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Zhang Y, Wang M, Zhu Q, Guo Y, Liu B, Li J, Yao X, Kong C, Zhang Y, Huang Y, Qi H, Wu J, Guo ZV, Dai Q. Long-term mesoscale imaging of 3D intercellular dynamics across a mammalian organ. Cell 2024:S0092-8674(24)00917-6. [PMID: 39276776 DOI: 10.1016/j.cell.2024.08.026] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2024] [Revised: 06/06/2024] [Accepted: 08/13/2024] [Indexed: 09/17/2024]
Abstract
A comprehensive understanding of physio-pathological processes necessitates non-invasive intravital three-dimensional (3D) imaging over varying spatial and temporal scales. However, huge data throughput, optical heterogeneity, surface irregularity, and phototoxicity pose great challenges, leading to an inevitable trade-off between volume size, resolution, speed, sample health, and system complexity. Here, we introduce a compact real-time, ultra-large-scale, high-resolution 3D mesoscope (RUSH3D), achieving uniform resolutions of 2.6 × 2.6 × 6 μm3 across a volume of 8,000 × 6,000 × 400 μm3 at 20 Hz with low phototoxicity. Through the integration of multiple computational imaging techniques, RUSH3D facilitates a 13-fold improvement in data throughput and an orders-of-magnitude reduction in system size and cost. With these advantages, we observed premovement neural activity and cross-day visual representational drift across the mouse cortex, the formation and progression of multiple germinal centers in mouse inguinal lymph nodes, and heterogeneous immune responses following traumatic brain injury-all at single-cell resolution, opening up a horizon for intravital mesoscale study of large-scale intercellular interactions at the organ level.
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Affiliation(s)
- Yuanlong Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Mingrui Wang
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China
| | - Qiyu Zhu
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Yuduo Guo
- Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518071, China
| | - Bo Liu
- School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Laboratory of Dynamic Immunobiology, Institute for Immunology, Tsinghua University, Beijing 100084, China
| | - Jiamin Li
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Xiao Yao
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Chui Kong
- School of Information Science and Technology, Fudan University, Shanghai 200433, China
| | - Yi Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Yuchao Huang
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China
| | - Hai Qi
- School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China; Laboratory of Dynamic Immunobiology, Institute for Immunology, Tsinghua University, Beijing 100084, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
| | - Zengcai V Guo
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China; School of Basic Medical Sciences, Tsinghua University, Beijing 100084, China; Tsinghua-Peking Center for Life Sciences, Beijing 100084, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
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2
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Xu C, Nedergaard M, Fowell DJ, Friedl P, Ji N. Multiphoton fluorescence microscopy for in vivo imaging. Cell 2024; 187:4458-4487. [PMID: 39178829 PMCID: PMC11373887 DOI: 10.1016/j.cell.2024.07.036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 07/18/2024] [Accepted: 07/22/2024] [Indexed: 08/26/2024]
Abstract
Multiphoton fluorescence microscopy (MPFM) has been a game-changer for optical imaging, particularly for studying biological tissues deep within living organisms. MPFM overcomes the strong scattering of light in heterogeneous tissue by utilizing nonlinear excitation that confines fluorescence emission mostly to the microscope focal volume. This enables high-resolution imaging deep within intact tissue and has opened new avenues for structural and functional studies. MPFM has found widespread applications and has led to numerous scientific discoveries and insights into complex biological processes. Today, MPFM is an indispensable tool in many research communities. Its versatility and effectiveness make it a go-to technique for researchers investigating biological phenomena at the cellular and subcellular levels in their native environments. In this Review, the principles, implementations, capabilities, and limitations of MPFM are presented. Three application areas of MPFM, neuroscience, cancer biology, and immunology, are reviewed in detail and serve as examples for applying MPFM to biological research.
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Affiliation(s)
- Chris Xu
- School of Applied and Engineering Physics, Cornell University, Ithaca, NY 14850, USA
| | - Maiken Nedergaard
- Center for Translational Neuromedicine, Faculty of Health and Medical Sciences, University of Copenhagen, Nørre Alle 3B, 2200 Copenhagen, Denmark; University of Rochester Medical School, 601 Elmwood Avenue, Rochester, NY 14642, USA
| | - Deborah J Fowell
- Department of Microbiology & Immunology, Cornell University, Ithaca, NY 14853, USA
| | - Peter Friedl
- Department of Medical BioSciences, Radboud University Medical Centre, Geert Grooteplein 26-28, Nijmegen HB 6500, the Netherlands
| | - Na Ji
- Department of Neuroscience, Department of Physics, University of California Berkeley, Berkeley, CA 94720, USA.
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3
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Chen W, Ge X, Zhang Q, Natan RG, Fan JL, Scanziani M, Ji N. High-throughput volumetric mapping of synaptic transmission. Nat Methods 2024; 21:1298-1305. [PMID: 38898094 DOI: 10.1038/s41592-024-02309-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 05/15/2024] [Indexed: 06/21/2024]
Abstract
Volumetric imaging of synaptic transmission in vivo requires high spatial and high temporal resolution. Shaping the wavefront of two-photon fluorescence excitation light, we developed Bessel-droplet foci for high-contrast and high-resolution volumetric imaging of synapses. Applying our method to imaging glutamate release, we demonstrated high-throughput mapping of excitatory inputs at >1,000 synapses per volume and >500 dendritic spines per neuron in vivo and unveiled previously unseen features of functional synaptic organization in the mouse primary visual cortex.
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Affiliation(s)
- Wei Chen
- Department of Physics, University of California, Berkeley, CA, USA
- School of Mechanical Science and Engineering - Advanced Biomedical Imaging Facility, Huazhong University of Science and Technology, Wuhan, China
| | - Xinxin Ge
- Department of Physiology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Qinrong Zhang
- Department of Physics, University of California, Berkeley, CA, USA
| | - Ryan G Natan
- Department of Physics, University of California, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA
| | - Jiang Lan Fan
- Joint Bioengineering Graduate Program, University of California, Berkeley, CA, USA
- Joint Bioengineering Graduate Program, University of California, San Francisco, CA, USA
| | - Massimo Scanziani
- Department of Physiology, University of California, San Francisco, San Francisco, CA, USA
- Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, CA, USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, CA, USA.
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, USA.
- Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA.
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA.
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4
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Mok AT, Wang T, Zhao S, Kolkman KE, Wu D, Ouzounov DG, Seo C, Wu C, Fetcho JR, Xu C. A Large Field-of-view, Single-cell-resolution Two- and Three-Photon Microscope for Deep Imaging. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.14.566970. [PMID: 38014101 PMCID: PMC10680773 DOI: 10.1101/2023.11.14.566970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
In vivo imaging of large-scale neuron activity plays a pivotal role in unraveling the function of the brain's network. Multiphoton microscopy, a powerful tool for deep-tissue imaging, has received sustained interest in advancing its speed, field of view and imaging depth. However, to avoid thermal damage in scattering biological tissue, field of view decreases exponentially as imaging depth increases. We present a suite of innovations to overcome constraints on the field of view in three-photon microscopy and to perform deep imaging that is inaccessible to two-photon microscopy. These innovations enable us to image neuronal activities in a ~3.5-mm diameter field-of-view at 4 Hz with single-cell resolution and in the deepest cortical layer of mouse brains. We further demonstrate simultaneous large field-of-view two-photon and three-photon imaging, subcortical imaging in the mouse brain, and whole-brain imaging in adult zebrafish. The demonstrated techniques can be integrated into any multiphoton microscope for large-field-of-view and system-level neural circuit research.
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Affiliation(s)
- Aaron T. Mok
- School of Applied Engineering Physics, Cornell University, NY, USA
- Meining School of Biomedical Engineering, Cornell University, NY, USA
| | - Tianyu Wang
- School of Applied Engineering Physics, Cornell University, NY, USA
| | - Shitong Zhao
- School of Applied Engineering Physics, Cornell University, NY, USA
| | | | - Danni Wu
- Department of Population Health, New York University, NY, USA
| | | | - Changwoo Seo
- Department of Neurobiology and Behavior, Cornell University, NY, USA
| | - Chunyan Wu
- College of Veterinary Medicine, Cornell University, NY, USA
| | - Joseph R. Fetcho
- Department of Neurobiology and Behavior, Cornell University, NY, USA
| | - Chris Xu
- School of Applied Engineering Physics, Cornell University, NY, USA
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5
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Ataka M, Otomo K, Enoki R, Ishii H, Tsutsumi M, Kozawa Y, Sato S, Nemoto T. Multibeam continuous axial scanning two-photon microscopy for in vivo volumetric imaging in mouse brain. BIOMEDICAL OPTICS EXPRESS 2024; 15:1089-1101. [PMID: 38404301 PMCID: PMC10890896 DOI: 10.1364/boe.514826] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 01/12/2024] [Accepted: 01/15/2024] [Indexed: 02/27/2024]
Abstract
This study presents an alternative approach for two-photon volumetric imaging that combines multibeam lateral scanning with continuous axial scanning using a confocal spinning-disk scanner and an electrically focus tunable lens. Using this proposed system, the brain of a living mouse could be imaged at a penetration depth of over 450 μm from the surface. In vivo volumetric Ca2+ imaging at a volume rate of 1.5 Hz within a depth range of 130-200 μm, was segmented with an axial pitch of approximately 5-µm and revealed spontaneous activity of neurons with their 3D positions. This study offers a practical microscope design equipped with compact scanners, a simple control system, and readily adjustable imaging parameters, which is crucial for the widespread adoption of two-photon volumetric imaging.
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Affiliation(s)
- Mitsutoshi Ataka
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8787, Japan
| | - Kohei Otomo
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- Graduate School of Medicine, Juntendo University, Tokyo 113-8421, Japan
| | - Ryosuke Enoki
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- School of Life Sciences, The Graduate School of Advanced Studies, SOKENDAI, Okazaki 444-8787, Japan
| | - Hirokazu Ishii
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- School of Life Sciences, The Graduate School of Advanced Studies, SOKENDAI, Okazaki 444-8787, Japan
| | - Motosuke Tsutsumi
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- School of Life Sciences, The Graduate School of Advanced Studies, SOKENDAI, Okazaki 444-8787, Japan
| | - Yuichi Kozawa
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Shunichi Sato
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai 980-8577, Japan
| | - Tomomi Nemoto
- National Institute for Physiological Sciences, National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, Okazaki 444-8787, Japan
- School of Life Sciences, The Graduate School of Advanced Studies, SOKENDAI, Okazaki 444-8787, Japan
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6
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Chai Y, Qi K, Wu Y, Li D, Tan G, Guo Y, Chu J, Mu Y, Shen C, Wen Q. All-optical interrogation of brain-wide activity in freely swimming larval zebrafish. iScience 2024; 27:108385. [PMID: 38205255 PMCID: PMC10776927 DOI: 10.1016/j.isci.2023.108385] [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: 06/12/2023] [Revised: 09/22/2023] [Accepted: 10/30/2023] [Indexed: 01/12/2024] Open
Abstract
We introduce an all-optical technique that enables volumetric imaging of brain-wide calcium activity and targeted optogenetic stimulation of specific brain regions in unrestrained larval zebrafish. The system consists of three main components: a 3D tracking module, a dual-color fluorescence imaging module, and a real-time activity manipulation module. Our approach uses a sensitive genetically encoded calcium indicator in combination with a long Stokes shift red fluorescence protein as a reference channel, allowing the extraction of Ca2+ activity from signals contaminated by motion artifacts. The method also incorporates rapid 3D image reconstruction and registration, facilitating real-time selective optogenetic stimulation of different regions of the brain. By demonstrating that selective light activation of the midbrain regions in larval zebrafish could reliably trigger biased turning behavior and changes of brain-wide neural activity, we present a valuable tool for investigating the causal relationship between distributed neural circuit dynamics and naturalistic behavior.
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Affiliation(s)
- Yuming Chai
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Kexin Qi
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Yubin Wu
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Daguang Li
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Guodong Tan
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Yuqi Guo
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology and Center for Biomedical Optics and Molecular Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Jun Chu
- Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology and Center for Biomedical Optics and Molecular Imaging, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China
| | - Yu Mu
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
| | - Chen Shen
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
| | - Quan Wen
- Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- Hefei National Research Center for Physical Sciences at the Microscale, Center for Integrative Imaging, University of Science and Technology of China, Hefei, China
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7
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Xue F, Li F, Zhang KM, Ding L, Wang Y, Zhao X, Xu F, Zhang D, Sun M, Lau PM, Zhu Q, Zhou P, Bi GQ. Multi-region calcium imaging in freely behaving mice with ultra-compact head-mounted fluorescence microscopes. Natl Sci Rev 2024; 11:nwad294. [PMID: 38288367 PMCID: PMC10824555 DOI: 10.1093/nsr/nwad294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 09/26/2023] [Accepted: 11/23/2023] [Indexed: 01/31/2024] Open
Abstract
To investigate the circuit-level neural mechanisms of behavior, simultaneous imaging of neuronal activity in multiple cortical and subcortical regions is highly desired. Miniature head-mounted microscopes offer the capability of calcium imaging in freely behaving animals. However, implanting multiple microscopes on a mouse brain remains challenging due to space constraints and the cumbersome weight of the equipment. Here, we present TINIscope, a Tightly Integrated Neuronal Imaging microscope optimized for electronic and opto-mechanical design. With its compact and lightweight design of 0.43 g, TINIscope enables unprecedented simultaneous imaging of behavior-relevant activity in up to four brain regions in mice. Proof-of-concept experiments with TINIscope recorded over 1000 neurons in four hippocampal subregions and revealed concurrent activity patterns spanning across these regions. Moreover, we explored potential multi-modal experimental designs by integrating additional modules for optogenetics, electrical stimulation or local field potential recordings. Overall, TINIscope represents a timely and indispensable tool for studying the brain-wide interregional coordination that underlies unrestrained behaviors.
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Affiliation(s)
- Feng Xue
- Department of Precision Machinery and Precision Instruments, University of Science and Technology of China, Hefei 230026, China
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Fei Li
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Faculty of Life and Health Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Ke-ming Zhang
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Lufeng Ding
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
| | - Yang Wang
- Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China
| | - Xingtao Zhao
- Department of Modern Life Sciences and Biotecnology, Xiongan Institute of Innovation, Xiongan New Area, Xiongan 071899, China
| | - Fang Xu
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Faculty of Life and Health Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
| | - Danke Zhang
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Mingzhai Sun
- Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China
| | - Pak-Ming Lau
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Qingyuan Zhu
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
| | - Pengcheng Zhou
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- Faculty of Life and Health Sciences, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Guo-Qiang Bi
- Hefei National Laboratory for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei 230026, China
- Interdisciplinary Center for Brain Information, the Brain Cognition and Brain Disease Institute, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
- School of Life Sciences, University of Science and Technology of China, Hefei 230026, China
- Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen 518055, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
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8
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Yang L, Liu W, Shi L, Wu J, Zhang W, Chuang YA, Redding-Ochoa J, Kirkwood A, Savonenko AV, Worley PF. NMDA Receptor-Arc Signaling Is Required for Memory Updating and Is Disrupted in Alzheimer's Disease. Biol Psychiatry 2023; 94:706-720. [PMID: 36796600 PMCID: PMC10423741 DOI: 10.1016/j.biopsych.2023.02.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 01/31/2023] [Accepted: 02/06/2023] [Indexed: 02/16/2023]
Abstract
BACKGROUND Memory deficits are central to many neuropsychiatric diseases. During acquisition of new information, memories can become vulnerable to interference, yet mechanisms that underlie interference are unknown. METHODS We describe a novel transduction pathway that links the NMDA receptor (NMDAR) to AKT signaling via the immediate early gene Arc and evaluate its role in memory. The signaling pathway is validated using biochemical tools and transgenic mice, and function is evaluated in assays of synaptic plasticity and behavior. The translational relevance is evaluated in human postmortem brain. RESULTS Arc is dynamically phosphorylated by CaMKII (calcium/calmodulin-dependent protein kinase II) and binds the NMDAR subunits NR2A/NR2B and a previously unstudied PI3K (phosphoinositide 3-kinase) adapter p55PIK (PIK3R3) in vivo in response to novelty or tetanic stimulation in acute slices. NMDAR-Arc-p55PIK recruits p110α PI3K and mTORC2 (mechanistic target of rapamycin complex 2) to activate AKT. NMDAR-Arc-p55PIK-PI3K-mTORC2-AKT assembly occurs within minutes of exploratory behavior and localizes to sparse synapses throughout hippocampal and cortical regions. Studies using conditional (Nestin-Cre) p55PIK deletion mice indicate that NMDAR-Arc-p55PIK-PI3K-mTORC2-AKT functions to inhibit GSK3 and mediates input-specific metaplasticity that protects potentiated synapses from subsequent depotentiation. p55PIK conditional knockout mice perform normally in multiple behaviors including working memory and long-term memory tasks but exhibit deficits indicative of increased vulnerability to interference in both short-term and long-term paradigms. The NMDAR-AKT transduction complex is reduced in postmortem brain of individuals with early Alzheimer's disease. CONCLUSIONS A novel function of Arc mediates synapse-specific NMDAR-AKT signaling and metaplasticity that contributes to memory updating and is disrupted in human cognitive disease.
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Affiliation(s)
- Liuqing Yang
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Wenxue Liu
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Linyuan Shi
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jing Wu
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Wenchi Zhang
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Yang-An Chuang
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Javier Redding-Ochoa
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Alfredo Kirkwood
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Alena V Savonenko
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Paul F Worley
- Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, Maryland.
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9
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Das A, Holden S, Borovicka J, Icardi J, O'Niel A, Chaklai A, Patel D, Patel R, Kaech Petrie S, Raber J, Dana H. Large-scale recording of neuronal activity in freely-moving mice at cellular resolution. Nat Commun 2023; 14:6399. [PMID: 37828016 PMCID: PMC10570384 DOI: 10.1038/s41467-023-42083-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 09/28/2023] [Indexed: 10/14/2023] Open
Abstract
Current methods for recording large-scale neuronal activity from behaving mice at single-cell resolution require either fixing the mouse head under a microscope or attachment of a recording device to the animal's skull. Both of these options significantly affect the animal behavior and hence also the recorded brain activity patterns. Here, we introduce a different method to acquire snapshots of single-cell cortical activity maps from freely-moving mice using a calcium sensor called CaMPARI. CaMPARI has a unique property of irreversibly changing its color from green to red inside active neurons when illuminated with 400 nm light. We capitalize on this property to demonstrate cortex-wide activity recording without any head fixation, tethering, or attachment of a miniaturized device to the mouse's head. Multiple cortical regions were recorded while the mouse was performing a battery of behavioral and cognitive tests. We identified task-dependent activity patterns across motor and somatosensory cortices, with significant differences across sub-regions of the motor cortex and correlations across several activity patterns and task parameters. This CaMPARI-based recording method expands the capabilities of recording neuronal activity from freely-moving and behaving mice under minimally-restrictive experimental conditions and provides large-scale volumetric data that are currently not accessible otherwise.
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Affiliation(s)
- Aniruddha Das
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Sarah Holden
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Julie Borovicka
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Jacob Icardi
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Abigail O'Niel
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Ariel Chaklai
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
| | - Davina Patel
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Rushik Patel
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | | | - Jacob Raber
- Department of Behavioral Neuroscience, Oregon Health and Science University, Portland, OR, USA
- Departments of Neurology and Radiation Medicine, Division of Neuroscience, ONPRC, Oregon Health and Science University, Portland, OR, USA
| | - Hod Dana
- Department of Neurosciences, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, USA.
- Department of Molecular Medicine, Cleveland Clinic Lerner College of Medicine, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
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10
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Paul TC, Johnson KA, Hagen GM. Super-Resolution Imaging of Neuronal Structures with Structured Illumination Microscopy. Bioengineering (Basel) 2023; 10:1081. [PMID: 37760183 PMCID: PMC10525192 DOI: 10.3390/bioengineering10091081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2023] [Accepted: 09/09/2023] [Indexed: 09/29/2023] Open
Abstract
Super-resolution structured illumination microscopy (SR-SIM) is an optical fluorescence microscopy method which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing raw data and coarser illumination patterns, we imaged through a 150-micrometer-thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7-fold beyond conventional widefield imaging.
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Affiliation(s)
| | | | - Guy M. Hagen
- UCCS BioFrontiers Center, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, CO 80918, USA; (T.C.P.); (K.A.J.)
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11
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Qiao G, Gulisashvili D, Jablonska A, Zhao G, Janowski M, Walczak P, Liang Y. 3D printing-based frugal manufacturing of glass pipettes for minimally invasive delivery of therapeutics to the brain. NEUROPROTECTION 2023; 1:58-65. [PMID: 37771648 PMCID: PMC10538625 DOI: 10.1002/nep3.20] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 04/04/2023] [Indexed: 09/30/2023]
Abstract
Objective Intracerebral delivery of agents in liquid form is usually achieved through commercially available and durable metal needles. However, their size and texture may contribute to mechanical brain damage. Glass pipettes with a thin tip may significantly reduce injection-associated brain damage but require access to prohibitively expensive programmable pipette pullers. This study is to remove the economic barrier to the application of minimally invasive delivery of therapeutics to the brain, such as chemical compounds, viral vectors, and cells. Methods We took advantage of the rapid development of free educational online resources and emerging low-cost 3D printers by designing an affordable pipette puller (APP) to remove the cost obstacle. Results We showed that our APP could produce glass pipettes with a sharp tip opening down to 20 μm or less, which is sufficiently thin for the delivery of therapeutics into the brain. A pipeline from pipette pulling to brain injection using low-cost and open-source equipment was established to facilitate the application of the APP. Conclusion In the spirit of frugal science, our device may democratize glass pipette-puling and substantially promote the application of minimally invasive and precisely controlled delivery of therapeutics to the brain for finding more effective therapies of brain diseases.
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Affiliation(s)
- Guanda Qiao
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - David Gulisashvili
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Anna Jablonska
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Guiling Zhao
- Laboratory of Molecular Cardiology, Department of Physiology, Center for Biomedical Engineering and Technology, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Miroslaw Janowski
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Piotr Walczak
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Yajie Liang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
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12
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Petersen ED, Lapan AP, Castellanos Franco EA, Fillion AJ, Crespo EL, Lambert GG, Grady CJ, Zanca AT, Orcutt R, Hochgeschwender U, Shaner NC, Gilad AA. Bioluminescent Genetically Encoded Glutamate Indicators for Molecular Imaging of Neuronal Activity. ACS Synth Biol 2023; 12:2301-2309. [PMID: 37450884 PMCID: PMC10443529 DOI: 10.1021/acssynbio.2c00687] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2022] [Indexed: 07/18/2023]
Abstract
Genetically encoded optical sensors and advancements in microscopy instrumentation and techniques have revolutionized the scientific toolbox available for probing complex biological processes such as release of specific neurotransmitters. Most genetically encoded optical sensors currently used are based on fluorescence and have been highly successful tools for single-cell imaging in superficial brain regions. However, there remains a need to develop new tools for reporting neuronal activity in vivo within deeper structures without the need for hardware such as lenses or fibers to be implanted within the brain. Our approach to this problem is to replace the fluorescent elements of the existing biosensors with bioluminescent elements. This eliminates the need of external light sources to illuminate the sensor, thus allowing deeper brain regions to be imaged noninvasively. Here, we report the development of the first genetically encoded neurotransmitter indicators based on bioluminescent light emission. These probes were optimized by high-throughput screening of linker libraries. The selected probes exhibit robust changes in light output in response to the extracellular presence of the excitatory neurotransmitter glutamate. We expect this new approach to neurotransmitter indicator design to enable the engineering of specific bioluminescent probes for multiple additional neurotransmitters in the future, ultimately allowing neuroscientists to monitor activity associated with a specific neurotransmitter as it relates to behavior in a variety of neuronal and psychiatric disorders, among many other applications.
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Affiliation(s)
- Eric D. Petersen
- Department
of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan 48824, United States
- College
of Medicine, Central Michigan University, Mount Pleasant, Michigan 48859, United States
| | - Alexandra P. Lapan
- Department
of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan 48824, United States
| | | | - Adam J. Fillion
- Department
of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan 48824, United States
| | - Emmanuel L. Crespo
- College
of Medicine, Central Michigan University, Mount Pleasant, Michigan 48859, United States
| | - Gerard G. Lambert
- Department
of Neurosciences, University of California
San Diego School of Medicine, La Jolla, California 92093, United States
| | - Connor J. Grady
- Department
of Biomedical Engineering, Michigan State
University, East Lansing, Michigan 48824, United States
| | - Albertina T. Zanca
- Department
of Neurosciences, University of California
San Diego School of Medicine, La Jolla, California 92093, United States
| | - Richard Orcutt
- Department
of Neurosciences, University of California
San Diego School of Medicine, La Jolla, California 92093, United States
| | - Ute Hochgeschwender
- College
of Medicine, Central Michigan University, Mount Pleasant, Michigan 48859, United States
| | - Nathan C. Shaner
- Department
of Neurosciences, University of California
San Diego School of Medicine, La Jolla, California 92093, United States
| | - Assaf A. Gilad
- Department
of Chemical Engineering and Materials Science, Michigan State University, East Lansing, Michigan 48824, United States
- Department
of Radiology, Michigan State University, East Lansing, Michigan 48824, United States
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13
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Pian Q, Alfadhel M, Tang J, Lee GV, Li B, Fu B, Ayata Y, Yaseen MA, Boas DA, Secomb TW, Sakadzic S. Cortical microvascular blood flow velocity mapping by combining dynamic light scattering optical coherence tomography and two-photon microscopy. JOURNAL OF BIOMEDICAL OPTICS 2023; 28:076003. [PMID: 37484973 PMCID: PMC10362155 DOI: 10.1117/1.jbo.28.7.076003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 05/30/2023] [Accepted: 06/05/2023] [Indexed: 07/25/2023]
Abstract
Significance The accurate large-scale mapping of cerebral microvascular blood flow velocity is crucial for a better understanding of cerebral blood flow (CBF) regulation. Although optical imaging techniques enable both high-resolution microvascular angiography and fast absolute CBF velocity measurements in the mouse cortex, they usually require different imaging techniques with independent system configurations to maximize their performances. Consequently, it is still a challenge to accurately combine functional and morphological measurements to co-register CBF speed distribution from hundreds of microvessels with high-resolution microvascular angiograms. Aim We propose a data acquisition and processing framework to co-register a large set of microvascular blood flow velocity measurements from dynamic light scattering optical coherence tomography (DLS-OCT) with the corresponding microvascular angiogram obtained using two-photon microscopy (2PM). Approach We used DLS-OCT to first rapidly acquire a large set of microvascular velocities through a sealed cranial window in mice and then to acquire high-resolution microvascular angiograms using 2PM. The acquired data were processed in three steps: (i) 2PM angiogram coregistration with the DLS-OCT angiogram, (ii) 2PM angiogram segmentation and graphing, and (iii) mapping of the CBF velocities to the graph representation of the 2PM angiogram. Results We implemented the developed framework on the three datasets acquired from the mice cortices to facilitate the coregistration of the large sets of DLS-OCT flow velocity measurements with 2PM angiograms. We retrieved the distributions of red blood cell velocities in arterioles, venules, and capillaries as a function of the branching order from precapillary arterioles and postcapillary venules from more than 1000 microvascular segments. Conclusions The proposed framework may serve as a useful tool for quantitative analysis of large microvascular datasets obtained by OCT and 2PM in studies involving normal brain functioning, progression of various diseases, and numerical modeling of the oxygen advection and diffusion in the realistic microvascular networks.
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Affiliation(s)
- Qi Pian
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Mohammed Alfadhel
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - Jianbo Tang
- Southern University of Science and Technology, Department of Biomedical Engineering, Shenzhen, China
| | - Grace V. Lee
- University of Arizona, Program in Applied Mathematics, Tucson, Arizona, United States
| | - Baoqiang Li
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Chinese Academy of Sciences, Shenzhen Institute of Advanced Technology, Brain Cognition and Brain Disease Institute; Shenzhen Fundamental Research Institutions, Shenzhen–Hong Kong Institute of Brain Science, Shenzhen, Guangdong, China
| | - Buyin Fu
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Yagmur Ayata
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
| | - Mohammad Abbas Yaseen
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
- Northeastern University, Department of Bioengineering, Boston, Massachusetts, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Timothy W. Secomb
- University of Arizona, Program in Applied Mathematics, Tucson, Arizona, United States
- University of Arizona, Department of Mathematics, Tucson, Arizona, United States
- University of Arizona, Department of Physiology, Tucson, Arizona, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Charlestown, Massachusetts, United States
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14
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Paul TC, Johnson KA, Hagen GM. Super-resolution imaging of neuronal structure with structured illumination microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.26.542523. [PMID: 37292949 PMCID: PMC10245995 DOI: 10.1101/2023.05.26.542523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Super-resolution structured illumination microscopy (SR-SIM) is a method in optical fluorescence microscopy which is suitable for imaging a wide variety of cells and tissues in biological and biomedical research. Typically, SIM methods use high spatial frequency illumination patterns generated by laser interference. This approach provides high resolution but is limited to thin samples such as cultured cells. Using a different strategy for processing the raw data and coarser illumination patterns, we imaged through a 150 µm thick coronal section of a mouse brain expressing GFP in a subset of neurons. The resolution reached 144 nm, an improvement of 1.7 fold beyond conventional widefield imaging.
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Affiliation(s)
- Tristan C. Paul
- UCCS BioFrontiers Center, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918
| | - Karl A. Johnson
- UCCS BioFrontiers Center, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918
| | - Guy M. Hagen
- UCCS BioFrontiers Center, University of Colorado Colorado Springs, 1420 Austin Bluffs Parkway, Colorado Springs, Colorado, 80918
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15
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Zhang Y, Zhang G, Han X, Wu J, Li Z, Li X, Xiao G, Xie H, Fang L, Dai Q. Rapid detection of neurons in widefield calcium imaging datasets after training with synthetic data. Nat Methods 2023; 20:747-754. [PMID: 37002377 PMCID: PMC10172132 DOI: 10.1038/s41592-023-01838-7] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 03/07/2023] [Indexed: 04/03/2023]
Abstract
Widefield microscopy can provide optical access to multi-millimeter fields of view and thousands of neurons in mammalian brains at video rate. However, tissue scattering and background contamination results in signal deterioration, making the extraction of neuronal activity challenging, laborious and time consuming. Here we present our deep-learning-based widefield neuron finder (DeepWonder), which is trained by simulated functional recordings and effectively works on experimental data to achieve high-fidelity neuronal extraction. Equipped with systematic background contribution priors, DeepWonder conducts neuronal inference with an order-of-magnitude-faster speed and improved accuracy compared with alternative approaches. DeepWonder removes background contaminations and is computationally efficient. Specifically, DeepWonder accomplishes 50-fold signal-to-background ratio enhancement when processing terabytes-scale cortex-wide functional recordings, with over 14,000 neurons extracted in 17 h.
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Affiliation(s)
- Yuanlong Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Guoxun Zhang
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Xiaofei Han
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
| | - Ziwei Li
- Shanghai Artificial Intelligence Laboratory, Shanghai, China
- School of Information Science and Technology, Fudan University, Shanghai, China
| | - Xinyang Li
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Guihua Xiao
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China
| | - Lu Fang
- Department of Electronic Engineering, Tsinghua University, Beijing, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing, China.
- IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing, China.
- Beijing Laboratory of Brain and Cognitive Intelligence, Beijing Municipal Education Commission, Beijing, China.
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16
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Abdeladim L, Shin H, Jagadisan UK, Ogando MB, Adesnik H. Probing inter-areal computations with a cellular resolution two-photon holographic mesoscope. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.02.530875. [PMID: 37090604 PMCID: PMC10120651 DOI: 10.1101/2023.03.02.530875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Brain computation depends on intricately connected yet highly distributed neural networks. Due to the absence of the requisite technologies, causally testing fundamental hypotheses on the nature of inter-areal processing have remained largely out-of-each. Here we developed the first two photon holographic mesoscope, a system capable of simultaneously reading and writing neural activity patterns with single cell resolution across large regions of the brain. We demonstrate the precise photo-activation of spatial and temporal sequences of neurons in one brain area while reading out the downstream effect in several other regions. Investigators can use this new platform to understand feed-forward and feed-back processing in distributed neural circuits with single cell precision for the first time.
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17
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Chen S, Yang Q, Lim S. Efficient inference of synaptic plasticity rule with Gaussian process regression. iScience 2023; 26:106182. [PMID: 36879810 PMCID: PMC9985048 DOI: 10.1016/j.isci.2023.106182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 01/24/2023] [Accepted: 02/07/2023] [Indexed: 02/16/2023] Open
Abstract
Finding the form of synaptic plasticity is critical to understanding its functions underlying learning and memory. We investigated an efficient method to infer synaptic plasticity rules in various experimental settings. We considered biologically plausible models fitting a wide range of in-vitro studies and examined the recovery of their firing-rate dependence from sparse and noisy data. Among the methods assuming low-rankness or smoothness of plasticity rules, Gaussian process regression (GPR), a nonparametric Bayesian approach, performs the best. Under the conditions measuring changes in synaptic weights directly or measuring changes in neural activities as indirect observables of synaptic plasticity, which leads to different inference problems, GPR performs well. Also, GPR could simultaneously recover multiple plasticity rules and robustly perform under various plasticity rules and noise levels. Such flexibility and efficiency, particularly at the low sampling regime, make GPR suitable for recent experimental developments and inferring a broader class of plasticity models.
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Affiliation(s)
- Shirui Chen
- Department of Applied Mathematics, University of Washington, Lewis Hall 201, Box 353925, Seattle, WA 98195-3925, USA
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Qixin Yang
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University, The Suzanne and Charles Goodman Brain Sciences Building, Edmond J. Safra Campus, Jerusalem, 9190401, Israel
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
| | - Sukbin Lim
- Neural Science, New York University Shanghai, 1555 Century Avenue, Shanghai, 200122, China
- NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, 3663 Zhongshan Road North, Shanghai, 200062, China
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18
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Xiao Y, Deng P, Zhao Y, Yang S, Li B. Three-photon excited fluorescence imaging in neuroscience: From principles to applications. Front Neurosci 2023; 17:1085682. [PMID: 36891460 PMCID: PMC9986337 DOI: 10.3389/fnins.2023.1085682] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 02/02/2023] [Indexed: 02/22/2023] Open
Abstract
The development of three-photon microscopy (3PM) has greatly expanded the capability of imaging deep within biological tissues, enabling neuroscientists to visualize the structure and activity of neuronal populations with greater depth than two-photon imaging. In this review, we outline the history and physical principles of 3PM technology. We cover the current techniques for improving the performance of 3PM. Furthermore, we summarize the imaging applications of 3PM for various brain regions and species. Finally, we discuss the future of 3PM applications for neuroscience.
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Affiliation(s)
| | | | | | | | - Bo Li
- State Key Laboratory of Medical Neurobiology, Department of Neurology, Ministry of Education (MOE), Frontiers Center for Brain Science, Institute for Translational Brain Research, Huashan Hospital, Fudan University, Shanghai, China
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19
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Zhou Y, Sun N, Hu S. Deep Learning-Powered Bessel-Beam Multiparametric Photoacoustic Microscopy. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3544-3551. [PMID: 35788453 PMCID: PMC9767649 DOI: 10.1109/tmi.2022.3188739] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Enabling simultaneous and high-resolution quantification of the total concentration of hemoglobin ( [Formula: see text]), oxygen saturation of hemoglobin (sO2), and cerebral blood flow (CBF), multi-parametric photoacoustic microscopy (PAM) has emerged as a promising tool for functional and metabolic imaging of the live mouse brain. However, due to the limited depth of focus imposed by the Gaussian-beam excitation, the quantitative measurements become inaccurate when the imaging object is out of focus. To address this problem, we have developed a hardware-software combined approach by integrating Bessel-beam excitation and conditional generative adversarial network (cGAN)-based deep learning. Side-by-side comparison of the new cGAN-powered Bessel-beam multi-parametric PAM against the conventional Gaussian-beam multi-parametric PAM shows that the new system enables high-resolution, quantitative imaging of [Formula: see text], sO2, and CBF over a depth range of [Formula: see text] in the live mouse brain, with errors 13-58 times lower than those of the conventional system. Better fulfilling the rigid requirement of light focusing for accurate hemodynamic measurements, the deep learning-powered Bessel-beam multi-parametric PAM may find applications in large-field functional recording across the uneven brain surface and beyond (e.g., tumor imaging).
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20
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Kathe C, Skinnider MA, Hutson TH, Regazzi N, Gautier M, Demesmaeker R, Komi S, Ceto S, James ND, Cho N, Baud L, Galan K, Matson KJE, Rowald A, Kim K, Wang R, Minassian K, Prior JO, Asboth L, Barraud Q, Lacour SP, Levine AJ, Wagner F, Bloch J, Squair JW, Courtine G. The neurons that restore walking after paralysis. Nature 2022; 611:540-547. [DOI: 10.1038/s41586-022-05385-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 09/23/2022] [Indexed: 11/10/2022]
Abstract
AbstractA spinal cord injury interrupts pathways from the brain and brainstem that project to the lumbar spinal cord, leading to paralysis. Here we show that spatiotemporal epidural electrical stimulation (EES) of the lumbar spinal cord1–3 applied during neurorehabilitation4,5 (EESREHAB) restored walking in nine individuals with chronic spinal cord injury. This recovery involved a reduction in neuronal activity in the lumbar spinal cord of humans during walking. We hypothesized that this unexpected reduction reflects activity-dependent selection of specific neuronal subpopulations that become essential for a patient to walk after spinal cord injury. To identify these putative neurons, we modelled the technological and therapeutic features underlying EESREHAB in mice. We applied single-nucleus RNA sequencing6–9 and spatial transcriptomics10,11 to the spinal cords of these mice to chart a spatially resolved molecular atlas of recovery from paralysis. We then employed cell type12,13 and spatial prioritization to identify the neurons involved in the recovery of walking. A single population of excitatory interneurons nested within intermediate laminae emerged. Although these neurons are not required for walking before spinal cord injury, we demonstrate that they are essential for the recovery of walking with EES following spinal cord injury. Augmenting the activity of these neurons phenocopied the recovery of walking enabled by EESREHAB, whereas ablating them prevented the recovery of walking that occurs spontaneously after moderate spinal cord injury. We thus identified a recovery-organizing neuronal subpopulation that is necessary and sufficient to regain walking after paralysis. Moreover, our methodology establishes a framework for using molecular cartography to identify the neurons that produce complex behaviours.
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21
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Nietz AK, Popa LS, Streng ML, Carter RE, Kodandaramaiah SB, Ebner TJ. Wide-Field Calcium Imaging of Neuronal Network Dynamics In Vivo. BIOLOGY 2022; 11:1601. [PMID: 36358302 PMCID: PMC9687960 DOI: 10.3390/biology11111601] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 10/28/2022] [Accepted: 10/31/2022] [Indexed: 11/06/2022]
Abstract
A central tenet of neuroscience is that sensory, motor, and cognitive behaviors are generated by the communications and interactions among neurons, distributed within and across anatomically and functionally distinct brain regions. Therefore, to decipher how the brain plans, learns, and executes behaviors requires characterizing neuronal activity at multiple spatial and temporal scales. This includes simultaneously recording neuronal dynamics at the mesoscale level to understand the interactions among brain regions during different behavioral and brain states. Wide-field Ca2+ imaging, which uses single photon excitation and improved genetically encoded Ca2+ indicators, allows for simultaneous recordings of large brain areas and is proving to be a powerful tool to study neuronal activity at the mesoscopic scale in behaving animals. This review details the techniques used for wide-field Ca2+ imaging and the various approaches employed for the analyses of the rich neuronal-behavioral data sets obtained. Also discussed is how wide-field Ca2+ imaging is providing novel insights into both normal and altered neural processing in disease. Finally, we examine the limitations of the approach and new developments in wide-field Ca2+ imaging that are bringing new capabilities to this important technique for investigating large-scale neuronal dynamics.
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Affiliation(s)
- Angela K. Nietz
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Laurentiu S. Popa
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Martha L. Streng
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | - Russell E. Carter
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
| | | | - Timothy J. Ebner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, USA
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22
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Zhou W, Ke S, Li W, Yuan J, Li X, Jin R, Jia X, Jiang T, Dai Z, He G, Fang Z, Shi L, Zhang Q, Gong H, Luo Q, Sun W, Li A, Li P. Mapping the Function of Whole-Brain Projection at the Single Neuron Level. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2022; 9:e2202553. [PMID: 36228099 PMCID: PMC9685445 DOI: 10.1002/advs.202202553] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 09/18/2022] [Indexed: 06/16/2023]
Abstract
Axonal projection conveys neural information. The divergent and diverse projections of individual neurons imply the complexity of information flow. It is necessary to investigate the relationship between the projection and functional information at the single neuron level for understanding the rules of neural circuit assembly, but a gap remains due to a lack of methods to map the function to whole-brain projection. Here an approach is developed to bridge two-photon calcium imaging in vivo with high-resolution whole-brain imaging based on sparse labeling with the genetically encoded calcium indicator GCaMP6. Reliable whole-brain projections are captured by the high-definition fluorescent micro-optical sectioning tomography (HD-fMOST). A cross-modality cell matching is performed and the functional annotation of whole-brain projection at the single-neuron level (FAWPS) is obtained. Applying it to the layer 2/3 (L2/3) neurons in mouse visual cortex, the relationship is investigated between functional preferences and axonal projection features. The functional preference of projection motifs and the correlation between axonal length in MOs and neuronal orientation selectivity, suggest that projection motif-defined neurons form a functionally specific information flow, and the projection strength in specific targets relates to the information clarity. This pipeline provides a new way to understand the principle of neuronal information transmission.
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Affiliation(s)
- Wei Zhou
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Shanshan Ke
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Wenwei Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Jing Yuan
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Xiangning Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Rui Jin
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Xueyan Jia
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Tao Jiang
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Zimin Dai
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Guannan He
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Zhiwei Fang
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Liang Shi
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Qi Zhang
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
| | - Hui Gong
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Qingming Luo
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical EngineeringHainan UniversityHaikou570228China
| | - Wenzhi Sun
- Chinese Institute for Brain ResearchBeijing102206China
- School of Basic Medical SciencesCapital Medical UniversityBeijing100069China
| | - Anan Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
| | - Pengcheng Li
- Britton Chance Center and MoE Key Laboratory for Biomedical PhotonicsWuhan National Laboratory for OptoelectronicsHuazhong University of Science and TechnologyWuhan430074China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and ImagingChinese Academy of Medical SciencesHUST‐Suzhou Institute for BrainsmaticsJITRISuzhou215100China
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23
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Machado TA, Kauvar IV, Deisseroth K. Multiregion neuronal activity: the forest and the trees. Nat Rev Neurosci 2022; 23:683-704. [PMID: 36192596 PMCID: PMC10327445 DOI: 10.1038/s41583-022-00634-0] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2022] [Indexed: 12/12/2022]
Abstract
The past decade has witnessed remarkable advances in the simultaneous measurement of neuronal activity across many brain regions, enabling fundamentally new explorations of the brain-spanning cellular dynamics that underlie sensation, cognition and action. These recently developed multiregion recording techniques have provided many experimental opportunities, but thoughtful consideration of methodological trade-offs is necessary, especially regarding field of view, temporal acquisition rate and ability to guarantee cellular resolution. When applied in concert with modern optogenetic and computational tools, multiregion recording has already made possible fundamental biological discoveries - in part via the unprecedented ability to perform unbiased neural activity screens for principles of brain function, spanning dozens of brain areas and from local to global scales.
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Affiliation(s)
- Timothy A Machado
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - Isaac V Kauvar
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Karl Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA.
- Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA.
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
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24
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Cai Y, Wu J, Dai Q. Review on data analysis methods for mesoscale neural imaging in vivo. NEUROPHOTONICS 2022; 9:041407. [PMID: 35450225 PMCID: PMC9010663 DOI: 10.1117/1.nph.9.4.041407] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/23/2022] [Indexed: 06/14/2023]
Abstract
Significance: Mesoscale neural imaging in vivo has gained extreme popularity in neuroscience for its capacity of recording large-scale neurons in action. Optical imaging with single-cell resolution and millimeter-level field of view in vivo has been providing an accumulated database of neuron-behavior correspondence. Meanwhile, optical detection of neuron signals is easily contaminated by noises, background, crosstalk, and motion artifacts, while neural-level signal processing and network-level coordinate are extremely complicated, leading to laborious and challenging signal processing demands. The existing data analysis procedure remains unstandardized, which could be daunting to neophytes or neuroscientists without computational background. Aim: We hope to provide a general data analysis pipeline of mesoscale neural imaging shared between imaging modalities and systems. Approach: We divide the pipeline into two main stages. The first stage focuses on extracting high-fidelity neural responses at single-cell level from raw images, including motion registration, image denoising, neuron segmentation, and signal extraction. The second stage focuses on data mining, including neural functional mapping, clustering, and brain-wide network deduction. Results: Here, we introduce the general pipeline of processing the mesoscale neural images. We explain the principles of these procedures and compare different approaches and their application scopes with detailed discussions about the shortcomings and remaining challenges. Conclusions: There are great challenges and opportunities brought by the large-scale mesoscale data, such as the balance between fidelity and efficiency, increasing computational load, and neural network interpretability. We believe that global circuits on single-neuron level will be more extensively explored in the future.
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Affiliation(s)
- Yeyi Cai
- Tsinghua University, Department of Automation, Beijing, China
| | - Jiamin Wu
- Tsinghua University, Department of Automation, Beijing, China
| | - Qionghai Dai
- Tsinghua University, Department of Automation, Beijing, China
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25
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Xu D, Ding JB, Peng L. Depth random-access two-photon Bessel light-sheet imaging in brain tissue. OPTICS EXPRESS 2022; 30:26396-26406. [PMID: 36236832 PMCID: PMC9363024 DOI: 10.1364/oe.456871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 05/27/2022] [Accepted: 06/07/2022] [Indexed: 06/16/2023]
Abstract
Two-photon light-sheet fluorescence microscopy enables high-resolution imaging of neural activity in brain tissue at a high frame rate. Traditionally, light-sheet microscopy builds up a 3D stack by multiple depth scans with uniform spatial intervals, which substantially limits the volumetric imaging speed. Here, we introduce the depth random-access light-sheet microscopy, allowing rapid switching scanning depth for light-sheet imaging. With a low-cost electrically tunable lens and minimum modification of an existing two-photon light-sheet imaging instrument, we demonstrated fast random depth hopping light-sheet imaging at 100 frames per second in the live brain slice. Through depth random-access, calcium activities for an astrocyte were recorded on four user-selected detection planes at a refreshing rate of 25 Hz.
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Affiliation(s)
- Dongli Xu
- College of Optical Science, The University of Arizona, Tucson, AZ 85721, USA
- Department of Neurosurgery, Department of Neurology and Neurological Sciences, Wu-Tsai Neuroscience Institute, Stanford University, Stanford, CA 94305, USA
| | - Jun B. Ding
- Department of Neurosurgery, Department of Neurology and Neurological Sciences, Wu-Tsai Neuroscience Institute, Stanford University, Stanford, CA 94305, USA
| | - Leilei Peng
- College of Optical Science, The University of Arizona, Tucson, AZ 85721, USA
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26
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Guilbert J, Légaré A, De Koninck P, Desrosiers P, Desjardins M. Toward an integrative neurovascular framework for studying brain networks. NEUROPHOTONICS 2022; 9:032211. [PMID: 35434179 PMCID: PMC8989057 DOI: 10.1117/1.nph.9.3.032211] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 03/11/2022] [Indexed: 05/28/2023]
Abstract
Brain functional connectivity based on the measure of blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI) signals has become one of the most widely used measurements in human neuroimaging. However, the nature of the functional networks revealed by BOLD fMRI can be ambiguous, as highlighted by a recent series of experiments that have suggested that typical resting-state networks can be replicated from purely vascular or physiologically driven BOLD signals. After going through a brief review of the key concepts of brain network analysis, we explore how the vascular and neuronal systems interact to give rise to the brain functional networks measured with BOLD fMRI. This leads us to emphasize a view of the vascular network not only as a confounding element in fMRI but also as a functionally relevant system that is entangled with the neuronal network. To study the vascular and neuronal underpinnings of BOLD functional connectivity, we consider a combination of methodological avenues based on multiscale and multimodal optical imaging in mice, used in combination with computational models that allow the integration of vascular information to explain functional connectivity.
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Affiliation(s)
- Jérémie Guilbert
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
| | - Antoine Légaré
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Paul De Koninck
- Centre de recherche CERVO, Québec, Canada
- Université Laval, Department of Biochemistry, Microbiology, and Bioinformatics, Québec, Canada
| | - Patrick Desrosiers
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Centre de recherche CERVO, Québec, Canada
| | - Michèle Desjardins
- Université Laval, Department of Physics, Physical Engineering, and Optics, Québec, Canada
- Université Laval, Centre de recherche du CHU de Québec, Québec, Canada
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27
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Papaioannou S, Medini P. Advantages, Pitfalls, and Developments of All Optical Interrogation Strategies of Microcircuits in vivo. Front Neurosci 2022; 16:859803. [PMID: 35837124 PMCID: PMC9274136 DOI: 10.3389/fnins.2022.859803] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 05/30/2022] [Indexed: 12/03/2022] Open
Abstract
The holy grail for every neurophysiologist is to conclude a causal relationship between an elementary behaviour and the function of a specific brain area or circuit. Our effort to map elementary behaviours to specific brain loci and to further manipulate neural activity while observing the alterations in behaviour is in essence the goal for neuroscientists. Recent advancements in the area of experimental brain imaging in the form of longer wavelength near infrared (NIR) pulsed lasers with the development of highly efficient optogenetic actuators and reporters of neural activity, has endowed us with unprecedented resolution in spatiotemporal precision both in imaging neural activity as well as manipulating it with multiphoton microscopy. This readily available toolbox has introduced a so called all-optical physiology and interrogation of circuits and has opened new horizons when it comes to precisely, fast and non-invasively map and manipulate anatomically, molecularly or functionally identified mesoscopic brain circuits. The purpose of this review is to describe the advantages and possible pitfalls of all-optical approaches in system neuroscience, where by all-optical we mean use of multiphoton microscopy to image the functional response of neuron(s) in the network so to attain flexible choice of the cells to be also optogenetically photostimulated by holography, in absence of electrophysiology. Spatio-temporal constraints will be compared toward the classical reference of electrophysiology methods. When appropriate, in relation to current limitations of current optical approaches, we will make reference to latest works aimed to overcome these limitations, in order to highlight the most recent developments. We will also provide examples of types of experiments uniquely approachable all-optically. Finally, although mechanically non-invasive, all-optical electrophysiology exhibits potential off-target effects which can ambiguate and complicate the interpretation of the results. In summary, this review is an effort to exemplify how an all-optical experiment can be designed, conducted and interpreted from the point of view of the integrative neurophysiologist.
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28
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Wang Y, Wang J. Intravital Imaging of Inflammatory Response in Liver Disease. Front Cell Dev Biol 2022; 10:922041. [PMID: 35837329 PMCID: PMC9274191 DOI: 10.3389/fcell.2022.922041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2022] [Accepted: 05/16/2022] [Indexed: 11/17/2022] Open
Abstract
The healthy liver requires a strictly controlled crosstalk between immune and nonimmune cells to maintain its function and homeostasis. A well-conditioned immune system can effectively recognize and clear noxious stimuli by a self-limited, small-scale inflammatory response. This regulated inflammatory process enables the liver to cope with daily microbial exposure and metabolic stress, which is beneficial for hepatic self-renewal and tissue remodeling. However, the failure to clear noxious stimuli or dysregulation of immune response can lead to uncontrolled liver inflammation, liver dysfunction, and severe liver disease. Numerous highly dynamic circulating immune cells and sessile resident immune and parenchymal cells interact and communicate with each other in an incredibly complex way to regulate the inflammatory response in both healthy and diseased liver. Intravital imaging is a powerful tool to visualize individual cells in vivo and has been widely used for dissecting the behavior and interactions between various cell types in the complex architecture of the liver. Here, we summarize some new findings obtained with the use of intravital imaging, which enhances our understanding of the complexity of immune cell behavior, cell–cell interaction, and spatial organization during the physiological and pathological liver inflammatory response.
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29
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Biomedical Microscopic Imaging in Computational Intelligence Using Deep Learning Ensemble Convolution Learning-Based Feature Extraction and Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:3531308. [PMID: 35795729 PMCID: PMC9252635 DOI: 10.1155/2022/3531308] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/30/2022] [Accepted: 05/05/2022] [Indexed: 11/22/2022]
Abstract
Microscopy image analysis gives quantitative support for enhancing the characterizations of various diseases, including breast cancer, lung cancer, and brain tumors. As a result, it is crucial in computer-assisted diagnosis and prognosis. Understanding the biological principles underlying these dynamic image sequences often necessitates precise analysis and statistical quantification, a major discipline issue. Deep learning methods are increasingly used in bioimage processing as they grow rapidly. This research proposes novel biomedical microscopic image analysis techniques using deep learning architectures based on feature extraction and classification. Here, the input image has been taken as microscopic image, and it has been processed and analyzed for noise removal, edge smoothening, and normalization. The processed image has been extracted based on their features in microscopic image analysis using ConVol_NN architecture with AlexNet model. Then, the features have been classified using ensemble of Inception-ResNet and VGG-16 (EN_InResNet_VGG-16) architectures. The experimental results show various dataset analyses in terms of accuracy of 98%, precision of 90%, computational time of 79%, SNR of 89%, and MSE of 62%.
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30
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Munck S, Cawthorne C, Escamilla‐Ayala A, Kerstens A, Gabarre S, Wesencraft K, Battistella E, Craig R, Reynaud EG, Swoger J, McConnell G. Challenges and advances in optical 3D mesoscale imaging. J Microsc 2022; 286:201-219. [PMID: 35460574 PMCID: PMC9325079 DOI: 10.1111/jmi.13109] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 03/02/2022] [Accepted: 04/14/2022] [Indexed: 12/14/2022]
Abstract
Optical mesoscale imaging is a rapidly developing field that allows the visualisation of larger samples than is possible with standard light microscopy, and fills a gap between cell and organism resolution. It spans from advanced fluorescence imaging of micrometric cell clusters to centimetre-size complete organisms. However, with larger volume specimens, new problems arise. Imaging deeper into tissues at high resolution poses challenges ranging from optical distortions to shadowing from opaque structures. This manuscript discusses the latest developments in mesoscale imaging and highlights limitations, namely labelling, clearing, absorption, scattering, and also sample handling. We then focus on approaches that seek to turn mesoscale imaging into a more quantitative technique, analogous to quantitative tomography in medical imaging, highlighting a future role for digital and physical phantoms as well as artificial intelligence.
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Affiliation(s)
- Sebastian Munck
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | | | - Abril Escamilla‐Ayala
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | - Axelle Kerstens
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | - Sergio Gabarre
- VIB‐KU Leuven Center for Brain & Disease ResearchLight Microscopy Expertise Unit & VIB BioImaging CoreLeuvenBelgium
- KU Leuven Department of NeurosciencesLeuven Brain InstituteLeuvenBelgium
| | | | | | - Rebecca Craig
- Department of Physics, SUPAUniversity of StrathclydeGlasgowUK
| | - Emmanuel G. Reynaud
- School of Biomolecular and Biomedical ScienceUniversity College DublinDublinBelfieldIreland
| | - Jim Swoger
- European Molecular Biology Laboratory (EMBL) BarcelonaBarcelonaSpain
| | - Gail McConnell
- Department of Physics, SUPAUniversity of StrathclydeGlasgowUK
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31
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Ota K, Uwamori H, Ode T, Murayama M. Breaking trade-offs: development of fast, high-resolution, wide-field two-photon microscopes to reveal the computational principles of the brain. Neurosci Res 2022; 179:3-14. [PMID: 35390357 DOI: 10.1016/j.neures.2022.03.010] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Revised: 02/26/2022] [Accepted: 03/07/2022] [Indexed: 11/29/2022]
Abstract
Information in the brain is represented by the collective and coordinated activity of single neurons. Activity is determined by a large amount of dynamic synaptic inputs from neurons in the same and/or distant brain regions. Therefore, the simultaneous recording of single neurons across several brain regions is critical for revealing the interactions among neurons that reflect the computational principles of the brain. Recently, several wide-field two-photon (2P) microscopes equipped with sizeable objective lenses have been reported. These microscopes enable large-scale in vivo calcium imaging and have the potential to make a significant contribution to the elucidation of information-processing mechanisms in the cerebral cortex. This review discusses recent reports on wide-field 2P microscopes and describes the trade-offs encountered in developing wide-field 2P microscopes. Large-scale imaging of neural activity allows us to test hypotheses proposed in theoretical neuroscience, and to identify rare but influential neurons that have potentially significant impacts on the whole-brain system.
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Affiliation(s)
- Keisuke Ota
- Department of Neurochemistry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo113-0033, Japan; Center for Brain Science, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama351-0198, Japan.
| | - Hiroyuki Uwamori
- Center for Brain Science, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama351-0198, Japan
| | - Takahiro Ode
- Center for Brain Science, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama351-0198, Japan; FOV Corporation, 2-12-3 Taru-machi, Kouhoku-ku, Yokohama, Kanagawa222-0001, Japan
| | - Masanori Murayama
- Center for Brain Science, RIKEN, 2-1 Hirosawa, Wako-shi, Saitama351-0198, Japan
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32
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Tran CHT. Toolbox for studying neurovascular coupling in vivo, with a focus on vascular activity and calcium dynamics in astrocytes. NEUROPHOTONICS 2022; 9:021909. [PMID: 35295714 PMCID: PMC8920490 DOI: 10.1117/1.nph.9.2.021909] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2021] [Accepted: 02/23/2022] [Indexed: 05/14/2023]
Abstract
Significance: Insights into the cellular activity of each member of the neurovascular unit (NVU) is critical for understanding their contributions to neurovascular coupling (NVC)-one of the key control mechanisms in cerebral blood flow regulation. Advances in imaging and genetic tools have enhanced our ability to observe, manipulate and understand the cellular activity of NVU components, namely neurons, astrocytes, microglia, endothelial cells, vascular smooth muscle cells, and pericytes. However, there are still many unresolved questions. Since astrocytes are considered electrically unexcitable,Ca 2 + signaling is the main parameter used to monitor their activity. It is therefore imperative to study astrocyticCa 2 + dynamics simultaneously with vascular activity using tools appropriate for the question of interest. Aim: To highlight currently available genetic and imaging tools for studying the NVU-and thus NVC-with a focus on astrocyteCa 2 + dynamics and vascular activity, and discuss the utility, technical advantages, and limitations of these tools for elucidating NVC mechanisms. Approach: We draw attention to some outstanding questions regarding the mechanistic basis of NVC and emphasize the role of astrocyticCa 2 + elevations in functional hyperemia. We further discuss commonly used genetic, and optical imaging tools, as well as some newly developed imaging modalities for studying NVC at the cellular level, highlighting their advantages and limitations. Results: We provide an overview of the current state of NVC research, focusing on the role of astrocyticCa 2 + elevations in functional hyperemia; summarize recent advances in genetically engineeredCa 2 + indicators, fluorescence microscopy techniques for studying NVC; and discuss the unmet challenges for future imaging development. Conclusions: Advances in imaging techniques together with improvements in genetic tools have significantly contributed to our understanding of NVC. Many pieces of the puzzle have been revealed, but many more remain to be discovered. Ultimately, optimizing NVC research will require a concerted effort to improve imaging techniques, available genetic tools, and analytical software.
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Affiliation(s)
- Cam Ha T. Tran
- University of Nevada, Reno School of Medicine, Department of Physiology and Cell Biology, Reno, Nevada, United States
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Drobizhev M, Molina RS, Franklin J. Multiphoton Bleaching of Red Fluorescent Proteins and the Ways to Reduce It. Int J Mol Sci 2022; 23:770. [PMID: 35054953 PMCID: PMC8775990 DOI: 10.3390/ijms23020770] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/06/2022] [Accepted: 01/07/2022] [Indexed: 11/16/2022] Open
Abstract
Red fluorescent proteins and biosensors built upon them are potentially beneficial for two-photon laser microscopy (TPLM) because they can image deeper layers of tissue, compared to green fluorescent proteins. However, some publications report on their very fast photobleaching, especially upon excitation at 750-800 nm. Here we study the multiphoton bleaching properties of mCherry, mPlum, tdTomato, and jREX-GECO1, measuring power dependences of photobleaching rates K at different excitation wavelengths across the whole two-photon absorption spectrum. Although all these proteins contain the chromophore with the same chemical structure, the mechanisms of their multiphoton bleaching are different. The number of photons required to initiate a photochemical reaction varies, depending on wavelength and power, from 2 (all four proteins) to 3 (jREX-GECO1) to 4 (mCherry, mPlum, tdTomato), and even up to 8 (tdTomato). We found that at sufficiently low excitation power P, the rate K often follows a quadratic power dependence, that turns into higher order dependence (K~Pα with α > 2) when the power surpasses a particular threshold P*. An optimum intensity for TPLM is close to the P*, because it provides the highest signal-to-background ratio and any further reduction of laser intensity would not improve the fluorescence/bleaching rate ratio. Additionally, one should avoid using wavelengths shorter than a particular threshold to avoid fast bleaching due to multiphoton ionization.
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Affiliation(s)
- Mikhail Drobizhev
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717, USA;
| | - Rosana S. Molina
- Department of Microbiology and Cell Biology, Montana State University, Bozeman, MT 59717, USA;
| | - Jacob Franklin
- Vidrio Technologies LLC, 19955 Highland Vista Drive Suite 150, Ashburn, VA 20147, USA;
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Kim TH, Schnitzer MJ. Fluorescence imaging of large-scale neural ensemble dynamics. Cell 2022; 185:9-41. [PMID: 34995519 PMCID: PMC8849612 DOI: 10.1016/j.cell.2021.12.007] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2021] [Revised: 12/06/2021] [Accepted: 12/07/2021] [Indexed: 12/14/2022]
Abstract
Recent progress in fluorescence imaging allows neuroscientists to observe the dynamics of thousands of individual neurons, identified genetically or by their connectivity, across multiple brain areas and for extended durations in awake behaving mammals. We discuss advances in fluorescent indicators of neural activity, viral and genetic methods to express these indicators, chronic animal preparations for long-term imaging studies, and microscopes to monitor and manipulate the activity of large neural ensembles. Ca2+ imaging studies of neural activity can track brain area interactions and distributed information processing at cellular resolution. Across smaller spatial scales, high-speed voltage imaging reveals the distinctive spiking patterns and coding properties of targeted neuron types. Collectively, these innovations will propel studies of brain function and dovetail with ongoing neuroscience initiatives to identify new neuron types and develop widely applicable, non-human primate models. The optical toolkit's growing sophistication also suggests that "brain observatory" facilities would be useful open resources for future brain-imaging studies.
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Affiliation(s)
- Tony Hyun Kim
- James Clark Center for Biomedical Engineering & Sciences, Stanford University, Stanford, CA 94305, USA; CNC Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
| | - Mark J Schnitzer
- James Clark Center for Biomedical Engineering & Sciences, Stanford University, Stanford, CA 94305, USA; CNC Program, Stanford University, Stanford, CA 94305, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA 94305, USA.
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Abdelfattah AS, Ahuja S, Akkin T, Allu SR, Brake J, Boas DA, Buckley EM, Campbell RE, Chen AI, Cheng X, Čižmár T, Costantini I, De Vittorio M, Devor A, Doran PR, El Khatib M, Emiliani V, Fomin-Thunemann N, Fainman Y, Fernandez-Alfonso T, Ferri CGL, Gilad A, Han X, Harris A, Hillman EMC, Hochgeschwender U, Holt MG, Ji N, Kılıç K, Lake EMR, Li L, Li T, Mächler P, Miller EW, Mesquita RC, Nadella KMNS, Nägerl UV, Nasu Y, Nimmerjahn A, Ondráčková P, Pavone FS, Perez Campos C, Peterka DS, Pisano F, Pisanello F, Puppo F, Sabatini BL, Sadegh S, Sakadzic S, Shoham S, Shroff SN, Silver RA, Sims RR, Smith SL, Srinivasan VJ, Thunemann M, Tian L, Tian L, Troxler T, Valera A, Vaziri A, Vinogradov SA, Vitale F, Wang LV, Uhlířová H, Xu C, Yang C, Yang MH, Yellen G, Yizhar O, Zhao Y. Neurophotonic tools for microscopic measurements and manipulation: status report. NEUROPHOTONICS 2022; 9:013001. [PMID: 35493335 PMCID: PMC9047450 DOI: 10.1117/1.nph.9.s1.013001] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Neurophotonics was launched in 2014 coinciding with the launch of the BRAIN Initiative focused on development of technologies for advancement of neuroscience. For the last seven years, Neurophotonics' agenda has been well aligned with this focus on neurotechnologies featuring new optical methods and tools applicable to brain studies. While the BRAIN Initiative 2.0 is pivoting towards applications of these novel tools in the quest to understand the brain, this status report reviews an extensive and diverse toolkit of novel methods to explore brain function that have emerged from the BRAIN Initiative and related large-scale efforts for measurement and manipulation of brain structure and function. Here, we focus on neurophotonic tools mostly applicable to animal studies. A companion report, scheduled to appear later this year, will cover diffuse optical imaging methods applicable to noninvasive human studies. For each domain, we outline the current state-of-the-art of the respective technologies, identify the areas where innovation is needed, and provide an outlook for the future directions.
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Affiliation(s)
- Ahmed S. Abdelfattah
- Brown University, Department of Neuroscience, Providence, Rhode Island, United States
| | - Sapna Ahuja
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Taner Akkin
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Srinivasa Rao Allu
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Joshua Brake
- Harvey Mudd College, Department of Engineering, Claremont, California, United States
| | - David A. Boas
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Erin M. Buckley
- Georgia Institute of Technology and Emory University, Wallace H. Coulter Department of Biomedical Engineering, Atlanta, Georgia, United States
- Emory University, Department of Pediatrics, Atlanta, Georgia, United States
| | - Robert E. Campbell
- University of Tokyo, Department of Chemistry, Tokyo, Japan
- University of Alberta, Department of Chemistry, Edmonton, Alberta, Canada
| | - Anderson I. Chen
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Xiaojun Cheng
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Tomáš Čižmár
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Irene Costantini
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Biology, Florence, Italy
- National Institute of Optics, National Research Council, Rome, Italy
| | - Massimo De Vittorio
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Anna Devor
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Patrick R. Doran
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Mirna El Khatib
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | | | - Natalie Fomin-Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Yeshaiahu Fainman
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Tomas Fernandez-Alfonso
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Christopher G. L. Ferri
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Ariel Gilad
- The Hebrew University of Jerusalem, Institute for Medical Research Israel–Canada, Department of Medical Neurobiology, Faculty of Medicine, Jerusalem, Israel
| | - Xue Han
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Andrew Harris
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | | | - Ute Hochgeschwender
- Central Michigan University, Department of Neuroscience, Mount Pleasant, Michigan, United States
| | - Matthew G. Holt
- University of Porto, Instituto de Investigação e Inovação em Saúde (i3S), Porto, Portugal
| | - Na Ji
- University of California Berkeley, Department of Physics, Berkeley, California, United States
| | - Kıvılcım Kılıç
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evelyn M. R. Lake
- Yale School of Medicine, Department of Radiology and Biomedical Imaging, New Haven, Connecticut, United States
| | - Lei Li
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Tianqi Li
- University of Minnesota, Department of Biomedical Engineering, Minneapolis, Minnesota, United States
| | - Philipp Mächler
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Evan W. Miller
- University of California Berkeley, Departments of Chemistry and Molecular & Cell Biology and Helen Wills Neuroscience Institute, Berkeley, California, United States
| | | | | | - U. Valentin Nägerl
- Interdisciplinary Institute for Neuroscience University of Bordeaux & CNRS, Bordeaux, France
| | - Yusuke Nasu
- University of Tokyo, Department of Chemistry, Tokyo, Japan
| | - Axel Nimmerjahn
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, California, United States
| | - Petra Ondráčková
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Francesco S. Pavone
- National Institute of Optics, National Research Council, Rome, Italy
- University of Florence, European Laboratory for Non-Linear Spectroscopy, Department of Physics, Florence, Italy
| | - Citlali Perez Campos
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Darcy S. Peterka
- Columbia University, Zuckerman Mind Brain Behavior Institute, New York, United States
| | - Filippo Pisano
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Ferruccio Pisanello
- Istituto Italiano di Tecnologia, Center for Biomolecular Nanotechnologies, Arnesano, Italy
| | - Francesca Puppo
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Bernardo L. Sabatini
- Harvard Medical School, Howard Hughes Medical Institute, Department of Neurobiology, Boston, Massachusetts, United States
| | - Sanaz Sadegh
- University of California San Diego, Departments of Neurosciences, La Jolla, California, United States
| | - Sava Sakadzic
- Massachusetts General Hospital, Harvard Medical School, Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Massachusetts, United States
| | - Shy Shoham
- New York University Grossman School of Medicine, Tech4Health and Neuroscience Institutes, New York, New York, United States
| | - Sanaya N. Shroff
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - R. Angus Silver
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Ruth R. Sims
- Sorbonne University, INSERM, CNRS, Institut de la Vision, Paris, France
| | - Spencer L. Smith
- University of California Santa Barbara, Department of Electrical and Computer Engineering, Santa Barbara, California, United States
| | - Vivek J. Srinivasan
- New York University Langone Health, Departments of Ophthalmology and Radiology, New York, New York, United States
| | - Martin Thunemann
- Boston University, Department of Biomedical Engineering, Boston, Massachusetts, United States
| | - Lei Tian
- Boston University, Departments of Electrical Engineering and Biomedical Engineering, Boston, Massachusetts, United States
| | - Lin Tian
- University of California Davis, Department of Biochemistry and Molecular Medicine, Davis, California, United States
| | - Thomas Troxler
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Antoine Valera
- University College London, Department of Neuroscience, Physiology and Pharmacology, London, United Kingdom
| | - Alipasha Vaziri
- Rockefeller University, Laboratory of Neurotechnology and Biophysics, New York, New York, United States
- The Rockefeller University, The Kavli Neural Systems Institute, New York, New York, United States
| | - Sergei A. Vinogradov
- University of Pennsylvania, Perelman School of Medicine, Department of Biochemistry and Biophysics, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, School of Arts and Sciences, Department of Chemistry, Philadelphia, Pennsylvania, United States
| | - Flavia Vitale
- Center for Neuroengineering and Therapeutics, Departments of Neurology, Bioengineering, Physical Medicine and Rehabilitation, Philadelphia, Pennsylvania, United States
| | - Lihong V. Wang
- California Institute of Technology, Andrew and Peggy Cherng Department of Medical Engineering, Department of Electrical Engineering, Pasadena, California, United States
| | - Hana Uhlířová
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, Czech Republic
| | - Chris Xu
- Cornell University, School of Applied and Engineering Physics, Ithaca, New York, United States
| | - Changhuei Yang
- California Institute of Technology, Departments of Electrical Engineering, Bioengineering and Medical Engineering, Pasadena, California, United States
| | - Mu-Han Yang
- University of California San Diego, Department of Electrical and Computer Engineering, La Jolla, California, United States
| | - Gary Yellen
- Harvard Medical School, Department of Neurobiology, Boston, Massachusetts, United States
| | - Ofer Yizhar
- Weizmann Institute of Science, Department of Brain Sciences, Rehovot, Israel
| | - Yongxin Zhao
- Carnegie Mellon University, Department of Biological Sciences, Pittsburgh, Pennsylvania, United States
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36
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Improving Multiphoton Microscopy by Combining Spherical Aberration Patterns and Variable Axicons. PHOTONICS 2021. [DOI: 10.3390/photonics8120573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Multiphoton (MP) microscopy is a well-established method for the non-invasive imaging of biological tissues. However, its optical sectioning capabilities are reduced due to specimen-induced aberrations. Both the manipulation of spherical aberration (SA) and the use of axicons have been reported to be useful techniques to bypass this limitation. We propose the combination of SA patterns and variable axicons to further improve the quality of MP microscopy images. This approach provides enhanced images at different depth locations whose quality is better than those corresponding to the use of SA or axicons separately. Thus, the procedure proposed herein facilitates the visualization of details and increases the depth observable at high resolution.
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37
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Yu CH, Stirman JN, Yu Y, Hira R, Smith SL. Diesel2p mesoscope with dual independent scan engines for flexible capture of dynamics in distributed neural circuitry. Nat Commun 2021; 12:6639. [PMID: 34789723 PMCID: PMC8599518 DOI: 10.1038/s41467-021-26736-4] [Citation(s) in RCA: 51] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2021] [Accepted: 10/05/2021] [Indexed: 11/09/2022] Open
Abstract
Imaging the activity of neurons that are widely distributed across brain regions deep in scattering tissue at high speed remains challenging. Here, we introduce an open-source system with Dual Independent Enhanced Scan Engines for Large field-of-view Two-Photon imaging (Diesel2p). Combining optical design, adaptive optics, and temporal multiplexing, the system offers subcellular resolution over a large field-of-view of ~25 mm2, encompassing distances up to 7 mm, with independent scan engines. We demonstrate the flexibility and various use cases of this system for calcium imaging of neurons in the living brain.
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Affiliation(s)
- Che-Hang Yu
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | | | - Yiyi Yu
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | - Riichiro Hira
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA
| | - Spencer L Smith
- Department of Electrical and Computer Engineering, University of California, Santa Barbara, CA, USA.
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38
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Clough M, Chen IA, Park SW, Ahrens AM, Stirman JN, Smith SL, Chen JL. Flexible simultaneous mesoscale two-photon imaging of neural activity at high speeds. Nat Commun 2021; 12:6638. [PMID: 34789730 PMCID: PMC8599611 DOI: 10.1038/s41467-021-26737-3] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 10/05/2021] [Indexed: 12/02/2022] Open
Abstract
Understanding brain function requires monitoring local and global brain dynamics. Two-photon imaging of the brain across mesoscopic scales has presented trade-offs between imaging area and acquisition speed. We describe a flexible cellular resolution two-photon microscope capable of simultaneous video rate acquisition of four independently targetable brain regions spanning an approximate five-millimeter field of view. With this system, we demonstrate the ability to measure calcium activity across mouse sensorimotor cortex at behaviorally relevant timescales.
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Affiliation(s)
- Mitchell Clough
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
| | - Ichun Anderson Chen
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
| | - Seong-Wook Park
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA
| | - Allison M Ahrens
- Department of Biology, Boston University, Boston, MA, 02215, USA
| | - Jeffrey N Stirman
- Neuroscience Center, University of North Carolina School of Medicine, Chapel Hill, NC, 27599, USA
| | - Spencer L Smith
- Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, 93106, USA
| | - Jerry L Chen
- Department of Biomedical Engineering, Boston University, Boston, MA, 02215, USA.
- Center for Neurophotonics, Boston University, Boston, MA, 02215, USA.
- Department of Biology, Boston University, Boston, MA, 02215, USA.
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39
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Chen W, Natan RG, Yang Y, Chou SW, Zhang Q, Isacoff EY, Ji N. In vivo volumetric imaging of calcium and glutamate activity at synapses with high spatiotemporal resolution. Nat Commun 2021; 12:6630. [PMID: 34785691 PMCID: PMC8595604 DOI: 10.1038/s41467-021-26965-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Accepted: 10/27/2021] [Indexed: 12/02/2022] Open
Abstract
Studying neuronal activity at synapses requires high spatiotemporal resolution. For high spatial resolution in vivo imaging at depth, adaptive optics (AO) is required to correct sample-induced aberrations. To improve temporal resolution, Bessel focus has been combined with two-photon fluorescence microscopy (2PFM) for fast volumetric imaging at subcellular lateral resolution. To achieve both high-spatial and high-temporal resolution at depth, we develop an efficient AO method that corrects the distorted wavefront of Bessel focus at the objective focal plane and recovers diffraction-limited imaging performance. Applying AO Bessel focus scanning 2PFM to volumetric imaging of zebrafish larval and mouse brains down to 500 µm depth, we demonstrate substantial improvements in the sensitivity and resolution of structural and functional measurements of synapses in vivo. This enables volumetric measurements of synaptic calcium and glutamate activity at high accuracy, including the simultaneous recording of glutamate activity of apical and basal dendritic spines in the mouse cortex.
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Affiliation(s)
- Wei Chen
- grid.47840.3f0000 0001 2181 7878Department of Physics, University of California, Berkeley, CA 97420 USA
| | - Ryan G. Natan
- grid.47840.3f0000 0001 2181 7878Department of Physics, University of California, Berkeley, CA 97420 USA
| | - Yuhan Yang
- grid.47840.3f0000 0001 2181 7878Department of Physics, University of California, Berkeley, CA 97420 USA
| | - Shih-Wei Chou
- grid.47840.3f0000 0001 2181 7878Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720 USA
| | - Qinrong Zhang
- grid.47840.3f0000 0001 2181 7878Department of Physics, University of California, Berkeley, CA 97420 USA
| | - Ehud Y. Isacoff
- grid.47840.3f0000 0001 2181 7878Department of Molecular and Cell Biology, University of California, Berkeley, CA 94720 USA ,grid.47840.3f0000 0001 2181 7878Helen Wills Neuroscience Institute, University of California, Berkeley, CA 94720 USA ,grid.184769.50000 0001 2231 4551Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 USA
| | - Na Ji
- Department of Physics, University of California, Berkeley, CA, 97420, USA. .,Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA. .,Helen Wills Neuroscience Institute, University of California, Berkeley, CA, 94720, USA. .,Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA.
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40
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Zhang Y, Lu Z, Wu J, Lin X, Jiang D, Cai Y, Xie J, Wang Y, Zhu T, Ji X, Dai Q. Computational optical sectioning with an incoherent multiscale scattering model for light-field microscopy. Nat Commun 2021; 12:6391. [PMID: 34737278 PMCID: PMC8568979 DOI: 10.1038/s41467-021-26730-w] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2020] [Accepted: 10/14/2021] [Indexed: 11/09/2022] Open
Abstract
Quantitative volumetric fluorescence imaging at high speed across a long term is vital to understand various cellular and subcellular behaviors in living organisms. Light-field microscopy provides a compact computational solution by imaging the entire volume in a tomographic way, while facing severe degradation in scattering tissue or densely-labelled samples. To address this problem, we propose an incoherent multiscale scattering model in a complete space for quantitative 3D reconstruction in complicated environments, which is called computational optical sectioning. Without the requirement of any hardware modifications, our method can be generally applied to different light-field schemes with reduction in background fluorescence, reconstruction artifacts, and computational costs, facilitating more practical applications of LFM in a broad community. We validate the superior performance by imaging various biological dynamics in Drosophila embryos, zebrafish larvae, and mice.
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Affiliation(s)
- Yi Zhang
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
| | - Zhi Lu
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
| | - Jiamin Wu
- Department of Automation, Tsinghua University, 100084, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
| | - Xing Lin
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
- Beijing National Research Center for Information Science and Technology, Tsinghua University, 100084, Beijing, China
| | - Dong Jiang
- State Key Laboratory of Membrane Biology, Tsinghua University-Peking University Joint Centre for Life Sciences, Beijing Frontier Research Centre for Biological Structure, School of Life Sciences, Tsinghua University, 100084, Beijing, China
| | - Yeyi Cai
- Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Jiachen Xie
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
| | - Yuling Wang
- Department of Automation, Tsinghua University, 100084, Beijing, China
| | - Tianyi Zhu
- Department of Automation, Tsinghua University, 100084, Beijing, China
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China
| | - Xiangyang Ji
- Department of Automation, Tsinghua University, 100084, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing National Research Center for Information Science and Technology, Tsinghua University, 100084, Beijing, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, 100084, Beijing, China.
- Institute for Brain and Cognitive Sciences, Tsinghua University, 100084, Beijing, China.
- Beijing National Research Center for Information Science and Technology, Tsinghua University, 100084, Beijing, China.
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Dai M, Xiao G, Fiondella L, Shao M, Zhang YS. Deep Learning-Enabled Resolution-Enhancement in Mini- and Regular Microscopy for Biomedical Imaging. SENSORS AND ACTUATORS. A, PHYSICAL 2021; 331:112928. [PMID: 34393376 PMCID: PMC8362924 DOI: 10.1016/j.sna.2021.112928] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Artificial intelligence algorithms that aid mini-microscope imaging are attractive for numerous applications. In this paper, we optimize artificial intelligence techniques to provide clear, and natural biomedical imaging. We demonstrate that a deep learning-enabled super-resolution method can significantly enhance the spatial resolution of mini-microscopy and regular-microscopy. This data-driven approach trains a generative adversarial network to transform low-resolution images into super-resolved ones. Mini-microscopic images and regular-microscopic images acquired with different optical microscopes under various magnifications are collected as our experimental benchmark datasets. The only input to this generative-adversarial-network-based method are images from the datasets down-sampled by the Bicubic interpolation. We use independent test set to evaluate this deep learning approach with other deep learning-based algorithms through qualitative and quantitative comparisons. To clearly present the improvements achieved by this generative-adversarial-network-based method, we zoom into the local features to explore and highlight the qualitative differences. We also employ the peak signal-to-noise ratio and the structural similarity, to quantitatively compare alternative super-resolution methods. The quantitative results illustrate that super-resolution images obtained from our approach with interpolation parameter α=0.25 more closely match those of the original high-resolution images than to those obtained by any of the alternative state-of-the-art method. These results are significant for fields that use microscopy tools, such as biomedical imaging of engineered living systems. We also utilize this generative adversarial network-based algorithm to optimize the resolution of biomedical specimen images and then generate three-dimensional reconstruction, so as to enhance the ability of three-dimensional imaging throughout the entire volumes for spatial-temporal analyses of specimen structures.
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Affiliation(s)
- Manna Dai
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA 02139, USA
| | - Gao Xiao
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA 02139, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
| | - Lance Fiondella
- Department of Electrical and Computer Engineering, College of Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA
| | - Ming Shao
- Department of Computer and Information Science, College of Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA
| | - Yu Shrike Zhang
- Division of Engineering in Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Cambridge, MA 02139, USA
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Hulse BK, Haberkern H, Franconville R, Turner-Evans D, Takemura SY, Wolff T, Noorman M, Dreher M, Dan C, Parekh R, Hermundstad AM, Rubin GM, Jayaraman V. A connectome of the Drosophila central complex reveals network motifs suitable for flexible navigation and context-dependent action selection. eLife 2021; 10:e66039. [PMID: 34696823 PMCID: PMC9477501 DOI: 10.7554/elife.66039] [Citation(s) in RCA: 122] [Impact Index Per Article: 40.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 09/07/2021] [Indexed: 11/13/2022] Open
Abstract
Flexible behaviors over long timescales are thought to engage recurrent neural networks in deep brain regions, which are experimentally challenging to study. In insects, recurrent circuit dynamics in a brain region called the central complex (CX) enable directed locomotion, sleep, and context- and experience-dependent spatial navigation. We describe the first complete electron microscopy-based connectome of the Drosophila CX, including all its neurons and circuits at synaptic resolution. We identified new CX neuron types, novel sensory and motor pathways, and network motifs that likely enable the CX to extract the fly's head direction, maintain it with attractor dynamics, and combine it with other sensorimotor information to perform vector-based navigational computations. We also identified numerous pathways that may facilitate the selection of CX-driven behavioral patterns by context and internal state. The CX connectome provides a comprehensive blueprint necessary for a detailed understanding of network dynamics underlying sleep, flexible navigation, and state-dependent action selection.
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Affiliation(s)
- Brad K Hulse
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Hannah Haberkern
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Romain Franconville
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Daniel Turner-Evans
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Shin-ya Takemura
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Tanya Wolff
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marcella Noorman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Marisa Dreher
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Chuntao Dan
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ruchi Parekh
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Ann M Hermundstad
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Gerald M Rubin
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
| | - Vivek Jayaraman
- Janelia Research Campus, Howard Hughes Medical InstituteAshburnUnited States
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43
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Contribution of animal models toward understanding resting state functional connectivity. Neuroimage 2021; 245:118630. [PMID: 34644593 DOI: 10.1016/j.neuroimage.2021.118630] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/06/2021] [Accepted: 09/29/2021] [Indexed: 12/27/2022] Open
Abstract
Functional connectivity, which reflects the spatial and temporal organization of intrinsic activity throughout the brain, is one of the most studied measures in human neuroimaging research. The noninvasive acquisition of resting state functional magnetic resonance imaging (rs-fMRI) allows the characterization of features designated as functional networks, functional connectivity gradients, and time-varying activity patterns that provide insight into the intrinsic functional organization of the brain and potential alterations related to brain dysfunction. Functional connectivity, hence, captures dimensions of the brain's activity that have enormous potential for both clinical and preclinical research. However, the mechanisms underlying functional connectivity have yet to be fully characterized, hindering interpretation of rs-fMRI studies. As in other branches of neuroscience, the identification of the neurophysiological processes that contribute to functional connectivity largely depends on research conducted on laboratory animals, which provide a platform where specific, multi-dimensional investigations that involve invasive measurements can be carried out. These highly controlled experiments facilitate the interpretation of the temporal correlations observed across the brain. Indeed, information obtained from animal experimentation to date is the basis for our current understanding of the underlying basis for functional brain connectivity. This review presents a compendium of some of the most critical advances in the field based on the efforts made by the animal neuroimaging community.
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Bang S, Hwang KS, Jeong S, Cho IJ, Choi N, Kim J, Kim HN. Engineered neural circuits for modeling brain physiology and neuropathology. Acta Biomater 2021; 132:379-400. [PMID: 34157452 DOI: 10.1016/j.actbio.2021.06.024] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 05/16/2021] [Accepted: 06/14/2021] [Indexed: 12/14/2022]
Abstract
The neural circuits of the central nervous system are the regulatory pathways for feeling, motion control, learning, and memory, and their dysfunction is closely related to various neurodegenerative diseases. Despite the growing demand for the unraveling of the physiology and functional connectivity of the neural circuits, their fundamental investigation is hampered because of the inability to access the components of neural circuits and the complex microenvironment. As an alternative approach, in vitro human neural circuits show principles of in vivo human neuronal circuit function. They allow access to the cellular compartment and permit real-time monitoring of neural circuits. In this review, we summarize recent advances in reconstituted in vitro neural circuits using engineering techniques. To this end, we provide an overview of the fabrication techniques and methods for stimulation and measurement of in vitro neural circuits. Subsequently, representative examples of in vitro neural circuits are reviewed with a particular focus on the recapitulation of structures and functions observed in vivo, and we summarize their application in the study of various brain diseases. We believe that the in vitro neural circuits can help neuroscience and the neuropharmacology. STATEMENT OF SIGNIFICANCE: Despite the growing demand to unravel the physiology and functional connectivity of the neural circuits, the studies on the in vivo neural circuits are frequently limited due to the poor accessibility. Furthermore, single neuron-based analysis has an inherent limitation in that it does not reflect the full spectrum of the neural circuit physiology. As an alternative approach, in vitro engineered neural circuit models have arisen because they can recapitulate the structural and functional characteristics of in vivo neural circuits. These in vitro neural circuits allow the mimicking of dysregulation of the neural circuits, including neurodegenerative diseases and traumatic brain injury. Emerging in vitro engineered neural circuits will provide a better understanding of the (patho-)physiology of neural circuits.
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Affiliation(s)
- Seokyoung Bang
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea
| | - Kyeong Seob Hwang
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; School of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea
| | - Sohyeon Jeong
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul 02792, Republic of Korea
| | - Il-Joo Cho
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul 02792, Republic of Korea; School of Electrical and Electronics Engineering, Yonsei University, Seoul 03722, Republic of Korea; Yonsei-KIST Convergence Research Institute, Yonsei University, Seoul 03722, Republic of Korea
| | - Nakwon Choi
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul 02792, Republic of Korea; KU-KIST Graduate School of Converging Science and Technology, Korea University, Seoul 02841, Republic of Korea.
| | - Jongbaeg Kim
- School of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea.
| | - Hong Nam Kim
- Brain Science Institute, Korea Institute of Science and Technology (KIST), Seoul 02792, Republic of Korea; Division of Bio-Medical Science & Technology, KIST School, Korea University of Science and Technology (UST), Seoul 02792, Republic of Korea.
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High-speed, cortex-wide volumetric recording of neuroactivity at cellular resolution using light beads microscopy. Nat Methods 2021; 18:1103-1111. [PMID: 34462592 PMCID: PMC8958902 DOI: 10.1038/s41592-021-01239-8] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 07/09/2021] [Indexed: 02/07/2023]
Abstract
Two-photon microscopy has enabled high-resolution imaging of neuroactivity at depth within scattering brain tissue. However, its various realizations have not overcome the tradeoffs between speed and spatiotemporal sampling that would be necessary to enable mesoscale volumetric recording of neuroactivity at cellular resolution and speed compatible with resolving calcium transients. Here, we introduce light beads microscopy (LBM), a scalable and spatiotemporally optimal acquisition approach limited only by fluorescence lifetime, where a set of axially separated and temporally distinct foci record the entire axial imaging range near-simultaneously, enabling volumetric recording at 1.41 × 108 voxels per second. Using LBM, we demonstrate mesoscopic and volumetric imaging at multiple scales in the mouse cortex, including cellular-resolution recordings within ~3 × 5 × 0.5 mm volumes containing >200,000 neurons at ~5 Hz and recordings of populations of ~1 million neurons within ~5.4 × 6 × 0.5 mm volumes at ~2 Hz, as well as higher speed (9.6 Hz) subcellular-resolution volumetric recordings. LBM provides an opportunity for discovering the neurocomputations underlying cortex-wide encoding and processing of information in the mammalian brain.
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Ichimura T, Kakizuka T, Horikawa K, Seiriki K, Kasai A, Hashimoto H, Fujita K, Watanabe TM, Nagai T. Exploring rare cellular activity in more than one million cells by a transscale scope. Sci Rep 2021; 11:16539. [PMID: 34400683 PMCID: PMC8368064 DOI: 10.1038/s41598-021-95930-7] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Accepted: 08/03/2021] [Indexed: 02/07/2023] Open
Abstract
In many phenomena of biological systems, not a majority, but a minority of cells act on the entire multicellular system causing drastic changes in the system properties. To understand the mechanisms underlying such phenomena, it is essential to observe the spatiotemporal dynamics of a huge population of cells at sub-cellular resolution, which is difficult with conventional tools such as microscopy and flow cytometry. Here, we describe an imaging system named AMATERAS that enables optical imaging with an over-one-centimeter field-of-view and a-few-micrometer spatial resolution. This trans-scale-scope has a simple configuration, composed of a low-power lens for machine vision and a hundred-megapixel image sensor. We demonstrated its high cell-throughput, capable of simultaneously observing more than one million cells. We applied it to dynamic imaging of calcium ions in HeLa cells and cyclic-adenosine-monophosphate in Dictyostelium discoideum, and successfully detected less than 0.01% of rare cells and observed multicellular events induced by these cells.
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Affiliation(s)
- T Ichimura
- Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan.
- PRESTO, Japan Science and Technology Agency, Tokyo, 113-0033, Japan.
| | - T Kakizuka
- Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan
| | - K Horikawa
- Department of Optical Imaging, Advanced Research Promotion Center, Tokushima University, Kuramoto-cho 3-18-15, Tokushima, Tokushima, 770-8503, Japan
| | - K Seiriki
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Yamadaoka 1-6, Suita, Osaka, 565-0871, Japan
| | - A Kasai
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Yamadaoka 1-6, Suita, Osaka, 565-0871, Japan
| | - H Hashimoto
- Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan
- Laboratory of Molecular Neuropharmacology, Graduate School of Pharmaceutical Sciences, Osaka University, Yamadaoka 1-6, Suita, Osaka, 565-0871, Japan
- Institute for Transdisciplinary Graduate Degree Programs, Osaka University, Yamadaoka 1-1, Suita, Osaka, 565-0871, Japan
| | - K Fujita
- Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan
- Department of Applied Physics, Graduate School of Engineering, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan
| | - T M Watanabe
- Laboratory for Comprehensive Bioimaging, RIKEN Center for Biosystems Dynamics Research (BDR), Minatomachi-minami 2-2-3, Chuo-ku, Kobe, Hyogo, 650-0047, Japan
- Department of Stem Cell Biology, Research Institute for Radiation Biology and Medicine, Hiroshima University, Kasumi 1-2-3, Minami-ku, Hiroshima, 734-8553, Japan
| | - T Nagai
- Transdimensional Life Imaging Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Yamadaoka 2-1, Suita, Osaka, 565-0871, Japan.
- SANKEN (The Institute of Scientific and Industrial Research), Osaka University, Mihogaoka 8-1, Ibaraki, Osaka, 567-0047, Japan.
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47
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Goetz L, Roth A, Häusser M. Active dendrites enable strong but sparse inputs to determine orientation selectivity. Proc Natl Acad Sci U S A 2021; 118:e2017339118. [PMID: 34301882 PMCID: PMC8325157 DOI: 10.1073/pnas.2017339118] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
The dendrites of neocortical pyramidal neurons are excitable. However, it is unknown how synaptic inputs engage nonlinear dendritic mechanisms during sensory processing in vivo, and how they in turn influence action potential output. Here, we provide a quantitative account of the relationship between synaptic inputs, nonlinear dendritic events, and action potential output. We developed a detailed pyramidal neuron model constrained by in vivo dendritic recordings. We drive this model with realistic input patterns constrained by sensory responses measured in vivo and connectivity measured in vitro. We show mechanistically that under realistic conditions, dendritic Na+ and NMDA spikes are the major determinants of neuronal output in vivo. We demonstrate that these dendritic spikes can be triggered by a surprisingly small number of strong synaptic inputs, in some cases even by single synapses. We predict that dendritic excitability allows the 1% strongest synaptic inputs of a neuron to control the tuning of its output. Active dendrites therefore allow smaller subcircuits consisting of only a few strongly connected neurons to achieve selectivity for specific sensory features.
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Affiliation(s)
- Lea Goetz
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, United Kingdom
| | - Arnd Roth
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, United Kingdom
| | - Michael Häusser
- Wolfson Institute for Biomedical Research, University College London, London WC1E 6BT, United Kingdom
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48
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Stuyt G, Godenzini L, Palmer LM. Local and Global Dynamics of Dendritic Activity in the Pyramidal Neuron. Neuroscience 2021; 489:176-184. [PMID: 34280492 DOI: 10.1016/j.neuroscience.2021.07.008] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2021] [Revised: 07/06/2021] [Accepted: 07/08/2021] [Indexed: 12/22/2022]
Abstract
There has been increasing interest in the measurement and comparison of activity across compartments of the pyramidal neuron. Dendritic activity can occur both locally, on a single dendritic segment, or globally, involving multiple compartments of the single neuron. Little is known about how these dendritic dynamics shape and contribute to information processing and behavior. Although it has been difficult to characterize local and global activity in vivo due to the technical challenge of simultaneously recording from the entire dendritic arbor and soma, the rise of calcium imaging has driven the increased feasibility and interest of these experiments. However, the distinction between local and global activity made by calcium imaging requires careful consideration. In this review we describe local and global activity, discuss the difficulties and caveats of this distinction, and present the evidence of local and global activity in information processing and behavior.
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Affiliation(s)
- George Stuyt
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Luca Godenzini
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3052, Australia
| | - Lucy M Palmer
- Florey Institute of Neuroscience and Mental Health, University of Melbourne, Parkville, Victoria 3052, Australia.
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49
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Wu J, Lu Z, Jiang D, Guo Y, Qiao H, Zhang Y, Zhu T, Cai Y, Zhang X, Zhanghao K, Xie H, Yan T, Zhang G, Li X, Jiang Z, Lin X, Fang L, Zhou B, Xi P, Fan J, Yu L, Dai Q. Iterative tomography with digital adaptive optics permits hour-long intravital observation of 3D subcellular dynamics at millisecond scale. Cell 2021; 184:3318-3332.e17. [PMID: 34038702 DOI: 10.1016/j.cell.2021.04.029] [Citation(s) in RCA: 83] [Impact Index Per Article: 27.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 01/04/2021] [Accepted: 04/16/2021] [Indexed: 01/03/2023]
Abstract
Long-term subcellular intravital imaging in mammals is vital to study diverse intercellular behaviors and organelle functions during native physiological processes. However, optical heterogeneity, tissue opacity, and phototoxicity pose great challenges. Here, we propose a computational imaging framework, termed digital adaptive optics scanning light-field mutual iterative tomography (DAOSLIMIT), featuring high-speed, high-resolution 3D imaging, tiled wavefront correction, and low phototoxicity with a compact system. By tomographic imaging of the entire volume simultaneously, we obtained volumetric imaging across 225 × 225 × 16 μm3, with a resolution of up to 220 nm laterally and 400 nm axially, at the millisecond scale, over hundreds of thousands of time points. To establish the capabilities, we investigated large-scale cell migration and neural activities in different species and observed various subcellular dynamics in mammals during neutrophil migration and tumor cell circulation.
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Affiliation(s)
- Jiamin Wu
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Zhi Lu
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Dong Jiang
- State Key Laboratory of Membrane Biology, Tsinghua University-Peking University Joint Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Yuduo Guo
- Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Hui Qiao
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Yi Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Tianyi Zhu
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Yeyi Cai
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Xu Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; Beijing Institute of Collaborative Innovation, Beijing 100094, China
| | - Karl Zhanghao
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Hao Xie
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China
| | - Tao Yan
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Guoxun Zhang
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Xiaoxu Li
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Zheng Jiang
- State Key Laboratory of Membrane Biology, Tsinghua University-Peking University Joint Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China
| | - Xing Lin
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China
| | - Lu Fang
- Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
| | - Bing Zhou
- Advanced Innovation Center for Big Data-based Precision Medicine, School of Biological Science and Medical Engineering, Beihang University, Beijing 100191, China
| | - Peng Xi
- Department of Biomedical Engineering, College of Engineering, Peking University, Beijing 100871, China
| | - Jingtao Fan
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
| | - Li Yu
- State Key Laboratory of Membrane Biology, Tsinghua University-Peking University Joint Center for Life Sciences, Beijing Frontier Research Center for Biological Structure, School of Life Sciences, Tsinghua University, Beijing 100084, China.
| | - Qionghai Dai
- Department of Automation, Tsinghua University, Beijing 100084, China; Institute for Brain and Cognitive Sciences, Tsinghua University, Beijing 100084, China; Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP), Tsinghua University, Beijing 100084, China; IDG/McGovern Institute for Brain Research, Tsinghua University, Beijing 100084, China.
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50
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Ren C, Komiyama T. Characterizing Cortex-Wide Dynamics with Wide-Field Calcium Imaging. J Neurosci 2021; 41:4160-4168. [PMID: 33893217 PMCID: PMC8143209 DOI: 10.1523/jneurosci.3003-20.2021] [Citation(s) in RCA: 39] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 03/26/2021] [Accepted: 03/30/2021] [Indexed: 12/27/2022] Open
Abstract
The brain functions through coordinated activity among distributed regions. Wide-field calcium imaging, combined with improved genetically encoded calcium indicators, allows sufficient signal-to-noise ratio and spatiotemporal resolution to afford a unique opportunity to capture cortex-wide dynamics on a moment-by-moment basis in behaving animals. Recent applications of this approach have been uncovering cortical dynamics at unprecedented scales during various cognitive processes, ranging from relatively simple sensorimotor integration to more complex decision-making tasks. In this review, we will highlight recent scientific advances enabled by wide-field calcium imaging in behaving mice. We then summarize several technical considerations and future opportunities for wide-field imaging to uncover large-scale circuit dynamics.
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Affiliation(s)
- Chi Ren
- Neurobiology Section, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California 92093
| | - Takaki Komiyama
- Neurobiology Section, Center for Neural Circuits and Behavior, Department of Neurosciences, and Halıcıoğlu Data Science Institute, University of California San Diego, La Jolla, California 92093
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