characterize tissue microstructure. The implications of in vivo histology are many, from discovering novel biomarkers to studying human development, to providing tools for disease diagnosis and monitoring the effects of novel treatments on tissue. This review focuses on quantitative MRI (qMRI) techniques that are used to map spinal cord microstructure. Opening with a rationale for non-invasive imaging of the spinal cord, this article continues with a brief overview of the existing MRI techniques for axon and myelin imaging, followed by the specific challenges and potential solutions for acquiring and processing such data. The final part of this review focuses on histological validation, with suggested tissue preparation, acquisition and processing protocols for large-scale microscopy.
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Affiliation(s)
- J Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, QC, Canada.
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32
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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: 145] [Impact Index Per Article: 20.7] [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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Large-scale 3-dimensional quantitative imaging of tissues: state-of-the-art and translational implications. Transl Res 2017; 189:1-12. [PMID: 28784428 PMCID: PMC5659947 DOI: 10.1016/j.trsl.2017.07.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2017] [Revised: 06/26/2017] [Accepted: 07/18/2017] [Indexed: 12/12/2022]
Abstract
Recent developments in automated optical sectioning microscope systems have enabled researchers to conduct high resolution, three-dimensional (3D) microscopy at the scale of millimeters in various types of tissues. This powerful technology allows the exploration of tissues at an unprecedented level of detail, while preserving the spatial context. By doing so, such technology will also enable researchers to explore cellular and molecular signatures within tissue and correlate with disease course. This will allow an improved understanding of pathophysiology and facilitate a precision medicine approach to assess the response to treatment. The ability to perform large-scale imaging in 3D cannot be realized without the widespread availability of accessible quantitative analysis. In this review, we will outline recent advances in large-scale 3D imaging and discuss the available methodologies to perform meaningful analysis and potential applications in translational research.
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Castonguay A, Lefebvre J, Pouliot P, Avti P, Moeini M, Lesage F. Serial optical coherence scanning reveals an association between cardiac function and the heart architecture in the aging rodent heart. BIOMEDICAL OPTICS EXPRESS 2017; 8:5027-5038. [PMID: 29188099 PMCID: PMC5695949 DOI: 10.1364/boe.8.005027] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 10/11/2017] [Accepted: 10/12/2017] [Indexed: 05/10/2023]
Abstract
Normal aging is accompanied by structural changes in the heart architecture. To explore this remodeling, we used a serial optical coherence tomography scanner to image entire mouse hearts at micron scale resolution. Ex vivo hearts of 7 young (4 months) and 5 old (24 months) C57BL/6 mice were acquired with the imaging platform. OCT of the myocardium revealed myofiber orientation changing linearly from the endocardium to the epicardium. In old mice, this rate of change was lower when compared to young mice while the average volume of old mice hearts was significantly larger (p<0.05). Myocardial wall thickening was also accompanied by extracellular spacing in the endocardium, resulting in a lower OCT attenuation coefficient in old mice endocardium (p<0.05). Prior to serial sectioning, cardiac function of the same hearts was imaged in vivo using MRI and revealed a reduced ejection fraction with aging. The use of a serial optical coherence tomography scanner allows new insight into fine age-related changes of the heart associated with changes in heart function.
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Affiliation(s)
- Alexandre Castonguay
- École Polytechnique de Montréal, C.P. 6079, Succ. Centre-Ville, Montréal QC, H3C3A7, Canada
| | - Joël Lefebvre
- École Polytechnique de Montréal, C.P. 6079, Succ. Centre-Ville, Montréal QC, H3C3A7, Canada
| | - Philippe Pouliot
- École Polytechnique de Montréal, C.P. 6079, Succ. Centre-Ville, Montréal QC, H3C3A7, Canada
- Institut de Cardiologie de Montréal, 5000 rue Bélanger Est, Montréal, QC, H1T1C8, Canada
| | - Pramod Avti
- Department of Biophysics, Postgraduate Institute of Medical Education and Research, Chandigarh, 160012, India
| | - Mohammad Moeini
- École Polytechnique de Montréal, C.P. 6079, Succ. Centre-Ville, Montréal QC, H3C3A7, Canada
- Institut de Cardiologie de Montréal, 5000 rue Bélanger Est, Montréal, QC, H1T1C8, Canada
| | - Frédéric Lesage
- École Polytechnique de Montréal, C.P. 6079, Succ. Centre-Ville, Montréal QC, H3C3A7, Canada
- Institut de Cardiologie de Montréal, 5000 rue Bélanger Est, Montréal, QC, H1T1C8, Canada
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Ishimaru T, Ishida J, Kim JD, Mizukami H, Hara K, Hashimoto M, Yagami KI, Sugiyama F, Fukamizu A. Angiodysplasia in embryo lacking protein arginine methyltransferase 1 in vascular endothelial cells. J Biochem 2017; 161:255-258. [PMID: 28003433 DOI: 10.1093/jb/mvw095] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2016] [Accepted: 12/13/2016] [Indexed: 01/29/2023] Open
Abstract
Protein arginine methyltransferase 1 (PRMT1) is involved in multiple cellular functions including proliferation and differentiation. Although PRMT1 is expressed in vascular endothelial cells (ECs), which are responsible for angiogenesis during embryonic development, its role has remained elusive. In this study, we generated endothelial-specific prmt1-knockout (Prmt1-ECKO) mice, and found that they died before embryonic day 15. The superficial temporal arteries in these embryos were poorly perfused with blood, and whole-mount 3D imaging revealed dilated and segmentalized luminal structures in Prmt1-ECKO fetuses in comparison with those of controls. Our findings provide evidence that PRMT1 is important for embryonic vascular formation.
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Affiliation(s)
- Tomohiro Ishimaru
- Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Junji Ishida
- Life science Center, Tsukuba Advanced Research Alliance, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Jun-Dal Kim
- Life science Center, Tsukuba Advanced Research Alliance, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Hayase Mizukami
- Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan
| | - Kanako Hara
- Master's Program in Medical Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan
| | - Misuzu Hashimoto
- PhD Program in Human Biology, School of Integrative and Global Majors, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
| | - Ken-Ichi Yagami
- Laboratory Animal Resource Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan
| | - Fumihiro Sugiyama
- Laboratory Animal Resource Center, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan
| | - Akiyoshi Fukamizu
- Graduate School of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan.,Life science Center, Tsukuba Advanced Research Alliance, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan.,Master's Program in Medical Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8575, Japan.,PhD Program in Human Biology, School of Integrative and Global Majors, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8577, Japan
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Gangolli M, Holleran L, Hee Kim J, Stein TD, Alvarez V, McKee AC, Brody DL. Quantitative validation of a nonlinear histology-MRI coregistration method using generalized Q-sampling imaging in complex human cortical white matter. Neuroimage 2017; 153:152-167. [PMID: 28365421 DOI: 10.1016/j.neuroimage.2017.03.059] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2016] [Revised: 03/24/2017] [Accepted: 03/29/2017] [Indexed: 12/14/2022] Open
Abstract
Advanced diffusion MRI methods have recently been proposed for detection of pathologies such as traumatic axonal injury and chronic traumatic encephalopathy which commonly affect complex cortical brain regions. However, radiological-pathological correlations in human brain tissue that detail the relationship between the multi-component diffusion signal and underlying pathology are lacking. We present a nonlinear voxel based two dimensional coregistration method that is useful for matching diffusion signals to quantitative metrics of high resolution histological images. When validated in ex vivo human cortical tissue at a 250×250×500 μm spatial resolution, the method proved robust in correlations between generalized q-sampling imaging and histologically based white matter fiber orientations, with r=0.94 for the primary fiber direction and r=0.88 for secondary fiber direction in each voxel. Importantly, however, the correlation was substantially worse with reduced spatial resolution or with fiber orientations derived using a diffusion tensor model. Furthermore, we have detailed a quantitative histological metric of white matter fiber integrity termed power coherence capable of distinguishing architecturally complex but intact white matter from disrupted white matter regions. These methods may allow for more sensitive and specific radiological-pathological correlations of neurodegenerative diseases affecting complex gray and white matter.
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Affiliation(s)
- Mihika Gangolli
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA
| | - Laurena Holleran
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Joong Hee Kim
- Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA
| | - Thor D Stein
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
| | - Victor Alvarez
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
| | - Ann C McKee
- Boston University Alzheimer's Disease and CTE Center, Boston University School of Medicine, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA
| | - David L Brody
- Department of Biomedical Engineering, Washington University School of Medicine, St. Louis, MO, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO, USA; Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO, USA.
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Cazemier JL, Clascá F, Tiesinga PHE. Connectomic Analysis of Brain Networks: Novel Techniques and Future Directions. Front Neuroanat 2016; 10:110. [PMID: 27881953 PMCID: PMC5101213 DOI: 10.3389/fnana.2016.00110] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2016] [Accepted: 10/25/2016] [Indexed: 12/31/2022] Open
Abstract
Brain networks, localized or brain-wide, exist only at the cellular level, i.e., between specific pre- and post-synaptic neurons, which are connected through functionally diverse synapses located at specific points of their cell membranes. "Connectomics" is the emerging subfield of neuroanatomy explicitly aimed at elucidating the wiring of brain networks with cellular resolution and a quantified accuracy. Such data are indispensable for realistic modeling of brain circuitry and function. A connectomic analysis, therefore, needs to identify and measure the soma, dendrites, axonal path, and branching patterns together with the synapses and gap junctions of the neurons involved in any given brain circuit or network. However, because of the submicron caliber, 3D complexity, and high packing density of most such structures, as well as the fact that axons frequently extend over long distances to make synapses in remote brain regions, creating connectomic maps is technically challenging and requires multi-scale approaches, Such approaches involve the combination of the most sensitive cell labeling and analysis methods available, as well as the development of new ones able to resolve individual cells and synapses with increasing high-throughput. In this review, we provide an overview of recently introduced high-resolution methods, which researchers wanting to enter the field of connectomics may consider. It includes several molecular labeling tools, some of which specifically label synapses, and covers a number of novel imaging tools such as brain clearing protocols and microscopy approaches. Apart from describing the tools, we also provide an assessment of their qualities. The criteria we use assess the qualities that tools need in order to contribute to deciphering the key levels of circuit organization. We conclude with a brief future outlook for neuroanatomic research, computational methods, and network modeling, where we also point out several outstanding issues like structure-function relations and the complexity of neural models.
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Affiliation(s)
- J Leonie Cazemier
- Department of Neuroinformatics, Donders Institute, Radboud UniversityNijmegen, Netherlands; Department of Cortical Structure and Function, Netherlands Institute for NeuroscienceAmsterdam, Netherlands
| | - Francisco Clascá
- Department of Anatomy, Histology and Neuroscience, School of Medicine, Autónoma University Madrid, Spain
| | - Paul H E Tiesinga
- Department of Neuroinformatics, Donders Institute, Radboud University Nijmegen, Netherlands
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Rockland KS. Lighting up Neuroanatomy. Front Neurosci 2016; 10:293. [PMID: 27444725 PMCID: PMC4917544 DOI: 10.3389/fnins.2016.00293] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2016] [Accepted: 06/10/2016] [Indexed: 11/13/2022] Open
Affiliation(s)
- Kathleen S Rockland
- Department of Anatomy & Neurobiology, Boston University School Medicine Boston, MA, USA
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