1
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Nagai H. Deciphering prefrontal circuits underlying stress and depression: exploring the potential of volume electron microscopy. Microscopy (Oxf) 2024; 73:391-404. [PMID: 39045685 DOI: 10.1093/jmicro/dfae036] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2024] [Revised: 06/07/2024] [Accepted: 07/23/2024] [Indexed: 07/25/2024] Open
Abstract
Adapting to environmental changes and formulating behavioral strategies are central to the nervous system, with the prefrontal cortex being crucial. Chronic stress impacts this region, leading to disorders including major depression. This review discusses the roles for prefrontal cortex and the effects of stress, highlighting similarities and differences between human/primates and rodent brains. Notably, the rodent medial prefrontal cortex is analogous to the human subgenual anterior cingulate cortex in terms of emotional regulation, sharing similarities in cytoarchitecture and circuitry, while also performing cognitive functions similar to the human dorsolateral prefrontal cortex. It has been shown that chronic stress induces atrophic changes in the rodent mPFC, which mirrors the atrophy observed in the subgenual anterior cingulate cortex and dorsolateral prefrontal cortex of depression patients. However, the precise alterations in neural circuitry due to chronic stress are yet to be fully unraveled. The use of advanced imaging techniques, particularly volume electron microscopy, is emphasized as critical for the detailed examination of synaptic changes, providing a deeper understanding of stress and depression at the molecular, cellular and circuit levels. This approach offers invaluable insights into the alterations in neuronal circuits within the medial prefrontal cortex caused by chronic stress, significantly enriching our understanding of stress and depression pathologies.
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Affiliation(s)
- Hirotaka Nagai
- Division of Pharmacology, Graduate School of Medicine, Kobe University, Research Building B 4F, 7-5-1 Kusunoki-cho, Chuo-ku, Kobe, 650-0017, Japan
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2
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Hong I, Kim J, Hainmueller T, Kim DW, Keijser J, Johnson RC, Park SH, Limjunyawong N, Yang Z, Cheon D, Hwang T, Agarwal A, Cholvin T, Krienen FM, McCarroll SA, Dong X, Leopold DA, Blackshaw S, Sprekeler H, Bergles DE, Bartos M, Brown SP, Huganir RL. Calcium-permeable AMPA receptors govern PV neuron feature selectivity. Nature 2024:10.1038/s41586-024-08027-2. [PMID: 39358515 DOI: 10.1038/s41586-024-08027-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 09/05/2024] [Indexed: 10/04/2024]
Abstract
The brain helps us survive by forming internal representations of the external world1,2. Excitatory cortical neurons are often precisely tuned to specific external stimuli3,4. However, inhibitory neurons, such as parvalbumin-positive (PV) interneurons, are generally less selective5. PV interneurons differ from excitatory neurons in their neurotransmitter receptor subtypes, including AMPA (α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid) receptors (AMPARs)6,7. Excitatory neurons express calcium-impermeable AMPARs that contain the GluA2 subunit (encoded by GRIA2), whereas PV interneurons express receptors that lack the GluA2 subunit and are calcium-permeable (CP-AMPARs). Here we demonstrate a causal relationship between CP-AMPAR expression and the low feature selectivity of PV interneurons. We find low expression stoichiometry of GRIA2 mRNA relative to other subunits in PV interneurons that is conserved across ferrets, rodents, marmosets and humans, and causes abundant CP-AMPAR expression. Replacing CP-AMPARs in PV interneurons with calcium-impermeable AMPARs increased their orientation selectivity in the visual cortex. Manipulations to induce sparse CP-AMPAR expression demonstrated that this increase was cell-autonomous and could occur with changes beyond development. Notably, excitatory-PV interneuron connectivity rates and unitary synaptic strength were unaltered by CP-AMPAR removal, which suggested that the selectivity of PV interneurons can be altered without markedly changing connectivity. In Gria2-knockout mice, in which all AMPARs are calcium-permeable, excitatory neurons showed significantly degraded orientation selectivity, which suggested that CP-AMPARs are sufficient to drive lower selectivity regardless of cell type. Moreover, hippocampal PV interneurons, which usually exhibit low spatial tuning, became more spatially selective after removing CP-AMPARs, which indicated that CP-AMPARs suppress the feature selectivity of PV interneurons independent of modality. These results reveal a new role of CP-AMPARs in maintaining low-selectivity sensory representation in PV interneurons and implicate a conserved molecular mechanism that distinguishes this cell type in the neocortex.
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Affiliation(s)
- Ingie Hong
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
| | - Juhyun Kim
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Emotion, Cognition and Behavior Research Group, Korea Brain Research Institute (KBRI), Daegu, Republic of Korea
| | - Thomas Hainmueller
- Institute for Physiology I, University of Freiburg, Medical Faculty, Freiburg, Germany
- Department of Psychiatry, New York University Langone Medical Center, New York, NY, USA
| | - Dong Won Kim
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Danish Research Institute of Translational Neuroscience (DANDRITE), Nordic EMBL Partnership for Molecular Medicine, Aarhus University, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Joram Keijser
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Charité-Universitätsmedizin Berlin, Einstein Center for Neurosciences Berlin, Berlin, Germany
| | - Richard C Johnson
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Soo Hyun Park
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, MD, USA
- Department of Brain and Cognitive Sciences, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Nathachit Limjunyawong
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Center of Research Excellence in Allergy and Immunology, Research Department, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
| | - Zhuonan Yang
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - David Cheon
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Taeyoung Hwang
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Amit Agarwal
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Chica and Heinz Schaller Research Group, Institute for Anatomy and Cell Biology, Heidelberg, Germany
- Interdisciplinary Center for Neurosciences, University of Heidelberg, Heidelberg, Germany
| | - Thibault Cholvin
- Institute for Physiology I, University of Freiburg, Medical Faculty, Freiburg, Germany
| | - Fenna M Krienen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Xinzhong Dong
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Howard Hughes Medical Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - David A Leopold
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, MD, USA
| | - Seth Blackshaw
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Ophthalmology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Henning Sprekeler
- Modelling of Cognitive Processes, Technical University of Berlin, Berlin, Germany
- Bernstein Center for Computational Neuroscience Berlin, Berlin, Germany
- Science of Intelligence, Research Cluster of Excellence, Berlin, Germany
| | - Dwight E Bergles
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Marlene Bartos
- Institute for Physiology I, University of Freiburg, Medical Faculty, Freiburg, Germany
| | - Solange P Brown
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Richard L Huganir
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
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3
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Celii B, Papadopoulos S, Ding Z, Fahey PG, Wang E, Papadopoulos C, Kunin A, Patel S, Bae JA, Bodor AL, Brittain D, Buchanan J, Bumbarger DJ, Castro MA, Cobos E, Dorkenwald S, Elabbady L, Halageri A, Jia Z, Jordan C, Kapner D, Kemnitz N, Kinn S, Lee K, Li K, Lu R, Macrina T, Mahalingam G, Mitchell E, Mondal SS, Mu S, Nehoran B, Popovych S, Schneider-Mizell CM, Silversmith W, Takeno M, Torres R, Turner NL, Wong W, Wu J, Yu SC, Yin W, Xenes D, Kitchell LM, Rivlin PK, Rose VA, Bishop CA, Wester B, Froudarakis E, Walker EY, Sinz FH, Seung HS, Collman F, da Costa NM, Reid RC, Pitkow X, Tolias AS, Reimer J. NEURD offers automated proofreading and feature extraction for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.14.532674. [PMID: 36993282 PMCID: PMC10055177 DOI: 10.1101/2023.03.14.532674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution. Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML). Automated segmentation methods produce exceptionally accurate reconstructions of cells, but post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons in these volumes contain detailed morphological information at multiple scales, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes meshed neurons into compact and extensively-annotated graph representations. With these feature-rich graphs, we automate a variety of tasks such as state of the art automated proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other annotations. These features enable many downstream analyses of neural morphology and connectivity, making these massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.
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4
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Zhang L, Huang L, Yuan Z, Hang Y, Zeng Y, Li K, Wang L, Zeng H, Chen X, Zhang H, Xi J, Chen D, Gao Z, Le L, Chen J, Ye W, Liu L, Wang Y, Peng H. Collaborative augmented reconstruction of 3D neuron morphology in mouse and human brains. Nat Methods 2024:10.1038/s41592-024-02401-8. [PMID: 39232199 DOI: 10.1038/s41592-024-02401-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Accepted: 07/30/2024] [Indexed: 09/06/2024]
Abstract
Digital reconstruction of the intricate 3D morphology of individual neurons from microscopic images is a crucial challenge in both individual laboratories and large-scale projects focusing on cell types and brain anatomy. This task often fails in both conventional manual reconstruction and state-of-the-art artificial intelligence (AI)-based automatic reconstruction algorithms. It is also challenging to organize multiple neuroanatomists to generate and cross-validate biologically relevant and mutually agreed upon reconstructions in large-scale data production. Based on collaborative group intelligence augmented by AI, we developed a collaborative augmented reconstruction (CAR) platform for neuron reconstruction at scale. This platform allows for immersive interaction and efficient collaborative editing of neuron anatomy using a variety of devices, such as desktop workstations, virtual reality headsets and mobile phones, enabling users to contribute anytime and anywhere and to take advantage of several AI-based automation tools. We tested CAR's applicability for challenging mouse and human neurons toward scaled and faithful data production.
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Affiliation(s)
- Lingli Zhang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lei Huang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Zexin Yuan
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
- School of Future Technology, Shanghai University, Shanghai, China
| | - Yuning Hang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Ying Zeng
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Kaixiang Li
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijun Wang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Haoyu Zeng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Xin Chen
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Hairuo Zhang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Jiaqi Xi
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
| | - Danni Chen
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
| | - Ziqin Gao
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Longxin Le
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
- School of Future Technology, Shanghai University, Shanghai, China
| | - Jie Chen
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Wen Ye
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Lijuan Liu
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China
| | - Yimin Wang
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
- Guangdong Institute of Intelligence Science and Technology, Hengqin, China.
| | - Hanchuan Peng
- New Cornerstone Science Laboratory, SEU-ALLEN Joint Center, Institute for Brain and Intelligence, Southeast University, Nanjing, China.
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5
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Shi J, Nutkovich B, Kushinsky D, Rao BY, Herrlinger SA, Tsivourakis E, Mihaila TS, Paredes MEC, Malina KCK, O’Toole CK, Yong HC, Sanner BM, Xie A, Varol E, Losonczy A, Spiegel I. 2P-NucTag: on-demand phototagging for molecular analysis of functionally identified cortical neurons. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.03.21.586118. [PMID: 38585980 PMCID: PMC10996538 DOI: 10.1101/2024.03.21.586118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/09/2024]
Abstract
Neural circuits are characterized by genetically and functionally diverse cell types. A mechanistic understanding of circuit function is predicated on linking the genetic and physiological properties of individual neurons. However, it remains highly challenging to map the transcriptional properties to functionally heterogeneous neuronal subtypes in mammalian cortical circuits in vivo. Here, we introduce a high-throughput two-photon nuclear phototagging (2P-NucTag) approach optimized for on-demand and indelible labeling of single neurons via a photoactivatable red fluorescent protein following in vivo functional characterization in behaving mice. We demonstrate the utility of this function-forward pipeline by selectively labeling and transcriptionally profiling previously inaccessible 'place' and 'silent' cells in the mouse hippocampus. Our results reveal unexpected differences in gene expression between these hippocampal pyramidal neurons with distinct spatial coding properties. Thus, 2P-NucTag opens a new way to uncover the molecular principles that govern the functional organization of neural circuits.
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Affiliation(s)
- Jingcheng Shi
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
- Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY, United States
| | - Boaz Nutkovich
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Dahlia Kushinsky
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Bovey Y. Rao
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
- Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY, United States
| | - Stephanie A. Herrlinger
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Emmanouil Tsivourakis
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Tiberiu S. Mihaila
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Margaret E. Conde Paredes
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
- Doctoral Program in Neurobiology and Behavior, Columbia University, New York, NY, United States
- Tandon School of Engineering, New York University, New York, NY, United States
| | - Katayun Cohen-Kashi Malina
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
- Department of Molecular Neuroscience, Weizmann Institute of Science, Rehovot, Israel
| | - Cliodhna K. O’Toole
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Hyun Choong Yong
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Brynn M. Sanner
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Angel Xie
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Erdem Varol
- Tandon School of Engineering, New York University, New York, NY, United States
| | - Attila Losonczy
- Department of Neuroscience, Columbia University, New York, NY, United States
- Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States
| | - Ivo Spiegel
- Department of Brain Sciences, Weizmann Institute of Science, Rehovot, Israel
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6
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Furutachi S, Franklin AD, Aldea AM, Mrsic-Flogel TD, Hofer SB. Cooperative thalamocortical circuit mechanism for sensory prediction errors. Nature 2024; 633:398-406. [PMID: 39198646 PMCID: PMC11390482 DOI: 10.1038/s41586-024-07851-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Accepted: 07/18/2024] [Indexed: 09/01/2024]
Abstract
The brain functions as a prediction machine, utilizing an internal model of the world to anticipate sensations and the outcomes of our actions. Discrepancies between expected and actual events, referred to as prediction errors, are leveraged to update the internal model and guide our attention towards unexpected events1-10. Despite the importance of prediction-error signals for various neural computations across the brain, surprisingly little is known about the neural circuit mechanisms responsible for their implementation. Here we describe a thalamocortical disinhibitory circuit that is required for generating sensory prediction-error signals in mouse primary visual cortex (V1). We show that violating animals' predictions by an unexpected visual stimulus preferentially boosts responses of the layer 2/3 V1 neurons that are most selective for that stimulus. Prediction errors specifically amplify the unexpected visual input, rather than representing non-specific surprise or difference signals about how the visual input deviates from the animal's predictions. This selective amplification is implemented by a cooperative mechanism requiring thalamic input from the pulvinar and cortical vasoactive-intestinal-peptide-expressing (VIP) inhibitory interneurons. In response to prediction errors, VIP neurons inhibit a specific subpopulation of somatostatin-expressing inhibitory interneurons that gate excitatory pulvinar input to V1, resulting in specific pulvinar-driven response amplification of the most stimulus-selective neurons in V1. Therefore, the brain prioritizes unpredicted sensory information by selectively increasing the salience of unpredicted sensory features through the synergistic interaction of thalamic input and neocortical disinhibitory circuits.
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Affiliation(s)
- Shohei Furutachi
- Sainsbury Wellcome Centre, University College London, London, UK.
| | | | - Andreea M Aldea
- Sainsbury Wellcome Centre, University College London, London, UK
| | | | - Sonja B Hofer
- Sainsbury Wellcome Centre, University College London, London, UK.
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7
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Heuer SE, Bloss EB, Howell GR. Strategies to dissect microglia-synaptic interactions during aging and in Alzheimer's disease. Neuropharmacology 2024; 254:109987. [PMID: 38705570 DOI: 10.1016/j.neuropharm.2024.109987] [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: 01/10/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/07/2024]
Abstract
Age is the largest risk factor for developing Alzheimer's disease (AD), a neurodegenerative disorder that causes a progressive and severe dementia. The underlying cause of cognitive deficits seen in AD is thought to be the disconnection of neural circuits that control memory and executive functions. Insight into the mechanisms by which AD diverges from normal aging will require identifying precisely which cellular events are driven by aging and which are impacted by AD-related pathologies. Since microglia, the brain-resident macrophages, are known to have critical roles in the formation and maintenance of neural circuits through synaptic pruning, they are well-positioned to modulate synaptic connectivity in circuits sensitive to aging or AD. In this review, we provide an overview of the current state of the field and on emerging technologies being employed to elucidate microglia-synaptic interactions in aging and AD. We also discuss the importance of leveraging genetic diversity to study how these interactions are shaped across more realistic contexts. We propose that these approaches will be essential to define specific aging- and disease-relevant trajectories for more personalized therapeutics aimed at reducing the effects of age or AD pathologies on the brain. This article is part of the Special Issue on "Microglia".
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Affiliation(s)
- Sarah E Heuer
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA; Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, 02111, USA
| | - Erik B Bloss
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA; Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, 02111, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, 04469, USA.
| | - Gareth R Howell
- The Jackson Laboratory, Bar Harbor, ME, 04609, USA; Graduate School of Biomedical Sciences, Tufts University School of Medicine, Boston, MA, 02111, USA; Graduate School of Biomedical Sciences and Engineering, University of Maine, Orono, ME, 04469, USA.
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8
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Zheng Z, Own CS, Wanner AA, Koene RA, Hammerschmith EW, Silversmith WM, Kemnitz N, Lu R, Tank DW, Seung HS. Fast imaging of millimeter-scale areas with beam deflection transmission electron microscopy. Nat Commun 2024; 15:6860. [PMID: 39127683 PMCID: PMC11316758 DOI: 10.1038/s41467-024-50846-4] [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: 12/15/2022] [Accepted: 07/23/2024] [Indexed: 08/12/2024] Open
Abstract
Serial section transmission electron microscopy (TEM) has proven to be one of the leading methods for millimeter-scale 3D imaging of brain tissues at nanoscale resolution. It is important to further improve imaging efficiency to acquire larger and more brain volumes. We report here a threefold increase in the speed of TEM by using a beam deflecting mechanism to enable highly efficient acquisition of multiple image tiles (nine) for each motion of the mechanical stage. For millimeter-scale areas, the duty cycle of imaging doubles to more than 30%, yielding a net average imaging rate of 0.3 gigapixels per second. If fully utilized, an array of four beam deflection TEMs should be capable of imaging a dataset of cubic millimeter scale in five weeks.
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Affiliation(s)
- Zhihao Zheng
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | - Adrian A Wanner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Paul Scherrer Institute, Villigen, Switzerland
| | | | | | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - David W Tank
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
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9
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Sato T, Shimada K, Akase Z, Magara H, Tomita T, Shindo D. Electron holography observation of electron spin polarization around charged insulating wire. Microscopy (Oxf) 2024; 73:367-375. [PMID: 37952124 PMCID: PMC11288191 DOI: 10.1093/jmicro/dfad056] [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: 02/15/2023] [Revised: 10/26/2023] [Accepted: 11/10/2023] [Indexed: 11/14/2023] Open
Abstract
We report direct observation by electron holography of the spin polarization of electrons in a vacuum region around a charged SiO2 wire coated with Pt-Pd. Irradiating the SiO2 wire with 300 keV electrons caused the wire to become positively charged due to the emission of secondary electrons. The spin polarization of these electrons interacting with the charged wire was observed in situ using a phase reconstruction process under an external magnetic field. The magnetic field of the spin-polarized electrons was simulated taking into account the distribution of secondary electrons and the effect of the external magnetic field.
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Affiliation(s)
- Takafumi Sato
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Keiko Shimada
- RIKEN Center for Emergent Matter Science (CEMS), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Zentaro Akase
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Hideyuki Magara
- Institute of Multidisciplinary Research for Advanced Materials, Tohoku University, 2-1-1 Katahira, Aoba-ku, Sendai, Miyagi 980-8577, Japan
| | - Takeshi Tomita
- RIKEN Center for Emergent Matter Science (CEMS), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
| | - Daisuke Shindo
- RIKEN Center for Emergent Matter Science (CEMS), 2-1 Hirosawa, Wako, Saitama 351-0198, Japan
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10
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Ohno N, Karube F, Fujiyama F. Volume electron microscopy for genetically and molecularly defined neural circuits. Neurosci Res 2024:S0168-0102(24)00074-9. [PMID: 38914208 DOI: 10.1016/j.neures.2024.06.002] [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: 06/08/2024] [Revised: 06/03/2024] [Accepted: 06/09/2024] [Indexed: 06/26/2024]
Abstract
The brain networks responsible for adaptive behavioral changes are based on the physical connections between neurons. Light and electron microscopy have long been used to study neural projections and the physical connections between neurons. Volume electron microscopy has recently expanded its scale of analysis due to methodological advances, resulting in complete wiring maps of neurites in a large volume of brain tissues and even entire nervous systems in a growing number of species. However, structural approaches frequently suffer from inherent limitations in which elements in images are identified solely by morphological criteria. Recently, an increasing number of tools and technologies have been developed to characterize cells and cellular components in the context of molecules and gene expression. These advancements include newly developed probes for visualization in electron microscopic images as well as correlative integration methods for the same elements across multiple microscopic modalities. Such approaches advance our understanding of interactions between specific neurons and circuits and may help to elucidate novel aspects of the basal ganglia network involving dopamine neurons. These advancements are expected to reveal mechanisms for processing adaptive changes in specific neural circuits that modulate brain functions.
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Affiliation(s)
- Nobuhiko Ohno
- Department of Anatomy, Division of Histology and Cell Biology, Jichi Medical University, Japan; Division of Ultrastructural Research, National Institute for Physiological Sciences, Japan.
| | - Fuyuki Karube
- Laboratory of Histology and Cytology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Japan
| | - Fumino Fujiyama
- Laboratory of Histology and Cytology, Faculty of Medicine and Graduate School of Medicine, Hokkaido University, Japan
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11
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Meirovitch Y, Park CF, Mi L, Potocek P, Sawmya S, Li Y, Chandok IS, Athey TL, Karlupia N, Wu Y, Berger DR, Schalek R, Pfister H, Schoenmakers R, Peemen M, Lichtman JW, Samuel ADT, Shavit N. SmartEM: machine-learning guided electron microscopy. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.05.561103. [PMID: 38915594 PMCID: PMC11195061 DOI: 10.1101/2023.10.05.561103] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/26/2024]
Abstract
Connectomics provides essential nanometer-resolution, synapse-level maps of neural circuits to understand brain activity and behavior. However, few researchers have access to the high-throughput electron microscopes necessary to generate enough data for whole circuit or brain reconstruction. To date, machine-learning methods have been used after the collection of images by electron microscopy (EM) to accelerate and improve neuronal segmentation, synapse reconstruction and other data analysis. With the computational improvements in processing EM images, acquiring EM images has now become the rate-limiting step. Here, in order to speed up EM imaging, we integrate machine-learning into real-time image acquisition in a singlebeam scanning electron microscope. This SmartEM approach allows an electron microscope to perform intelligent, data-aware imaging of specimens. SmartEM allocates the proper imaging time for each region of interest - scanning all pixels equally rapidly, then re-scanning small subareas more slowly where a higher quality signal is required to achieve accurate segmentability, in significantly less time. We demonstrate that this pipeline achieves a 7-fold acceleration of image acquisition time for connectomics using a commercial single-beam SEM. We apply SmartEM to reconstruct a portion of mouse cortex with the same accuracy as traditional microscopy but in less time.
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Affiliation(s)
- Yaron Meirovitch
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Core Francisco Park
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Lu Mi
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Pavel Potocek
- Thermo Fisher Scientific, Eindhoven, the Netherlands
- Saarland University, 66123, Saarbrücken, Germany
| | - Shashata Sawmya
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Yicong Li
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | - Ishaan Singh Chandok
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Thomas L Athey
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Neha Karlupia
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Yuelong Wu
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Daniel R Berger
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Richard Schalek
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Hanspeter Pfister
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA 02134, USA
| | | | | | - Jeff W Lichtman
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA 02138, USA
| | - Aravinthan D T Samuel
- Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Physics, Harvard University, Cambridge, MA 02138, USA
| | - Nir Shavit
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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12
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Qiao M. Deciphering the genetic code of neuronal type connectivity through bilinear modeling. eLife 2024; 12:RP91532. [PMID: 38857169 PMCID: PMC11164534 DOI: 10.7554/elife.91532] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/12/2024] Open
Abstract
Understanding how different neuronal types connect and communicate is critical to interpreting brain function and behavior. However, it has remained a formidable challenge to decipher the genetic underpinnings that dictate the specific connections formed between neuronal types. To address this, we propose a novel bilinear modeling approach that leverages the architecture similar to that of recommendation systems. Our model transforms the gene expressions of presynaptic and postsynaptic neuronal types, obtained from single-cell transcriptomics, into a covariance matrix. The objective is to construct this covariance matrix that closely mirrors a connectivity matrix, derived from connectomic data, reflecting the known anatomical connections between these neuronal types. When tested on a dataset of Caenorhabditis elegans, our model achieved a performance comparable to, if slightly better than, the previously proposed spatial connectome model (SCM) in reconstructing electrical synaptic connectivity based on gene expressions. Through a comparative analysis, our model not only captured all genetic interactions identified by the SCM but also inferred additional ones. Applied to a mouse retinal neuronal dataset, the bilinear model successfully recapitulated recognized connectivity motifs between bipolar cells and retinal ganglion cells, and provided interpretable insights into genetic interactions shaping the connectivity. Specifically, it identified unique genetic signatures associated with different connectivity motifs, including genes important to cell-cell adhesion and synapse formation, highlighting their role in orchestrating specific synaptic connections between these neurons. Our work establishes an innovative computational strategy for decoding the genetic programming of neuronal type connectivity. It not only sets a new benchmark for single-cell transcriptomic analysis of synaptic connections but also paves the way for mechanistic studies of neural circuit assembly and genetic manipulation of circuit wiring.
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Affiliation(s)
- Mu Qiao
- LinkedInMountain ViewUnited States
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13
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Ott CM, Torres R, Kuan TS, Kuan A, Buchanan J, Elabbady L, Seshamani S, Bodor AL, Collman F, Bock DD, Lee WC, da Costa NM, Lippincott-Schwartz J. Ultrastructural differences impact cilia shape and external exposure across cell classes in the visual cortex. Curr Biol 2024; 34:2418-2433.e4. [PMID: 38749425 PMCID: PMC11217952 DOI: 10.1016/j.cub.2024.04.043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2023] [Revised: 03/27/2024] [Accepted: 04/22/2024] [Indexed: 06/06/2024]
Abstract
A primary cilium is a membrane-bound extension from the cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. Primary cilia in the brain are less accessible than cilia on cultured cells or epithelial tissues because in the brain they protrude into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs) but were absent from oligodendrocytes and microglia. Ultrastructural comparisons revealed that the base of the cilium and the microtubule organization differed between neurons and glia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting that cilia are poised to encounter locally released signaling molecules. Our analysis indicated that synapse proximity is likely due to random encounters in the neuropil, with no evidence that cilia modulate synapse activity as would be expected in tetrapartite synapses. The observed cell class differences in proximity to synapses were largely due to differences in external cilia length. Many key structural features that differed between neuronal and glial cilia influenced both cilium placement and shape and, thus, exposure to processes and synapses outside the cilium. Together, the ultrastructure both within and around neuronal and glial cilia suggest differences in cilia formation and function across cell types in the brain.
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Affiliation(s)
- Carolyn M Ott
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA 20147, USA.
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Tung-Sheng Kuan
- Department of Physics, University at Albany, State University of New York, Albany, NY 12222, USA
| | - Aaron Kuan
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA; University of Washington, Seattle, WA 98195, USA
| | | | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Davi D Bock
- Larner College of Medicine, University of Vermont, Burlington, VT 05405, USA
| | - Wei Chung Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA 02115, USA
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14
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Kang SC, Sarn NB, Venegas J, Tan Z, Hitomi M, Eng C. Germline PTEN genotype-dependent phenotypic divergence during the early neural developmental process of forebrain organoids. Mol Psychiatry 2024; 29:1767-1781. [PMID: 38030818 DOI: 10.1038/s41380-023-02325-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 10/22/2023] [Accepted: 11/13/2023] [Indexed: 12/01/2023]
Abstract
PTEN germline mutations account for ~0.2-1% of all autism spectrum disorder (ASD) cases, as well as ~17% of ASD patients with macrocephaly, making it one of the top ASD-associated risk genes. Individuals with germline PTEN mutations receive the molecular diagnosis of PTEN Hamartoma Tumor Syndrome (PHTS), an inherited cancer predisposition syndrome, about 20-23% of whom are diagnosed with ASD. We generated forebrain organoid cultures from gene-edited isogenic human induced pluripotent stem cells (hiPSCs) harboring a PTENG132D (ASD) or PTENM134R (cancer) mutant allele to model how these mutations interrupt neurodevelopmental processes. Here, we show that the PTENG132D allele disrupts early neuroectoderm formation during the first several days of organoid generation, and results in deficient electrophysiology. While organoids generated from PTENM134R hiPSCs remained morphologically similar to wild-type organoids during this early stage in development, we observed disrupted neuronal differentiation, radial glia positioning, and cortical layering in both PTEN-mutant organoids at the later stage of 72+ days of development. Perifosine, an AKT inhibitor, reduced over-activated AKT and partially corrected the abnormalities in cellular organization observed in PTENG132D organoids. Single cell RNAseq analyses on early-stage organoids revealed that genes related to neural cell fate were decreased in PTENG132D mutant organoids, and AKT inhibition was capable of upregulating gene signatures related to neuronal cell fate and CNS maturation pathways. These findings demonstrate that different PTEN missense mutations can have a profound impact on neurodevelopment at diverse stages which in turn may predispose PHTS individuals to ASD. Further study will shed light on ways to mitigate pathological impact of PTEN mutants on neurodevelopment by stage-specific manipulation of downstream PTEN signaling components.
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Affiliation(s)
- Shin Chung Kang
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Nicholas B Sarn
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Juan Venegas
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
| | - Zhibing Tan
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
| | - Masahiro Hitomi
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA
| | - Charis Eng
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
- Cleveland Clinic Lerner College of Medicine, Case Western Reserve University, Cleveland, OH, 44195, USA.
- Center for Personalized Genetic Healthcare, Medical Specialties Institute, Cleveland Clinic, Cleveland, OH, 44195, USA.
- Case Comprehensive Cancer Center, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
- Taussig Cancer Institute, Cleveland Clinic Foundation, Cleveland, OH, 44195, USA.
- Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine, Cleveland, OH, 44106, USA.
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15
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Shapson-Coe A, Januszewski M, Berger DR, Pope A, Wu Y, Blakely T, Schalek RL, Li PH, Wang S, Maitin-Shepard J, Karlupia N, Dorkenwald S, Sjostedt E, Leavitt L, Lee D, Troidl J, Collman F, Bailey L, Fitzmaurice A, Kar R, Field B, Wu H, Wagner-Carena J, Aley D, Lau J, Lin Z, Wei D, Pfister H, Peleg A, Jain V, Lichtman JW. A petavoxel fragment of human cerebral cortex reconstructed at nanoscale resolution. Science 2024; 384:eadk4858. [PMID: 38723085 DOI: 10.1126/science.adk4858] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Accepted: 03/27/2024] [Indexed: 05/31/2024]
Abstract
To fully understand how the human brain works, knowledge of its structure at high resolution is needed. Presented here is a computationally intensive reconstruction of the ultrastructure of a cubic millimeter of human temporal cortex that was surgically removed to gain access to an underlying epileptic focus. It contains about 57,000 cells, about 230 millimeters of blood vessels, and about 150 million synapses and comprises 1.4 petabytes. Our analysis showed that glia outnumber neurons 2:1, oligodendrocytes were the most common cell, deep layer excitatory neurons could be classified on the basis of dendritic orientation, and among thousands of weak connections to each neuron, there exist rare powerful axonal inputs of up to 50 synapses. Further studies using this resource may bring valuable insights into the mysteries of the human brain.
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Affiliation(s)
- Alexander Shapson-Coe
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Queen Mary, University of London, London E1 4NS, UK
| | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Art Pope
- Google Research, Mountain View, CA 94043, USA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Richard L Schalek
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Peter H Li
- Google Research, Mountain View, CA 94043, USA
| | - Shuohong Wang
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Neha Karlupia
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Sven Dorkenwald
- Google Research, Mountain View, CA 94043, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
- Computer Science Department, Princeton University, Princeton, NJ 08540, USA
| | - Evelina Sjostedt
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | | | - Dongil Lee
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea
| | - Jakob Troidl
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | | | - Luke Bailey
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Angerica Fitzmaurice
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Rohin Kar
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Benjamin Field
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Hank Wu
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Northeastern University, Boston, MA 02115, USA
| | - Julian Wagner-Carena
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - David Aley
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Joanna Lau
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA 02467, USA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Adi Peleg
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
- Google, Cambridge, MA 02142, USA
| | - Viren Jain
- Google Research, Mountain View, CA 94043, USA
| | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
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16
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Schmidt M, Motta A, Sievers M, Helmstaedter M. RoboEM: automated 3D flight tracing for synaptic-resolution connectomics. Nat Methods 2024; 21:908-913. [PMID: 38514779 PMCID: PMC11093750 DOI: 10.1038/s41592-024-02226-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/26/2024] [Indexed: 03/23/2024]
Abstract
Mapping neuronal networks from three-dimensional electron microscopy (3D-EM) data still poses substantial reconstruction challenges, in particular for thin axons. Currently available automated image segmentation methods require manual proofreading for many types of connectomic analysis. Here we introduce RoboEM, an artificial intelligence-based self-steering 3D 'flight' system trained to navigate along neurites using only 3D-EM data as input. Applied to 3D-EM data from mouse and human cortex, RoboEM substantially improves automated state-of-the-art segmentations and can replace manual proofreading for more complex connectomic analysis problems, yielding computational annotation cost for cortical connectomes about 400-fold lower than the cost of manual error correction.
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Affiliation(s)
- Martin Schmidt
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
| | - Alessandro Motta
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
| | - Meike Sievers
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany
- Faculty of Science, Radboud University, Nijmegen, the Netherlands
| | - Moritz Helmstaedter
- Department of Connectomics, Max Planck Institute for Brain Research, Frankfurt, Germany.
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17
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Son R, Yamazawa K, Oguchi A, Suga M, Tamura M, Yanagita M, Murakawa Y, Kume S. Morphomics via next-generation electron microscopy. J Mol Cell Biol 2024; 15:mjad081. [PMID: 38148118 PMCID: PMC11167312 DOI: 10.1093/jmcb/mjad081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 10/02/2022] [Accepted: 12/23/2023] [Indexed: 12/28/2023] Open
Abstract
The living body is composed of innumerable fine and complex structures. Although these structures have been studied in the past, a vast amount of information pertaining to them still remains unknown. When attempting to observe these ultra-structures, the use of electron microscopy (EM) has become indispensable. However, conventional EM settings are limited to a narrow tissue area, which can bias observations. Recently, new trends in EM research have emerged, enabling coverage of far broader, nano-scale fields of view for two-dimensional wide areas and three-dimensional large volumes. Moreover, cutting-edge bioimage informatics conducted via deep learning has accelerated the quantification of complex morphological bioimages. Taken together, these technological and analytical advances have led to the comprehensive acquisition and quantification of cellular morphology, which now arises as a new omics science termed 'morphomics'.
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Affiliation(s)
- Raku Son
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Kenji Yamazawa
- Advanced Manufacturing Support Team, RIKEN Center for Advanced Photonics, Wako 351-0198, Japan
| | - Akiko Oguchi
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
| | - Mitsuo Suga
- Multimodal Microstructure Analysis Unit, RIKEN–JEOL Collaboration Center, Kobe 650-0047, Japan
| | - Masaru Tamura
- Technology and Development Team for Mouse Phenotype Analysis, RIKEN BioResource Research Center, Tsukuba 305-0074, Japan
| | - Motoko Yanagita
- Department of Nephrology, Graduate School of Medicine, Kyoto University, Kyoto 606-8507, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
| | - Yasuhiro Murakawa
- RIKEN-IFOM Joint Laboratory for Cancer Genomics, RIKEN Center for Integrative Medical Sciences, Yokohama 230-0045, Japan
- Institute for the Advanced Study of Human Biology (ASHBi), Kyoto University, Kyoto 606-8501, Japan
- IFOM—The FIRC Institute of Molecular Oncology, Milan 20139, Italy
| | - Satoshi Kume
- Laboratory for Pathophysiological and Health Science, RIKEN Center for Biosystems Dynamics Research, Kobe 650-0047, Japan
- Center for Health Science Innovation, Osaka City University, Osaka 530-0011, Japan
- Osaka Electro-Communication University, Neyagawa 572-8530, Japan
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18
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Duszkiewicz AJ, Orhan P, Skromne Carrasco S, Brown EH, Owczarek E, Vite GR, Wood ER, Peyrache A. Local origin of excitatory-inhibitory tuning equivalence in a cortical network. Nat Neurosci 2024; 27:782-792. [PMID: 38491324 PMCID: PMC11001581 DOI: 10.1038/s41593-024-01588-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 01/24/2024] [Indexed: 03/18/2024]
Abstract
The interplay between excitation and inhibition determines the fidelity of cortical representations. The receptive fields of excitatory neurons are often finely tuned to encoded features, but the principles governing the tuning of inhibitory neurons remain elusive. In this study, we recorded populations of neurons in the mouse postsubiculum (PoSub), where the majority of excitatory neurons are head-direction (HD) cells. We show that the tuning of fast-spiking (FS) cells, the largest class of cortical inhibitory neurons, was broad and frequently radially symmetrical. By decomposing tuning curves using the Fourier transform, we identified an equivalence in tuning between PoSub-FS and PoSub-HD cell populations. Furthermore, recordings, optogenetic manipulations of upstream thalamic populations and computational modeling provide evidence that the tuning of PoSub-FS cells has a local origin. These findings support the notion that the equivalence of neuronal tuning between excitatory and inhibitory cell populations is an intrinsic property of local cortical networks.
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Affiliation(s)
- Adrian J Duszkiewicz
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK.
- Department of Psychology, University of Stirling, Stirling, UK.
| | - Pierre Orhan
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
- Ecole normale supérieure, PSL University, CNRS, Paris, France
| | - Sofia Skromne Carrasco
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Eleanor H Brown
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Eliott Owczarek
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Gilberto R Vite
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada
| | - Emma R Wood
- Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
- Simons Initiative for the Developing Brain, University of Edinburgh, Edinburgh, UK
| | - Adrien Peyrache
- Montreal Neurological Institute and Hospital, McGill University, Montreal, Quebec, Canada.
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19
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Micheva KD, Burden JJ, Schifferer M. Array tomography: trails to discovery. METHODS IN MICROSCOPY 2024; 1:9-17. [PMID: 39119254 PMCID: PMC11308915 DOI: 10.1515/mim-2024-0001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Accepted: 06/06/2024] [Indexed: 08/10/2024]
Abstract
Tissue slicing is at the core of many approaches to studying biological structures. Among the modern volume electron microscopy (vEM) methods, array tomography (AT) is based on serial ultramicrotomy, section collection onto solid support, imaging via light and/or scanning electron microscopy, and re-assembly of the serial images into a volume for analysis. While AT largely uses standard EM equipment, it provides several advantages, including long-term preservation of the sample and compatibility with multi-scale and multi-modal imaging. Furthermore, the collection of serial ultrathin sections improves axial resolution and provides access for molecular labeling, which is beneficial for light microscopy and immunolabeling, and facilitates correlation with EM. Despite these benefits, AT techniques are underrepresented in imaging facilities and labs, due to their perceived difficulty and lack of training opportunities. Here we point towards novel developments in serial sectioning and image analysis that facilitate the AT pipeline, and solutions to overcome constraints. Because no single vEM technique can serve all needs regarding field of view and resolution, we sketch a decision tree to aid researchers in navigating the plethora of options available. Lastly, we elaborate on the unexplored potential of AT approaches to add valuable insight in diverse biological fields.
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Affiliation(s)
| | | | - Martina Schifferer
- Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
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20
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Kievits AJ, Duinkerken BHP, Lane R, de Heus C, van Beijeren Bergen en Henegouwen D, Höppener T, Wolters AHG, Liv N, Giepmans BNG, Hoogenboom JP. FAST-EM array tomography: a workflow for multibeam volume electron microscopy. METHODS IN MICROSCOPY 2024; 1:49-64. [PMID: 39119255 PMCID: PMC11308914 DOI: 10.1515/mim-2024-0005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Accepted: 06/17/2024] [Indexed: 08/10/2024]
Abstract
Elucidating the 3D nanoscale structure of tissues and cells is essential for understanding the complexity of biological processes. Electron microscopy (EM) offers the resolution needed for reliable interpretation, but the limited throughput of electron microscopes has hindered its ability to effectively image large volumes. We report a workflow for volume EM with FAST-EM, a novel multibeam scanning transmission electron microscope that speeds up acquisition by scanning the sample in parallel with 64 electron beams. FAST-EM makes use of optical detection to separate the signals of the individual beams. The acquisition and 3D reconstruction of ultrastructural data from multiple biological samples is demonstrated. The results show that the workflow is capable of producing large reconstructed volumes with high resolution and contrast to address biological research questions within feasible acquisition time frames.
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Affiliation(s)
- Arent J. Kievits
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - B. H. Peter Duinkerken
- Department of Biomedical Sciences, University Medical Center Groningen, Groningen, The Netherlands
| | - Ryan Lane
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Cecilia de Heus
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | | | - Tibbe Höppener
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Anouk H. G. Wolters
- Department of Biomedical Sciences, University Medical Center Groningen, Groningen, The Netherlands
| | - Nalan Liv
- Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Ben N. G. Giepmans
- Department of Biomedical Sciences, University Medical Center Groningen, Groningen, The Netherlands
| | - Jacob P. Hoogenboom
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
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21
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Zhao J, Yu X, Shentu X, Li D. The application and development of electron microscopy for three-dimensional reconstruction in life science: a review. Cell Tissue Res 2024; 396:1-18. [PMID: 38416172 DOI: 10.1007/s00441-024-03878-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 02/13/2024] [Indexed: 02/29/2024]
Abstract
Imaging technologies have played a pivotal role in advancing biological research by enabling visualization of biological structures and processes. While traditional electron microscopy (EM) produces two-dimensional images, emerging techniques now allow high-resolution three-dimensional (3D) characterization of specimens in situ, meeting growing needs in molecular and cellular biology. Combining transmission electron microscopy (TEM) with serial sectioning inaugurated 3D imaging, attracting biologists seeking to explore cell ultrastructure and driving advancement of 3D EM reconstruction. By comprehensively and precisely rendering internal structure and distribution, 3D TEM reconstruction provides unparalleled ultrastructural insights into cells and molecules, holding tremendous value for elucidating structure-function relationships and broadly propelling structural biology. Here, we first introduce the principle of 3D reconstruction of cells and tissues by classical approaches in TEM and then discuss modern technologies utilizing TEM and on new SEM-based as well as cryo-electron microscope (cryo-EM) techniques. 3D reconstruction techniques from serial sections, electron tomography (ET), and the recent single-particle analysis (SPA) are examined; the focused ion beam scanning electron microscopy (FIB-SEM), the serial block-face scanning electron microscopy (SBF-SEM), and automatic tape-collecting lathe ultramicrotome (ATUM-SEM) for 3D reconstruction of large volumes are discussed. Finally, we review the challenges and development prospects of these technologies in life science. It aims to provide an informative reference for biological researchers.
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Affiliation(s)
- Jingjing Zhao
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Xiaoping Yu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Xuping Shentu
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China
| | - Danting Li
- Zhejiang Provincial Key Laboratory of Biometrology and Inspection and Quarantine, College of Life Science, China , Jiliang University, Hangzhou, 310018, China.
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22
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Boergens KM, Wildenberg G, Li R, Lambert L, Moradi A, Stam G, Tromp R, van der Molen SJ, King SB, Kasthuri N. Photoemission electron microscopy for connectomics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.05.556423. [PMID: 37771915 PMCID: PMC10525389 DOI: 10.1101/2023.09.05.556423] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/30/2023]
Abstract
Detailing the physical basis of neural circuits with large-volume serial electron microscopy (EM), 'connectomics', has emerged as an invaluable tool in the neuroscience armamentarium. However, imaging synaptic resolution connectomes is currently limited to either transmission electron microscopy (TEM) or scanning electron microscopy (SEM). Here, we describe a third way, using photoemission electron microscopy (PEEM) which illuminates ultra-thin brain slices collected on solid substrates with UV light and images the photoelectron emission pattern with a wide-field electron microscope. PEEM works with existing sample preparations for EM and routinely provides sufficient resolution and contrast to reveal myelinated axons, somata, dendrites, and sub-cellular organelles. Under optimized conditions, PEEM provides synaptic resolution; and simulation and experiments show that PEEM can be transformatively fast, at Gigahertz pixel rates. We conclude that PEEM imaging leverages attractive aspects of SEM and TEM, namely reliable sample collection on robust substrates combined with fast wide-field imaging, and could enable faster data acquisition for next-generation circuit mapping.
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23
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Fulton KA, Watkins PV, Briggman KL. GAUSS-EM, guided accumulation of ultrathin serial sections with a static magnetic field for volume electron microscopy. CELL REPORTS METHODS 2024; 4:100720. [PMID: 38452770 PMCID: PMC10985227 DOI: 10.1016/j.crmeth.2024.100720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/30/2023] [Accepted: 02/09/2024] [Indexed: 03/09/2024]
Abstract
Serial sectioning electron microscopy (EM) of millimeter-scale three-dimensional (3D) anatomical volumes requires the collection of thousands of ultrathin sections. Here, we report a high-throughput automated approach, GAUSS-EM (guided accumulation of ultrathin serial sections-EM), utilizing a static magnetic field to collect and densely pack thousands of sections onto individual silicon wafers. The method is capable of sectioning hundreds of microns of tissue per day at section thicknesses down to 35 nm. Relative to other automated volume EM approaches, GAUSS-EM democratizes the ability to collect large 3D EM volumes because it is simple and inexpensive to implement. We present two exemplar EM volumes of a zebrafish eye and mouse olfactory bulb collected with the method.
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Affiliation(s)
- Kara A Fulton
- Department of Computational Neuroethology, Max Planck Institute for Neurobiology of Behavior - caesar, 53175 Bonn, NRW, Germany
| | - Paul V Watkins
- Department of Computational Neuroethology, Max Planck Institute for Neurobiology of Behavior - caesar, 53175 Bonn, NRW, Germany
| | - Kevin L Briggman
- Department of Computational Neuroethology, Max Planck Institute for Neurobiology of Behavior - caesar, 53175 Bonn, NRW, Germany.
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24
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Znamenskiy P, Kim MH, Muir DR, Iacaruso MF, Hofer SB, Mrsic-Flogel TD. Functional specificity of recurrent inhibition in visual cortex. Neuron 2024; 112:991-1000.e8. [PMID: 38244539 DOI: 10.1016/j.neuron.2023.12.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Revised: 10/31/2023] [Accepted: 12/19/2023] [Indexed: 01/22/2024]
Abstract
In the neocortex, neural activity is shaped by the interaction of excitatory and inhibitory neurons, defined by the organization of their synaptic connections. Although connections among excitatory pyramidal neurons are sparse and functionally tuned, inhibitory connectivity is thought to be dense and largely unstructured. By measuring in vivo visual responses and synaptic connectivity of parvalbumin-expressing (PV+) inhibitory cells in mouse primary visual cortex, we show that the synaptic weights of their connections to nearby pyramidal neurons are specifically tuned according to the similarity of the cells' responses. Individual PV+ cells strongly inhibit those pyramidal cells that provide them with strong excitation and share their visual selectivity. This structured organization of inhibitory synaptic weights provides a circuit mechanism for tuned inhibition onto pyramidal cells despite dense connectivity, stabilizing activity within feature-specific excitatory ensembles while supporting competition between them.
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Affiliation(s)
- Petr Znamenskiy
- Specification and Function of Neural Circuits Laboratory, The Francis Crick Institute, 1 Midland Road, London NW1 1AT, UK; Sainsbury Wellcome Centre, 25 Howland Street, London W1T 4JG, UK; Biozentrum, University of Basel, Klingelbergstrasse 70, 4056 Basel, Switzerland.
| | - Mean-Hwan Kim
- Biozentrum, University of Basel, Klingelbergstrasse 70, 4056 Basel, Switzerland
| | - Dylan R Muir
- Biozentrum, University of Basel, Klingelbergstrasse 70, 4056 Basel, Switzerland
| | | | - Sonja B Hofer
- Sainsbury Wellcome Centre, 25 Howland Street, London W1T 4JG, UK; Biozentrum, University of Basel, Klingelbergstrasse 70, 4056 Basel, Switzerland
| | - Thomas D Mrsic-Flogel
- Sainsbury Wellcome Centre, 25 Howland Street, London W1T 4JG, UK; Biozentrum, University of Basel, Klingelbergstrasse 70, 4056 Basel, Switzerland.
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25
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Kuan AT, Bondanelli G, Driscoll LN, Han J, Kim M, Hildebrand DGC, Graham BJ, Wilson DE, Thomas LA, Panzeri S, Harvey CD, Lee WCA. Synaptic wiring motifs in posterior parietal cortex support decision-making. Nature 2024; 627:367-373. [PMID: 38383788 PMCID: PMC11162200 DOI: 10.1038/s41586-024-07088-7] [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: 04/13/2022] [Accepted: 01/17/2024] [Indexed: 02/23/2024]
Abstract
The posterior parietal cortex exhibits choice-selective activity during perceptual decision-making tasks1-10. However, it is not known how this selective activity arises from the underlying synaptic connectivity. Here we combined virtual-reality behaviour, two-photon calcium imaging, high-throughput electron microscopy and circuit modelling to analyse how synaptic connectivity between neurons in the posterior parietal cortex relates to their selective activity. We found that excitatory pyramidal neurons preferentially target inhibitory interneurons with the same selectivity. In turn, inhibitory interneurons preferentially target pyramidal neurons with opposite selectivity, forming an opponent inhibition motif. This motif was present even between neurons with activity peaks in different task epochs. We developed neural-circuit models of the computations performed by these motifs, and found that opponent inhibition between neural populations with opposite selectivity amplifies selective inputs, thereby improving the encoding of trial-type information. The models also predict that opponent inhibition between neurons with activity peaks in different task epochs contributes to creating choice-specific sequential activity. These results provide evidence for how synaptic connectivity in cortical circuits supports a learned decision-making task.
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Affiliation(s)
- Aaron T Kuan
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | - Giulio Bondanelli
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy
- Department of Excellence for Neural Information Processing, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Laura N Driscoll
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Allen Institute for Neural Dynamics, Allen Institute, Seattle, WA, USA
| | - Julie Han
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Khoury College of Computer Sciences, Northeastern University, Seattle, WA, USA
| | - Minsu Kim
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - David G C Hildebrand
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | - Brett J Graham
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Space Telescope Science Institute, Baltimore, MD, USA
| | - Daniel E Wilson
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Logan A Thomas
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Biophysics Graduate Group, University of California Berkeley, Berkeley, CA, USA
| | - Stefano Panzeri
- Neural Computation Laboratory, Istituto Italiano di Tecnologia, Genoa, Italy.
- Department of Excellence for Neural Information Processing, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
| | | | - Wei-Chung Allen Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA.
- FM Kirby Neurobiology Center, Boston Children's Hospital, Boston, MA, USA.
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26
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Lin A, Yang R, Dorkenwald S, Matsliah A, Sterling AR, Schlegel P, Yu SC, McKellar CE, Costa M, Eichler K, Bates AS, Eckstein N, Funke J, Jefferis GSXE, Murthy M. Network Statistics of the Whole-Brain Connectome of Drosophila. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.07.29.551086. [PMID: 37547019 PMCID: PMC10402125 DOI: 10.1101/2023.07.29.551086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
Brains comprise complex networks of neurons and connections. Network analysis applied to the wiring diagrams of brains can offer insights into how brains support computations and regulate information flow. The completion of the first whole-brain connectome of an adult Drosophila, the largest connectome to date, containing 130,000 neurons and millions of connections, offers an unprecedented opportunity to analyze its network properties and topological features. To gain insights into local connectivity, we computed the prevalence of two- and three-node network motifs, examined their strengths and neurotransmitter compositions, and compared these topological metrics with wiring diagrams of other animals. We discovered that the network of the fly brain displays rich club organization, with a large population (30% percent of the connectome) of highly connected neurons. We identified subsets of rich club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex and will serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.
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Affiliation(s)
- Albert Lin
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Center for the Physics of Biological Function, Princeton University, Princeton, NJ, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Arie Matsliah
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Amy R Sterling
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Philipp Schlegel
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Claire E McKellar
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Marta Costa
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Katharina Eichler
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Alexander Shakeel Bates
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
- Centre for Neural Circuits and Behaviour, University of Oxford, Oxford, UK
| | - Nils Eckstein
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Jan Funke
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, USA
| | - Gregory S X E Jefferis
- Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK
- Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK
| | - Mala Murthy
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
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27
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Qiu T, An Q, Wang J, Wang J, Qiu CW, Li S, Lv H, Cai M, Wang J, Cong L, Qu S. Vision-driven metasurfaces for perception enhancement. Nat Commun 2024; 15:1631. [PMID: 38388545 PMCID: PMC10883922 DOI: 10.1038/s41467-024-45296-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Accepted: 01/16/2024] [Indexed: 02/24/2024] Open
Abstract
Metasurfaces have exhibited unprecedented degree of freedom in manipulating electromagnetic (EM) waves and thus provide fantastic front-end interfaces for smart systems. Here we show a framework for perception enhancement based on vision-driven metasurface. Human's eye movements are matched with microwave radiations to extend the humans' perception spectrum. By this means, our eyes can "sense" visual information and invisible microwave information. Several experimental demonstrations are given for specific implementations, including a physiological-signal-monitoring system, an "X-ray-glasses" system, a "glimpse-and-forget" tracking system and a speech reception system for deaf people. Both the simulation and experiment results verify evident advantages in perception enhancement effects and improving information acquisition efficiency. This framework can be readily integrated into healthcare systems to monitor physiological signals and to offer assistance for people with disabilities. This work provides an alternative framework for perception enhancement and may find wide applications in healthcare, wearable devices, search-and-rescue and others.
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Affiliation(s)
- Tianshuo Qiu
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
- Fundamentals Department, Air Force Engineering University, Xi'an, China
- State Key Laboratory of Millimeter Waves, Southeast University, Nanjing, China
| | - Qiang An
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Jianqi Wang
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
| | - Jiafu Wang
- Aerospace metamaterials laboratory of SuZhou National Laboratory, Suzhou, China.
| | - Cheng-Wei Qiu
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore.
| | - Shiyong Li
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Hao Lv
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China.
| | - Ming Cai
- Fundamentals Department, Air Force Engineering University, Xi'an, China
| | - Jianyi Wang
- Department of Neurology, the First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Lin Cong
- Department of Biomedical Engineering, Fourth Military Medical University, Xi'an, China
| | - Shaobo Qu
- Aerospace metamaterials laboratory of SuZhou National Laboratory, Suzhou, China.
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28
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Popovych S, Macrina T, Kemnitz N, Castro M, Nehoran B, Jia Z, Bae JA, Mitchell E, Mu S, Trautman ET, Saalfeld S, Li K, Seung HS. Petascale pipeline for precise alignment of images from serial section electron microscopy. Nat Commun 2024; 15:289. [PMID: 38177169 PMCID: PMC10767115 DOI: 10.1038/s41467-023-44354-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 12/07/2023] [Indexed: 01/06/2024] Open
Abstract
The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.
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Affiliation(s)
- Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Barak Nehoran
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Zhen Jia
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Electrical and Computer Engineering Department, Princeton University, Princeton, NJ, USA
| | - Eric Mitchell
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | | | | | - Kai Li
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Computer Science Department, Princeton University, Princeton, NJ, USA.
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29
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Oldenburg IA, Hendricks WD, Handy G, Shamardani K, Bounds HA, Doiron B, Adesnik H. The logic of recurrent circuits in the primary visual cortex. Nat Neurosci 2024; 27:137-147. [PMID: 38172437 PMCID: PMC10774145 DOI: 10.1038/s41593-023-01510-5] [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: 09/20/2022] [Accepted: 10/27/2023] [Indexed: 01/05/2024]
Abstract
Recurrent cortical activity sculpts visual perception by refining, amplifying or suppressing visual input. However, the rules that govern the influence of recurrent activity remain enigmatic. We used ensemble-specific two-photon optogenetics in the mouse visual cortex to isolate the impact of recurrent activity from external visual input. We found that the spatial arrangement and the visual feature preference of the stimulated ensemble and the neighboring neurons jointly determine the net effect of recurrent activity. Photoactivation of these ensembles drives suppression in all cells beyond 30 µm but uniformly drives activation in closer similarly tuned cells. In nonsimilarly tuned cells, compact, cotuned ensembles drive net suppression, while diffuse, cotuned ensembles drive activation. Computational modeling suggests that highly local recurrent excitatory connectivity and selective convergence onto inhibitory neurons explain these effects. Our findings reveal a straightforward logic in which space and feature preference of cortical ensembles determine their impact on local recurrent activity.
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Affiliation(s)
- Ian Antón Oldenburg
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
- Department of Neuroscience and Cell Biology, Robert Wood Johnson Medical School, and Center for Advanced Biotechnology and Medicine, Rutgers University, Piscataway, NJ, USA.
| | - William D Hendricks
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Gregory Handy
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA.
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA.
- Department of Mathematics, University of Minnesota, Minneapolis, MN, USA.
| | - Kiarash Shamardani
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA
- Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA, USA
| | - Hayley A Bounds
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA
| | - Brent Doiron
- Department of Neurobiology and Statistics, University of Chicago, Chicago, IL, USA
- Grossman Center for Quantitative Biology and Human Behavior, University of Chicago, Chicago, IL, USA
| | - Hillel Adesnik
- Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA.
- The Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA.
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30
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Watkins PV, Jelli E, Briggman KL. msemalign: a pipeline for serial section multibeam scanning electron microscopy volume alignment. Front Neurosci 2023; 17:1281098. [PMID: 38148945 PMCID: PMC10749929 DOI: 10.3389/fnins.2023.1281098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Accepted: 11/23/2023] [Indexed: 12/28/2023] Open
Abstract
Serial section multibeam scanning electron microscopy (ssmSEM) is currently among the fastest technologies available for acquiring 3D anatomical data spanning relatively large neural tissue volumes, on the order of 1 mm3 or larger, at a resolution sufficient to resolve the fine detail of neuronal morphologies and synapses. These petabyte-scale volumes can be analyzed to create connectomes, datasets that contain detailed anatomical information including synaptic connectivity, neuronal morphologies and distributions of cellular organelles. The mSEM acquisition process creates hundreds of millions of individual image tiles for a single cubic-millimeter-sized dataset and these tiles must be aligned to create 3D volumes. Here we introduce msemalign, an alignment pipeline that strives for scalability and design simplicity. The pipeline can align petabyte-scale datasets such that they contain smooth transitions as the dataset is navigated in all directions, but critically that does so in a fashion that minimizes the overall magnitude of section distortions relative to the originally acquired micrographs.
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Affiliation(s)
| | | | - Kevin L. Briggman
- Max Planck Institute for Neurobiology of Behavior—caesar, Bonn, Germany
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31
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Dorkenwald S, Li PH, Januszewski M, Berger DR, Maitin-Shepard J, Bodor AL, Collman F, Schneider-Mizell CM, da Costa NM, Lichtman JW, Jain V. Multi-layered maps of neuropil with segmentation-guided contrastive learning. Nat Methods 2023; 20:2011-2020. [PMID: 37985712 PMCID: PMC10703674 DOI: 10.1038/s41592-023-02059-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 10/02/2023] [Indexed: 11/22/2023]
Abstract
Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.
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Affiliation(s)
- Sven Dorkenwald
- Google Research, Mountain View, CA, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Computer Science Department, Princeton University, Princeton, NJ, USA
| | | | | | - Daniel R Berger
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | | | | | | | | | | | - Jeff W Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard, Cambridge, MA, USA
| | - Viren Jain
- Google Research, Mountain View, CA, USA.
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32
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Ott CM, Torres R, Kuan TS, Kuan A, Buchanan J, Elabbady L, Seshamani S, Bodor AL, Collman F, Bock DD, Lee WC, da Costa NM, Lippincott-Schwartz J. Nanometer-scale views of visual cortex reveal anatomical features of primary cilia poised to detect synaptic spillover. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.31.564838. [PMID: 37961618 PMCID: PMC10635062 DOI: 10.1101/2023.10.31.564838] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
A primary cilium is a thin membrane-bound extension off a cell surface that contains receptors for perceiving and transmitting signals that modulate cell state and activity. While many cell types have a primary cilium, little is known about primary cilia in the brain, where they are less accessible than cilia on cultured cells or epithelial tissues and protrude from cell bodies into a deep, dense network of glial and neuronal processes. Here, we investigated cilia frequency, internal structure, shape, and position in large, high-resolution transmission electron microscopy volumes of mouse primary visual cortex. Cilia extended from the cell bodies of nearly all excitatory and inhibitory neurons, astrocytes, and oligodendrocyte precursor cells (OPCs), but were absent from oligodendrocytes and microglia. Structural comparisons revealed that the membrane structure at the base of the cilium and the microtubule organization differed between neurons and glia. OPC cilia were distinct in that they were the shortest and contained pervasive internal vesicles only occasionally observed in neuron and astrocyte cilia. Investigating cilia-proximal features revealed that many cilia were directly adjacent to synapses, suggesting cilia are well poised to encounter locally released signaling molecules. Cilia proximity to synapses was random, not enriched, in the synapse-rich neuropil. The internal anatomy, including microtubule changes and centriole location, defined key structural features including cilium placement and shape. Together, the anatomical insights both within and around neuron and glia cilia provide new insights into cilia formation and function across cell types in the brain.
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Affiliation(s)
- Carolyn M. Ott
- Janelia Research Campus, Howard Hughes Medical Institute
| | | | | | - Aaron Kuan
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
- Current address Department of Neuroscience, Yale School of Medicine, New Haven, CT, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science
- University of Washington, Seattle, WA, USA
| | | | | | | | - Davi D. Bock
- Larner College of Medicine, University of Vermont, Burlington, VT, USA
| | - Wei Chung Lee
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
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33
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Nakajima R, Shirakami A, Tsumura H, Matsuda K, Nakamura E, Shimono M. Mutual generation in neuronal activity across the brain via deep neural approach, and its network interpretation. Commun Biol 2023; 6:1105. [PMID: 37907640 PMCID: PMC10618281 DOI: 10.1038/s42003-023-05453-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Accepted: 10/11/2023] [Indexed: 11/02/2023] Open
Abstract
In the brain, many regions work in a network-like association, yet it is not known how durable these associations are in terms of activity and could survive without structural connections. To assess the association or similarity between brain regions with a generating approach, this study evaluated the similarity of activities of neurons within each region after disconnecting between regions. The "generation" approach here refers to using a multi-layer LSTM (Long Short-Term Memory) model to learn the rules of activity generation in one region and then apply that knowledge to generate activity in other regions. Surprisingly, the results revealed that activity generation from one region to disconnected regions was possible with similar accuracy to generation between the same regions in many cases. Notably, firing rates and synchronization of firing between neuron pairs, often used as neuronal representations, could be reproduced with precision. Additionally, accuracies were associated with the relative angle between brain regions and the strength of the structural connections that initially connected them. This outcome enables us to look into trends governing non-uniformity of the cortex based on the potential to generate informative data and reduces the need for animal experiments.
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Affiliation(s)
- Ryota Nakajima
- Kyoto University, Graduate School of Medicine, Kyoto, Japan
| | | | - Hayato Tsumura
- Kyoto University, Graduate School of Medicine, Kyoto, Japan
| | - Kouki Matsuda
- Kyoto University, Graduate School of Medicine, Kyoto, Japan
| | - Eita Nakamura
- Kyoto University, Graduate School of Informatics, Kyoto, Japan
- Kyoto University, The Hakubi Center for Advanced Research, Kyoto, Japan
| | - Masanori Shimono
- Kyoto University, Graduate School of Medicine, Kyoto, Japan.
- Kyoto University, The Hakubi Center for Advanced Research, Kyoto, Japan.
- Osaka University, Graduate School of Informatics, Kyoto, Japan.
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34
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Kievits AJ, Duinkerken BHP, Fermie J, Lane R, Giepmans BNG, Hoogenboom JP. Optical STEM detection for scanning electron microscopy. Ultramicroscopy 2023; 256:113877. [PMID: 37931528 DOI: 10.1016/j.ultramic.2023.113877] [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: 07/12/2023] [Revised: 10/13/2023] [Accepted: 10/21/2023] [Indexed: 11/08/2023]
Abstract
Recent advances in electron microscopy techniques have led to a significant scale up in volumetric imaging of biological tissue. The throughput of electron microscopes, however, remains a limiting factor for the volume that can be imaged in high resolution within reasonable time. Faster detection methods will improve throughput. Here, we have characterized and benchmarked a novel detection technique for scanning electron microscopy: optical scanning transmission electron microscopy (OSTEM). A qualitative and quantitative comparison was performed between OSTEM, secondary and backscattered electron detection and annular dark field detection in scanning transmission electron microscopy. Our analysis shows that OSTEM produces images similar to backscattered electron detection in terms of contrast, resolution and signal-to-noise ratio. OSTEM can complement large scale imaging with (scanning) transmission electron microscopy and has the potential to speed up imaging in single-beam scanning electron microscope.
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Affiliation(s)
- Arent J Kievits
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands.
| | - B H Peter Duinkerken
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | | | - Ryan Lane
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Ben N G Giepmans
- Department of Biomedical Sciences of Cells and Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Jacob P Hoogenboom
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
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35
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Pérez-Garza J, Parrish-Mulliken E, Deane Z, Ostroff LE. Rehydration of Freeze Substituted Brain Tissue for Pre-embedding Immunoelectron Microscopy. MICROSCOPY AND MICROANALYSIS : THE OFFICIAL JOURNAL OF MICROSCOPY SOCIETY OF AMERICA, MICROBEAM ANALYSIS SOCIETY, MICROSCOPICAL SOCIETY OF CANADA 2023; 29:1694-1704. [PMID: 37584524 PMCID: PMC10541149 DOI: 10.1093/micmic/ozad077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/27/2023] [Accepted: 07/16/2023] [Indexed: 08/17/2023]
Abstract
Electron microscopy (EM) volume reconstruction is a powerful tool for investigating the fundamental structure of brain circuits, but the full potential of this technique is limited by the difficulty of integrating molecular information. High quality ultrastructural preservation is necessary for EM reconstruction, and intact, highly contrasted cell membranes are essential for following small neuronal processes through serial sections. Unfortunately, the antibody labeling methods used to identify most endogenous molecules result in compromised morphology, especially of membranes. Cryofixation can produce superior morphological preservation and has the additional advantage of allowing indefinite storage of valuable samples. We have developed a method based on cryofixation that allows sensitive immunolabeling of endogenous molecules, preserves excellent ultrastructure, and is compatible with high-contrast staining for serial EM reconstruction.
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Affiliation(s)
- Janeth Pérez-Garza
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
| | - Emily Parrish-Mulliken
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
| | - Zachary Deane
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
| | - Linnaea E Ostroff
- Department of Physiology and Neurobiology, University of Connecticut, 75 North Eagleville Rd. Unit 3156, Storrs, CT 06269-3156, USA
- Connecticut Institute for the Brain and Cognitive Sciences, University of Connecticut, 337 Mansfield Rd. Unit 1272, Storrs, CT 06269-1272, USA
- Institute of Materials Science, University of Connecticut, 25 King Hill Rd. Unit 3136, Storrs, CT 06269-3136, USA
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36
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Hayashi S, Ohno N, Knott G, Molnár Z. Correlative light and volume electron microscopy to study brain development. Microscopy (Oxf) 2023; 72:279-286. [PMID: 36620906 DOI: 10.1093/jmicro/dfad002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Accepted: 01/06/2023] [Indexed: 01/10/2023] Open
Abstract
Recent advances in volume electron microscopy (EM) have been driving our thorough understanding of the brain architecture. Volume EM becomes increasingly powerful when cells and their subcellular structures that are imaged in light microscopy are correlated to those in ultramicrographs obtained with EM. This correlative approach, called correlative light and volume electron microscopy (vCLEM), is used to link three-dimensional ultrastructural information with physiological data such as intracellular Ca2+ dynamics. Genetic tools to express fluorescent proteins and/or an engineered form of a soybean ascorbate peroxidase allow us to perform vCLEM using natural landmarks including blood vessels without immunohistochemical staining. This immunostaining-free vCLEM has been successfully employed in two-photon Ca2+ imaging in vivo as well as in studying complex synaptic connections in thalamic neurons that receive a variety of specialized inputs from the cerebral cortex. In this mini-review, we overview how volume EM and vCLEM have contributed to studying the developmental processes of the brain. We also discuss potential applications of genetic manipulation of target cells using clustered regularly interspaced short palindromic repeats-associated protein 9 and subsequent volume EM to the analysis of protein localization as well as to loss-of-function studies of genes regulating brain development. We give examples for the combinatorial usage of genetic tools with vCLEM that will further enhance our understanding of regulatory mechanisms underlying brain development.
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Affiliation(s)
- Shuichi Hayashi
- Department of Anatomy, Kawasaki Medical School, 577 Matsushima, Kurashiki, Okayama 701-0192, Japan
| | - Nobuhiko Ohno
- Department of Anatomy, Division of Histology and Cell Biology, School of Medicine, Jichi Medical University, 3311-1 Yakushiji, Shimotsuke, Tochigi 329-0498, Japan
- Division of Ultrastructural Research, National Institute for Physiological Sciences, 5-1 Higashiyama Myodaiji, Okazaki, Aichi 444-8787, Japan
| | - Graham Knott
- Biological Electron Microscopy Facility, Faculty of Life Sciences, École Polytechnique Fédérale de Lausanne (EPFL), Route Cantonale, Lausanne CH-1015, Switzerland
| | - Zoltán Molnár
- Department of Physiology, Anatomy and Genetics, University of Oxford, Sherrington Building, Parks Road, Oxford OX1 3PT, UK
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37
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Xin T, Lv Y, Chen H, Li L, Shen L, Shan G, Chen X, Han H. A novel registration method for long-serial section images of EM with a serial split technique based on unsupervised optical flow network. Bioinformatics 2023; 39:btad436. [PMID: 37462605 PMCID: PMC10403427 DOI: 10.1093/bioinformatics/btad436] [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: 05/04/2023] [Revised: 06/06/2023] [Accepted: 07/17/2023] [Indexed: 08/06/2023] Open
Abstract
MOTIVATION The registration of serial section electron microscope images is a critical step in reconstructing biological tissue volumes, and it aims to eliminate complex nonlinear deformations from sectioning and replicate the correct neurite structure. However, due to the inherent properties of biological structures and the challenges posed by section preparation of biological tissues, achieving an accurate registration of serial sections remains a significant challenge. Conventional nonlinear registration techniques, which are effective in eliminating nonlinear deformation, can also eliminate the natural morphological variation of neurites across sections. Additionally, accumulation of registration errors alters the neurite structure. RESULTS This article proposes a novel method for serial section registration that utilizes an unsupervised optical flow network to measure feature similarity rather than pixel similarity to eliminate nonlinear deformation and achieve pairwise registration between sections. The optical flow network is then employed to estimate and compensate for cumulative registration error, thereby allowing for the reconstruction of the structure of biological tissues. Based on the novel serial section registration method, a serial split technique is proposed for long-serial sections. Experimental results demonstrate that the state-of-the-art method proposed here effectively improves the spatial continuity of serial sections, leading to more accurate registration and improved reconstruction of the structure of biological tissues. AVAILABILITY AND IMPLEMENTATION The source code and data are available at https://github.com/TongXin-CASIA/EFSR.
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Affiliation(s)
- Tong Xin
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190,China
| | - Yanan Lv
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Haoran Chen
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190,China
| | - Linlin Li
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lijun Shen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Guangcun Shan
- School of Instrumentation Science and Opto-electronics Engineering & Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Beihang University, Beijing 100083, China
| | - Xi Chen
- Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Hua Han
- State Key Laboratory of Multimodal Artificial Intelligence Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190,China
- School of Future Technology, University of Chinese Academy of Sciences, Beijing 100190, China
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38
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Marshall AG, Neikirk K, Stephens DC, Vang L, Vue Z, Beasley HK, Crabtree A, Scudese E, Lopez EG, Shao B, Krystofiak E, Rutledge S, Davis J, Murray SA, Damo SM, Katti P, Hinton A. Serial Block Face-Scanning Electron Microscopy as a Burgeoning Technology. Adv Biol (Weinh) 2023; 7:e2300139. [PMID: 37246236 PMCID: PMC10950369 DOI: 10.1002/adbi.202300139] [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: 04/08/2023] [Revised: 05/09/2023] [Indexed: 05/30/2023]
Abstract
Serial block face scanning electron microscopy (SBF-SEM), also referred to as serial block-face electron microscopy, is an advanced ultrastructural imaging technique that enables three-dimensional visualization that provides largerx- and y-axis ranges than other volumetric EM techniques. While SEM is first introduced in the 1930s, SBF-SEM is developed as a novel method to resolve the 3D architecture of neuronal networks across large volumes with nanometer resolution by Denk and Horstmann in 2004. Here, the authors provide an accessible overview of the advantages and challenges associated with SBF-SEM. Beyond this, the applications of SBF-SEM in biochemical domains as well as potential future clinical applications are briefly reviewed. Finally, the alternative forms of artificial intelligence-based segmentation which may contribute to devising a feasible workflow involving SBF-SEM, are also considered.
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Affiliation(s)
- Andrea G Marshall
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Kit Neikirk
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Dominique C Stephens
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Larry Vang
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Zer Vue
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Heather K Beasley
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Amber Crabtree
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Estevão Scudese
- Laboratory of Biosciences of Human Motricity (LABIMH) of the Federal University of State of Rio de Janeiro (UNIRIO), Rio de Janeiro, Brazil
- Sport Sciences and Exercise Laboratory (LaCEE), Catholic University of Petrópolis (UCP), Catholic, 25685-100, Brazil
| | - Edgar Garza Lopez
- Department of Internal Medicine, University of Iowa, Iowa City, IA, 52242, USA
| | - Bryanna Shao
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
| | - Evan Krystofiak
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Sharifa Rutledge
- Department of Chemistry, University of Alabama in Huntsville, Huntsville, AL, 35899, USA
| | - Jaimaine Davis
- Department of Biochemistry, Cancer Biology, Neuroscience, Pharmacology, Meharry Medical College, Nashville, TN, 37232, USA
| | - Sandra A Murray
- Department of Cell Biology, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Steven M Damo
- Department of Life and Physical Sciences, Fisk University, Nashville, TN, 37208, USA
- Center for Structural Biology, Vanderbilt University, Nashville, TN, 37232, USA
| | - Prasanna Katti
- National Heart, Lung and Blood Institute, National Institutes of Health, 9000 Rockville Pike, Bethesda, MD, 20892, USA
| | - Antentor Hinton
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, TN, 37232, USA
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39
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Bernáez Timón L, Ekelmans P, Kraynyukova N, Rose T, Busse L, Tchumatchenko T. How to incorporate biological insights into network models and why it matters. J Physiol 2023; 601:3037-3053. [PMID: 36069408 DOI: 10.1113/jp282755] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Accepted: 08/24/2022] [Indexed: 11/08/2022] Open
Abstract
Due to the staggering complexity of the brain and its neural circuitry, neuroscientists rely on the analysis of mathematical models to elucidate its function. From Hodgkin and Huxley's detailed description of the action potential in 1952 to today, new theories and increasing computational power have opened up novel avenues to study how neural circuits implement the computations that underlie behaviour. Computational neuroscientists have developed many models of neural circuits that differ in complexity, biological realism or emergent network properties. With recent advances in experimental techniques for detailed anatomical reconstructions or large-scale activity recordings, rich biological data have become more available. The challenge when building network models is to reflect experimental results, either through a high level of detail or by finding an appropriate level of abstraction. Meanwhile, machine learning has facilitated the development of artificial neural networks, which are trained to perform specific tasks. While they have proven successful at achieving task-oriented behaviour, they are often abstract constructs that differ in many features from the physiology of brain circuits. Thus, it is unclear whether the mechanisms underlying computation in biological circuits can be investigated by analysing artificial networks that accomplish the same function but differ in their mechanisms. Here, we argue that building biologically realistic network models is crucial to establishing causal relationships between neurons, synapses, circuits and behaviour. More specifically, we advocate for network models that consider the connectivity structure and the recorded activity dynamics while evaluating task performance.
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Affiliation(s)
- Laura Bernáez Timón
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
| | - Pierre Ekelmans
- Frankfurt Institute for Advanced Studies, Frankfurt, Germany
| | - Nataliya Kraynyukova
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Tobias Rose
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
| | - Laura Busse
- Division of Neurobiology, Faculty of Biology, LMU Munich, Munich, Germany
- Bernstein Center for Computational Neuroscience, Munich, Germany
| | - Tatjana Tchumatchenko
- Institute for Physiological Chemistry, University of Mainz Medical Center, Mainz, Germany
- Institute of Experimental Epileptology and Cognition Research, University of Bonn Medical Center, Bonn, Germany
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40
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Dipietro L, Gonzalez-Mego P, Ramos-Estebanez C, Zukowski LH, Mikkilineni R, Rushmore RJ, Wagner T. The evolution of Big Data in neuroscience and neurology. JOURNAL OF BIG DATA 2023; 10:116. [PMID: 37441339 PMCID: PMC10333390 DOI: 10.1186/s40537-023-00751-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/28/2023] [Accepted: 05/08/2023] [Indexed: 07/15/2023]
Abstract
Neurological diseases are on the rise worldwide, leading to increased healthcare costs and diminished quality of life in patients. In recent years, Big Data has started to transform the fields of Neuroscience and Neurology. Scientists and clinicians are collaborating in global alliances, combining diverse datasets on a massive scale, and solving complex computational problems that demand the utilization of increasingly powerful computational resources. This Big Data revolution is opening new avenues for developing innovative treatments for neurological diseases. Our paper surveys Big Data's impact on neurological patient care, as exemplified through work done in a comprehensive selection of areas, including Connectomics, Alzheimer's Disease, Stroke, Depression, Parkinson's Disease, Pain, and Addiction (e.g., Opioid Use Disorder). We present an overview of research and the methodologies utilizing Big Data in each area, as well as their current limitations and technical challenges. Despite the potential benefits, the full potential of Big Data in these fields currently remains unrealized. We close with recommendations for future research aimed at optimizing the use of Big Data in Neuroscience and Neurology for improved patient outcomes. Supplementary Information The online version contains supplementary material available at 10.1186/s40537-023-00751-2.
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Affiliation(s)
| | - Paola Gonzalez-Mego
- Spaulding Rehabilitation/Neuromodulation Lab, Harvard Medical School, Cambridge, MA USA
| | | | | | | | | | - Timothy Wagner
- Highland Instruments, Cambridge, MA USA
- Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA USA
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41
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Han X, Lu X, Li PH, Wang S, Schalek R, Meirovitch Y, Lin Z, Adhinarta J, Berger D, Wu Y, Fang T, Meral ES, Asraf S, Ploegh H, Pfister H, Wei D, Jain V, Trimmer JS, Lichtman JW. Multiplexed volumetric CLEM enabled by antibody derivatives provides new insights into the cytology of the mouse cerebellar cortex. RESEARCH SQUARE 2023:rs.3.rs-3121892. [PMID: 37461609 PMCID: PMC10350204 DOI: 10.21203/rs.3.rs-3121892/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/25/2023]
Abstract
Mapping neuronal networks that underlie behavior has become a central focus in neuroscience. While serial section electron microscopy (ssEM) can reveal the fine structure of neuronal networks (connectomics), it does not provide the molecular information that helps identify cell types or their functional properties. Volumetric correlated light and electron microscopy (vCLEM) combines ssEM and volumetric fluorescence microscopy to incorporate molecular labeling into ssEM datasets. We developed an approach that uses small fluorescent single-chain variable fragment (scFv) immuno-probes to perform multiplexed detergent-free immuno-labeling and ssEM on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (green fluorescent protein, glial fibrillary acidic protein, calbindin, parvalbumin, voltage-gated potassium channel subfamily A member 2, vesicular glutamate transporter 1, postsynaptic density protein 95, and neuropeptide Y). To test the vCLEM approach, six different fluorescent probes were imaged in a sample of the cortex of a cerebellar lobule (Crus 1), using confocal microscopy with spectral unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure with superimposition of the multiple fluorescence channels. Using this approach we could document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of one type of ion channel. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable molecular overlays for connectomic studies.
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Affiliation(s)
- Xiaomeng Han
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Xiaotang Lu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | | | - Shuohong Wang
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Richard Schalek
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Jason Adhinarta
- Computer Science Department, Boston College, Chestnut Hill, MA
| | - Daniel Berger
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Yuelong Wu
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
| | - Tao Fang
- Program of Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | | | - Shadnan Asraf
- School of Public Health, University of Massachusetts Amherst, Amherst, MA
| | - Hidde Ploegh
- Program of Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
| | - Donglai Wei
- Computer Science Department, Boston College, Chestnut Hill, MA
| | | | - James S. Trimmer
- Department of Physiology and Membrane Biology, University of California Davis School of Medicine, Davis, CA
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
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42
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Song JU, Choi K, Oh SM, Kahng B. Exploring nonlinear dynamics and network structures in Kuramoto systems using machine learning approaches. CHAOS (WOODBURY, N.Y.) 2023; 33:073148. [PMID: 37486666 DOI: 10.1063/5.0153229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/03/2023] [Indexed: 07/25/2023]
Abstract
Recent advances in machine learning (ML) have facilitated its application to a wide range of systems, from complex to quantum. Reservoir computing algorithms have proven particularly effective for studying nonlinear dynamical systems that exhibit collective behaviors, such as synchronizations and chaotic phenomena, some of which still remain unclear. Here, we apply ML approaches to the Kuramoto model to address several intriguing problems, including identifying the transition point and criticality of a hybrid synchronization transition, predicting future chaotic behaviors, and understanding network structures from chaotic patterns. Our proposed method also has further implications, such as inferring the structure of neural networks from electroencephalogram signals. This study, finally, highlights the potential of ML approaches for advancing our understanding of complex systems.
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Affiliation(s)
- Je Ung Song
- CTP and Department of Physics and Astronomy, Seoul National University, Seoul 08826, Korea
| | - Kwangjong Choi
- CTP and Department of Physics and Astronomy, Seoul National University, Seoul 08826, Korea
| | - Soo Min Oh
- Wireless Information and Network Sciences Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
- Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
| | - B Kahng
- Center for Complex Systems and KI for Grid Modernization, Korea Institute of Energy Technology, Naju 58217, Korea
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43
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Pavarino EC, Yang E, Dhanyasi N, Wang MD, Bidel F, Lu X, Yang F, Francisco Park C, Bangalore Renuka M, Drescher B, Samuel ADT, Hochner B, Katz PS, Zhen M, Lichtman JW, Meirovitch Y. mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops. Front Neural Circuits 2023; 17:952921. [PMID: 37396399 PMCID: PMC10309043 DOI: 10.3389/fncir.2023.952921] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Accepted: 04/17/2023] [Indexed: 07/04/2023] Open
Abstract
Connectomics is fundamental in propelling our understanding of the nervous system's organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from four different animals and five datasets, amounting to around 180 h of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of four pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/. With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.
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Affiliation(s)
- Elisa C. Pavarino
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | - Emma Yang
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | - Nagaraju Dhanyasi
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | - Mona D. Wang
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
| | - Flavie Bidel
- Department of Neurobiology, Silberman Institute of Life Sciences, The Hebrew University, Jerusalem, Israel
| | - Xiaotang Lu
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | - Fuming Yang
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | | | - Mukesh Bangalore Renuka
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | - Brandon Drescher
- Department of Biology, University of Massachusetts Amherst, Amherst, MA, United States
| | | | - Binyamin Hochner
- Department of Neurobiology, Silberman Institute of Life Sciences, The Hebrew University, Jerusalem, Israel
| | - Paul S. Katz
- Department of Biology, University of Massachusetts Amherst, Amherst, MA, United States
| | - Mei Zhen
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada
| | - Jeff W. Lichtman
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
| | - Yaron Meirovitch
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, United States
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44
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Spirou GA, Kersting M, Carr S, Razzaq B, Yamamoto Alves Pinto C, Dawson M, Ellisman MH, Manis PB. High-resolution volumetric imaging constrains compartmental models to explore synaptic integration and temporal processing by cochlear nucleus globular bushy cells. eLife 2023; 12:e83393. [PMID: 37288824 PMCID: PMC10435236 DOI: 10.7554/elife.83393] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2022] [Accepted: 06/07/2023] [Indexed: 06/09/2023] Open
Abstract
Globular bushy cells (GBCs) of the cochlear nucleus play central roles in the temporal processing of sound. Despite investigation over many decades, fundamental questions remain about their dendrite structure, afferent innervation, and integration of synaptic inputs. Here, we use volume electron microscopy (EM) of the mouse cochlear nucleus to construct synaptic maps that precisely specify convergence ratios and synaptic weights for auditory nerve innervation and accurate surface areas of all postsynaptic compartments. Detailed biophysically based compartmental models can help develop hypotheses regarding how GBCs integrate inputs to yield their recorded responses to sound. We established a pipeline to export a precise reconstruction of auditory nerve axons and their endbulb terminals together with high-resolution dendrite, soma, and axon reconstructions into biophysically detailed compartmental models that could be activated by a standard cochlear transduction model. With these constraints, the models predict auditory nerve input profiles whereby all endbulbs onto a GBC are subthreshold (coincidence detection mode), or one or two inputs are suprathreshold (mixed mode). The models also predict the relative importance of dendrite geometry, soma size, and axon initial segment length in setting action potential threshold and generating heterogeneity in sound-evoked responses, and thereby propose mechanisms by which GBCs may homeostatically adjust their excitability. Volume EM also reveals new dendritic structures and dendrites that lack innervation. This framework defines a pathway from subcellular morphology to synaptic connectivity, and facilitates investigation into the roles of specific cellular features in sound encoding. We also clarify the need for new experimental measurements to provide missing cellular parameters, and predict responses to sound for further in vivo studies, thereby serving as a template for investigation of other neuron classes.
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Affiliation(s)
- George A Spirou
- Department of Medical Engineering, University of South FloridaTampaUnited States
| | - Matthew Kersting
- Department of Medical Engineering, University of South FloridaTampaUnited States
| | - Sean Carr
- Department of Medical Engineering, University of South FloridaTampaUnited States
| | - Bayan Razzaq
- Department of Otolaryngology, Head and Neck Surgery, West Virginia UniversityMorgantownUnited States
| | | | - Mariah Dawson
- Department of Otolaryngology, Head and Neck Surgery, West Virginia UniversityMorgantownUnited States
| | - Mark H Ellisman
- Department of Neurosciences, University of California, San DiegoSan DiegoUnited States
- National Center for Microscopy and Imaging Research,University of California, San DiegoSan DiegoUnited States
| | - Paul B Manis
- Department of Otolaryngology/Head and Neck Surgery, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, University of North CarolinaChapel HillUnited States
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45
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Xu YKT, Graves AR, Coste GI, Huganir RL, Bergles DE, Charles AS, Sulam J. Cross-modality supervised image restoration enables nanoscale tracking of synaptic plasticity in living mice. Nat Methods 2023; 20:935-944. [PMID: 37169928 PMCID: PMC10250193 DOI: 10.1038/s41592-023-01871-6] [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: 01/28/2022] [Accepted: 04/04/2023] [Indexed: 05/13/2023]
Abstract
Learning is thought to involve changes in glutamate receptors at synapses, submicron structures that mediate communication between neurons in the central nervous system. Due to their small size and high density, synapses are difficult to resolve in vivo, limiting our ability to directly relate receptor dynamics to animal behavior. Here we developed a combination of computational and biological methods to overcome these challenges. First, we trained a deep-learning image-restoration algorithm that combines the advantages of ex vivo super-resolution and in vivo imaging modalities to overcome limitations specific to each optical system. When applied to in vivo images from transgenic mice expressing fluorescently labeled glutamate receptors, this restoration algorithm super-resolved synapses, enabling the tracking of behavior-associated synaptic plasticity with high spatial resolution. This method demonstrates the capabilities of image enhancement to learn from ex vivo data and imaging techniques to improve in vivo imaging resolution.
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Affiliation(s)
- Yu Kang T Xu
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Austin R Graves
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Engineering, Baltimore, MD, USA
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA
| | - Gabrielle I Coste
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Richard L Huganir
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Dwight E Bergles
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Adam S Charles
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University School of Engineering, Baltimore, MD, USA.
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
| | - Jeremias Sulam
- Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University School of Engineering, Baltimore, MD, USA.
- Center for Imaging Science, Johns Hopkins University, Baltimore, MD, USA.
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46
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Langdon C, Genkin M, Engel TA. A unifying perspective on neural manifolds and circuits for cognition. Nat Rev Neurosci 2023; 24:363-377. [PMID: 37055616 PMCID: PMC11058347 DOI: 10.1038/s41583-023-00693-x] [Citation(s) in RCA: 19] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/06/2023] [Indexed: 04/15/2023]
Abstract
Two different perspectives have informed efforts to explain the link between the brain and behaviour. One approach seeks to identify neural circuit elements that carry out specific functions, emphasizing connectivity between neurons as a substrate for neural computations. Another approach centres on neural manifolds - low-dimensional representations of behavioural signals in neural population activity - and suggests that neural computations are realized by emergent dynamics. Although manifolds reveal an interpretable structure in heterogeneous neuronal activity, finding the corresponding structure in connectivity remains a challenge. We highlight examples in which establishing the correspondence between low-dimensional activity and connectivity has been possible, unifying the neural manifold and circuit perspectives. This relationship is conspicuous in systems in which the geometry of neural responses mirrors their spatial layout in the brain, such as the fly navigational system. Furthermore, we describe evidence that, in systems in which neural responses are heterogeneous, the circuit comprises interactions between activity patterns on the manifold via low-rank connectivity. We suggest that unifying the manifold and circuit approaches is important if we are to be able to causally test theories about the neural computations that underlie behaviour.
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Affiliation(s)
- Christopher Langdon
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Mikhail Genkin
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Tatiana A Engel
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
- Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA.
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47
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Ramdya P, Ijspeert AJ. The neuromechanics of animal locomotion: From biology to robotics and back. Sci Robot 2023; 8:eadg0279. [PMID: 37256966 DOI: 10.1126/scirobotics.adg0279] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 05/05/2023] [Indexed: 06/02/2023]
Abstract
Robotics and neuroscience are sister disciplines that both aim to understand how agile, efficient, and robust locomotion can be achieved in autonomous agents. Robotics has already benefitted from neuromechanical principles discovered by investigating animals. These include the use of high-level commands to control low-level central pattern generator-like controllers, which, in turn, are informed by sensory feedback. Reciprocally, neuroscience has benefited from tools and intuitions in robotics to reveal how embodiment, physical interactions with the environment, and sensory feedback help sculpt animal behavior. We illustrate and discuss exemplar studies of this dialog between robotics and neuroscience. We also reveal how the increasing biorealism of simulations and robots is driving these two disciplines together, forging an integrative science of autonomous behavioral control with many exciting future opportunities.
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Affiliation(s)
- Pavan Ramdya
- Neuroengineering Laboratory, Brain Mind Institute and Institute of Bioengineering, EPFL, Lausanne, Switzerland
| | - Auke Jan Ijspeert
- Biorobotics Laboratory, Institute of Bioengineering, EPFL, Lausanne, Switzerland
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48
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Ekelmans P, Kraynyukovas N, Tchumatchenko T. Targeting operational regimes of interest in recurrent neural networks. PLoS Comput Biol 2023; 19:e1011097. [PMID: 37186668 DOI: 10.1371/journal.pcbi.1011097] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 05/25/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
Neural computations emerge from local recurrent neural circuits or computational units such as cortical columns that comprise hundreds to a few thousand neurons. Continuous progress in connectomics, electrophysiology, and calcium imaging require tractable spiking network models that can consistently incorporate new information about the network structure and reproduce the recorded neural activity features. However, for spiking networks, it is challenging to predict which connectivity configurations and neural properties can generate fundamental operational states and specific experimentally reported nonlinear cortical computations. Theoretical descriptions for the computational state of cortical spiking circuits are diverse, including the balanced state where excitatory and inhibitory inputs balance almost perfectly or the inhibition stabilized state (ISN) where the excitatory part of the circuit is unstable. It remains an open question whether these states can co-exist with experimentally reported nonlinear computations and whether they can be recovered in biologically realistic implementations of spiking networks. Here, we show how to identify spiking network connectivity patterns underlying diverse nonlinear computations such as XOR, bistability, inhibitory stabilization, supersaturation, and persistent activity. We establish a mapping between the stabilized supralinear network (SSN) and spiking activity which allows us to pinpoint the location in parameter space where these activity regimes occur. Notably, we find that biologically-sized spiking networks can have irregular asynchronous activity that does not require strong excitation-inhibition balance or large feedforward input and we show that the dynamic firing rate trajectories in spiking networks can be precisely targeted without error-driven training algorithms.
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Affiliation(s)
- Pierre Ekelmans
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Frankfurt Institute for Advanced Studies, Frankfurt am Main, Germany
| | - Nataliya Kraynyukovas
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Institute of Experimental Epileptology and Cognition Research, Life and Brain Center, Universitätsklinikum Bonn, Bonn, Germany
| | - Tatjana Tchumatchenko
- Theory of Neural Dynamics group, Max Planck Institute for Brain Research, Frankfurt am Main, Germany
- Institute of Experimental Epileptology and Cognition Research, Life and Brain Center, Universitätsklinikum Bonn, Bonn, Germany
- Institute of physiological chemistry, Medical center of the Johannes Gutenberg-University Mainz, Mainz, Germany
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49
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Goz RU, Hooks BM. Correlated Somatosensory Input in Parvalbumin/Pyramidal Cells in Mouse Motor Cortex. eNeuro 2023; 10:ENEURO.0488-22.2023. [PMID: 37094939 PMCID: PMC10167893 DOI: 10.1523/eneuro.0488-22.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 04/02/2023] [Accepted: 04/18/2023] [Indexed: 04/26/2023] Open
Abstract
In mammalian cortex, feedforward excitatory connections recruit feedforward inhibition. This is often carried by parvalbumin (PV+) interneurons, which may densely connect to local pyramidal (Pyr) neurons. Whether this inhibition affects all local excitatory cells indiscriminately or is targeted to specific subnetworks is unknown. Here, we test how feedforward inhibition is recruited by using two-channel circuit mapping to excite cortical and thalamic inputs to PV+ interneurons and Pyr neurons to mouse primary vibrissal motor cortex (M1). Single Pyr and PV+ neurons receive input from both cortex and thalamus. Connected pairs of PV+ interneurons and excitatory Pyr neurons receive correlated cortical and thalamic inputs. While PV+ interneurons are more likely to form local connections to Pyr neurons, Pyr neurons are much more likely to form reciprocal connections with PV+ interneurons that inhibit them. This suggests that Pyr and PV ensembles may be organized based on their local and long-range connections, an organization that supports the idea of local subnetworks for signal transduction and processing. Excitatory inputs to M1 can thus target inhibitory networks in a specific pattern which permits recruitment of feedforward inhibition to specific subnetworks within the cortical column.
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Affiliation(s)
- Roman U Goz
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
| | - Bryan M Hooks
- Department of Neurobiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213
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50
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Pavarino EC, Yang E, Dhanyasi N, Wang M, Bidel F, Lu X, Yang F, Park CF, Renuka MB, Drescher B, Samuel AD, Hochner B, Katz PS, Zhen M, Lichtman JW, Meirovitch Y. mEMbrain: an interactive deep learning MATLAB tool for connectomic segmentation on commodity desktops. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.17.537196. [PMID: 37131600 PMCID: PMC10153173 DOI: 10.1101/2023.04.17.537196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Connectomics is fundamental in propelling our understanding of the nervous system’s organization, unearthing cells and wiring diagrams reconstructed from volume electron microscopy (EM) datasets. Such reconstructions, on the one hand, have benefited from ever more precise automatic segmentation methods, which leverage sophisticated deep learning architectures and advanced machine learning algorithms. On the other hand, the field of neuroscience at large, and of image processing in particular, has manifested a need for user-friendly and open source tools which enable the community to carry out advanced analyses. In line with this second vein, here we propose mEMbrain, an interactive MATLAB-based software which wraps algorithms and functions that enable labeling and segmentation of electron microscopy datasets in a user-friendly user interface compatible with Linux and Windows. Through its integration as an API to the volume annotation and segmentation tool VAST, mEMbrain encompasses functions for ground truth generation, image preprocessing, training of deep neural networks, and on-the-fly predictions for proofreading and evaluation. The final goals of our tool are to expedite manual labeling efforts and to harness MATLAB users with an array of semi-automatic approaches for instance segmentation. We tested our tool on a variety of datasets that span different species at various scales, regions of the nervous system and developmental stages. To further expedite research in connectomics, we provide an EM resource of ground truth annotation from 4 different animals and 5 datasets, amounting to around 180 hours of expert annotations, yielding more than 1.2 GB of annotated EM images. In addition, we provide a set of 4 pre-trained networks for said datasets. All tools are available from https://lichtman.rc.fas.harvard.edu/mEMbrain/ . With our software, our hope is to provide a solution for lab-based neural reconstructions which does not require coding by the user, thus paving the way to affordable connectomics.
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Affiliation(s)
| | - Emma Yang
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, USA
| | - Nagaraju Dhanyasi
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, USA
| | - Mona Wang
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada
| | - Flavie Bidel
- Department of Neurobiology, Silberman Institute of Life Sciences, The Hebrew University, Jerusalem, Israel
| | - Xiaotang Lu
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, USA
| | - Fuming Yang
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, USA
| | | | | | - Brandon Drescher
- Department Biology, University of Massachusetts Amherst, Amherst, MA, USA
| | | | - Binyamin Hochner
- Department of Neurobiology, Silberman Institute of Life Sciences, The Hebrew University, Jerusalem, Israel
| | - Paul S. Katz
- Department Biology, University of Massachusetts Amherst, Amherst, MA, USA
| | - Mei Zhen
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Ontario, Canada
| | - Jeff W. Lichtman
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, USA
| | - Yaron Meirovitch
- Department of Cellular and Molecular Biology, Harvard University, Cambridge, MA, USA
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