1
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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] [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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2
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Uytiepo M, Zhu Y, Bushong E, Polli F, Chou K, Zhao E, Kim C, Luu D, Chang L, Quach T, Haberl M, Patapoutian L, Beutter E, Zhang W, Dong B, McCue E, Ellisman M, Maximov A. Synaptic architecture of a memory engram in the mouse hippocampus. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.23.590812. [PMID: 38712256 PMCID: PMC11071366 DOI: 10.1101/2024.04.23.590812] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
Memory engrams are formed through experience-dependent remodeling of neural circuits, but their detailed architectures have remained unresolved. Using 3D electron microscopy, we performed nanoscale reconstructions of the hippocampal CA3-CA1 pathway following chemogenetic labeling of cellular ensembles with a remote history of correlated excitation during associative learning. Projection neurons involved in memory acquisition expanded their connectomes via multi-synaptic boutons without altering the numbers and spatial arrangements of individual axonal terminals and dendritic spines. This expansion was driven by presynaptic activity elicited by specific negative valence stimuli, regardless of the co-activation state of postsynaptic partners. The rewiring of initial ensembles representing an engram coincided with local, input-specific changes in the shapes and organelle composition of glutamatergic synapses, reflecting their weights and potential for further modifications. Our findings challenge the view that the connectivity among neuronal substrates of memory traces is governed by Hebbian mechanisms, and offer a structural basis for representational drifts.
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3
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Friedrichsen K, Hsiang JC, Lin CI, McCoy L, Valkova K, Kerschensteiner D, Morgan JL. Subcellular pathways through VGluT3-expressing mouse amacrine cells provide locally tuned object-motion-selective signals in the retina. Nat Commun 2024; 15:2965. [PMID: 38580652 PMCID: PMC10997783 DOI: 10.1038/s41467-024-46996-0] [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: 06/19/2023] [Accepted: 03/15/2024] [Indexed: 04/07/2024] Open
Abstract
VGluT3-expressing mouse retinal amacrine cells (VG3s) respond to small-object motion and connect to multiple types of bipolar cells (inputs) and retinal ganglion cells (RGCs, outputs). Because these input and output connections are intermixed on the same dendrites, making sense of VG3 circuitry requires comparing the distribution of synapses across their arbors to the subcellular flow of signals. Here, we combine subcellular calcium imaging and electron microscopic connectomic reconstruction to analyze how VG3s integrate and transmit visual information. VG3s receive inputs from all nearby bipolar cell types but exhibit a strong preference for the fast type 3a bipolar cells. By comparing input distributions to VG3 dendrite responses, we show that VG3 dendrites have a short functional length constant that likely depends on inhibitory shunting. This model predicts that RGCs that extend dendrites into the middle layers of the inner plexiform encounter VG3 dendrites whose responses vary according to the local bipolar cell response type.
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Affiliation(s)
- Karl Friedrichsen
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Graduate Program in Neuroscience, Washington University in St. Louis, St. Louis, USA
| | - Jen-Chun Hsiang
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Graduate Program in Neuroscience, Washington University in St. Louis, St. Louis, USA
| | - Chin-I Lin
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
- Graduate Program in Neuroscience, Washington University in St. Louis, St. Louis, USA
| | - Liam McCoy
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Katia Valkova
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA
| | - Daniel Kerschensteiner
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
| | - Josh L Morgan
- Department of Ophthalmology and Visual Sciences, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Neuroscience, Washington University in St. Louis, St. Louis, MO, USA.
- Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
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4
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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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5
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Chen J, Ke R. Spatial analysis toolkits for RNA in situ sequencing. WILEY INTERDISCIPLINARY REVIEWS. RNA 2024; 15:e1842. [PMID: 38605484 DOI: 10.1002/wrna.1842] [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: 12/15/2023] [Revised: 03/13/2024] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Spatial transcriptomics (ST) is featured by high-throughput gene expression profiling within their native cell and tissue context, offering a means to investigate gene regulatory networks in tissue microenvironment. In situ sequencing (ISS) is an imaging-based ST technology that simultaneously detects hundreds to thousands of genes at subcellular resolution. As a highly reproducible and robust technique, ISS has been widely adapted and undergone a series of technical iterations. As the interest in ISS-based spatial transcriptomic analysis grows, scalable and integrated data analysis workflows are needed to facilitate the applications of ISS in different research fields. This review presents the state-of-the-art bioinformatic toolkits for ISS data analysis, which covers the upstream and downstream analysis workflows, including image analysis, cell segmentation, clustering, functional enrichment, detection of spatially variable genes and cell clusters, spatial cell-cell interactions, and trajectory inference. To assist the community in choosing the right tools for their research, the application of each tool and its compatibility with ISS data are reviewed in detailed. Finally, future perspectives and challenges concerning how to integrate heterogeneous tools into a user-friendly analysis pipeline are discussed. This article is categorized under: RNA Methods > RNA Analyses In Vitro and In Silico.
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Affiliation(s)
- Jiayu Chen
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
| | - Rongqin Ke
- School of Medicine, Huaqiao University, Xiamen, Fujian, China
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6
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Drescher B, Sant HH, Schalek RL, Lichtman JW, Katz PS. Sample preparation methods for volume electron microscopy in mollusc Berghia stephanieae. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.25.581936. [PMID: 38464069 PMCID: PMC10925166 DOI: 10.1101/2024.02.25.581936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Creating a high-resolution brain atlas in diverse species offers crucial insights into general principles underlying brain function and development. A volume electron microscopy approach to generate such neural maps has been gaining importance due to advances in imaging, data storage capabilities, and data analysis protocols. Sample preparation remains challenging and is a crucial step to accelerate the imaging and data processing pipeline. Here, we introduce several replicable methods for processing the brains of the gastropod mollusc, Berghia stephanieae for volume electron microscopy. Although high-pressure freezing is the most reliable method, the depth of cryopreservation is a severe limitation for large tissue samples. We introduce a BROPA-based method using pyrogallol and methods to rapidly process samples that can save hours at the bench. This is the first report on sample preparation and imaging pipeline for volume electron microscopy in a gastropod mollusc, opening up the potential for connectomic analysis and comparisons with other phyla.
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7
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Yim H, Choe DT, Bae JA, Choi MK, Kang HM, Nguyen KCQ, Ahn S, Bahn SK, Yang H, Hall DH, Kim JS, Lee J. Comparative connectomics of dauer reveals developmental plasticity. Nat Commun 2024; 15:1546. [PMID: 38413604 PMCID: PMC10899629 DOI: 10.1038/s41467-024-45943-3] [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: 07/06/2023] [Accepted: 02/06/2024] [Indexed: 02/29/2024] Open
Abstract
A fundamental question in neurodevelopmental biology is how flexibly the nervous system changes during development. To address this, we reconstructed the chemical connectome of dauer, an alternative developmental stage of nematodes with distinct behavioral characteristics, by volumetric reconstruction and automated synapse detection using deep learning. With the basic architecture of the nervous system preserved, structural changes in neurons, large or small, were closely associated with connectivity changes, which in turn evoked dauer-specific behaviors such as nictation. Graph theoretical analyses revealed significant dauer-specific rewiring of sensory neuron connectivity and increased clustering within motor neurons in the dauer connectome. We suggest that the nervous system in the nematode has evolved to respond to harsh environments by developing a quantitatively and qualitatively differentiated connectome.
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Affiliation(s)
- Hyunsoo Yim
- Department of Biological Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Daniel T Choe
- Department of Biological Sciences, Seoul National University, Seoul, 08826, South Korea
| | - J Alexander Bae
- Research Institute of Basic Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Myung-Kyu Choi
- Research Institute of Basic Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Hae-Mook Kang
- Research Institute of Basic Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Ken C Q Nguyen
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Soungyub Ahn
- Department of Biological Sciences, Seoul National University, Seoul, 08826, South Korea
| | - Sang-Kyu Bahn
- Neural Circuits Research Group, Korea Brain Research Institute, Daegu, 41062, South Korea
- Cognitive Science Research Group, Korea Brain Research Institute, Daegu, 41062, South Korea
| | - Heeseung Yang
- Department of Biological Sciences, Seoul National University, Seoul, 08826, South Korea
| | - David H Hall
- Dominick P. Purpura Department of Neuroscience, Albert Einstein College of Medicine, Bronx, NY, 10461, USA
| | - Jinseop S Kim
- Neural Circuits Research Group, Korea Brain Research Institute, Daegu, 41062, South Korea.
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, Gyeonggi-do, 16419, South Korea.
| | - Junho Lee
- Department of Biological Sciences, Seoul National University, Seoul, 08826, South Korea.
- Research Institute of Basic Sciences, Seoul National University, Seoul, 08826, South Korea.
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8
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Iyer M, Kantarci H, Cooper MH, Ambiel N, Novak SW, Andrade LR, Lam M, Jones G, Münch AE, Yu X, Khakh BS, Manor U, Zuchero JB. Oligodendrocyte calcium signaling promotes actin-dependent myelin sheath extension. Nat Commun 2024; 15:265. [PMID: 38177161 PMCID: PMC10767123 DOI: 10.1038/s41467-023-44238-3] [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: 06/07/2023] [Accepted: 12/05/2023] [Indexed: 01/06/2024] Open
Abstract
Myelin is essential for rapid nerve signaling and is increasingly found to play important roles in learning and in diverse diseases of the CNS. Morphological parameters of myelin such as sheath length are thought to precisely tune conduction velocity, but the mechanisms controlling sheath morphology are poorly understood. Local calcium signaling has been observed in nascent myelin sheaths and can be modulated by neuronal activity. However, the role of calcium signaling in sheath formation remains incompletely understood. Here, we use genetic tools to attenuate oligodendrocyte calcium signaling during myelination in the developing mouse CNS. Surprisingly, genetic calcium attenuation does not grossly affect the number of myelinated axons or myelin thickness. Instead, calcium attenuation causes myelination defects resulting in shorter, dysmorphic sheaths. Mechanistically, calcium attenuation reduces actin filaments in oligodendrocytes, and an intact actin cytoskeleton is necessary and sufficient to achieve accurate myelin morphology. Together, our work reveals a cellular mechanism required for accurate CNS myelin formation and may provide mechanistic insight into how oligodendrocytes respond to neuronal activity to sculpt and refine myelin sheaths.
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Affiliation(s)
- Manasi Iyer
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Husniye Kantarci
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Madeline H Cooper
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Nicholas Ambiel
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Sammy Weiser Novak
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Leonardo R Andrade
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Mable Lam
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Graham Jones
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Alexandra E Münch
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA
| | - Xinzhu Yu
- Department of Molecular and Integrative Physiology, Beckman Institute, University of Illinois at Urbana-, Champaign, IL, USA
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Baljit S Khakh
- Department of Physiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Uri Manor
- Waitt Advanced Biophotonics Center, Salk Institute for Biological Studies, La Jolla, CA, USA
- Department of Cell and Developmental Biology, University of California, San Diego, San Diego, CA, USA
| | - J Bradley Zuchero
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, CA, USA.
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9
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Bame X, Hill RA. Mitochondrial network reorganization and transient expansion during oligodendrocyte generation. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570104. [PMID: 38106204 PMCID: PMC10723275 DOI: 10.1101/2023.12.05.570104] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Oligodendrocyte precursor cells (OPCs) give rise to myelinating oligodendrocytes of the central nervous system. This process persists throughout life and is essential for recovery from neurodegeneration. To better understand the cellular checkpoints that occur during oligodendrogenesis, we determined the mitochondrial distribution and morphometrics across the oligodendrocyte lineage in mouse and human cerebral cortex. During oligodendrocyte generation, mitochondrial content expanded concurrently with a change in subcellular partitioning towards the distal processes. These changes were followed by an abrupt loss of mitochondria in the oligodendrocyte processes and myelin, coinciding with sheath compaction. This reorganization and extensive expansion and depletion took 3 days. Oligodendrocyte mitochondria were stationary over days while OPC mitochondrial motility was modulated by animal arousal state within minutes. Aged OPCs also displayed decreased mitochondrial size, content, and motility. Thus, mitochondrial dynamics are linked to oligodendrocyte generation, dynamically modified by their local microenvironment, and altered in the aging brain.
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Affiliation(s)
- Xhoela Bame
- Department of Biological Sciences, Dartmouth College, Hanover, NH, USA
| | - Robert A Hill
- Department of Biological Sciences, Dartmouth College, Hanover, NH, USA
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10
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Wildenberg G, Li H, Sampathkumar V, Sorokina A, Kasthuri N. Isochronic development of cortical synapses in primates and mice. Nat Commun 2023; 14:8018. [PMID: 38049416 PMCID: PMC10695974 DOI: 10.1038/s41467-023-43088-3] [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: 08/08/2023] [Accepted: 10/31/2023] [Indexed: 12/06/2023] Open
Abstract
The neotenous, or delayed, development of primate neurons, particularly human ones, is thought to underlie primate-specific abilities like cognition. We tested whether synaptic development follows suit-would synapses, in absolute time, develop slower in longer-lived, highly cognitive species like non-human primates than in shorter-lived species with less human-like cognitive abilities, e.g., the mouse? Instead, we find that excitatory and inhibitory synapses in the male Mus musculus (mouse) and Rhesus macaque (primate) cortex form at similar rates, at similar times after birth. Primate excitatory and inhibitory synapses and mouse excitatory synapses also prune in such an isochronic fashion. Mouse inhibitory synapses are the lone exception, which are not pruned and instead continuously added throughout life. The monotony of synaptic development clocks across species with disparate lifespans, experiences, and cognitive abilities argues that such programs are likely orchestrated by genetic events rather than experience.
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Affiliation(s)
- Gregg Wildenberg
- Department of Neurobiology, The University of Chicago, Chicago, USA.
- Argonne National Laboratory, Biosciences Division, Lemont, USA.
| | - Hanyu Li
- Department of Neurobiology, The University of Chicago, Chicago, USA
- Argonne National Laboratory, Biosciences Division, Lemont, USA
| | - Vandana Sampathkumar
- Department of Neurobiology, The University of Chicago, Chicago, USA
- Argonne National Laboratory, Biosciences Division, Lemont, USA
| | - Anastasia Sorokina
- Department of Neurobiology, The University of Chicago, Chicago, USA
- Argonne National Laboratory, Biosciences Division, Lemont, USA
| | - Narayanan Kasthuri
- Department of Neurobiology, The University of Chicago, Chicago, USA.
- Argonne National Laboratory, Biosciences Division, Lemont, USA.
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11
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Khare H, Dongo Mendoza N, Zurzolo C. CellWalker: a user-friendly and modular computational pipeline for morphological analysis of microscopy images. Bioinformatics 2023; 39:btad710. [PMID: 38060265 PMCID: PMC10713108 DOI: 10.1093/bioinformatics/btad710] [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: 02/13/2023] [Revised: 11/07/2023] [Accepted: 12/06/2023] [Indexed: 12/08/2023] Open
Abstract
SUMMARY The implementation of computational tools for analysis of microscopy images has been one of the most important technological innovations in biology, providing researchers unmatched capabilities to comprehend cell shape and connectivity. While numerous tools exist for image annotation and segmentation, there is a noticeable gap when it comes to morphometric analysis of microscopy images. Most existing tools often measure features solely on 2D serial images, which can be difficult to extrapolate to 3D. For this reason, we introduce CellWalker, a computational toolbox that runs inside Blender, an open-source computer graphics software. This add-on improves the morphological analysis by seamlessly integrating analysis tools into the Blender workflow, providing visual feedback through a powerful 3D visualization, and leveraging the resources of Blender's community. CellWalker provides several morphometric analysis tools that can be used to calculate distances, volume, surface areas and to determine cross-sectional properties. It also includes tools to build skeletons, calculate distributions of subcellular organelles. In addition, this python-based tool contains 'visible-source' IPython notebooks accessories for segmentation of 2D/3D microscopy images using deep learning and visualization of the segmented images that are required as input to CellWalker. Overall, CellWalker provides practical tools for segmentation and morphological analysis of microscopy images in the form of an open-source and modular pipeline which allows a complete access to fine-tuning of algorithms through visible-source code while still retaining a result-oriented interface. AVAILABILITY AND IMPLEMENTATION CellWalker source code is available on GitHub (https://github.com/utraf-pasteur-institute/Cellwalker-blender and https://github.com/utraf-pasteur-institute/Cellwalker-notebooks) under a GPL-3 license.
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Affiliation(s)
- Harshavardhan Khare
- Membrane Traffic and Pathogenesis Unit, Department of Cell Biology and Infection, CNRS UMR 3691, Université de Paris, Institut Pasteur, Paris, 75015, France
| | - Nathaly Dongo Mendoza
- Membrane Traffic and Pathogenesis Unit, Department of Cell Biology and Infection, CNRS UMR 3691, Université de Paris, Institut Pasteur, Paris, 75015, France
- Centro de Investigación en Bioingeniería – BIO, Universidad de Ingeniería y Tecnología – UTEC, Lima, 15063, Perú
| | - Chiara Zurzolo
- Membrane Traffic and Pathogenesis Unit, Department of Cell Biology and Infection, CNRS UMR 3691, Université de Paris, Institut Pasteur, Paris, 75015, France
- Department of Molecular Medicine and Medical Biotechnology, University of Naples Federico II, Naples, 80131, Italy
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12
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Tsukamoto Y. Electrical synapses for a pooling layer of the convolutional neural network in retinas. Front Cell Neurosci 2023; 17:1281786. [PMID: 38026698 PMCID: PMC10648117 DOI: 10.3389/fncel.2023.1281786] [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: 08/23/2023] [Accepted: 10/03/2023] [Indexed: 12/01/2023] Open
Abstract
We have an example of a synergetic effect between neuroscience and connectome via artificial intelligence. The invention of Neocognitron, a machine learning algorithm, was inspired by the visual cortical circuitry for complex cells to be made by combinations of simple cells, which uses a hierarchical convolutional neural network (CNN). The CNN machine learning algorithm is powerful in classifying neuron borderlines on electron micrograph images for automatized connectomic analysis. CNN is also useful as a functional framework to analyze the neurocircuitry of the visual system. The visual system encodes visual patterns in the retina and decodes them in the corresponding cortical areas. The knowledge of evolutionarily chosen mechanisms in retinas may help the innovation of new algorithms. Since over a half-century ago, a classical style of serial section transmission electron microscopy has vastly contributed to cell biology. It is still useful to comprehensively analyze the small area of retinal neurocircuitry that is rich in natural intelligence of pattern recognition. I discuss the perspective of our study on the primary rod signal pathway in mouse and macaque retinas with special reference to electrical synapses. Photon detection under the scotopic condition needs absolute sensitivity but no intricate pattern recognition. This extreme case is regarded as the most simplified pattern recognition of the input with no autocorrelation. A comparative study of mouse and macaque retinas, where exists the 7-fold difference in linear size, may give us the underlying principle with quantitative verification of their adaptational designs of neurocircuitry.
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Affiliation(s)
- Yoshihiko Tsukamoto
- Department of Biology, Hyogo Medical University, Nishinomiya, Hyogo, Japan
- Studio EM-Retina, Satonaka, Nishinomiya, Hyogo, Japan
- Center for Systems Vision Science, Organization of Science and Technology, Ritsumeikan University, Kusatsu, Shiga, Japan
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13
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Joyce J, Chalavadi R, Chan J, Tanna S, Xenes D, Kuo N, Rose V, Matelsky J, Kitchell L, Bishop C, Rivlin PK, Villafañe-Delgado M, Wester B. A Novel Semi-automated Proofreading and Mesh Error Detection Pipeline for Neuron Extension. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.20.563359. [PMID: 37961670 PMCID: PMC10634717 DOI: 10.1101/2023.10.20.563359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The immense scale and complexity of neuronal electron microscopy (EM) datasets pose significant challenges in data processing, validation, and interpretation, necessitating the development of efficient, automated, and scalable error-detection methodologies. This paper proposes a novel approach that employs mesh processing techniques to identify potential error locations near neuronal tips. Error detection at tips is a particularly important challenge since these errors usually indicate that many synapses are falsely split from their parent neuron, injuring the integrity of the connectomic reconstruction. Additionally, we draw implications and results from an implementation of this error detection in a semi-automated proofreading pipeline. Manual proofreading is a laborious, costly, and currently necessary method for identifying the errors in the machine learning based segmentation of neural tissue. This approach streamlines the process of proofreading by systematically highlighting areas likely to contain inaccuracies and guiding proofreaders towards potential continuations, accelerating the rate at which errors are corrected.
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Affiliation(s)
- Justin Joyce
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | - Rupasri Chalavadi
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
- Johns Hopkins University Krieger School of Arts and Sciences
| | - Joey Chan
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
- Johns Hopkins University Krieger School of Arts and Sciences
| | - Sheel Tanna
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
- Johns Hopkins University Krieger School of Arts and Sciences
| | - Daniel Xenes
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | - Nathanael Kuo
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | - Victoria Rose
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | - Jordan Matelsky
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | - Lindsey Kitchell
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | - Caitlyn Bishop
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | - Patricia K. Rivlin
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
| | | | - Brock Wester
- Research & Exploratory Development, Johns Hopkins University Applied Physics Laboratory
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14
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Michalska JM, Lyudchik J, Velicky P, Štefaničková H, Watson JF, Cenameri A, Sommer C, Amberg N, Venturino A, Roessler K, Czech T, Höftberger R, Siegert S, Novarino G, Jonas P, Danzl JG. Imaging brain tissue architecture across millimeter to nanometer scales. Nat Biotechnol 2023:10.1038/s41587-023-01911-8. [PMID: 37653226 DOI: 10.1038/s41587-023-01911-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 07/20/2023] [Indexed: 09/02/2023]
Abstract
Mapping the complex and dense arrangement of cells and their connectivity in brain tissue demands nanoscale spatial resolution imaging. Super-resolution optical microscopy excels at visualizing specific molecules and individual cells but fails to provide tissue context. Here we developed Comprehensive Analysis of Tissues across Scales (CATS), a technology to densely map brain tissue architecture from millimeter regional to nanometer synaptic scales in diverse chemically fixed brain preparations, including rodent and human. CATS uses fixation-compatible extracellular labeling and optical imaging, including stimulated emission depletion or expansion microscopy, to comprehensively delineate cellular structures. It enables three-dimensional reconstruction of single synapses and mapping of synaptic connectivity by identification and analysis of putative synaptic cleft regions. Applying CATS to the mouse hippocampal mossy fiber circuitry, we reconstructed and quantified the synaptic input and output structure of identified neurons. We furthermore demonstrate applicability to clinically derived human tissue samples, including formalin-fixed paraffin-embedded routine diagnostic specimens, for visualizing the cellular architecture of brain tissue in health and disease.
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Affiliation(s)
- Julia M Michalska
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Julia Lyudchik
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Philipp Velicky
- Institute of Science and Technology Austria, Klosterneuburg, Austria
- Core Facility Imaging, Medical University of Vienna, Vienna, Austria
| | - Hana Štefaničková
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Jake F Watson
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Alban Cenameri
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Christoph Sommer
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Nicole Amberg
- Department of Neurology, Division of Neuropathology and Neurochemistry, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | | | - Karl Roessler
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Thomas Czech
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
- Department of Neurosurgery, Medical University of Vienna, Vienna, Austria
| | - Romana Höftberger
- Department of Neurology, Division of Neuropathology and Neurochemistry, Medical University of Vienna, Vienna, Austria
- Comprehensive Center for Clinical Neurosciences and Mental Health, Medical University of Vienna, Vienna, Austria
| | - Sandra Siegert
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Gaia Novarino
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Peter Jonas
- Institute of Science and Technology Austria, Klosterneuburg, Austria
| | - Johann G Danzl
- Institute of Science and Technology Austria, Klosterneuburg, Austria.
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15
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Velicky P, Miguel E, Michalska JM, Lyudchik J, Wei D, Lin Z, Watson JF, Troidl J, Beyer J, Ben-Simon Y, Sommer C, Jahr W, Cenameri A, Broichhagen J, Grant SGN, Jonas P, Novarino G, Pfister H, Bickel B, Danzl JG. Dense 4D nanoscale reconstruction of living brain tissue. Nat Methods 2023; 20:1256-1265. [PMID: 37429995 PMCID: PMC10406607 DOI: 10.1038/s41592-023-01936-6] [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: 08/03/2022] [Accepted: 05/22/2023] [Indexed: 07/12/2023]
Abstract
Three-dimensional (3D) reconstruction of living brain tissue down to an individual synapse level would create opportunities for decoding the dynamics and structure-function relationships of the brain's complex and dense information processing network; however, this has been hindered by insufficient 3D resolution, inadequate signal-to-noise ratio and prohibitive light burden in optical imaging, whereas electron microscopy is inherently static. Here we solved these challenges by developing an integrated optical/machine-learning technology, LIONESS (live information-optimized nanoscopy enabling saturated segmentation). This leverages optical modifications to stimulated emission depletion microscopy in comprehensively, extracellularly labeled tissue and previous information on sample structure via machine learning to simultaneously achieve isotropic super-resolution, high signal-to-noise ratio and compatibility with living tissue. This allows dense deep-learning-based instance segmentation and 3D reconstruction at a synapse level, incorporating molecular, activity and morphodynamic information. LIONESS opens up avenues for studying the dynamic functional (nano-)architecture of living brain tissue.
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Affiliation(s)
- Philipp Velicky
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
- Core Facility Imaging, Medical University of Vienna, Vienna, Austria
| | - Eder Miguel
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Julia M Michalska
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Julia Lyudchik
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Donglai Wei
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Department of Computer Science, Boston College, Boston, MA, USA
| | - Zudi Lin
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Jake F Watson
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Jakob Troidl
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Johanna Beyer
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Yoav Ben-Simon
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
- Allen Institute for Brain Science, Seattle, WA, USA
| | - Christoph Sommer
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Wiebke Jahr
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
- In-Vision Technologies, Guntramsdorf, Austria
| | - Alban Cenameri
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | | | - Seth G N Grant
- Genes to Cognition Program, Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
- Simons Initiative for the Developing Brain (SIDB), Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Peter Jonas
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Gaia Novarino
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Hanspeter Pfister
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Bernd Bickel
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria
| | - Johann G Danzl
- Institute of Science and Technology Austria (ISTA), Klosterneuburg, Austria.
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16
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Lu X, Han X, Meirovitch Y, Sjöstedt E, Schalek RL, Lichtman JW. Preserving extracellular space for high-quality optical and ultrastructural studies of whole mammalian brains. CELL REPORTS METHODS 2023; 3:100520. [PMID: 37533653 PMCID: PMC10391564 DOI: 10.1016/j.crmeth.2023.100520] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 04/08/2023] [Accepted: 06/07/2023] [Indexed: 08/04/2023]
Abstract
Analysis of brain structure, connectivity, and molecular diversity relies on effective tissue fixation. Conventional tissue fixation causes extracellular space (ECS) loss, complicating the segmentation of cellular objects from electron microscopy datasets. Previous techniques for preserving ECS in mammalian brains utilizing high-pressure perfusion can give inconsistent results owing to variations in the hydrostatic pressure within the vasculature. A more reliable fixation protocol that uniformly preserves the ECS throughout whole brains would greatly benefit a wide range of neuroscience studies. Here, we report a straightforward transcardial perfusion strategy that preserves ECS throughout the whole rodent brain. No special setup is needed besides sequential solution changes, and the protocol offers excellent reproducibility. In addition to better capturing tissue ultrastructure, preservation of ECS has many downstream advantages such as accelerating heavy-metal staining for electron microscopy, improving detergent-free immunohistochemistry for correlated light and electron microscopy, and facilitating lipid removal for tissue clearing.
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Affiliation(s)
- Xiaotang Lu
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Xiaomeng Han
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Yaron Meirovitch
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Evelina Sjöstedt
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Richard L. Schalek
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology and Center for Brain Science, Harvard University, Cambridge, MA, USA
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17
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Ziegler KA, Ahles A, Dueck A, Esfandyari D, Pichler P, Weber K, Kotschi S, Bartelt A, Sinicina I, Graw M, Leonhardt H, Weckbach LT, Massberg S, Schifferer M, Simons M, Hoeher L, Luo J, Ertürk A, Schiattarella GG, Sassi Y, Misgeld T, Engelhardt S. Immune-mediated denervation of the pineal gland underlies sleep disturbance in cardiac disease. Science 2023; 381:285-290. [PMID: 37471539 DOI: 10.1126/science.abn6366] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 06/01/2023] [Indexed: 07/22/2023]
Abstract
Disruption of the physiologic sleep-wake cycle and low melatonin levels frequently accompany cardiac disease, yet the underlying mechanism has remained enigmatic. Immunostaining of sympathetic axons in optically cleared pineal glands from humans and mice with cardiac disease revealed their substantial denervation compared with controls. Spatial, single-cell, nuclear, and bulk RNA sequencing traced this defect back to the superior cervical ganglia (SCG), which responded to cardiac disease with accumulation of inflammatory macrophages, fibrosis, and the selective loss of pineal gland-innervating neurons. Depletion of macrophages in the SCG prevented disease-associated denervation of the pineal gland and restored physiological melatonin secretion. Our data identify the mechanism by which diurnal rhythmicity in cardiac disease is disturbed and suggest a target for therapeutic intervention.
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Affiliation(s)
- Karin A Ziegler
- Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Andrea Ahles
- Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Anne Dueck
- Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Dena Esfandyari
- Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Pauline Pichler
- Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
| | - Karolin Weber
- Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
| | - Stefan Kotschi
- Institute for Cardiovascular Prevention (IPEK), Faculty of Medicine, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
| | - Alexander Bartelt
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Institute for Cardiovascular Prevention (IPEK), Faculty of Medicine, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
- Institute for Diabetes and Cancer, Helmholtz Center Munich, Neuherberg, Germany
- Department of Molecular Metabolism & Sabri Ülker Center for Metabolic Research, Harvard. T.H. Chan School of Public Health, Boston, MA, USA
| | - Inga Sinicina
- Institute of Legal Medicine, Faculty of Medicine, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
| | - Matthias Graw
- Institute of Legal Medicine, Faculty of Medicine, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
| | - Heinrich Leonhardt
- Human Biology & Bioimaging, Faculty of Biology, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
| | - Ludwig T Weckbach
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Munich, Germany
- Institute of Cardiovascular Physiology and Pathophysiology, Biomedical Center, Ludwig-Maximilians-Universität (LMU), Planegg-Martinsried, Germany
| | - Steffen Massberg
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Medizinische Klinik und Poliklinik I, Klinikum der Universität München, Munich, Germany
| | - Martina Schifferer
- DZNE (German Center for Neurodegenerative Diseases), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Mikael Simons
- DZNE (German Center for Neurodegenerative Diseases), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich (TUM), Munich, Germany
| | - Luciano Hoeher
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center Munich, Neuherberg, Germany
| | - Jie Luo
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
| | - Ali Ertürk
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians-Universität (LMU) München, Munich, Germany
| | - Gabriele G Schiattarella
- DZHK (German Centre for Cardiovascular Research), Partner Site Berlin, Berlin, Germany
- Max Rubner Center for Cardiovascular Metabolic Renal Research (MRC), Deutsches Herzzentrum der Charité (DHZC), Charité-Universitätsmedizin Berlin, Berlin, Germany
- Translational Approaches in Heart Failure and Cardiometabolic Disease, Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Yassine Sassi
- Fralin Biomedical Research Institute at Virginia Tech Carilion, Roanoke, VA, USA
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA
- Department of Internal Medicine, Virginia Tech Carilion School of Medicine, Roanoke, VA, USA
| | - Thomas Misgeld
- DZNE (German Center for Neurodegenerative Diseases), Munich, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich (TUM), Munich, Germany
| | - Stefan Engelhardt
- Institute of Pharmacology and Toxicology, Technical University Munich (TUM), Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
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18
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Androvic P, Schifferer M, Perez Anderson K, Cantuti-Castelvetri L, Jiang H, Ji H, Liu L, Gouna G, Berghoff SA, Besson-Girard S, Knoferle J, Simons M, Gokce O. Spatial Transcriptomics-correlated Electron Microscopy maps transcriptional and ultrastructural responses to brain injury. Nat Commun 2023; 14:4115. [PMID: 37433806 DOI: 10.1038/s41467-023-39447-9] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2022] [Accepted: 06/14/2023] [Indexed: 07/13/2023] Open
Abstract
Understanding the complexity of cellular function within a tissue necessitates the combination of multiple phenotypic readouts. Here, we developed a method that links spatially-resolved gene expression of single cells with their ultrastructural morphology by integrating multiplexed error-robust fluorescence in situ hybridization (MERFISH) and large area volume electron microscopy (EM) on adjacent tissue sections. Using this method, we characterized in situ ultrastructural and transcriptional responses of glial cells and infiltrating T-cells after demyelinating brain injury in male mice. We identified a population of lipid-loaded "foamy" microglia located in the center of remyelinating lesion, as well as rare interferon-responsive microglia, oligodendrocytes, and astrocytes that co-localized with T-cells. We validated our findings using immunocytochemistry and lipid staining-coupled single-cell RNA sequencing. Finally, by integrating these datasets, we detected correlations between full-transcriptome gene expression and ultrastructural features of microglia. Our results offer an integrative view of the spatial, ultrastructural, and transcriptional reorganization of single cells after demyelinating brain injury.
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Affiliation(s)
- Peter Androvic
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Katrin Perez Anderson
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Ludovico Cantuti-Castelvetri
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Hanyi Jiang
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Hao Ji
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Lu Liu
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Garyfallia Gouna
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Stefan A Berghoff
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Simon Besson-Girard
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
| | - Johanna Knoferle
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany
| | - Mikael Simons
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany
| | - Ozgun Gokce
- Institute for Stroke and Dementia Research, University Hospital of Munich, LMU Munich, Munich, Germany.
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
- Department of Neurodegenerative Diseases and Geriatric Psychiatry, University Hospital Bonn, Bonn, Germany.
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19
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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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20
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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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21
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Wu L, Chen A, Salama P, Winfree S, Dunn KW, Delp EJ. NISNet3D: three-dimensional nuclear synthesis and instance segmentation for fluorescence microscopy images. Sci Rep 2023; 13:9533. [PMID: 37308499 DOI: 10.1038/s41598-023-36243-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Accepted: 05/31/2023] [Indexed: 06/14/2023] Open
Abstract
The primary step in tissue cytometry is the automated distinction of individual cells (segmentation). Since cell borders are seldom labeled, cells are generally segmented by their nuclei. While tools have been developed for segmenting nuclei in two dimensions, segmentation of nuclei in three-dimensional volumes remains a challenging task. The lack of effective methods for three-dimensional segmentation represents a bottleneck in the realization of the potential of tissue cytometry, particularly as methods of tissue clearing present the opportunity to characterize entire organs. Methods based on deep learning have shown enormous promise, but their implementation is hampered by the need for large amounts of manually annotated training data. In this paper, we describe 3D Nuclei Instance Segmentation Network (NISNet3D) that directly segments 3D volumes through the use of a modified 3D U-Net, 3D marker-controlled watershed transform, and a nuclei instance segmentation system for separating touching nuclei. NISNet3D is unique in that it provides accurate segmentation of even challenging image volumes using a network trained on large amounts of synthetic nuclei derived from relatively few annotated volumes, or on synthetic data obtained without annotated volumes. We present a quantitative comparison of results obtained from NISNet3D with results obtained from a variety of existing nuclei segmentation techniques. We also examine the performance of the methods when no ground truth is available and only synthetic volumes were used for training.
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Affiliation(s)
- Liming Wu
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Alain Chen
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA
| | - Paul Salama
- Department of Electrical and Computer Engineering, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202, USA
| | - Seth Winfree
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Kenneth W Dunn
- School of Medicine, Indiana University, Indianapolis, IN, 46202, USA
| | - Edward J Delp
- Video and Image Processing Laboratory, School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, 47907, USA.
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22
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Wen C, Matsumoto M, Sawada M, Sawamoto K, Kimura KD. Seg2Link: an efficient and versatile solution for semi-automatic cell segmentation in 3D image stacks. Sci Rep 2023; 13:7109. [PMID: 37217545 DOI: 10.1038/s41598-023-34232-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 04/26/2023] [Indexed: 05/24/2023] Open
Abstract
Recent advances in microscopy techniques, especially in electron microscopy, are transforming biomedical studies by acquiring large quantities of high-precision 3D cell image stacks. To examine cell morphology and connectivity in organs such as the brain, scientists need to conduct cell segmentation, which extracts individual cell regions of different shapes and sizes from a 3D image. This is challenging due to the indistinct images often encountered in real biomedical research: in many cases, automatic segmentation methods inevitably contain numerous mistakes in the segmentation results, even when using advanced deep learning methods. To analyze 3D cell images effectively, a semi-automated software solution is needed that combines powerful deep learning techniques with the ability to perform post-processing, generate accurate segmentations, and incorporate manual corrections. To address this gap, we developed Seg2Link, which takes deep learning predictions as inputs and use watershed 2D + cross-slice linking to generate more accurate automatic segmentations than previous methods. Additionally, it provides various manual correction tools essential for correcting mistakes in 3D segmentation results. Moreover, our software has been optimized for efficiently processing large 3D images in diverse organisms. Thus, Seg2Link offers an practical solution for scientists to study cell morphology and connectivity in 3D image stacks.
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Affiliation(s)
- Chentao Wen
- Graduate School of Science, Nagoya City University, Nagoya, Japan.
- RIKEN Center for Biosystems Dynamics Research, Kobe, Japan.
| | - Mami Matsumoto
- Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Division of Neural Development and Regeneration, National Institute for Physiological Sciences, Okazaki, Japan
| | - Masato Sawada
- Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Division of Neural Development and Regeneration, National Institute for Physiological Sciences, Okazaki, Japan
| | - Kazunobu Sawamoto
- Department of Developmental and Regenerative Neurobiology, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan
- Division of Neural Development and Regeneration, National Institute for Physiological Sciences, Okazaki, Japan
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23
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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. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.20.540091. [PMID: 37292964 PMCID: PMC10245788 DOI: 10.1101/2023.05.20.540091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
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 serial electron microscopy on the same samples. We generated eight such fluorescent scFvs that targeted useful markers for brain studies (GFP, GFAP, calbindin, parvalbumin, Kv1.2, VGluT1, PSD-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 linear unmixing, followed by ssEM imaging of the same sample. The results show excellent ultrastructure and superimposition of the different fluorescence channels. We document a poorly described cell type in the cerebellum, two types of mossy fiber terminals, and the subcellular localization of ion channels. Because scFvs can be derived from existing monoclonal antibodies, hundreds of such probes can be generated to enable a wide range of connectomic studies.
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24
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Shcherbakov E. Functional morphology of the praying mantis male genitalia (Insecta: Mantodea). ARTHROPOD STRUCTURE & DEVELOPMENT 2023; 74:101267. [PMID: 37119794 DOI: 10.1016/j.asd.2023.101267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Revised: 04/02/2023] [Accepted: 04/08/2023] [Indexed: 06/02/2023]
Abstract
Male genitalia in praying mantids are highly complex, but we know little of how they function. I combined the micro-computed tomography of a copulating pair of the European mantis (Mantis religiosa) with public videos of copulation in various species of Mantodea and an analysis of literature. The function of each major element is reviewed. Copulation is divided into three phases: opening, anchoring and deposition. The opening is achieved by pulling the female subgenital plate with the male apical process. Multiple cases of female cooperation or resistance were observed and one case of coercion by the male. In species with the reduced apical process, female cooperation is mandatory. The male subgenital plate may participate in the opening as an integral part of the genitalia. After the opening, the conformation of the genitalia drastically changes, revealing activity of the genital papilla. Tight grasp on female genitalia is maintained solely by the clamp on the right phallomere, despite the overall complexity and predictions of sexual conflict theory. Other prominent elements show rhythmic motions, but their functions are not entirely clear and evidently involve spermatophore deposition, female stimulation or rival sperm removal. The opening and anchoring are similar in Mantodea and Blattodea, but achieved with non-homologous elements.
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Affiliation(s)
- Evgeny Shcherbakov
- Department of Entomology, Faculty of Biology, Lomonosov Moscow State University, Leninskie Gory st. 1 bldg 12, 119991, Moscow, Russia.
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25
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Wang T, Shi P, Luo D, Guo J, Liu H, Yuan J, Jin H, Wu X, Zhang Y, Xiong Z, Zhu J, Zhou R, Zhang R. A Convenient All-Cell Optical Imaging Method Compatible with Serial SEM for Brain Mapping. Brain Sci 2023; 13:brainsci13050711. [PMID: 37239183 DOI: 10.3390/brainsci13050711] [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: 03/03/2023] [Revised: 04/04/2023] [Accepted: 04/12/2023] [Indexed: 05/28/2023] Open
Abstract
The mammalian brain, with its complexity and intricacy, poses significant challenges for researchers aiming to understand its inner workings. Optical multilayer interference tomography (OMLIT) is a novel, promising imaging technique that enables the mapping and reconstruction of mesoscale all-cell brain atlases and is seamlessly compatible with tape-based serial scanning electron microscopy (SEM) for microscale mapping in the same tissue. However, currently, OMLIT suffers from imperfect coatings, leading to background noise and image contamination. In this study, we introduced a new imaging configuration using carbon spraying to eliminate the tape-coating step, resulting in reduced noise and enhanced imaging quality. We demonstrated the improved imaging quality and validated its applicability through a correlative light-electron imaging workflow. Our method successfully reconstructed all cells and vasculature within a large OMLIT dataset, enabling basic morphological classification and analysis. We also show that this approach can perform effectively on thicker sections, extending its applicability to sub-micron scale slices, saving sample preparation and imaging time, and increasing imaging throughput. Consequently, this method emerges as a promising candidate for high-speed, high-throughput brain tissue reconstruction and analysis. Our findings open new avenues for exploring the structure and function of the brain using OMLIT images.
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Affiliation(s)
- Tianyi Wang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Peiyao Shi
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Dingsan Luo
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jun Guo
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Hui Liu
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Jinyun Yuan
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
| | - Haiqun Jin
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Xiaolong Wu
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Yueyi Zhang
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Zhiwei Xiong
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Jinlong Zhu
- State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
| | - Renjie Zhou
- Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Ruobing Zhang
- School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Suzhou 215163, China
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
- Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, Hefei 230088, China
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26
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Malik N, Ferreira BI, Hollstein PE, Curtis SD, Trefts E, Novak SW, Yu J, Gilson R, Hellberg K, Fang L, Sheridan A, Hah N, Shadel GS, Manor U, Shaw RJ. Induction of lysosomal and mitochondrial biogenesis by AMPK phosphorylation of FNIP1. Science 2023; 380:eabj5559. [PMID: 37079666 PMCID: PMC10794112 DOI: 10.1126/science.abj5559] [Citation(s) in RCA: 32] [Impact Index Per Article: 32.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 03/22/2023] [Indexed: 04/22/2023]
Abstract
Cells respond to mitochondrial poisons with rapid activation of the adenosine monophosphate-activated protein kinase (AMPK), causing acute metabolic changes through phosphorylation and prolonged adaptation of metabolism through transcriptional effects. Transcription factor EB (TFEB) is a major effector of AMPK that increases expression of lysosome genes in response to energetic stress, but how AMPK activates TFEB remains unresolved. We demonstrate that AMPK directly phosphorylates five conserved serine residues in folliculin-interacting protein 1 (FNIP1), suppressing the function of the folliculin (FLCN)-FNIP1 complex. FNIP1 phosphorylation is required for AMPK to induce nuclear translocation of TFEB and TFEB-dependent increases of peroxisome proliferator-activated receptor gamma coactivator 1-alpha (PGC1α) and estrogen-related receptor alpha (ERRα) messenger RNAs. Thus, mitochondrial damage triggers AMPK-FNIP1-dependent nuclear translocation of TFEB, inducing sequential waves of lysosomal and mitochondrial biogenesis.
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Affiliation(s)
- Nazma Malik
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Bibiana I. Ferreira
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Pablo E. Hollstein
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Stephanie D. Curtis
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Elijah Trefts
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Sammy Weiser Novak
- Biophotonics Core, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Jingting Yu
- Bioinformatics Core, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Rebecca Gilson
- Biophotonics Core, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Kristina Hellberg
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Lingjing Fang
- Biophotonics Core, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Arlo Sheridan
- Biophotonics Core, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Nasun Hah
- Next Generation Sequencing Core, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Gerald S. Shadel
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Uri Manor
- Biophotonics Core, The Salk Institute for Biological Studies, La Jolla, CA, USA
| | - Reuben J. Shaw
- Molecular and Cell Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
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27
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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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28
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Glavis-Bloom C, Vanderlip CR, Weiser Novak S, Kuwajima M, Kirk L, Harris KM, Manor U, Reynolds JH. Violation of the ultrastructural size principle in the dorsolateral prefrontal cortex underlies working memory impairment in the aged common marmoset (Callithrix jacchus). Front Aging Neurosci 2023; 15:1146245. [PMID: 37122384 PMCID: PMC10132463 DOI: 10.3389/fnagi.2023.1146245] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/21/2023] [Indexed: 05/02/2023] Open
Abstract
Morphology and function of the dorsolateral prefrontal cortex (dlPFC), and corresponding working memory performance, are affected early in the aging process, but nearly half of aged individuals are spared of working memory deficits. Translationally relevant model systems are critical for determining the neurobiological drivers of this variability. The common marmoset (Callithrix jacchus) is advantageous as a model for these investigations because, as a non-human primate, marmosets have a clearly defined dlPFC that enables measurement of prefrontal-dependent cognitive functions, and their short (∼10 year) lifespan facilitates longitudinal studies of aging. Previously, we characterized working memory capacity in a cohort of marmosets that collectively covered the lifespan, and found age-related working memory impairment. We also found a remarkable degree of heterogeneity in performance, similar to that found in humans. Here, we tested the hypothesis that changes to synaptic ultrastructure that affect synaptic efficacy stratify marmosets that age with cognitive impairment from those that age without cognitive impairment. We utilized electron microscopy to visualize synapses in the marmoset dlPFC and measured the sizes of boutons, presynaptic mitochondria, and synapses. We found that coordinated scaling of the sizes of synapses and mitochondria with their associated boutons is essential for intact working memory performance in aged marmosets. Further, lack of synaptic scaling, due to a remarkable failure of synaptic mitochondria to scale with presynaptic boutons, selectively underlies age-related working memory impairment. We posit that this decoupling results in mismatched energy supply and demand, leading to impaired synaptic transmission. We also found that aged marmosets have fewer synapses in dlPFC than young, though the severity of synapse loss did not predict whether aging occurred with or without cognitive impairment. This work identifies a novel mechanism of synapse dysfunction that stratifies marmosets that age with cognitive impairment from those that age without cognitive impairment. The process by which synaptic scaling is regulated is yet unknown and warrants future investigation.
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Affiliation(s)
- Courtney Glavis-Bloom
- Salk Institute for Biological Studies, Systems Neurobiology Laboratory, La Jolla, CA, United States
| | - Casey R. Vanderlip
- Salk Institute for Biological Studies, Systems Neurobiology Laboratory, La Jolla, CA, United States
| | - Sammy Weiser Novak
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, CA, United States
| | - Masaaki Kuwajima
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Lyndsey Kirk
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Kristen M. Harris
- Department of Neuroscience, Center for Learning and Memory, University of Texas at Austin, Austin, TX, United States
| | - Uri Manor
- Salk Institute for Biological Studies, Waitt Advanced Biophotonics Center, La Jolla, CA, United States
| | - John H. Reynolds
- Salk Institute for Biological Studies, Systems Neurobiology Laboratory, La Jolla, CA, United States
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29
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Cordero Cervantes D, Khare H, Wilson AM, Mendoza ND, Coulon-Mahdi O, Lichtman JW, Zurzolo C. 3D reconstruction of the cerebellar germinal layer reveals tunneling connections between developing granule cells. SCIENCE ADVANCES 2023; 9:eadf3471. [PMID: 37018410 PMCID: PMC10075961 DOI: 10.1126/sciadv.adf3471] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 03/02/2023] [Indexed: 06/19/2023]
Abstract
The difficulty of retrieving high-resolution, in vivo evidence of the proliferative and migratory processes occurring in neural germinal zones has limited our understanding of neurodevelopmental mechanisms. Here, we used a connectomic approach using a high-resolution, serial-sectioning scanning electron microscopy volume to investigate the laminar cytoarchitecture of the transient external granular layer (EGL) of the developing cerebellum, where granule cells coordinate a series of mitotic and migratory events. By integrating image segmentation, three-dimensional reconstruction, and deep-learning approaches, we found and characterized anatomically complex intercellular connections bridging pairs of cerebellar granule cells throughout the EGL. Connected cells were either mitotic, migratory, or transitioning between these two cell stages, displaying a chronological continuum of proliferative and migratory events never previously observed in vivo at this resolution. This unprecedented ultrastructural characterization poses intriguing hypotheses about intercellular connectivity between developing progenitors and its possible role in the development of the central nervous system.
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Affiliation(s)
- Diégo Cordero Cervantes
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
- Université Paris-Saclay, 91405 Orsay, France
| | - Harshavardhan Khare
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
| | - Alyssa Michelle Wilson
- Department of Neurology, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nathaly Dongo Mendoza
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
- Research Center in Bioengineering, Universidad de Ingeniería y Tecnología-UTEC, Lima 15049, Peru
| | - Orfane Coulon-Mahdi
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
| | - Jeff William Lichtman
- Department of Molecular and Cellular Biology, Center for Brain Science, Harvard University, Cambridge, MA 02138, USA
| | - Chiara Zurzolo
- Membrane Traffic and Pathogenesis, Institut Pasteur, Université Paris Cité, CNRS UMR 3691, F-75015 Paris, France
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30
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Kislinger G, Niemann C, Rodriguez L, Jiang H, Fard MK, Snaidero N, Schumacher AM, Kerschensteiner M, Misgeld T, Schifferer M. Neurons on tape: Automated Tape Collecting Ultramicrotomy-mediated volume EM for targeting neuropathology. Methods Cell Biol 2023; 177:125-170. [PMID: 37451765 DOI: 10.1016/bs.mcb.2023.01.012] [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: 03/19/2023]
Abstract
In this chapter, we review Automated Tape Collecting Ultramicrotomy (ATUM), which, among other array tomography methods, substantially simplified large-scale volume electron microscopy (vEM) projects. vEM reveals biological structures at nanometer resolution in three dimensions and resolves ambiguities of two-dimensional representations. However, as the structures of interest-like disease hallmarks emerging from neuropathology-are often rare but the field of view is small, this can easily turn a vEM project into a needle in a haystack problem. One solution for this is correlated light and electron microscopy (CLEM), providing tissue context, dynamic and molecular features before switching to targeted vEM to hone in on the object's ultrastructure. This requires precise coordinate transfer between the two imaging modalities (e.g., by micro computed tomography), especially for block face vEM which relies on physical destruction of sections. With array tomography methods, serial ultrathin sections are collected into a tissue library, thus allowing storage of precious samples like human biopsies and enabling repetitive imaging at different resolution levels for an SEM-based search strategy. For this, ATUM has been developed to reliably collect serial ultrathin sections via a conveyor belt onto a plastic tape that is later mounted onto silicon wafers for serial scanning EM (SEM). The ATUM-SEM procedure is highly modular and can be divided into sample preparation, serial ultramicrotomy onto tape, mounting, serial image acquisition-after which the acquired image stacks can be used for analysis. Here, we describe the steps of this workflow and how ATUM-SEM enables targeting and high resolution imaging of specific structures. ATUM-SEM is widely applicable. To illustrate this, we exemplify the approach by reconstructions of focal pathology in an Alzheimer mouse model and CLEM of a specific cortical synapse.
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Affiliation(s)
- Georg Kislinger
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Cornelia Niemann
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Lucia Rodriguez
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Hanyi Jiang
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Maryam K Fard
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
| | - Nicolas Snaidero
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; Hertie institute for Clinical Brain Research, Tuebingen University Hospital, Tuebingen, Germany
| | - Adrian-Minh Schumacher
- Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-University Munich, Munich, Germany; Institute of Clinical Neuroimmunology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Martin Kerschensteiner
- Faculty of Medicine, Biomedical Center (BMC), Ludwig-Maximilians-University Munich, Munich, Germany; Institute of Clinical Neuroimmunology, University Hospital, Ludwig-Maximilians-University Munich, Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Thomas Misgeld
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany; Munich Cluster of Systems Neurology (SyNergy), Munich, Germany.
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31
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Wildenberg G, Li H, Kasthuri N. The Development of Synapses in Mouse and Macaque Primary Sensory Cortices. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.15.528564. [PMID: 36824798 PMCID: PMC9949058 DOI: 10.1101/2023.02.15.528564] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
We report that the rate of synapse development in primary sensory cortices of mice and macaques is unrelated to lifespan, as was previously thought. We analyzed 28,084 synapses over multiple developmental time points in both species and find, instead, that net excitatory synapse development of mouse and macaque neurons primarily increased at similar rates in the first few postnatal months, and then decreased over a span of 1-1.5 years of age. The development of inhibitory synapses differed qualitatively across species. In macaques, net inhibitory synapses first increase and then decrease on excitatory soma at similar ages as excitatory synapses. In mice, however, such synapses are added throughout life. These findings contradict the long-held belief that the cycle of synapse formation and pruning occurs earlier in shorter-lived animals. Instead, our results suggest more nuanced rules, with the development of different types of synapses following different timing rules or different trajectories across species.
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Affiliation(s)
- Gregg Wildenberg
- Department of Neurobiology, The University of Chicago
- Argonne National Laboratory, Biosciences Division
| | - Hanyu Li
- Department of Neurobiology, The University of Chicago
- Argonne National Laboratory, Biosciences Division
| | - Narayanan Kasthuri
- Department of Neurobiology, The University of Chicago
- Argonne National Laboratory, Biosciences Division
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32
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Gallusser B, Maltese G, Di Caprio G, Vadakkan TJ, Sanyal A, Somerville E, Sahasrabudhe M, O’Connor J, Weigert M, Kirchhausen T. Deep neural network automated segmentation of cellular structures in volume electron microscopy. J Cell Biol 2023; 222:e202208005. [PMID: 36469001 PMCID: PMC9728137 DOI: 10.1083/jcb.202208005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 10/03/2022] [Accepted: 11/14/2022] [Indexed: 12/12/2022] Open
Abstract
Volume electron microscopy is an important imaging modality in contemporary cell biology. Identification of intracellular structures is a laborious process limiting the effective use of this potentially powerful tool. We resolved this bottleneck with automated segmentation of intracellular substructures in electron microscopy (ASEM), a new pipeline to train a convolutional neural network to detect structures of a wide range in size and complexity. We obtained dedicated models for each structure based on a small number of sparsely annotated ground truth images from only one or two cells. Model generalization was improved with a rapid, computationally effective strategy to refine a trained model by including a few additional annotations. We identified mitochondria, Golgi apparatus, endoplasmic reticulum, nuclear pore complexes, caveolae, clathrin-coated pits, and vesicles imaged by focused ion beam scanning electron microscopy. We uncovered a wide range of membrane-nuclear pore diameters within a single cell and derived morphological metrics from clathrin-coated pits and vesicles, consistent with the classical constant-growth assembly model.
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Affiliation(s)
- Benjamin Gallusser
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Giorgio Maltese
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Giuseppe Di Caprio
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
| | - Tegy John Vadakkan
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Anwesha Sanyal
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Department of Cell Biology, Harvard Medical School, Boston, MA
| | - Elliott Somerville
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
| | - Mihir Sahasrabudhe
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Université Paris-Saclay, CentraleSupélec, Mathématiques et Informatique pour la Complexité et les Systèmes, Gif-sur-Yvette, France
| | - Justin O’Connor
- Department of Biological Chemistry & Molecular Pharmacology, Harvard Medical School, Boston, MA
| | - Martin Weigert
- Institute of Bioengineering, School of Life Sciences, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Tom Kirchhausen
- Program in Cellular and Molecular Medicine, Boston Children’s Hospital, Boston, MA
- Department of Pediatrics, Harvard Medical School, Boston, MA
- Department of Cell Biology, Harvard Medical School, Boston, MA
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33
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Galbraith CG. Pumping up the volume. J Cell Biol 2023; 222:e202212042. [PMID: 36696087 PMCID: PMC9930139 DOI: 10.1083/jcb.202212042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
The time and cost of annotating ground-truth images and network training are major challenges to utilizing machine learning to automate the mining of volume electron microscopy data. In this issue, Gallusser et al. (2023. J. Cell Biol.https://doi.org/10.1083/jcb.202208005) present a less computationally intense pipeline to detect a single type of organelle using a limited number of loosely annotated images.
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Affiliation(s)
- Catherine G. Galbraith
- Oregon Health and Science University, Portland, OR, USA
- Quantitative and Systems Biology Program in Biomedical Engineering and The Knight Cancer Institute, Portland, OR, USA
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34
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Djannatian M, Radha S, Weikert U, Safaiyan S, Wrede C, Deichsel C, Kislinger G, Rhomberg A, Ruhwedel T, Campbell DS, van Ham T, Schmid B, Hegermann J, Möbius W, Schifferer M, Simons M. Myelination generates aberrant ultrastructure that is resolved by microglia. J Biophys Biochem Cytol 2023; 222:213804. [PMID: 36637807 PMCID: PMC9856851 DOI: 10.1083/jcb.202204010] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 10/18/2022] [Accepted: 12/21/2022] [Indexed: 01/14/2023] Open
Abstract
To enable rapid propagation of action potentials, axons are ensheathed by myelin, a multilayered insulating membrane formed by oligodendrocytes. Most of the myelin is generated early in development, resulting in the generation of long-lasting stable membrane structures. Here, we explored structural and dynamic changes in central nervous system myelin during development. To achieve this, we performed an ultrastructural analysis of mouse optic nerves by serial block face scanning electron microscopy (SBF-SEM) and confocal time-lapse imaging in the zebrafish spinal cord. We found that myelin undergoes extensive ultrastructural changes during early postnatal development. Myelin degeneration profiles were engulfed and phagocytosed by microglia using exposed phosphatidylserine as one "eat me" signal. In contrast, retractions of entire myelin sheaths occurred independently of microglia and involved uptake of myelin by the oligodendrocyte itself. Our findings show that the generation of myelin early in development is an inaccurate process associated with aberrant ultrastructural features that require substantial refinement.
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Affiliation(s)
- Minou Djannatian
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany,Department of Neurology, Klinikum rechts der Isar, TUM School of Medicine, Technical University of Munich, Munich, Germany,Munich Cluster of Systems Neurology (SyNergy), Munich, Germany,Minou Djannatian:
| | - Swathi Radha
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany
| | - Ulrich Weikert
- Max-Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Shima Safaiyan
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany
| | - Christoph Wrede
- https://ror.org/00f2yqf98Institute of Functional and Applied Anatomy, Research Core Unit Electron Microscopy, Hannover Medical School, Hannover, Germany
| | - Cassandra Deichsel
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany
| | - Georg Kislinger
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany
| | - Agata Rhomberg
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany
| | - Torben Ruhwedel
- Max-Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Douglas S. Campbell
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany
| | - Tjakko van Ham
- https://ror.org/018906e22Department of Clinical Genetics, Erasmus University Medical Center, University Medical Center Rotterdam, Rotterdam, Netherlands
| | - Bettina Schmid
- https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany
| | - Jan Hegermann
- https://ror.org/00f2yqf98Institute of Functional and Applied Anatomy, Research Core Unit Electron Microscopy, Hannover Medical School, Hannover, Germany
| | - Wiebke Möbius
- Max-Planck Institute of Experimental Medicine, Göttingen, Germany
| | - Martina Schifferer
- https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany,Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Mikael Simons
- Institute of Neuronal Cell Biology, Technical University Munich, Munich, Germany,https://ror.org/043j0f473German Center for Neurodegenerative Diseases, Munich, Germany,Munich Cluster of Systems Neurology (SyNergy), Munich, Germany,Institute for Stroke and Dementia Research, University Hospital of Munich, Ludwig Maximilian University of Munich, Munich, Germany,Correspondence to Mikael Simons:
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35
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Kole K, Voesenek BJB, Brinia ME, Petersen N, Kole MHP. Parvalbumin basket cell myelination accumulates axonal mitochondria to internodes. Nat Commun 2022; 13:7598. [PMID: 36494349 PMCID: PMC9734141 DOI: 10.1038/s41467-022-35350-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 11/30/2022] [Indexed: 12/13/2022] Open
Abstract
Parvalbumin-expressing (PV+) basket cells are fast-spiking inhibitory interneurons that exert critical control over local circuit activity and oscillations. PV+ axons are often myelinated, but the electrical and metabolic roles of interneuron myelination remain poorly understood. Here, we developed viral constructs allowing cell type-specific investigation of mitochondria with genetically encoded fluorescent probes. Single-cell reconstructions revealed that mitochondria selectively cluster to myelinated segments of PV+ basket cells, confirmed by analyses of a high-resolution electron microscopy dataset. In contrast to the increased mitochondrial densities in excitatory axons cuprizone-induced demyelination abolished mitochondrial clustering in PV+ axons. Furthermore, with genetic deletion of myelin basic protein the mitochondrial clustering was still observed at internodes wrapped by noncompacted myelin, indicating that compaction is dispensable. Finally, two-photon imaging of action potential-evoked calcium (Ca2+) responses showed that interneuron myelination attenuates both the cytosolic and mitochondrial Ca2+ transients. These findings suggest that oligodendrocyte ensheathment of PV+ axons assembles mitochondria to branch selectively fine-tune metabolic demands.
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Affiliation(s)
- Koen Kole
- grid.418101.d0000 0001 2153 6865Axonal Signaling Group, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
| | - Bas J. B. Voesenek
- grid.418101.d0000 0001 2153 6865Axonal Signaling Group, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
| | - Maria E. Brinia
- grid.418101.d0000 0001 2153 6865Axonal Signaling Group, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands ,grid.5216.00000 0001 2155 0800Medical School, National Kapodistrian University of Athens, Athens, 11527 Greece
| | - Naomi Petersen
- grid.418101.d0000 0001 2153 6865Axonal Signaling Group, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands
| | - Maarten H. P. Kole
- grid.418101.d0000 0001 2153 6865Axonal Signaling Group, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA Amsterdam, the Netherlands ,grid.5477.10000000120346234Cell Biology, Neurobiology and Biophysics, Department of Biology, Faculty of Science, Utrecht University, Padualaan 8, 3584 CH Utrecht, the Netherlands
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36
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Silversmith W, Zlateski A, Bae JA, Tartavull I, Kemnitz N, Wu J, Seung HS. Igneous: Distributed dense 3D segmentation meshing, neuron skeletonization, and hierarchical downsampling. Front Neural Circuits 2022; 16:977700. [PMID: 36506593 PMCID: PMC9732676 DOI: 10.3389/fncir.2022.977700] [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: 06/24/2022] [Accepted: 11/09/2022] [Indexed: 11/27/2022] Open
Abstract
Three-dimensional electron microscopy images of brain tissue and their dense segmentations are now petascale and growing. These volumes require the mass production of dense segmentation-derived neuron skeletons, multi-resolution meshes, image hierarchies (for both modalities) for visualization and analysis, and tools to manage the large amount of data. However, open tools for large-scale meshing, skeletonization, and data management have been missing. Igneous is a Python-based distributed computing framework that enables economical meshing, skeletonization, image hierarchy creation, and data management using cloud or cluster computing that has been proven to scale horizontally. We sketch Igneous's computing framework, show how to use it, and characterize its performance and data storage.
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Affiliation(s)
- William Silversmith
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States,*Correspondence: William Silversmith
| | - Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States,Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - J. Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States,Department of Electrical and Computer Engineering, Princeton University, Princeton, NJ, United States
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States
| | - H. Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, United States,Department of Computer Science, Princeton University, Princeton, NJ, United States
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37
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Dorkenwald S, Turner NL, Macrina T, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Kemnitz N, Silversmith WM, Ih D, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Wong W, Castro M, Jordan CS, Wilson AM, Froudarakis E, Buchanan J, Takeno MM, Torres R, Mahalingam G, Collman F, Schneider-Mizell CM, Bumbarger DJ, Li Y, Becker L, Suckow S, Reimer J, Tolias AS, Macarico da Costa N, Reid RC, Seung HS. Binary and analog variation of synapses between cortical pyramidal neurons. eLife 2022; 11:e76120. [PMID: 36382887 PMCID: PMC9704804 DOI: 10.7554/elife.76120] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 11/15/2022] [Indexed: 11/17/2022] Open
Abstract
Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.
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Affiliation(s)
- Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Nicholas L Turner
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Brain & Cognitive Sciences Department, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Agnes L Bodor
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | | | - Dodam Ih
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Aleksandar Zlateski
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
| | - William Wong
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
| | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | | | - Marc M Takeno
- Allen Institute for Brain ScienceSeattleUnited States
| | - Russel Torres
- Allen Institute for Brain ScienceSeattleUnited States
| | | | | | | | | | - Yang Li
- Allen Institute for Brain ScienceSeattleUnited States
| | - Lynne Becker
- Allen Institute for Brain ScienceSeattleUnited States
| | - Shelby Suckow
- Allen Institute for Brain ScienceSeattleUnited States
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of MedicineHoustonUnited States
- Center for Neuroscience and Artificial Intelligence, Baylor College of MedicineHoustonUnited States
- Department of Electrical and Computer Engineering, Rice UniversityHoustonUnited States
| | | | - R Clay Reid
- Allen Institute for Brain ScienceSeattleUnited States
| | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton UniversityPrincetonUnited States
- Computer Science Department, Princeton UniversityPrincetonUnited States
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38
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Post-embryonic remodeling of the C. elegans motor circuit. Curr Biol 2022; 32:4645-4659.e3. [DOI: 10.1016/j.cub.2022.09.065] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Revised: 07/28/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022]
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39
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A serotonergic axon-cilium synapse drives nuclear signaling to alter chromatin accessibility. Cell 2022; 185:3390-3407.e18. [PMID: 36055200 PMCID: PMC9789380 DOI: 10.1016/j.cell.2022.07.026] [Citation(s) in RCA: 46] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Revised: 05/16/2022] [Accepted: 07/25/2022] [Indexed: 12/27/2022]
Abstract
Chemical synapses between axons and dendrites mediate neuronal intercellular communication. Here, we describe a synapse between axons and primary cilia: the axo-ciliary synapse. Using enhanced focused ion beam-scanning electron microscopy on samples with optimally preserved ultrastructure, we discovered synapses between brainstem serotonergic axons and the primary cilia of hippocampal CA1 pyramidal neurons. Functionally, these cilia are enriched in a ciliary-restricted serotonin receptor, the 5-hydroxytryptamine receptor 6 (5-HTR6). Using a cilia-targeted serotonin sensor, we show that opto- and chemogenetic stimulation of serotonergic axons releases serotonin onto cilia. Ciliary 5-HTR6 stimulation activates a non-canonical Gαq/11-RhoA pathway, which modulates nuclear actin and increases histone acetylation and chromatin accessibility. Ablation of this pathway reduces chromatin accessibility in CA1 pyramidal neurons. As a signaling apparatus with proximity to the nucleus, axo-ciliary synapses short circuit neurotransmission to alter the postsynaptic neuron's epigenetic state.
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40
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Varsano N, Wolf SG. Electron microscopy of cellular ultrastructure in three dimensions. Curr Opin Struct Biol 2022; 76:102444. [PMID: 36041268 DOI: 10.1016/j.sbi.2022.102444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 07/05/2022] [Accepted: 07/19/2022] [Indexed: 11/03/2022]
Abstract
Electron microscopy in three dimensions (3D) of cells and tissues can be essential for understanding the ultrastructural aspects of biological processes. The quest for 3D information reveals challenges at many stages of the workflow, from sample preparation, to imaging, data analysis and segmentation. Here, we outline several available methods, including volume SEM imaging, cryo-TEM and cryo-STEM tomography, each one occupying a different domain in the basic tradeoff between field-of-view and resolution. We discuss the considerations for choosing a suitable method depending on research needs and highlight recent developments that are essential for making 3D volume imaging of cells and tissues a standard tool for cellular and structural biologists.
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Affiliation(s)
- Neta Varsano
- Department of Chemical Research Support, Weizmann Institute of Science, 234 Herzl St., Rehovot 76100, Israel
| | - Sharon Grayer Wolf
- Department of Chemical Research Support, Weizmann Institute of Science, 234 Herzl St., Rehovot 76100, Israel.
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41
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Kievits AJ, Lane R, Carroll EC, Hoogenboom JP. How innovations in methodology offer new prospects for volume electron microscopy. J Microsc 2022; 287:114-137. [PMID: 35810393 PMCID: PMC9546337 DOI: 10.1111/jmi.13134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 06/29/2022] [Accepted: 07/06/2022] [Indexed: 11/29/2022]
Abstract
Detailed knowledge of biological structure has been key in understanding biology at several levels of organisation, from organs to cells and proteins. Volume electron microscopy (volume EM) provides high resolution 3D structural information about tissues on the nanometre scale. However, the throughput rate of conventional electron microscopes has limited the volume size and number of samples that can be imaged. Recent improvements in methodology are currently driving a revolution in volume EM, making possible the structural imaging of whole organs and small organisms. In turn, these recent developments in image acquisition have created or stressed bottlenecks in other parts of the pipeline, like sample preparation, image analysis and data management. While the progress in image analysis is stunning due to the advent of automatic segmentation and server‐based annotation tools, several challenges remain. Here we discuss recent trends in volume EM, emerging methods for increasing throughput and implications for sample preparation, image analysis and data management.
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Affiliation(s)
- Arent J. Kievits
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
| | - Ryan Lane
- Imaging Physics Delft University of Technology Delft 2624CJ The Netherlands
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Peddie CJ, Genoud C, Kreshuk A, Meechan K, Micheva KD, Narayan K, Pape C, Parton RG, Schieber NL, Schwab Y, Titze B, Verkade P, Aubrey A, Collinson LM. Volume electron microscopy. NATURE REVIEWS. METHODS PRIMERS 2022; 2:51. [PMID: 37409324 PMCID: PMC7614724 DOI: 10.1038/s43586-022-00131-9] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 05/10/2022] [Indexed: 07/07/2023]
Abstract
Life exists in three dimensions, but until the turn of the century most electron microscopy methods provided only 2D image data. Recently, electron microscopy techniques capable of delving deep into the structure of cells and tissues have emerged, collectively called volume electron microscopy (vEM). Developments in vEM have been dubbed a quiet revolution as the field evolved from established transmission and scanning electron microscopy techniques, so early publications largely focused on the bioscience applications rather than the underlying technological breakthroughs. However, with an explosion in the uptake of vEM across the biosciences and fast-paced advances in volume, resolution, throughput and ease of use, it is timely to introduce the field to new audiences. In this Primer, we introduce the different vEM imaging modalities, the specialized sample processing and image analysis pipelines that accompany each modality and the types of information revealed in the data. We showcase key applications in the biosciences where vEM has helped make breakthrough discoveries and consider limitations and future directions. We aim to show new users how vEM can support discovery science in their own research fields and inspire broader uptake of the technology, finally allowing its full adoption into mainstream biological imaging.
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Affiliation(s)
- Christopher J. Peddie
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
| | - Christel Genoud
- Electron Microscopy Facility, Faculty of Biology and Medicine, University of Lausanne, Lausanne, Switzerland
| | - Anna Kreshuk
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Kimberly Meechan
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
- Present address: Faculty of Biosciences, Heidelberg University, Heidelberg, Germany
| | - Kristina D. Micheva
- Department of Molecular and Cellular Physiology, Stanford University, Palo Alto, CA, USA
| | - Kedar Narayan
- Center for Molecular Microscopy, Center for Cancer Research, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
- Cancer Research Technology Program, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
| | - Constantin Pape
- Cell Biology and Biophysics Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Robert G. Parton
- The Institute for Molecular Bioscience, The University of Queensland, Brisbane, Queensland, Australia
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Nicole L. Schieber
- Centre for Microscopy and Microanalysis, The University of Queensland, Brisbane, Queensland, Australia
| | - Yannick Schwab
- Cell Biology and Biophysics Unit/ Electron Microscopy Core Facility, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Paul Verkade
- School of Biochemistry, University of Bristol, Bristol, UK
| | - Aubrey Aubrey
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Lucy M. Collinson
- Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, UK
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Herwerth M, Kenet S, Schifferer M, Winkler A, Weber M, Snaidero N, Wang M, Lohrberg M, Bennett JL, Stadelmann C, Hemmer B, Misgeld T. A new form of axonal pathology in a spinal model of neuromyelitis optica. Brain 2022; 145:1726-1742. [PMID: 35202467 PMCID: PMC9166560 DOI: 10.1093/brain/awac079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 01/31/2022] [Accepted: 02/12/2022] [Indexed: 11/14/2022] Open
Abstract
Neuromyelitis optica is a chronic neuroinflammatory disease, which primarily targets astrocytes and often results in severe axon injury of unknown mechanism. Neuromyelitis optica patients harbour autoantibodies against the astrocytic water channel protein, aquaporin-4 (AQP4-IgG), which induce complement-mediated astrocyte lysis and subsequent axon damage. Using spinal in vivo imaging in a mouse model of such astrocytopathic lesions, we explored the mechanism underlying neuromyelitis optica-related axon injury. Many axons showed a swift and morphologically distinct 'pearls-on-string' transformation also readily detectable in human neuromyelitis optica lesions, which especially affected small calibre axons independently of myelination. Functional imaging revealed that calcium homeostasis was initially preserved in this 'acute axonal beading' state, ruling out disruption of the axonal membrane, which sets this form of axon injury apart from previously described forms of traumatic and inflammatory axon damage. Morphological, pharmacological and genetic analyses showed that AQP4-IgG-induced axon injury involved osmotic stress and ionic overload, but does not appear to use canonical pathways of Wallerian-like degeneration. Subcellular analysis demonstrated remodelling of the axonal cytoskeleton in beaded axons, especially local loss of microtubules. Treatment with the microtubule stabilizer epothilone, a putative therapy approach for traumatic and degenerative axonopathies, prevented axonal beading, while destabilizing microtubules sensitized axons for beading. Our results reveal a distinct form of immune-mediated axon pathology in neuromyelitis optica that mechanistically differs from known cascades of post-traumatic and inflammatory axon loss, and suggest a new strategy for neuroprotection in neuromyelitis optica and related diseases.
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Affiliation(s)
- Marina Herwerth
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Selin Kenet
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians University, Munich, Germany
| | - Martina Schifferer
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Anne Winkler
- Institute of Neuropathology, University Medical Center Göttingen, Göttingen, Germany
| | - Melanie Weber
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
| | - Nicolas Snaidero
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
| | - Mengzhe Wang
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
| | - Melanie Lohrberg
- Institute of Neuropathology, University Medical Center Göttingen, Göttingen, Germany
| | - Jeffrey L. Bennett
- Departments of Neurology and Ophthalmology, Programs in Neuroscience and Immunology, University of Colorado School of Medicine, Aurora, USA
| | - Christine Stadelmann
- Institute of Neuropathology, University Medical Center Göttingen, Göttingen, Germany
| | - Bernhard Hemmer
- Department of Neurology, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
| | - Thomas Misgeld
- Institute of Neuronal Cell Biology, Technical University of Munich, Munich, Germany
- German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
- Munich Cluster of Systems Neurology (SyNergy), Munich, Germany
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Sanchez M, Moore D, Johnson EC, Wester B, Lichtman JW, Gray-Roncal W. Connectomics Annotation Metadata Standardization for Increased Accessibility and Queryability. Front Neuroinform 2022; 16:828458. [PMID: 35651719 PMCID: PMC9150677 DOI: 10.3389/fninf.2022.828458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Neuroscientists can leverage technological advances to image neural tissue across a range of different scales, potentially forming the basis for the next generation of brain atlases and circuit reconstructions at submicron resolution, using Electron Microscopy and X-ray Microtomography modalities. However, there is variability in data collection, annotation, and storage approaches, which limits effective comparative and secondary analysis. There has been great progress in standardizing interfaces for large-scale spatial image data, but more work is needed to standardize annotations, especially metadata associated with neuroanatomical entities. Standardization will enable validation, sharing, and replication, greatly amplifying investment throughout the connectomics community. We share key design considerations and a usecase developed for metadata for a recent large-scale dataset.
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Affiliation(s)
- Morgan Sanchez
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Dymon Moore
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Erik C. Johnson
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Brock Wester
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
| | - Jeff W. Lichtman
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, United States
| | - William Gray-Roncal
- Research and Exploratory Development Department, Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States
- *Correspondence: William Gray-Roncal
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Automatic Gray Image Coloring Method Based on Convolutional Network. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5273698. [PMID: 35515498 PMCID: PMC9064524 DOI: 10.1155/2022/5273698] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Revised: 03/30/2022] [Accepted: 04/04/2022] [Indexed: 11/24/2022]
Abstract
Image coloring is a time-consuming and laborious work. For a work, color collocation is an important factor to determine its quality. Therefore, automatic image coloring is a topic with great research significance and application value. With the development of computer hardware, deep learning technology has achieved satisfactory results in the field of automatic coloring. According to the source of color information, this paper can divide automatic coloring methods into three types: image coloring based on prior knowledge, image coloring based on reference pictures, and interactive coloring. The coloring method can meet the needs of most users, but there are disadvantages such as users cannot get the multiple objects in a picture of different reference graph coloring. Aiming at this problem, based on the instance of color image segmentation and image fusion technology, the use of deep learning is proposed to implement regional mixed color more and master the method. It can be divided into foreground color based on reference picture and background color based on prior knowledge. In order to identify multiple objects and background areas in the image and fuse the final coloring results together, a method of image coloring based on CNN is proposed in this paper. Firstly, CNN is used to extract their semantic information, respectively. According to the extractive semantic information, the color of the designated area of the reference image is transferred to the designated area of the grayscale image. During the transformation, images combined with semantic information are input into CNN model to obtain the content feature map of grayscale image and the style feature map of reference image. Then, a random noise map is iterated to make the noise map approach the content feature map as a whole and the specific target region approach the designated area of the style feature map. Experimental results show that the proposed method has good effect on image coloring and has great advantages in network volume and coloring effect.
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46
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Khalin I, Adarsh N, Schifferer M, Wehn A, Groschup B, Misgeld T, Klymchenko A, Plesnila N. Size-Selective Transfer of Lipid Nanoparticle-Based Drug Carriers Across the Blood Brain Barrier Via Vascular Occlusions Following Traumatic Brain Injury. SMALL (WEINHEIM AN DER BERGSTRASSE, GERMANY) 2022; 18:e2200302. [PMID: 35384294 DOI: 10.1002/smll.202200302] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/15/2022] [Revised: 03/10/2022] [Indexed: 06/14/2023]
Abstract
The current lack of understanding about how nanocarriers cross the blood-brain barrier (BBB) in the healthy and injured brain is hindering the clinical translation of nanoscale brain-targeted drug-delivery systems. Here, the bio-distribution of lipid nano-emulsion droplets (LNDs) of two sizes (30 and 80 nm) in the mouse brain after traumatic brain injury (TBI) is investigated. The highly fluorescent LNDs are prepared by loading them with octadecyl rhodamine B and a bulky hydrophobic counter-ion, tetraphenylborate. Using in vivo two-photon and confocal imaging, the circulation kinetics and bio-distribution of LNDs in the healthy and injured mouse brain are studied. It is found that after TBI, LNDs of both sizes accumulate at vascular occlusions, where specifically 30 nm LNDs extravasate into the brain parenchyma and reach neurons. The vascular occlusions are not associated with bleedings, but instead are surrounded by processes of activated microglia, suggesting a specific opening of the BBB. Finally, correlative light-electron microscopy reveals 30 nm LNDs in endothelial vesicles, while 80 nm particles remain in the vessel lumen, indicating size-selective vesicular transport across the BBB via vascular occlusions. The data suggest that microvascular occlusions serve as "gates" for the transport of nanocarriers across the BBB.
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Affiliation(s)
- Igor Khalin
- Institute for Stroke and Dementia Research, University of Munich Medical Center, 81377, Munich, Germany
- Cluster for Systems Neurology, Munich, Germany
| | - Nagappanpillai Adarsh
- Laboratory de Biophotonique et Pharmacologie, University of Strasbourg, Strasbourg, 67401, France
- Department of Polymer Chemistry, Government College Attingal, Kerala, 695101, India
| | - Martina Schifferer
- Cluster for Systems Neurology, Munich, Germany
- German Center for Neurodegenerative Diseases, 81377, Munich, Germany
| | - Antonia Wehn
- Institute for Stroke and Dementia Research, University of Munich Medical Center, 81377, Munich, Germany
| | - Bernhard Groschup
- Institute for Stroke and Dementia Research, University of Munich Medical Center, 81377, Munich, Germany
| | - Thomas Misgeld
- Cluster for Systems Neurology, Munich, Germany
- German Center for Neurodegenerative Diseases, 81377, Munich, Germany
- Institute of Neuronal Cell Biology, School of Medicine, Technical University of Munich, 80802, Munich, Germany
| | - Andrey Klymchenko
- Laboratory de Biophotonique et Pharmacologie, University of Strasbourg, Strasbourg, 67401, France
| | - Nikolaus Plesnila
- Institute for Stroke and Dementia Research, University of Munich Medical Center, 81377, Munich, Germany
- Cluster for Systems Neurology, Munich, Germany
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Pallikaras V, Shizgal P. The Convergence Model of Brain Reward Circuitry: Implications for Relief of Treatment-Resistant Depression by Deep-Brain Stimulation of the Medial Forebrain Bundle. Front Behav Neurosci 2022; 16:851067. [PMID: 35431828 PMCID: PMC9011331 DOI: 10.3389/fnbeh.2022.851067] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Accepted: 02/10/2022] [Indexed: 12/28/2022] Open
Abstract
Deep-brain stimulation of the medial forebrain bundle (MFB) can provide effective, enduring relief of treatment-resistant depression. Panksepp provided an explanatory framework: the MFB constitutes the core of the neural circuitry subserving the anticipation and pursuit of rewards: the “SEEKING” system. On that view, the SEEKING system is hypoactive in depressed individuals; background electrical stimulation of the MFB alleviates symptoms by normalizing activity. Panksepp attributed intracranial self-stimulation to excitation of the SEEKING system in which the ascending projections of midbrain dopamine neurons are an essential component. In parallel with Panksepp’s qualitative work, intracranial self-stimulation has long been studied quantitatively by psychophysical means. That work argues that the predominant directly stimulated substrate for MFB self-stimulation are myelinated, non-dopaminergic fibers, more readily excited by brief electrical current pulses than the thin, unmyelinated axons of the midbrain dopamine neurons. The series-circuit hypothesis reconciles this view with the evidence implicating dopamine in MFB self-stimulation as follows: direct activation of myelinated MFB fibers is rewarding due to their trans-synaptic activation of midbrain dopamine neurons. A recent study in which rats worked for optogenetic stimulation of midbrain dopamine neurons challenges the series-circuit hypothesis and provides a new model of intracranial self-stimulation in which the myelinated non-dopaminergic neurons and the midbrain dopamine projections access the behavioral final common path for reward seeking via separate, converging routes. We explore the potential implications of this convergence model for the interpretation of the antidepressant effect of MFB stimulation. We also discuss the consistent finding that psychomotor stimulants, which boost dopaminergic neurotransmission, fail to provide a monotherapy for depression. We propose that non-dopaminergic MFB components may contribute to the therapeutic effect in parallel to, in synergy with, or even instead of, a dopaminergic component.
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Turner NL, Macrina T, Bae JA, Yang R, Wilson AM, Schneider-Mizell C, Lee K, Lu R, Wu J, Bodor AL, Bleckert AA, Brittain D, Froudarakis E, Dorkenwald S, Collman F, Kemnitz N, Ih D, Silversmith WM, Zung J, Zlateski A, Tartavull I, Yu SC, Popovych S, Mu S, Wong W, Jordan CS, Castro M, Buchanan J, Bumbarger DJ, Takeno M, Torres R, Mahalingam G, Elabbady L, Li Y, Cobos E, Zhou P, Suckow S, Becker L, Paninski L, Polleux F, Reimer J, Tolias AS, Reid RC, da Costa NM, Seung HS. Reconstruction of neocortex: Organelles, compartments, cells, circuits, and activity. Cell 2022; 185:1082-1100.e24. [PMID: 35216674 PMCID: PMC9337909 DOI: 10.1016/j.cell.2022.01.023] [Citation(s) in RCA: 51] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 07/26/2021] [Accepted: 01/27/2022] [Indexed: 12/31/2022]
Abstract
We assembled a semi-automated reconstruction of L2/3 mouse primary visual cortex from ∼250 × 140 × 90 μm3 of electron microscopic images, including pyramidal and non-pyramidal neurons, astrocytes, microglia, oligodendrocytes and precursors, pericytes, vasculature, nuclei, mitochondria, and synapses. Visual responses of a subset of pyramidal cells are included. The data are publicly available, along with tools for programmatic and three-dimensional interactive access. Brief vignettes illustrate the breadth of potential applications relating structure to function in cortical circuits and neuronal cell biology. Mitochondria and synapse organization are characterized as a function of path length from the soma. Pyramidal connectivity motif frequencies are predicted accurately using a configuration model of random graphs. Pyramidal cells receiving more connections from nearby cells exhibit stronger and more reliable visual responses. Sample code shows data access and analysis.
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Affiliation(s)
- Nicholas L Turner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Thomas Macrina
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - J Alexander Bae
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Electrical and Computer Engineering Department, Princeton University, Princeton, NJ 08544, USA
| | - Runzhe Yang
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Alyssa M Wilson
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Kisuk Lee
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Brain & Cognitive Sciences Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ran Lu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Jingpeng Wu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Agnes L Bodor
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | | | - Emmanouil Froudarakis
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Sven Dorkenwald
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | | | - Nico Kemnitz
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Dodam Ih
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | | | - Jonathan Zung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Aleksandar Zlateski
- Electrical Engineering and Computer Science Department, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Ignacio Tartavull
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Szi-Chieh Yu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Sergiy Popovych
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA
| | - Shang Mu
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - William Wong
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Chris S Jordan
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - Manuel Castro
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA
| | - JoAnn Buchanan
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Marc Takeno
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Russel Torres
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - Leila Elabbady
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Yang Li
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Erick Cobos
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Pengcheng Zhou
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA
| | - Shelby Suckow
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Lynne Becker
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | - Liam Paninski
- Department of Statistics, Columbia University, New York, NY 10027, USA; Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA; Grossman Center for the Statistics of Mind, Columbia University, New York, NY 10027, USA; Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Franck Polleux
- Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA; Department of Neuroscience, Columbia University, New York, NY 10027, USA; Kavli Institute for Brain Science at Columbia University, New York, NY 10027, USA
| | - Jacob Reimer
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA
| | - Andreas S Tolias
- Department of Neuroscience, Baylor College of Medicine, Houston, TX 77030, USA; Center for Neuroscience and Artificial Intelligence, Baylor College of Medicine, Houston, TX 77030, USA; Department of Electrical and Computer Engineering, Rice University, Houston, TX 77005, USA
| | - R Clay Reid
- Allen Institute for Brain Science, Seattle, WA 98109, USA
| | | | - H Sebastian Seung
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08544, USA; Computer Science Department, Princeton University, Princeton, NJ 08544, USA.
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Park C, Gim J, Lee S, Lee KJ, Kim JS. Automated Synapse Detection Method for Cerebellar Connectomics. Front Neuroanat 2022; 16:760279. [PMID: 35360651 PMCID: PMC8963724 DOI: 10.3389/fnana.2022.760279] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Accepted: 02/14/2022] [Indexed: 11/17/2022] Open
Abstract
The connectomic analyses of large-scale volumetric electron microscope (EM) images enable the discovery of hidden neural connectivity. While the technologies for neuronal reconstruction of EM images are under rapid progress, the technologies for synapse detection are lagging behind. Here, we propose a method that automatically detects the synapses in the 3D EM images, specifically for the mouse cerebellar molecular layer (CML). The method aims to accurately detect the synapses between the reconstructed neuronal fragments whose types can be identified. It extracts the contacts between the reconstructed neuronal fragments and classifies them as synaptic or non-synaptic with the help of type information and two deep learning artificial intelligences (AIs). The method can also assign the pre- and postsynaptic sides of a synapse and determine excitatory and inhibitory synapse types. The accuracy of this method is estimated to be 0.955 in F1-score for a test volume of CML containing 508 synapses. To demonstrate the usability, we measured the size and number of the synapses in the volume and investigated the subcellular connectivity between the CML neuronal fragments. The basic idea of the method to exploit tissue-specific properties can be extended to other brain regions.
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Affiliation(s)
- Changjoo Park
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Department of Brain and Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Jawon Gim
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Sungjin Lee
- Department of Electrical Engineering and Computer Science, Daegu Gyeongbuk Institute of Science and Technology, Daegu, South Korea
| | - Kea Joo Lee
- Laboratory of Synaptic Circuit Plasticity in Neural Circuits Research Group, Korea Brain Research Institute, Daegu, South Korea
| | - Jinseop S. Kim
- Department of Biological Sciences, Sungkyunkwan University, Suwon-si, South Korea
- Laboratory of Computational Neuroscience, Korea Brain Research Institute, Daegu, South Korea
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50
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Ma Z, Lytle NK, Chen B, Jyotsana N, Novak SW, Cho CJ, Caplan L, Ben-Levy O, Neininger AC, Burnette DT, Trinh VQ, Tan MCB, Patterson EA, Arrojo E Drigo R, Giraddi RR, Ramos C, Means AL, Matsumoto I, Manor U, Mills JC, Goldenring JR, Lau KS, Wahl GM, DelGiorno KE. Single-Cell Transcriptomics Reveals a Conserved Metaplasia Program in Pancreatic Injury. Gastroenterology 2022; 162:604-620.e20. [PMID: 34695382 PMCID: PMC8792222 DOI: 10.1053/j.gastro.2021.10.027] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 09/15/2021] [Accepted: 10/09/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND & AIMS Acinar to ductal metaplasia (ADM) occurs in the pancreas in response to tissue injury and is a potential precursor for adenocarcinoma. The goal of these studies was to define the populations arising from ADM, the associated transcriptional changes, and markers of disease progression. METHODS Acinar cells were lineage-traced with enhanced yellow fluorescent protein (EYFP) to follow their fate post-injury. Transcripts of more than 13,000 EYFP+ cells were determined using single-cell RNA sequencing (scRNA-seq). Developmental trajectories were generated. Data were compared with gastric metaplasia, KrasG12D-induced neoplasia, and human pancreatitis. Results were confirmed by immunostaining and electron microscopy. KrasG12D was expressed in injury-induced ADM using several inducible Cre drivers. Surgical specimens of chronic pancreatitis from 15 patients were evaluated by immunostaining. RESULTS scRNA-seq of ADM revealed emergence of a mucin/ductal population resembling gastric pyloric metaplasia. Lineage trajectories suggest that some pyloric metaplasia cells can generate tuft and enteroendocrine cells (EECs). Comparison with KrasG12D-induced ADM identifies populations associated with disease progression. Activation of KrasG12D expression in HNF1B+ or POU2F3+ ADM populations leads to neoplastic transformation and formation of MUC5AC+ gastric-pit-like cells. Human pancreatitis samples also harbor pyloric metaplasia with a similar transcriptional phenotype. CONCLUSIONS Under conditions of chronic injury, acinar cells undergo a pyloric-type metaplasia to mucinous progenitor-like populations, which seed disparate tuft cell and EEC lineages. ADM-derived EEC subtypes are diverse. KrasG12D expression is sufficient to drive neoplasia when targeted to injury-induced ADM populations and offers an alternative origin for tumorigenesis. This program is conserved in human pancreatitis, providing insight into early events in pancreas diseases.
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Affiliation(s)
- Zhibo Ma
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Nikki K Lytle
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Bob Chen
- Program in Chemical and Physical Biology, Vanderbilt University School of Medicine, Nashville, Tennessee; Epithelial Biology Center, Vanderbilt University School of Medicine, Nashville, Tennessee
| | - Nidhi Jyotsana
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee
| | - Sammy Weiser Novak
- Waitt Advanced Biophotonics Center, Salk Insitute for Biological Studies, La Jolla, California
| | - Charles J Cho
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas
| | - Leah Caplan
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee
| | - Olivia Ben-Levy
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee
| | - Abigail C Neininger
- Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee
| | - Dylan T Burnette
- Epithelial Biology Center, Vanderbilt University School of Medicine, Nashville, Tennessee; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee; Vanderbilt Ingram Cancer Center, Nashville, Tennessee
| | - Vincent Q Trinh
- Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Marcus C B Tan
- Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt Digestive Disease Research Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Emilee A Patterson
- Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | - Rafael Arrojo E Drigo
- Epithelial Biology Center, Vanderbilt University School of Medicine, Nashville, Tennessee; Department of Molecular Physiology and Biophysics, Vanderbilt University, Nashville, Tennessee
| | - Rajshekhar R Giraddi
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Cynthia Ramos
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Anna L Means
- Vanderbilt Ingram Cancer Center, Nashville, Tennessee; Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt Digestive Disease Research Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | | | - Uri Manor
- Waitt Advanced Biophotonics Center, Salk Insitute for Biological Studies, La Jolla, California
| | - Jason C Mills
- Section of Gastroenterology and Hepatology, Baylor College of Medicine, Houston, Texas
| | - James R Goldenring
- Epithelial Biology Center, Vanderbilt University School of Medicine, Nashville, Tennessee; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee; Vanderbilt Ingram Cancer Center, Nashville, Tennessee; Department of Surgery, Vanderbilt University Medical Center, Nashville, Tennessee; Vanderbilt Digestive Disease Research Center, Vanderbilt University Medical Center, Nashville, Tennessee; Nashville VA Medical Center, Nashville, Tennessee
| | - Ken S Lau
- Epithelial Biology Center, Vanderbilt University School of Medicine, Nashville, Tennessee; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee; Vanderbilt Ingram Cancer Center, Nashville, Tennessee; Vanderbilt Digestive Disease Research Center, Vanderbilt University Medical Center, Nashville, Tennessee
| | - Geoffrey M Wahl
- Gene Expression Laboratory, Salk Institute for Biological Studies, La Jolla, California
| | - Kathleen E DelGiorno
- Epithelial Biology Center, Vanderbilt University School of Medicine, Nashville, Tennessee; Department of Cell and Developmental Biology, Vanderbilt University, Nashville, Tennessee; Vanderbilt Ingram Cancer Center, Nashville, Tennessee; Vanderbilt Digestive Disease Research Center, Vanderbilt University Medical Center, Nashville, Tennessee.
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