151
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Sands GB, Ashton JL, Trew ML, Baddeley D, Walton RD, Benoist D, Efimov IR, Smith NP, Bernus O, Smaill BH. It's clearly the heart! Optical transparency, cardiac tissue imaging, and computer modelling. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2021; 168:18-32. [PMID: 34126113 DOI: 10.1016/j.pbiomolbio.2021.06.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 05/10/2021] [Accepted: 06/07/2021] [Indexed: 12/19/2022]
Abstract
Recent developments in clearing and microscopy enable 3D imaging with cellular resolution up to the whole organ level. These methods have been used extensively in neurobiology, but their uptake in other fields has been much more limited. Application of this approach to the human heart and effective use of the data acquired present challenges of scale and complexity. Four interlinked issues need to be addressed: 1) efficient clearing and labelling of heart tissue, 2) fast microscopic imaging of human-scale samples, 3) handling and processing of multi-terabyte 3D images, and 4) extraction of structural information in computationally tractable structure-based models of cardiac function. Preliminary studies show that each of these requirements can be achieved with the appropriate application and development of existing technologies.
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Affiliation(s)
- Gregory B Sands
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand.
| | - Jesse L Ashton
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - Mark L Trew
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
| | - David Baddeley
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Department of Cell Biology, Yale University, New Haven CT, 06520, USA
| | - Richard D Walton
- IHU Liryc, Fondation Bordeaux Université, Bordeaux, France; Univ. Bordeaux, Inserm, Centre de Recherche Cardio-Thoracique, U1045, 33000, Bordeaux, France
| | - David Benoist
- IHU Liryc, Fondation Bordeaux Université, Bordeaux, France; Univ. Bordeaux, Inserm, Centre de Recherche Cardio-Thoracique, U1045, 33000, Bordeaux, France
| | - Igor R Efimov
- IHU Liryc, Fondation Bordeaux Université, Bordeaux, France; Department of Biomedical Engineering, The George Washington University, Washington DC, 20052, USA
| | - Nicolas P Smith
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand; Queensland University of Technology, Brisbane 4000, Australia
| | - Olivier Bernus
- IHU Liryc, Fondation Bordeaux Université, Bordeaux, France; Univ. Bordeaux, Inserm, Centre de Recherche Cardio-Thoracique, U1045, 33000, Bordeaux, France
| | - Bruce H Smaill
- Auckland Bioengineering Institute, The University of Auckland, Auckland, New Zealand
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152
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Yu T, Li D, Zhu D. Tissue Optical Clearing for Biomedical Imaging: From In Vitro to In Vivo. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2021; 3233:217-255. [PMID: 34053030 DOI: 10.1007/978-981-15-7627-0_11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Tissue optical clearing technique provides a prospective solution for the application of advanced optical methods in life sciences. This chapter firstly gives a brief introduction to mechanisms of tissue optical clearing techniques, from the physical mechanism to chemical mechanism, which is the most important foundation to develop tissue optical clearing methods. During the past years, in vitro and in vivo tissue optical clearing methods were developed. In vitro tissue optical clearing techniques, including the solvent-based clearing methods and the hydrophilic reagents-based clearing methods, combined with labeling technique and advanced microscopy, can be applied to image 3D microstructure of tissue blocks or whole organs such as brain and spinal cord with high resolution. In vivo skin or skull optical clearing, promise various optical imaging techniques to detect cutaneous or cortical cell and vascular structure and function without surgical window.
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Affiliation(s)
- Tingting Yu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Dongyu Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China.,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Dan Zhu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China. .,MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei, China.
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153
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Costantini I, Mazzamuto G, Roffilli M, Laurino A, Maria Castelli F, Neri M, Lughi G, Simonetto A, Lazzeri E, Pesce L, Destrieux C, Silvestri L, Conti V, Guerrini R, Saverio Pavone F. Large-scale, cell-resolution volumetric mapping allows layer-specific investigation of human brain cytoarchitecture. BIOMEDICAL OPTICS EXPRESS 2021; 12:3684-3699. [PMID: 34221688 PMCID: PMC8221968 DOI: 10.1364/boe.415555] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 03/23/2021] [Accepted: 03/30/2021] [Indexed: 06/01/2023]
Abstract
Although neuronal density analysis on human brain slices is available from stereological studies, data on the spatial distribution of neurons in 3D are still missing. Since the neuronal organization is very inhomogeneous in the cerebral cortex, it is critical to map all neurons in a given volume rather than relying on sparse sampling methods. To achieve this goal, we implement a new tissue transformation protocol to clear and label human brain tissues and we exploit the high-resolution optical sectioning of two-photon fluorescence microscopy to perform 3D mesoscopic reconstruction. We perform neuronal mapping of 100mm3 human brain samples and evaluate the volume and density distribution of neurons from various areas of the cortex originating from different subjects (young, adult, and elderly, both healthy and pathological). The quantitative evaluation of the density in combination with the mean volume of the thousands of neurons identified within the specimens, allow us to determine the layer-specific organization of the cerebral architecture.
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Affiliation(s)
- Irene Costantini
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Department of Biology, University of Florence, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- These authors contributed equally to this work
| | - Giacomo Mazzamuto
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- These authors contributed equally to this work
| | | | - Annunziatina Laurino
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Present address: Department of Neurofarba, Section of Pharmacology and Toxicology, University of Florence, Italy
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Department of Physics, University of Florence, Italy
| | | | | | | | - Erica Lazzeri
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Department of Physics, University of Florence, Italy
| | | | - Ludovico Silvestri
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics, University of Florence, Italy
| | - Valerio Conti
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, A. Meyer Children’s Hospital, University of Florence, Florence, Italy
| | - Renzo Guerrini
- Pediatric Neurology, Neurogenetics and Neurobiology Unit and Laboratories, A. Meyer Children’s Hospital, University of Florence, Florence, Italy
| | - Francesco Saverio Pavone
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics, University of Florence, Italy
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154
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Gao R, Yu CCJ, Gao L, Piatkevich KD, Neve RL, Munro JB, Upadhyayula S, Boyden ES. A highly homogeneous polymer composed of tetrahedron-like monomers for high-isotropy expansion microscopy. NATURE NANOTECHNOLOGY 2021; 16:698-707. [PMID: 33782587 PMCID: PMC8197733 DOI: 10.1038/s41565-021-00875-7] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 02/11/2021] [Indexed: 05/08/2023]
Abstract
Expansion microscopy (ExM) physically magnifies biological specimens to enable nanoscale-resolution imaging using conventional microscopes. Current ExM methods permeate specimens with free-radical-chain-growth-polymerized polyacrylate hydrogels, whose network structure limits the local isotropy of expansion as well as the preservation of morphology and shape at the nanoscale. Here we report that ExM is possible using hydrogels that have a more homogeneous network structure, assembled via non-radical terminal linking of tetrahedral monomers. As with earlier forms of ExM, such 'tetra-gel'-embedded specimens can be iteratively expanded for greater physical magnification. Iterative tetra-gel expansion of herpes simplex virus type 1 (HSV-1) virions by ~10× in linear dimension results in a median spatial error of 9.2 nm for localizing the viral envelope layer, rather than 14.3 nm from earlier versions of ExM. Moreover, tetra-gel-based expansion better preserves the virion spherical shape. Thus, tetra-gels may support ExM with reduced spatial errors and improved local isotropy, pointing the way towards single-biomolecule accuracy ExM.
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Affiliation(s)
- Ruixuan Gao
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
| | - Chih-Chieh Jay Yu
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
| | - Linyi Gao
- Media Arts and Sciences, MIT, Cambridge, MA, USA
- Department of Biological Engineering, MIT, Cambridge, MA, USA
- Broad Institute, MIT, Cambridge, MA, USA
| | - Kiryl D Piatkevich
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA
- Media Arts and Sciences, MIT, Cambridge, MA, USA
| | - Rachael L Neve
- Department of Neurology, Massachusetts General Hospital, Cambridge, MA, USA
| | - James B Munro
- Department of Microbiology and Physiological Systems, University of Massachusetts Medical School, Worcester, MA, USA
| | - Srigokul Upadhyayula
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
- Department of Cell Biology, Harvard Medical School, Boston, MA, USA
- Program in Cellular and Molecular Medicine, Boston Children's Hospital, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Advanced Bioimaging Center, University of California at Berkeley, Berkeley, CA, USA
- Department of Molecular and Cell Biology, University of California at Berkeley, Berkeley, CA, USA
| | - Edward S Boyden
- McGovern Institute for Brain Research, MIT, Cambridge, MA, USA.
- Media Arts and Sciences, MIT, Cambridge, MA, USA.
- Department of Biological Engineering, MIT, Cambridge, MA, USA.
- MIT Center for Neurobiological Engineering, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Koch Institute, MIT, Cambridge, MA, USA.
- Howard Hughes Medical Institute, Cambridge, MA, USA.
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155
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Weiss KR, Voigt FF, Shepherd DP, Huisken J. Tutorial: practical considerations for tissue clearing and imaging. Nat Protoc 2021; 16:2732-2748. [PMID: 34021294 PMCID: PMC10542857 DOI: 10.1038/s41596-021-00502-8] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2020] [Accepted: 01/18/2021] [Indexed: 02/06/2023]
Abstract
Tissue clearing has become a powerful technique for studying anatomy and morphology at scales ranging from entire organisms to subcellular features. With the recent proliferation of tissue-clearing methods and imaging options, it can be challenging to determine the best clearing protocol for a particular tissue and experimental question. The fact that so many clearing protocols exist suggests there is no one-size-fits-all approach to tissue clearing and imaging. Even in cases where a basic level of clearing has been achieved, there are many factors to consider, including signal retention, staining (labeling), uniformity of transparency, image acquisition and analysis. Despite reviews citing features of clearing protocols, it is often unknown a priori whether a protocol will work for a given experiment, and thus some optimization is required by the end user. In addition, the capabilities of available imaging setups often dictate how the sample needs to be prepared. After imaging, careful evaluation of volumetric image data is required for each combination of clearing protocol, tissue type, biological marker, imaging modality and biological question. Rather than providing a direct comparison of the many clearing methods and applications available, in this tutorial we address common pitfalls and provide guidelines for designing, optimizing and imaging in a successful tissue-clearing experiment with a focus on light-sheet fluorescence microscopy (LSFM).
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Affiliation(s)
- Kurt R Weiss
- Morgridge Institute for Research, Madison, WI, USA
| | - Fabian F Voigt
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich & ETH Zurich, Zurich, Switzerland
| | - Douglas P Shepherd
- Department of Physics, Arizona State University, Tempe, AZ, USA
- Center for Biological Physics, Arizona State University, Tempe, AZ, USA
| | - Jan Huisken
- Morgridge Institute for Research, Madison, WI, USA.
- Department of Integrative Biology, University of Wisconsin, Madison, WI, USA.
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156
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Hintiryan H, Bowman I, Johnson DL, Korobkova L, Zhu M, Khanjani N, Gou L, Gao L, Yamashita S, Bienkowski MS, Garcia L, Foster NN, Benavidez NL, Song MY, Lo D, Cotter KR, Becerra M, Aquino S, Cao C, Cabeen RP, Stanis J, Fayzullina M, Ustrell SA, Boesen T, Tugangui AJ, Zhang ZG, Peng B, Fanselow MS, Golshani P, Hahn JD, Wickersham IR, Ascoli GA, Zhang LI, Dong HW. Connectivity characterization of the mouse basolateral amygdalar complex. Nat Commun 2021; 12:2859. [PMID: 34001873 PMCID: PMC8129205 DOI: 10.1038/s41467-021-22915-5] [Citation(s) in RCA: 55] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 03/25/2021] [Indexed: 11/08/2022] Open
Abstract
The basolateral amygdalar complex (BLA) is implicated in behaviors ranging from fear acquisition to addiction. Optogenetic methods have enabled the association of circuit-specific functions to uniquely connected BLA cell types. Thus, a systematic and detailed connectivity profile of BLA projection neurons to inform granular, cell type-specific interrogations is warranted. Here, we apply machine-learning based computational and informatics analysis techniques to the results of circuit-tracing experiments to create a foundational, comprehensive BLA connectivity map. The analyses identify three distinct domains within the anterior BLA (BLAa) that house target-specific projection neurons with distinguishable morphological features. We identify brain-wide targets of projection neurons in the three BLAa domains, as well as in the posterior BLA, ventral BLA, posterior basomedial, and lateral amygdalar nuclei. Inputs to each nucleus also are identified via retrograde tracing. The data suggests that connectionally unique, domain-specific BLAa neurons are associated with distinct behavior networks.
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Affiliation(s)
- Houri Hintiryan
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
| | - Ian Bowman
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - David L Johnson
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Laura Korobkova
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Muye Zhu
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Neda Khanjani
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Lin Gou
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Lei Gao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Seita Yamashita
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Michael S Bienkowski
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Luis Garcia
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nicholas N Foster
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Nora L Benavidez
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Monica Y Song
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Darrick Lo
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Kaelan R Cotter
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Marlene Becerra
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarvia Aquino
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Chunru Cao
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Ryan P Cabeen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Jim Stanis
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Marina Fayzullina
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Sarah A Ustrell
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Tyler Boesen
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Amanda J Tugangui
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
| | - Zheng-Gang Zhang
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Department of Physiology, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China
| | - Bo Peng
- Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Michael S Fanselow
- Brain Research Institute, Department of Psychology, University of California, Los Angeles, CA, USA
| | - Peyman Golshani
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA
- Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, CA, USA
- West Los Angeles Veterans Administration Medical Center, Los Angeles, CA, USA
| | - Joel D Hahn
- Department of Biological Sciences, University of Southern California, Los Angeles, CA, USA
| | - Ian R Wickersham
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Giorgio A Ascoli
- Krasnow Institute for Advanced Study, George Mason University, Fairfax, VA, USA
| | - Li I Zhang
- Center for Neural Circuitry & Sensory Processing Disorders, Zilkha Neurogenetic Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Hong-Wei Dong
- Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
- UCLA Brain Research & Artificial Intelligence Nexus, Department of Neurobiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.
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157
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Chen Y, Li X, Zhang D, Wang C, Feng R, Li X, Wen Y, Xu H, Zhang XS, Yang X, Chen Y, Feng Y, Zhou B, Chen BC, Lei K, Cai S, Jia JM, Gao L. A Versatile Tiling Light Sheet Microscope for Imaging of Cleared Tissues. Cell Rep 2021; 33:108349. [PMID: 33147464 DOI: 10.1016/j.celrep.2020.108349] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2020] [Revised: 08/13/2020] [Accepted: 10/13/2020] [Indexed: 01/14/2023] Open
Abstract
We present a tiling light sheet microscope compatible with all tissue clearing methods for rapid multicolor 3D imaging of cleared tissues with micron-scale (4 × 4 × 10 μm3) to submicron-scale (0.3 × 0.3 × 1 μm3) spatial resolution. The resolving ability is improved to sub-100 nm (70 × 70 × 200 nm3) via tissue expansion. The microscope uses tiling light sheets to achieve higher spatial resolution and better optical sectioning ability than conventional light sheet microscopes. The illumination light is phase modulated to adjust the position and intensity profile of the light sheet based on the desired spatial resolution and imaging speed and to keep the microscope aligned. The ability of the microscope to align via phase modulation alone also ensures its accuracy for multicolor 3D imaging and makes the microscope reliable and easy to operate. Here we describe the working principle and design of the microscope. We demonstrate its utility by imaging various cleared tissues.
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Affiliation(s)
- Yanlu Chen
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Xiaoliang Li
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Dongdong Zhang
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Chunhui Wang
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Ruili Feng
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Xuzhao Li
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Yao Wen
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China
| | - Hao Xu
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Xinyi Shirley Zhang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Xiao Yang
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences, Beijing Institute of Lifeomics, Beijing 102206, China
| | - Yongyi Chen
- Department of Clinical laboratory, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310000, China
| | - Yi Feng
- Department of Integrative Medicine and Neurobiology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Bo Zhou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai 200031, China
| | - Bi-Chang Chen
- Research Center for Applied Sciences, Academia Sinica, Taipei 11529, Taiwan
| | - Kai Lei
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China
| | - Shang Cai
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China.
| | - Jie-Min Jia
- Key Laboratory of Growth Regulation and Translation Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Biology, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China.
| | - Liang Gao
- Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang 310024, China; Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang 310024, China; Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, Zhejiang 310024, China.
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158
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Helseth AR, Hernandez-Martinez R, Hall VL, Oliver ML, Turner BD, Caffall ZF, Rittiner JE, Shipman MK, King CS, Gradinaru V, Gerfen C, Costa-Mattioli M, Calakos N. Cholinergic neurons constitutively engage the ISR for dopamine modulation and skill learning in mice. Science 2021; 372:372/6540/eabe1931. [PMID: 33888613 DOI: 10.1126/science.abe1931] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/22/2020] [Accepted: 03/12/2021] [Indexed: 12/25/2022]
Abstract
The integrated stress response (ISR) maintains proteostasis by modulating protein synthesis and is important in synaptic plasticity, learning, and memory. We developed a reporter, SPOTlight, for brainwide imaging of ISR state with cellular resolution. Unexpectedly, we found a class of neurons in mouse brain, striatal cholinergic interneurons (CINs), in which the ISR was activated at steady state. Genetic and pharmacological manipulations revealed that ISR signaling was necessary in CINs for normal type 2 dopamine receptor (D2R) modulation. Inhibiting the ISR inverted the sign of D2R modulation of CIN firing and evoked dopamine release and altered skill learning. Thus, a noncanonical, steady-state mode of ISR activation is found in CINs, revealing a neuromodulatory role for the ISR in learning.
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Affiliation(s)
- Ashley R Helseth
- Department of Neurology, Duke University Medical Center, Durham, NC 27715, USA.
| | | | - Victoria L Hall
- Department of Neurobiology, Duke University Medical Center, Durham, NC 27715, USA
| | - Matthew L Oliver
- Department of Cell Biology, Duke University Medical Center, Durham, NC 27715, USA
| | - Brandon D Turner
- Department of Neurology, Duke University Medical Center, Durham, NC 27715, USA
| | - Zachary F Caffall
- Department of Neurology, Duke University Medical Center, Durham, NC 27715, USA
| | - Joseph E Rittiner
- Department of Neurology, Duke University Medical Center, Durham, NC 27715, USA
| | - Miranda K Shipman
- Department of Neurology, Duke University Medical Center, Durham, NC 27715, USA
| | - Connor S King
- Department of Neurology, Duke University Medical Center, Durham, NC 27715, USA
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125, USA
| | - Charles Gerfen
- Section on Neuroanatomy, National Institute of Mental Health, Bethesda, MD 20892, USA
| | | | - Nicole Calakos
- Department of Neurology, Duke University Medical Center, Durham, NC 27715, USA. .,Department of Neurobiology, Duke University Medical Center, Durham, NC 27715, USA.,Department of Cell Biology, Duke University Medical Center, Durham, NC 27715, USA.,Duke Institute for Brain Sciences, Duke University, Durham, NC 27715, USA
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159
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Li Q, Zhang Y, Liang H, Gong H, Jiang L, Liu Q, Shen L. Deep learning based neuronal soma detection and counting for Alzheimer's disease analysis. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 203:106023. [PMID: 33744751 DOI: 10.1016/j.cmpb.2021.106023] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Accepted: 02/21/2021] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Alzheimer's Disease (AD) is associated with neuronal damage and decrease. Micro-Optical Sectioning Tomography (MOST) provides an approach to acquire high-resolution images for neuron analysis in the whole-brain. Application of this technique to AD mouse brain enables us to investigate neuron changes during the progression of AD pathology. However, how to deal with the huge amount of data becomes the bottleneck. METHODS Using MOST technology, we acquired 3D whole-brain images of six AD mice, and sampled the imaging data of four regions in each mouse brain for AD progression analysis. To count the number of neurons, we proposed a deep learning based method by detecting neuronal soma in the neuronal images. In our method, the neuronal images were first cut into small cubes, then a Convolutional Neural Network (CNN) classifier was designed to detect the neuronal soma by classifying the cubes into three categories, "soma", "fiber", and "background". RESULTS Compared with the manual method and currently available NeuroGPS software, our method demonstrates faster speed and higher accuracy in identifying neurons from the MOST images. By applying our method to various brain regions of 6-month-old and 12-month-old AD mice, we found that the amount of neurons in three brain regions (lateral entorhinal cortex, medial entorhinal cortex, and presubiculum) decreased slightly with the increase of age, which is consistent with the experimental results previously reported. CONCLUSION This paper provides a new method to automatically handle the huge amounts of data and accurately identify neuronal soma from the MOST images. It also provides the potential possibility to construct a whole-brain neuron projection to reveal the impact of AD pathology on mouse brain.
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Affiliation(s)
- Qiufu Li
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Yu Zhang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China
| | - Hanbang Liang
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China
| | - Hui Gong
- National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan 430074, China
| | - Liang Jiang
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China.
| | - Qiong Liu
- College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, Guangdong, 518055, China; Shenzhen Bay Laboratory, Shenzhen, 518055, China; Shenzhen-Hong Kong Institute of Brain Science-Shenzhen Fundamental Research Institutions, Shenzhen, 518055, China.
| | - Linlin Shen
- Computer Vision Institute, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, Guangdong, 518060, China; AI Research Center for Medical Image Analysis and Diagnosis, Shenzhen University, Shenzhen 518060, China; Guangdong Key Laboratory of Intelligent Information Processing, Shenzhen University, Shenzhen 518060, China.
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160
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谷 沛, 沈 建, 诸 颖, 李 江, 王 丽. [Development in Tissue Clearing Technology and Its Application in Neurodegenerative Diseases]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2021; 52:350-356. [PMID: 34018350 PMCID: PMC10409203 DOI: 10.12182/20210560302] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 12/10/2020] [Indexed: 11/23/2022]
Abstract
Modern tissue clearing techniques have made it possible to have high-resolution imaging of cell populations and three-dimensional reconstruction of tissue structures, and we are able to obtain more complete three-dimensional brain structures and spatial connections between the various components of brain tissues through tissue clearing techniques. Over the past decade, scientists have developed and improved a number of tissue clearing techniques that are now widely used in neuroscience research, allowing us to extract important information from complex neural networks. Moreover, tissue clearing technology also provides research tools for the stem cell therapy and neurogeneration of neurodegenerative diseases. In this paper, we reviewed the major types of existing tissue clearing techniques and their respective strengths and weaknesses. We summarized the application of these techniques in neurodegenerative disease research and their unique merits. In addition, we explored the development requirements of tissue clearing technology, improvements in the supporting equipment, and its potential to be used as research tools for stem cell therapy and regenerative medicine in the future.
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Affiliation(s)
- 沛霖 谷
- 中国科学院上海应用物理研究所 中国科学院界面物理与技术重点实验室 (上海 201800)CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
- 中国科学院大学 (北京 100049)University of Chinese Academy of Sciences, Beijing 100049, China
| | - 建磊 沈
- 中国科学院上海应用物理研究所 中国科学院界面物理与技术重点实验室 (上海 201800)CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - 颖 诸
- 中国科学院上海应用物理研究所 中国科学院界面物理与技术重点实验室 (上海 201800)CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - 江 李
- 中国科学院上海应用物理研究所 中国科学院界面物理与技术重点实验室 (上海 201800)CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
| | - 丽华 王
- 中国科学院上海应用物理研究所 中国科学院界面物理与技术重点实验室 (上海 201800)CAS Key Laboratory of Interfacial Physics and Technology, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201800, China
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161
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Simpson S, Chen Y, Wellmeyer E, Smith LC, Aragon Montes B, George O, Kimbrough A. The Hidden Brain: Uncovering Previously Overlooked Brain Regions by Employing Novel Preclinical Unbiased Network Approaches. Front Syst Neurosci 2021; 15:595507. [PMID: 33967705 PMCID: PMC8097000 DOI: 10.3389/fnsys.2021.595507] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2020] [Accepted: 03/26/2021] [Indexed: 12/18/2022] Open
Abstract
A large focus of modern neuroscience has revolved around preselected brain regions of interest based on prior studies. While there are reasons to focus on brain regions implicated in prior work, the result has been a biased assessment of brain function. Thus, many brain regions that may prove crucial in a wide range of neurobiological problems, including neurodegenerative diseases and neuropsychiatric disorders, have been neglected. Advances in neuroimaging and computational neuroscience have made it possible to make unbiased assessments of whole-brain function and identify previously overlooked regions of the brain. This review will discuss the tools that have been developed to advance neuroscience and network-based computational approaches used to further analyze the interconnectivity of the brain. Furthermore, it will survey examples of neural network approaches that assess connectivity in clinical (i.e., human) and preclinical (i.e., animal model) studies and discuss how preclinical studies of neurodegenerative diseases and neuropsychiatric disorders can greatly benefit from the unbiased nature of whole-brain imaging and network neuroscience.
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Affiliation(s)
- Sierra Simpson
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Yueyi Chen
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States.,Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States
| | - Emma Wellmeyer
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Lauren C Smith
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Brianna Aragon Montes
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Olivier George
- Department of Psychiatry, School of Medicine, University of California, San Diego, San Diego, CA, United States
| | - Adam Kimbrough
- Department of Basic Medical Sciences, College of Veterinary Medicine, Purdue University, West Lafayette, IN, United States.,Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN, United States.,Purdue Institute for Inflammation, Immunology, and Infectious Disease, West Lafayette, IN, United States
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162
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Lee K, Lai HM, Soerensen MH, Hui ES, Ma VW, Cho WC, Ho Y, Chang RC. Optimised tissue clearing minimises distortion and destruction during tissue delipidation. Neuropathol Appl Neurobiol 2021; 47:441-453. [PMID: 33107057 PMCID: PMC8048831 DOI: 10.1111/nan.12673] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 09/04/2020] [Accepted: 10/15/2020] [Indexed: 01/24/2023]
Abstract
AIMS A variety of tissue clearing techniques have been developed to render intact tissue transparent. For thicker samples, additional partial tissue delipidation is required before immersion into the final refractive index (RI)-matching solution, which alone is often inadequate to achieve full tissue transparency. However, it is difficult to determine a sufficient degree of tissue delipidation, excess of which can result in tissue distortion and protein loss. Here, we aim to develop a clearing strategy that allows better monitoring and more precise determination of delipidation progress. METHODS We combined the detergent sodium dodecyl sulphate (SDS) with OPTIClear, a RI-matching solution, to form a strategy termed Accurate delipidation with Optimal Clearing (Accu-OptiClearing). Accu-OptiClearing allows for a better preview of the final tissue transparency achieved when immersed in OPTIClear alone just before imaging. We assessed for the changes in clearing rate, protein loss, degree of tissue distortion, and preservation of antigens. RESULTS Partial delipidation using Accu-OptiClearing accelerated tissue clearing and better preserved tissue structure and antigens than delipidation with SDS alone. Despite achieving similar transparency in the final OPTIClear solution, more lipids were retained in samples cleared with Accu-OptiClearing compared to SDS. CONCLUSIONS Combining the RI-matching solution OPTIClear with detergents, Accu-OptiClearing, can avoid excessive delipidation, leading to accelerated tissue clearing, less tissue damage and better preserved antigens.
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Affiliation(s)
- Krit Lee
- Laboratory of Neurodegenerative DiseasesSchool of Biomedical SciencesLKS Faculty of MedicineThe University of Hong KongPokfulam, Hong Kong SARChina
| | - Hei Ming Lai
- School of Biomedical SciencesLKS Faculty of MedicineThe University of Hong KongPokfulam, Hong Kong SARChina
- Department of PsychiatryFaculty of MedicineThe Chinese University of Hong KongHong Kong SARChina
| | - Maja Hoejvang Soerensen
- Laboratory of Neurodegenerative DiseasesSchool of Biomedical SciencesLKS Faculty of MedicineThe University of Hong KongPokfulam, Hong Kong SARChina
| | - Edward Sai‐Kam Hui
- Department of Diagnostic RadiologyLKS Faculty of MedicineThe University of Hong KongPokfulam, Hong Kong SARChina
| | - Victor Wan‐San Ma
- Department of Clinical OncologyQueen Elizabeth HospitalKowloon, Hong Kong SARChina
| | | | - Yuen‐Shan Ho
- School of NursingThe Hong Kong Polytechnic UniversityHung Hom, Hong Kong SARChina
| | - Raymond Chuen‐Chung Chang
- Laboratory of Neurodegenerative DiseasesSchool of Biomedical SciencesLKS Faculty of MedicineThe University of Hong KongPokfulam, Hong Kong SARChina
- State Key Laboratory of Brain and Cognitive SciencesThe University of Hong KongPokfulam, Hong Kong SARChina
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163
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Zhang N, Li X, Czajkowsky DM, Zhang H, Alam MS, Shao Z. Efficient and Fast Immuno-Labeling of Clarified Tissues Using Low-Field Enhanced Diffusion. IEEE Trans Biomed Eng 2021; 68:3301-3307. [PMID: 33788676 DOI: 10.1109/tbme.2021.3070146] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE To alleviate the severe limitation of the prohibitively long process of immune-fluorescence labeling on the routine applications of revolutionary intact tissue clearing techniques in diverse biomedical arenas. METHODS We proposed an easily adaptable approach, electro-enhanced rapid staining (EERS), for highly efficient and fast immuno-labeling of thick clarified tissues. In EERS, an optimized and precisely controlled weak external electric field is engineered into a compact device to enable efficient and uniform transport of antibodies into clarified tissues while minimizing the detrimental effect of macromolecular crowding at the tissue-solution interface. RESULTS AND CONCLUSIONS The experimental results show that, with EERS, a current density of only ∼0.2 mA mm-2 is sufficient to achieve uniform labeling of clarified tissues of several millimeters thick in a few hours without detectable tissue damage. In addition, the amount of antibodies required is also several-fold lower than conventional immuno-labeling assays under comparable conditions. SIGNIFICANCE It is expected that the implementation of EERS in most laboratories should significantly expedite the application of tissue clearing in a broad range of research explorations, both basic and clinical.
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164
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Lilascharoen V, Wang EHJ, Do N, Pate SC, Tran AN, Yoon CD, Choi JH, Wang XY, Pribiag H, Park YG, Chung K, Lim BK. Divergent pallidal pathways underlying distinct Parkinsonian behavioral deficits. Nat Neurosci 2021; 24:504-515. [PMID: 33723433 PMCID: PMC8907079 DOI: 10.1038/s41593-021-00810-y] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Accepted: 01/26/2021] [Indexed: 01/31/2023]
Abstract
The basal ganglia regulate a wide range of behaviors, including motor control and cognitive functions, and are profoundly affected in Parkinson's disease (PD). However, the functional organization of different basal ganglia nuclei has not been fully elucidated at the circuit level. In this study, we investigated the functional roles of distinct parvalbumin-expressing neuronal populations in the external globus pallidus (GPe-PV) and their contributions to different PD-related behaviors. We demonstrate that substantia nigra pars reticulata (SNr)-projecting GPe-PV neurons and parafascicular thalamus (PF)-projecting GPe-PV neurons are associated with locomotion and reversal learning, respectively. In a mouse model of PD, we found that selective manipulation of the SNr-projecting GPe-PV neurons alleviated locomotor deficit, whereas manipulation of the PF-projecting GPe-PV neurons rescued the impaired reversal learning. Our findings establish the behavioral importance of two distinct GPe-PV neuronal populations and, thereby, provide a new framework for understanding the circuit basis of different behavioral deficits in the Parkinsonian state.
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Affiliation(s)
- Varoth Lilascharoen
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA.,Biological Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.,These authors contributed equally: Varoth Lilascharoen, Eric Hou-Jen Wang
| | - Eric Hou-Jen Wang
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA.,Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.,These authors contributed equally: Varoth Lilascharoen, Eric Hou-Jen Wang
| | - Nam Do
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Stefan Carl Pate
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Amanda Ngoc Tran
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Christopher Dabin Yoon
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Jun-Hyeok Choi
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Xiao-Yun Wang
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Horia Pribiag
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Young-Gyun Park
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge MA, USA
| | - Kwanghun Chung
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge MA, USA
| | - Byung Kook Lim
- Neurobiology Section, Division of Biological Sciences, University of California, San Diego, La Jolla, CA, USA.,Biological Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Biomedical Sciences Graduate Program, University of California, San Diego, La Jolla, CA, USA.,Correspondence and requests for materials should be addressed to B.K.L.,
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165
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Abstract
Advanced optical methods combined with various probes pave the way toward molecular imaging within living cells. However, major challenges are associated with the need to enhance the imaging resolution even further to the subcellular level for the imaging of larger tissues, as well as for in vivo studies. High scattering and absorption of opaque tissues limit the penetration of light into deep tissues and thus the optical imaging depth. Tissue optical clearing technique provides an innovative way to perform deep-tissue imaging. Recently, various optical clearing methods have been developed, which provide tissue clearing based on similar physical principles via different chemical approaches. Here, we introduce the mechanisms of the current clearing methods from fundamental physical and chemical perspectives, including the main physical principle, refractive index matching via various chemical approaches, such as dissociation of collagen, delipidation, decalcification, dehydration, and hyperhydration, to reduce scattering, as well as decolorization to reduce absorption.
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Affiliation(s)
- Tingting Yu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Jingtan Zhu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Dongyu Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
| | - Dan Zhu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
- MoE Key Laboratory for Biomedical Photonics, School of Engineering Sciences, Huazhong University of Science and Technology, Wuhan, Hubei 430074, China
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166
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Avilov SV. Navigating across multi-dimensional space of tissue clearing parameters. Methods Appl Fluoresc 2021; 9:022001. [PMID: 33592593 DOI: 10.1088/2050-6120/abe6fb] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Optical tissue clearing refers to physico-chemical treatments which make thick biological samples transparent by removal of refractive index gradients and light absorbing substances. Although tissue clearing was first reported in 1914, it was not widely used in light microscopy until 21th century, because instrumentation of that time did not permit to acquire and handle images of thick (mm to cm) samples as whole. Rapid progress in optical instrumentation, computers and software over the last decades made micrograph acquisition of centimeter-thick samples feasible. This boosted tissue clearing use and development. Numerous diverse protocols have been developed. They use organic solvents or water-miscible substances, such as detergents and chaotropic agents; some protocols require application of electric field or perfusion with special devices. There is no 'best-for-all' tissue clearing method. Depending on the case, one or another protocol is more suitable. Most of protocols require days or even weeks to complete, thus choosing an unsuitable protocol may cause an important waste of time. Several inter-dependent parameters should be taken into account to choose a tissue clearing protocol, such as: (1) required image quality (resolution, contrast, signal to noise ratio etc), (2) nature and size of the sample, (3) type of labels, (4) characteristics of the available instrumentation, (5) budget, (6) time budget, and (7) feasibility. Present review focusses on the practical aspects of various tissue clearing techniques. It is aimed to help non-experts to choose tissue clearing techniques which are optimal for their particular cases.
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Affiliation(s)
- Sergiy V Avilov
- Max Planck Institute of Immunobiology and Epigenetics, Freiburg, Germany
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167
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He L, Pu W, Liu X, Zhang Z, Han M, Li Y, Huang X, Han X, Li Y, Liu K, Shi M, Lai L, Sun R, Wang QD, Ji Y, Tchorz JS, Zhou B. Proliferation tracing reveals regional hepatocyte generation in liver homeostasis and repair. Science 2021; 371:371/6532/eabc4346. [PMID: 33632818 DOI: 10.1126/science.abc4346] [Citation(s) in RCA: 110] [Impact Index Per Article: 36.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2020] [Accepted: 11/30/2020] [Indexed: 12/11/2022]
Abstract
Organ homeostasis is orchestrated by time- and spatially restricted cell proliferation. Studies identifying cells with superior proliferative capacities often rely on the lineage tracing of a subset of cell populations, which introduces a potential selective bias. In this work, we developed a genetic system [proliferation tracer (ProTracer)] by incorporating dual recombinases to seamlessly record the proliferation events of entire cell populations over time in multiple organs. In the mouse liver, ProTracer revealed more hepatocyte proliferation in distinct zones during liver homeostasis, injury repair, and regrowth. Clonal analysis showed that most of the hepatocytes labeled by ProTracer had undergone cell division. By genetically recording proliferation events of entire cell populations, ProTracer enables the unbiased detection of specific cellular compartments with enhanced regenerative capacities.
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Affiliation(s)
- Lingjuan He
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Wenjuan Pu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Xiuxiu Liu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Zhenqian Zhang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Maoying Han
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China.,School of Life Science and Technology, ShanghaiTech University, Shanghai, China
| | - Yi Li
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Xiuzhen Huang
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Ximeng Han
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Yan Li
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Kuo Liu
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Mengyang Shi
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China
| | - Liang Lai
- Shanghai Model Organisms Center, Inc., Shanghai, China
| | - Ruilin Sun
- Shanghai Model Organisms Center, Inc., Shanghai, China
| | - Qing-Dong Wang
- Bioscience Cardiovascular, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Gothenburg, Sweden
| | - Yong Ji
- The Collaborative Innovation Center for Cardiovascular Disease Translational Medicine, Nanjing Medical University, Nanjing, China
| | - Jan S Tchorz
- Novartis Institutes for BioMedical Research, Novartis Pharma AG, Basel, Switzerland
| | - Bin Zhou
- State Key Laboratory of Cell Biology, Shanghai Institute of Biochemistry and Cell Biology, Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China. .,School of Life Science and Technology, ShanghaiTech University, Shanghai, China.,School of Life Science, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China.,Institute for Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China
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168
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Liu JTC, Glaser AK, Bera K, True LD, Reder NP, Eliceiri KW, Madabhushi A. Harnessing non-destructive 3D pathology. Nat Biomed Eng 2021; 5:203-218. [PMID: 33589781 PMCID: PMC8118147 DOI: 10.1038/s41551-020-00681-x] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 12/17/2020] [Indexed: 02/08/2023]
Abstract
High-throughput methods for slide-free three-dimensional (3D) pathological analyses of whole biopsies and surgical specimens offer the promise of modernizing traditional histology workflows and delivering improvements in diagnostic performance. Advanced optical methods now enable the interrogation of orders of magnitude more tissue than previously possible, where volumetric imaging allows for enhanced quantitative analyses of cell distributions and tissue structures that are prognostic and predictive. Non-destructive imaging processes can simplify laboratory workflows, potentially reducing costs, and can ensure that samples are available for subsequent molecular assays. However, the large size of the feature-rich datasets that they generate poses challenges for data management and computer-aided analysis. In this Perspective, we provide an overview of the imaging technologies that enable 3D pathology, and the computational tools-machine learning, in particular-for image processing and interpretation. We also discuss the integration of various other diagnostic modalities with 3D pathology, along with the challenges and opportunities for clinical adoption and regulatory approval.
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Affiliation(s)
- Jonathan T C Liu
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA.
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA.
- Department of Bioengineering, University of Washington, Seattle, WA, USA.
| | - Adam K Glaser
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Lawrence D True
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Nicholas P Reder
- Department of Mechanical Engineering, University of Washington, Seattle, WA, USA
- Department of Laboratory Medicine and Pathology, University of Washington, Seattle, WA, USA
| | - Kevin W Eliceiri
- Department of Medical Physics, University of Wisconsin, Madison, WI, USA.
- Department of Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
- Morgridge Institute for Research, Madison, WI, USA.
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, USA.
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169
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Leuze C, Goubran M, Barakovic M, Aswendt M, Tian Q, Hsueh B, Crow A, Weber EMM, Steinberg GK, Zeineh M, Plowey ED, Daducci A, Innocenti G, Thiran JP, Deisseroth K, McNab JA. Comparison of diffusion MRI and CLARITY fiber orientation estimates in both gray and white matter regions of human and primate brain. Neuroimage 2021; 228:117692. [PMID: 33385546 PMCID: PMC7953593 DOI: 10.1016/j.neuroimage.2020.117692] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/16/2020] [Accepted: 12/18/2020] [Indexed: 11/30/2022] Open
Abstract
Diffusion MRI (dMRI) represents one of the few methods for mapping brain fiber orientations non-invasively. Unfortunately, dMRI fiber mapping is an indirect method that relies on inference from measured diffusion patterns. Comparing dMRI results with other modalities is a way to improve the interpretation of dMRI data and help advance dMRI technologies. Here, we present methods for comparing dMRI fiber orientation estimates with optical imaging of fluorescently labeled neurofilaments and vasculature in 3D human and primate brain tissue cuboids cleared using CLARITY. The recent advancements in tissue clearing provide a new opportunity to histologically map fibers projecting in 3D, which represents a captivating complement to dMRI measurements. In this work, we demonstrate the capability to directly compare dMRI and CLARITY in the same human brain tissue and assess multiple approaches for extracting fiber orientation estimates from CLARITY data. We estimate the three-dimensional neuronal fiber and vasculature orientations from neurofilament and vasculature stained CLARITY images by calculating the tertiary eigenvector of structure tensors. We then extend CLARITY orientation estimates to an orientation distribution function (ODF) formalism by summing multiple sub-voxel structure tensor orientation estimates. In a sample containing part of the human thalamus, there is a mean angular difference of 19o±15o between the primary eigenvectors of the dMRI tensors and the tertiary eigenvectors from the CLARITY neurofilament stain. We also demonstrate evidence that vascular compartments do not affect the dMRI orientation estimates by showing an apparent lack of correspondence (mean angular difference = 49o±23o) between the orientation of the dMRI tensors and the structure tensors in the vasculature stained CLARITY images. In a macaque brain dataset, we examine how the CLARITY feature extraction depends on the chosen feature extraction parameters. By varying the volume of tissue over which the structure tensor estimates are derived, we show that orientation estimates are noisier with more spurious ODF peaks for sub-voxels below 30 µm3 and that, for our data, the optimal gray matter sub-voxel size is between 62.5 µm3 and 125 µm3. The example experiments presented here represent an important advancement towards robust multi-modal MRI-CLARITY comparisons.
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Affiliation(s)
- C Leuze
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - M Goubran
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - M Barakovic
- Department of Radiology, Stanford University, Stanford, CA, USA; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - M Aswendt
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Q Tian
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - B Hsueh
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - A Crow
- Department of Bioengineering, Stanford University, Stanford, CA, USA
| | - E M M Weber
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - G K Steinberg
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - M Zeineh
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - E D Plowey
- Department of Pathology, Stanford University, Stanford, CA, USA
| | - A Daducci
- Department of Computer Science, University of Verona, Verona, Italy
| | - G Innocenti
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Brain and Mind Institute, Ecole Polytechnique Federale de Lausanne, Lausanne, Switzerland
| | - J-P Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - K Deisseroth
- Department of Bioengineering, Stanford University, Stanford, CA, USA; Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | - J A McNab
- Department of Radiology, Stanford University, Stanford, CA, USA
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170
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Molbay M, Kolabas ZI, Todorov MI, Ohn T, Ertürk A. A guidebook for DISCO tissue clearing. Mol Syst Biol 2021; 17:e9807. [PMID: 33769689 PMCID: PMC7995442 DOI: 10.15252/msb.20209807] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2020] [Revised: 12/29/2020] [Accepted: 01/14/2021] [Indexed: 12/14/2022] Open
Abstract
Histological analysis of biological tissues by mechanical sectioning is significantly time-consuming and error-prone due to loss of important information during sample slicing. In the recent years, the development of tissue clearing methods overcame several of these limitations and allowed exploring intact biological specimens by rendering tissues transparent and subsequently imaging them by laser scanning fluorescence microscopy. In this review, we provide a guide for scientists who would like to perform a clearing protocol from scratch without any prior knowledge, with an emphasis on DISCO clearing protocols, which have been widely used not only due to their robustness, but also owing to their relatively straightforward application. We discuss diverse tissue-clearing options and propose solutions for several possible pitfalls. Moreover, after surveying more than 30 researchers that employ tissue clearing techniques in their laboratories, we compiled the most frequently encountered issues and propose solutions. Overall, this review offers an informative and detailed guide through the growing literature of tissue clearing and can help with finding the easiest way for hands-on implementation.
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Affiliation(s)
- Muge Molbay
- Institute for Tissue Engineering and Regenerative Medicine (iTERM)Helmholtz CenterNeuherberg, MunichGermany
- Institute for Stroke and Dementia ResearchKlinikum der Universität MünchenLudwig‐Maximilians‐University MunichMunichGermany
- Munich Medical Research School (MMRS)MunichGermany
| | - Zeynep Ilgin Kolabas
- Institute for Tissue Engineering and Regenerative Medicine (iTERM)Helmholtz CenterNeuherberg, MunichGermany
- Institute for Stroke and Dementia ResearchKlinikum der Universität MünchenLudwig‐Maximilians‐University MunichMunichGermany
- Graduate School for Systemic Neurosciences (GSN)MunichGermany
| | - Mihail Ivilinov Todorov
- Institute for Tissue Engineering and Regenerative Medicine (iTERM)Helmholtz CenterNeuherberg, MunichGermany
- Institute for Stroke and Dementia ResearchKlinikum der Universität MünchenLudwig‐Maximilians‐University MunichMunichGermany
- Graduate School for Systemic Neurosciences (GSN)MunichGermany
| | - Tzu‐Lun Ohn
- Institute for Tissue Engineering and Regenerative Medicine (iTERM)Helmholtz CenterNeuherberg, MunichGermany
- Institute for Stroke and Dementia ResearchKlinikum der Universität MünchenLudwig‐Maximilians‐University MunichMunichGermany
| | - Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine (iTERM)Helmholtz CenterNeuherberg, MunichGermany
- Institute for Stroke and Dementia ResearchKlinikum der Universität MünchenLudwig‐Maximilians‐University MunichMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
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171
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Tian T, Yang Z, Li X. Tissue clearing technique: Recent progress and biomedical applications. J Anat 2021; 238:489-507. [PMID: 32939792 PMCID: PMC7812135 DOI: 10.1111/joa.13309] [Citation(s) in RCA: 61] [Impact Index Per Article: 20.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2020] [Revised: 04/19/2020] [Accepted: 08/24/2020] [Indexed: 02/03/2023] Open
Abstract
Organisms are inherently three dimensional, thus comprehensive understanding of the complicated biological system requires analysis of organs or even whole bodies in the context of three dimensions. However, this is a tremendous task since the biological specimens are naturally opaque, a major obstacle in whole-body and whole-organ imaging. Tissue clearing technique provides a prospective solution and has become a powerful tool for three-dimensional imaging and quantification of organisms. Tissue clearing technique aims to make tissue transparent by minimizing light scattering and light absorption, thus allowing deep imaging of large volume samples. When combined with diverse molecular labeling methods and high-throughput optical sectioning microscopes, tissue clearing technique enables whole-body and whole-organ imaging at cellular or subcellular resolution, providing detailed and comprehensive information about the intact biological systems. Here, we give an overview of recent progress and biomedical applications of tissue clearing technique. We introduce the mechanisms and basic principles of tissue clearing, and summarize the current tissue clearing methods. Moreover, the available imaging techniques and software packages for data processing are also presented. Finally, we introduce the recent advances in applications of tissue clearing in biomedical fields. Tissue clearing contributes to the investigation of structure-function relationships in intact mammalian organs, and opens new avenues for cellular and molecular mapping of intact human organs. We hope this review contributes to a better understanding of tissue clearing technique and can help researchers to select the best-suited clearing protocol for their experiments.
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Affiliation(s)
- Ting Tian
- Beijing Key Laboratory for Biomaterials and Neural RegenerationSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina
| | - Zhaoyang Yang
- Department of NeurobiologySchool of Basic Medical SciencesCapital Medical UniversityBeijingChina,Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural RegenerationBeijing Advanced Innovation Center for Biomedical EngineeringBeihang UniversityBeijingChina
| | - Xiaoguang Li
- Beijing Key Laboratory for Biomaterials and Neural RegenerationSchool of Biological Science and Medical EngineeringBeihang UniversityBeijingChina,Department of NeurobiologySchool of Basic Medical SciencesCapital Medical UniversityBeijingChina,Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural RegenerationBeijing Advanced Innovation Center for Biomedical EngineeringBeihang UniversityBeijingChina
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172
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Zhao J, Lai HM, Qi Y, He D, Sun H. Current Status of Tissue Clearing and the Path Forward in Neuroscience. ACS Chem Neurosci 2021; 12:5-29. [PMID: 33326739 DOI: 10.1021/acschemneuro.0c00563] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
Due to the complexity and limited availability of human brain tissues, for decades, pathologists have sought to maximize information gained from individual samples, based on which (patho)physiological processes could be inferred. Recently, new understandings of chemical and physical properties of biological tissues and multiple chemical profiling have given rise to the development of scalable tissue clearing methods allowing superior optical clearing of across-the-scale samples. In the past decade, tissue clearing techniques, molecular labeling methods, advanced laser scanning microscopes, and data visualization and analysis have become commonplace. Combined, they have made 3D visualization of brain tissues with unprecedented resolution and depth widely accessible. To facilitate further advancements and applications, here we provide a critical appraisal of these techniques. We propose a classification system of current tissue clearing and expansion methods that allows users to judge the applicability of individual ones to their questions, followed by a review of the current progress in molecular labeling, optical imaging, and data processing to demonstrate the whole 3D imaging pipeline based on tissue clearing and downstream techniques for visualizing the brain. We also raise the path forward of tissue-clearing-based imaging technology, that is, integrating with state-of-the-art techniques, such as multiplexing protein imaging, in situ signal amplification, RNA detection and sequencing, super-resolution imaging techniques, multiomics studies, and deep learning, for drawing the complete atlas of the human brain and building a 3D pathology platform for central nervous system disorders.
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Affiliation(s)
- Jiajia Zhao
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
| | - Hei Ming Lai
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Yuwei Qi
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
| | - Dian He
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
| | - Haitao Sun
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
- Microbiome Medicine Center, Department of Laboratory Medicine, Clinical Biobank Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
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173
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Kumar V, Krolewski DM, Hebda-Bauer EK, Parsegian A, Martin B, Foltz M, Akil H, Watson SJ. Optimization and evaluation of fluorescence in situ hybridization chain reaction in cleared fresh-frozen brain tissues. Brain Struct Funct 2021; 226:481-499. [PMID: 33386994 DOI: 10.1007/s00429-020-02194-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2019] [Accepted: 11/27/2020] [Indexed: 11/27/2022]
Abstract
Transcript labeling in intact tissues using in situ hybridization chain reaction has potential to provide vital spatiotemporal information for molecular characterization of heterogeneous neuronal populations. However, large tissue labeling in non-perfused or fresh-frozen rodent and postmortem human samples, which provide more flexible utilization than perfused tissues, is largely unexplored. In the present study, we optimized the combination of in situ hybridization chain reaction in fresh-frozen rodent brains and then evaluated the uniformity of neuronal labeling between two clearing methods, CLARITY and iDISCO+. We found that CLARITY yielded higher signal-to-noise ratios but more limited imaging depth and required longer clearing times, whereas, iDISCO+ resulted in better tissue clearing, greater imaging depth and a more uniform labeling of larger samples. Based on these results, we used iDISCO+-cleared fresh-frozen rodent brains to further validate this combination and map the expression of a few genes of interest pertaining to mood disorders. We then examined the potential of in situ hybridization chain reaction to label transcripts in cleared postmortem human brain tissues. The combination failed to produce adequate mRNA labeling in postmortem human cortical slices but produced visually adequate labeling in the cerebellum tissues. We next, investigated the multiplexing ability of in situ hybridization chain reaction in cleared tissues which revealed inconsistent fluorescence output depending upon the fluorophore conjugated to the hairpins. Finally, we applied our optimized protocol to assess the effect of glucocorticoid receptor overexpression on basal somatostatin expression in the mouse cortex. The constitutive glucocorticoid receptor overexpression resulted in lower number density of somatostatin-expressing neurons compared to wild type. Overall, the combination of in situ hybridization chain reaction with clearing methods, especially iDISCO+, may find broad application in the transcript analysis in rodent studies, but its limited use in postmortem human tissues can be improved by further optimizations.
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Affiliation(s)
- Vivek Kumar
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA.
| | - David M Krolewski
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA
| | - Elaine K Hebda-Bauer
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA
| | - Aram Parsegian
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA
| | - Brian Martin
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA
| | - Matthew Foltz
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA
| | - Huda Akil
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA
| | - Stanley J Watson
- Michigan Neuroscience Institute, University of Michigan, 205 Zina Pitcher pl, Ann Arbor, MI, 48109, USA
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174
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Light-Sheet Fluorescence Microscopy for Multiscale Biological Imaging. Mol Imaging 2021. [DOI: 10.1016/b978-0-12-816386-3.00026-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
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175
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Liang X, Luo H. Optical Tissue Clearing: Illuminating Brain Function and Dysfunction. Theranostics 2021; 11:3035-3051. [PMID: 33537072 PMCID: PMC7847687 DOI: 10.7150/thno.53979] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 12/08/2020] [Indexed: 12/15/2022] Open
Abstract
Tissue optical clearing technology has been developing rapidly in the past decade due to advances in microscopy equipment and various labeling techniques. Consistent modification of primary methods for optical tissue transparency has allowed observation of the whole mouse body at single-cell resolution or thick tissue slices at the nanoscale level, with the final aim to make intact primate and human brains or thick human brain tissues optically transparent. Optical clearance combined with flexible large-volume tissue labeling technology can not only preserve the anatomical structure but also visualize multiple molecular information from intact samples in situ. It also provides a new strategy for studying complex tissues, which is of great significance for deciphering the functional structure of healthy brains and the mechanisms of neurological pathologies. In this review, we briefly introduce the existing optical clearing technology and discuss its application in deciphering connection and structure, brain development, and brain diseases. Besides, we discuss the standard computational analysis tools for large-scale imaging dataset processing and information extraction. In general, we hope that this review will provide a valuable reference for researchers who intend to use optical clearing technology in studying the brain.
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Affiliation(s)
- Xiaohan Liang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
| | - Haiming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
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176
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Bharat A, Querrey M, Markov NS, Kim S, Kurihara C, Garza-Castillon R, Manerikar A, Shilatifard A, Tomic R, Politanska Y, Abdala-Valencia H, Yeldandi AV, Lomasney JW, Misharin AV, Budinger GRS. Lung transplantation for patients with severe COVID-19. Sci Transl Med 2020; 12:eabe4282. [PMID: 33257409 PMCID: PMC8050952 DOI: 10.1126/scitranslmed.abe4282] [Citation(s) in RCA: 228] [Impact Index Per Article: 57.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 11/23/2020] [Indexed: 12/15/2022]
Abstract
Lung transplantation can potentially be a life-saving treatment for patients with nonresolving COVID-19-associated respiratory failure. Concerns limiting lung transplantation include recurrence of SARS-CoV-2 infection in the allograft, technical challenges imposed by viral-mediated injury to the native lung, and the potential risk for allograft infection by pathogens causing ventilator-associated pneumonia in the native lung. Additionally, the native lung might recover, resulting in long-term outcomes preferable to those of transplant. Here, we report the results of lung transplantation in three patients with nonresolving COVID-19-associated respiratory failure. We performed single-molecule fluorescence in situ hybridization (smFISH) to detect both positive and negative strands of SARS-CoV-2 RNA in explanted lung tissue from the three patients and in additional control lung tissue samples. We conducted extracellular matrix imaging and single-cell RNA sequencing on explanted lung tissue from the three patients who underwent transplantation and on warm postmortem lung biopsies from two patients who had died from COVID-19-associated pneumonia. Lungs from these five patients with prolonged COVID-19 disease were free of SARS-CoV-2 as detected by smFISH, but pathology showed extensive evidence of injury and fibrosis that resembled end-stage pulmonary fibrosis. Using machine learning, we compared single-cell RNA sequencing data from the lungs of patients with late-stage COVID-19 to that from the lungs of patients with pulmonary fibrosis and identified similarities in gene expression across cell lineages. Our findings suggest that some patients with severe COVID-19 develop fibrotic lung disease for which lung transplantation is their only option for survival.
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Affiliation(s)
- Ankit Bharat
- Division of Thoracic Surgery, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
| | - Melissa Querrey
- Division of Thoracic Surgery, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Nikolay S Markov
- Division of Pulmonary and Critical Care Medicine, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Samuel Kim
- Division of Thoracic Surgery, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Chitaru Kurihara
- Division of Thoracic Surgery, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Rafael Garza-Castillon
- Division of Thoracic Surgery, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Adwaiy Manerikar
- Division of Thoracic Surgery, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Ali Shilatifard
- Department of Biochemistry and Molecular Genetics, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Rade Tomic
- Division of Pulmonary and Critical Care Medicine, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Yuliya Politanska
- Division of Pulmonary and Critical Care Medicine, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Hiam Abdala-Valencia
- Division of Pulmonary and Critical Care Medicine, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Anjana V Yeldandi
- Department of Pathology, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Jon W Lomasney
- Department of Pathology, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Alexander V Misharin
- Division of Pulmonary and Critical Care Medicine, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - G R Scott Budinger
- Division of Pulmonary and Critical Care Medicine, Northwestern Memorial Hospital, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
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177
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Albanese A, Swaney JM, Yun DH, Evans NB, Antonucci JM, Velasco S, Sohn CH, Arlotta P, Gehrke L, Chung K. Multiscale 3D phenotyping of human cerebral organoids. Sci Rep 2020; 10:21487. [PMID: 33293587 PMCID: PMC7723053 DOI: 10.1038/s41598-020-78130-7] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Accepted: 10/27/2020] [Indexed: 01/28/2023] Open
Abstract
Brain organoids grown from human pluripotent stem cells self-organize into cytoarchitectures resembling the developing human brain. These three-dimensional models offer an unprecedented opportunity to study human brain development and dysfunction. Characterization currently sacrifices spatial information for single-cell or histological analysis leaving whole-tissue analysis mostly unexplored. Here, we present the SCOUT pipeline for automated multiscale comparative analysis of intact cerebral organoids. Our integrated technology platform can rapidly clear, label, and image intact organoids. Algorithmic- and convolutional neural network-based image analysis extract hundreds of features characterizing molecular, cellular, spatial, cytoarchitectural, and organoid-wide properties from fluorescence microscopy datasets. Comprehensive analysis of 46 intact organoids and ~ 100 million cells reveals quantitative multiscale "phenotypes" for organoid development, culture protocols and Zika virus infection. SCOUT provides a much-needed framework for comparative analysis of emerging 3D in vitro models using fluorescence microscopy.
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Affiliation(s)
- Alexandre Albanese
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | | | - Dae Hee Yun
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA
| | - Nicholas B Evans
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | - Jenna M Antonucci
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
| | - Silvia Velasco
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Chang Ho Sohn
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | - Paola Arlotta
- Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Lee Gehrke
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA
- Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA, 02115, USA
- Harvard-MIT Program in Health Sciences and Technology, Cambridge, MA, 02139, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, MIT, Cambridge, MA, USA.
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
- Department of Chemical Engineering, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea.
- Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea.
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178
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Franceschini A, Costantini I, Pavone FS, Silvestri L. Dissecting Neuronal Activation on a Brain-Wide Scale With Immediate Early Genes. Front Neurosci 2020; 14:569517. [PMID: 33192255 PMCID: PMC7645181 DOI: 10.3389/fnins.2020.569517] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Accepted: 09/28/2020] [Indexed: 11/13/2022] Open
Abstract
Visualizing neuronal activation on a brain-wide scale yet with cellular resolution is a fundamental technical challenge for neuroscience. This would enable analyzing how different neuronal circuits are disrupted in pathology and how they could be rescued by pharmacological treatments. Although this goal would have appeared visionary a decade ago, recent technological advances make it eventually feasible. Here, we review the latest developments in the fields of genetics, sample preparation, imaging, and image analysis that could be combined to afford whole-brain cell-resolution activation mapping. We show how the different biochemical and optical methods have been coupled to study neuronal circuits at different spatial and temporal scales, and with cell-type specificity. The inventory of techniques presented here could be useful to find the tools best suited for a specific experiment. We envision that in the next years, mapping of neuronal activation could become routine in many laboratories, allowing dissecting the neuronal counterpart of behavior.
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Affiliation(s)
| | - Irene Costantini
- European Laboratory for Non-linear Spectroscopy (LENS), Sesto Fiorentino, Italy.,National Institute of Optics, National Research Council (INO-CNR), Sesto Fiorentino, Italy
| | - Francesco S Pavone
- European Laboratory for Non-linear Spectroscopy (LENS), Sesto Fiorentino, Italy.,National Institute of Optics, National Research Council (INO-CNR), Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
| | - Ludovico Silvestri
- European Laboratory for Non-linear Spectroscopy (LENS), Sesto Fiorentino, Italy.,National Institute of Optics, National Research Council (INO-CNR), Sesto Fiorentino, Italy.,Department of Physics and Astronomy, University of Florence, Florence, Italy
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179
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Abstract
Neurotransmitter switching is a form of brain plasticity in which an environmental stimulus causes neurons to replace one neurotransmitter with another, often resulting in changes in behavior. This raises the possibility of applying a specific environmental stimulus to induce a switch that can enhance a desirable behavior or ameliorate symptoms of a specific pathology. For example, a stimulus inducing an increase in the number of neurons expressing dopamine could treat Parkinson's disease, or one affecting the number expressing serotonin could alleviate depression. This may already be producing successful treatment outcomes without our knowing that transmitter switching is involved, with improvement of motor function through physical activity and cure of seasonal depression with phototherapy. This review presents prospects for future investigation of neurotransmitter switching, considering opportunities and challenges for future research and describing how the investigation of transmitter switching is likely to evolve with new tools, thus reshaping our understanding of both normal brain function and mental illness.
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180
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Parra-Damas A, Saura CA. Tissue Clearing and Expansion Methods for Imaging Brain Pathology in Neurodegeneration: From Circuits to Synapses and Beyond. Front Neurosci 2020; 14:914. [PMID: 33122983 PMCID: PMC7571329 DOI: 10.3389/fnins.2020.00914] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2020] [Accepted: 08/07/2020] [Indexed: 11/30/2022] Open
Abstract
Studying the structural alterations occurring during diseases of the nervous system requires imaging heterogeneous cell populations at the circuit, cellular and subcellular levels. Recent advancements in brain tissue clearing and expansion methods allow unprecedented detailed imaging of the nervous system through its entire scale, from circuits to synapses, including neurovascular and brain lymphatics elements. Here, we review the state-of-the-art of brain tissue clearing and expansion methods, mentioning their main advantages and limitations, and suggest their parallel implementation for circuits-to-synapses brain imaging using conventional (diffraction-limited) light microscopy -such as confocal, two-photon and light-sheet microscopy- to interrogate the cellular and molecular basis of neurodegenerative diseases. We discuss recent studies in which clearing and expansion methods have been successfully applied to study neuropathological processes in mouse models and postmortem human brain tissue. Volumetric imaging of cleared intact mouse brains and large human brain samples has allowed unbiased assessment of neuropathological hallmarks. In contrast, nanoscale imaging of expanded cells and brain tissue has been used to study the effect of protein aggregates on specific subcellular structures. Therefore, these approaches can be readily applied to study a wide range of brain processes and pathological mechanisms with cellular and subcellular resolution in a time- and cost-efficient manner. We consider that a broader implementation of these technologies is necessary to reveal the full landscape of cellular and molecular mechanisms underlying neurodegenerative diseases.
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Affiliation(s)
- Arnaldo Parra-Damas
- Institut de Neurociències, Departament de Bioquímica i Biologia Molecular, Facultat de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain
| | - Carlos A Saura
- Institut de Neurociències, Departament de Bioquímica i Biologia Molecular, Facultat de Medicina, Universitat Autònoma de Barcelona, Barcelona, Spain.,Centro de Investigación Biomédica en Red Enfermedades Neurodegenerativas, Instituto de Salud Carlos III, Madrid, Spain
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181
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Carrier M, Robert MÈ, González Ibáñez F, Desjardins M, Tremblay MÈ. Imaging the Neuroimmune Dynamics Across Space and Time. Front Neurosci 2020; 14:903. [PMID: 33071723 PMCID: PMC7539119 DOI: 10.3389/fnins.2020.00903] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 08/04/2020] [Indexed: 12/13/2022] Open
Abstract
The immune system is essential for maintaining homeostasis, as well as promoting growth and healing throughout the brain and body. Considering that immune cells respond rapidly to changes in their microenvironment, they are very difficult to study without affecting their structure and function. The advancement of non-invasive imaging methods greatly contributed to elucidating the physiological roles performed by immune cells in the brain across stages of the lifespan and contexts of health and disease. For instance, techniques like two-photon in vivo microscopy were pivotal for studying microglial functional dynamics in the healthy brain. Through these observations, their interactions with neurons, astrocytes, blood vessels and synapses were uncovered. High-resolution electron microscopy with immunostaining and 3D-reconstruction, as well as super-resolution fluorescence microscopy, provided complementary insights by revealing microglial interventions at synapses (phagocytosis, trogocytosis, synaptic stripping, etc.). In addition, serial block-face scanning electron microscopy has provided the first 3D reconstruction of a microglial cell at nanoscale resolution. This review will discuss the technical toolbox that currently allows to study microglia and other immune cells in the brain, as well as introduce emerging methods that were developed and could be used to increase the spatial and temporal resolution of neuroimmune imaging. A special attention will also be placed on positron emission tomography and the development of selective functional radiotracers for microglia and peripheral macrophages, considering their strong potential for research translation between animals and humans, notably when paired with other imaging modalities such as magnetic resonance imaging.
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Affiliation(s)
- Micaël Carrier
- Axe Neurosciences, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
| | - Marie-Ève Robert
- Axe Neurosciences, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
| | - Fernando González Ibáñez
- Axe Neurosciences, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
| | - Michèle Desjardins
- Axe Oncologie, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
- Department of Physics, Physical Engineering and Optics, Université Laval, Québec City, QC, Canada
| | - Marie-Ève Tremblay
- Axe Neurosciences, Centre de Recherche du CHU de Québec, Université Laval, Québec City, QC, Canada
- Department of Molecular Medicine, Université Laval, Québec City, QC, Canada
- Division of Medical Sciences, University of Victoria, Victoria, BC, Canada
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182
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Shen FY, Harrington MM, Walker LA, Cheng HPJ, Boyden ES, Cai D. Light microscopy based approach for mapping connectivity with molecular specificity. Nat Commun 2020; 11:4632. [PMID: 32934230 PMCID: PMC7493953 DOI: 10.1038/s41467-020-18422-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 08/21/2020] [Indexed: 11/28/2022] Open
Abstract
Mapping neuroanatomy is a foundational goal towards understanding brain function. Electron microscopy (EM) has been the gold standard for connectivity analysis because nanoscale resolution is necessary to unambiguously resolve synapses. However, molecular information that specifies cell types is often lost in EM reconstructions. To address this, we devise a light microscopy approach for connectivity analysis of defined cell types called spectral connectomics. We combine multicolor labeling (Brainbow) of neurons with multi-round immunostaining Expansion Microscopy (miriEx) to simultaneously interrogate morphology, molecular markers, and connectivity in the same brain section. We apply this strategy to directly link inhibitory neuron cell types with their morphologies. Furthermore, we show that correlative Brainbow and endogenous synaptic machinery immunostaining can define putative synaptic connections between neurons, as well as map putative inhibitory and excitatory inputs. We envision that spectral connectomics can be applied routinely in neurobiology labs to gain insights into normal and pathophysiological neuroanatomy.
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Affiliation(s)
- Fred Y Shen
- Medical Scientist Training Program, University of Michigan, Ann Arbor, MI, USA
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA
| | - Margaret M Harrington
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Logan A Walker
- LS & A, Program in Biophysics, University of Michigan, Ann Arbor, MI, USA
| | - Hon Pong Jimmy Cheng
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA
| | - Edward S Boyden
- McGovern Institute, Koch Institute, Department of Media Arts and Sciences, Department of Biological Engineering, and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Howard Hughes Medical Institute, Cambridge, MA, USA
| | - Dawen Cai
- Neuroscience Graduate Program, University of Michigan, Ann Arbor, MI, USA.
- Department of Cell and Developmental Biology, University of Michigan, Ann Arbor, MI, USA.
- LS & A, Program in Biophysics, University of Michigan, Ann Arbor, MI, USA.
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183
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Tian T, Li X. Applications of tissue clearing in the spinal cord. Eur J Neurosci 2020; 52:4019-4036. [DOI: 10.1111/ejn.14938] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2020] [Revised: 07/22/2020] [Accepted: 08/03/2020] [Indexed: 12/12/2022]
Affiliation(s)
- Ting Tian
- Beijing Key Laboratory for Biomaterials and Neural Regeneration School of Biological Science and Medical Engineering Beihang University Beijing China
| | - Xiaoguang Li
- Beijing Key Laboratory for Biomaterials and Neural Regeneration School of Biological Science and Medical Engineering Beihang University Beijing China
- Beijing International Cooperation Bases for Science and Technology on Biomaterials and Neural Regeneration Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing China
- Department of Neurobiology School of Basic Medical Sciences Capital Medical University Beijing China
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184
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Gómez-Gaviro MV, Sanderson D, Ripoll J, Desco M. Biomedical Applications of Tissue Clearing and Three-Dimensional Imaging in Health and Disease. iScience 2020; 23:101432. [PMID: 32805648 PMCID: PMC7452225 DOI: 10.1016/j.isci.2020.101432] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2020] [Revised: 07/29/2020] [Accepted: 07/30/2020] [Indexed: 12/27/2022] Open
Abstract
Three-dimensional (3D) optical imaging techniques can expand our knowledge about physiological and pathological processes that cannot be fully understood with 2D approaches. Standard diagnostic tests frequently are not sufficient to unequivocally determine the presence of a pathological condition. Whole-organ optical imaging requires tissue transparency, which can be achieved by using tissue clearing procedures enabling deeper image acquisition and therefore making possible the analysis of large-scale biological tissue samples. Here, we review currently available clearing agents, methods, and their application in imaging of physiological or pathological conditions in different animal and human organs. We also compare different optical tissue clearing methods discussing their advantages and disadvantages and review the use of different 3D imaging techniques for the visualization and image acquisition of cleared tissues. The use of optical tissue clearing resources for large-scale biological tissues 3D imaging paves the way for future applications in translational and clinical research.
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Affiliation(s)
- Maria Victoria Gómez-Gaviro
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain.
| | - Daniel Sanderson
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Jorge Ripoll
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain
| | - Manuel Desco
- Instituto de Investigación Sanitaria Gregorio Marañón, Madrid, Spain; Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Madrid, Spain; Departamento de Bioingeniería e Ingeniería Aeroespacial, Universidad Carlos III de Madrid, Madrid, Spain; Centro Nacional de Investigaciones Cardiovasculares Carlos III, Madrid, Spain
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185
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Wen G, Vanheusden M, Acke A, Valli D, Neely RK, Leen V, Hofkens J. Evaluation of Direct Grafting Strategies via Trivalent Anchoring for Enabling Lipid Membrane and Cytoskeleton Staining in Expansion Microscopy. ACS NANO 2020; 14:7860-7867. [PMID: 32176475 DOI: 10.1101/696039] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
Super-resolution fluorescence microscopy is a key tool in the elucidation of biological fine structures, providing insights into the distribution and interactions of biomolecular complexes down to the nanometer scale. Expansion microscopy is a recently developed approach for achieving nanoscale resolution on a conventional microscope. Here, biological samples are embedded in an isotropically swollen hydrogel. This physical expansion of the sample allows imaging with resolutions down to the tens-of-nanometers. However, because of the requirement that fluorescent labels are covalently bound to the hydrogel, standard, small-molecule targeting of fluorophores has proven incompatible with expansion microscopy. Here, we show a chemical linking approach that enables direct, covalent grafting of a targeting molecule and fluorophore to the hydrogel in expansion microscopy. We show application of this series of molecules in the antibody-free targeting of the cell cytoskeleton and in an example of lipid membrane staining for expansion microscopy. Furthermore, using this trivalent linker strategy, we demonstrate the benefit of introducing fluorescent labels post-expansion by visualizing an immunostaining through fluorescent oligonucleotide hybridization after expanding the polymer. Our probes allow different labeling approaches that are compatible with expansion microscopy.
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Affiliation(s)
- Gang Wen
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | | | - Aline Acke
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | - Donato Valli
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | - Robert K Neely
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Volker Leen
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | - Johan Hofkens
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
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186
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Wen G, Vanheusden M, Acke A, Valli D, Neely RK, Leen V, Hofkens J. Evaluation of Direct Grafting Strategies via Trivalent Anchoring for Enabling Lipid Membrane and Cytoskeleton Staining in Expansion Microscopy. ACS NANO 2020; 14:7860-7867. [PMID: 32176475 DOI: 10.1021/acsnano.9b09259] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Super-resolution fluorescence microscopy is a key tool in the elucidation of biological fine structures, providing insights into the distribution and interactions of biomolecular complexes down to the nanometer scale. Expansion microscopy is a recently developed approach for achieving nanoscale resolution on a conventional microscope. Here, biological samples are embedded in an isotropically swollen hydrogel. This physical expansion of the sample allows imaging with resolutions down to the tens-of-nanometers. However, because of the requirement that fluorescent labels are covalently bound to the hydrogel, standard, small-molecule targeting of fluorophores has proven incompatible with expansion microscopy. Here, we show a chemical linking approach that enables direct, covalent grafting of a targeting molecule and fluorophore to the hydrogel in expansion microscopy. We show application of this series of molecules in the antibody-free targeting of the cell cytoskeleton and in an example of lipid membrane staining for expansion microscopy. Furthermore, using this trivalent linker strategy, we demonstrate the benefit of introducing fluorescent labels post-expansion by visualizing an immunostaining through fluorescent oligonucleotide hybridization after expanding the polymer. Our probes allow different labeling approaches that are compatible with expansion microscopy.
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Affiliation(s)
- Gang Wen
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | | | - Aline Acke
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | - Donato Valli
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | - Robert K Neely
- School of Chemistry, University of Birmingham, Edgbaston, Birmingham B15 2TT, United Kingdom
| | - Volker Leen
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
| | - Johan Hofkens
- Department of Chemistry, KU Leuven, Leuven, 3001, Belgium
- Max Planck Institute for Polymer Research, Ackermannweg 10, 55128 Mainz, Germany
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187
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Three-dimensional single-cell imaging for the analysis of RNA and protein expression in intact tumour biopsies. Nat Biomed Eng 2020; 4:875-888. [PMID: 32601394 DOI: 10.1038/s41551-020-0576-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2019] [Accepted: 05/21/2020] [Indexed: 12/20/2022]
Abstract
Microscopy analysis of tumour samples is commonly performed on fixed, thinly sectioned and protein-labelled tissues. However, these examinations do not reveal the intricate three-dimensional structures of tumours, nor enable the detection of aberrant transcripts. Here, we report a method, which we name DIIFCO (for diagnosing in situ immunofluorescence-labelled cleared oncosamples), for the multimodal volumetric imaging of RNAs and proteins in intact tumour volumes and organoids. We used DIIFCO to spatially profile the expression of diverse coding RNAs and non-coding RNAs at the single-cell resolution in a variety of cancer tissues. Quantitative single-cell analysis revealed spatial niches of cancer stem-like cells, and showed that the niches were present at a higher density in triple-negative breast cancer tissue. The improved molecular phenotyping and histopathological diagnosis of cancers may lead to new insights into the biology of tumours of patients.
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188
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Tan Y, Chiam CPL, Zhang Y, Tey HL, Ng LG. Research Techniques Made Simple: Optical Clearing and Three-Dimensional Volumetric Imaging of Skin Biopsies. J Invest Dermatol 2020; 140:1305-1314.e1. [PMID: 32571496 DOI: 10.1016/j.jid.2020.04.014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 03/31/2020] [Accepted: 04/27/2020] [Indexed: 11/25/2022]
Abstract
Skin histology is traditionally carried out using two-dimensional tissue sections, which allows for rapid staining, but these sections cannot accurately represent three-dimensional structures in skin such as nerves, vasculature, hair follicles, and sebaceous glands. Although it may be ideal to image skin in a three-dimensional manner, it is technically challenging to image deep into tissue because of light scattering from collagen fibrils in the dermis and refractive index mismatch owing to the presence of differing biological materials such as cytoplasm, and lipids in the skin. Different optical clearing methods have been developed recently, making it possible to render tissues transparent using different approaches. Here, we discuss the steps involved in tissue preparation for three-dimensional volumetric imaging and provide a brief overview of the different optical clearing methods as well as different imaging modalities for three-dimensional imaging.
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Affiliation(s)
- Yingrou Tan
- Department of Research, National Skin Centre, Singapore; Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore
| | - Carolyn Pei Lyn Chiam
- School of Medicine, Dentistry & Nursing, University of Glasgow, Glasgow, United Kingdom
| | - Yuning Zhang
- Faculty of Science, National University of Singapore, Singapore
| | - Hong Liang Tey
- Department of Research, National Skin Centre, Singapore; Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore
| | - Lai Guan Ng
- Singapore Immunology Network (SIgN), A*STAR (Agency for Science, Technology and Research), Biopolis, Singapore.
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189
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Ku T, Guan W, Evans NB, Sohn CH, Albanese A, Kim JG, Frosch MP, Chung K. Elasticizing tissues for reversible shape transformation and accelerated molecular labeling. Nat Methods 2020; 17:609-613. [PMID: 32424271 PMCID: PMC8056749 DOI: 10.1038/s41592-020-0823-y] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 04/01/2020] [Indexed: 01/13/2023]
Abstract
We developed entangled link-augmented stretchable tissue-hydrogel (ELAST), a technology that transforms tissues into elastic hydrogels to enhance macromolecular accessibility and mechanical stability simultaneously. ELASTicized tissues are highly stretchable and compressible, which enables reversible shape transformation and faster delivery of probes into intact tissue specimens via mechanical thinning. This universal platform may facilitate rapid and scalable molecular phenotyping of large-scale biological systems, such as human organs.
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Affiliation(s)
- Taeyun Ku
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
- Graduate School of Medical Science and Engineering, KAIST, Daejeon, Republic of Korea
| | - Webster Guan
- Department of Chemical Engineering, MIT, Cambridge, MA, USA
| | - Nicholas B Evans
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | - Chang Ho Sohn
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA
| | - Alexandre Albanese
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA
| | - Joon-Goon Kim
- Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Matthew P Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology (MIT), Cambridge, MA, USA.
- Picower Institute for Learning and Memory, MIT, Cambridge, MA, USA.
- Department of Chemical Engineering, MIT, Cambridge, MA, USA.
- Department of Brain and Cognitive Sciences, MIT, Cambridge, MA, USA.
- Broad Institute of Harvard University and MIT, Cambridge, MA, USA.
- Center for Nanomedicine, Institute for Basic Science (IBS), Seoul, Republic of Korea.
- Nano Biomedical Engineering (Nano BME) Graduate Program, Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea.
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190
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Ueda HR, Dodt HU, Osten P, Economo MN, Chandrashekar J, Keller PJ. Whole-Brain Profiling of Cells and Circuits in Mammals by Tissue Clearing and Light-Sheet Microscopy. Neuron 2020; 106:369-387. [PMID: 32380050 PMCID: PMC7213014 DOI: 10.1016/j.neuron.2020.03.004] [Citation(s) in RCA: 129] [Impact Index Per Article: 32.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2019] [Revised: 01/11/2020] [Accepted: 03/04/2020] [Indexed: 01/12/2023]
Abstract
Tissue clearing and light-sheet microscopy have a 100-year-plus history, yet these fields have been combined only recently to facilitate novel experiments and measurements in neuroscience. Since tissue-clearing methods were first combined with modernized light-sheet microscopy a decade ago, the performance of both technologies has rapidly improved, broadening their applications. Here, we review the state of the art of tissue-clearing methods and light-sheet microscopy and discuss applications of these techniques in profiling cells and circuits in mice. We examine outstanding challenges and future opportunities for expanding these techniques to achieve brain-wide profiling of cells and circuits in primates and humans. Such integration will help provide a systems-level understanding of the physiology and pathology of our central nervous system.
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Affiliation(s)
- Hiroki R Ueda
- Department of Systems Pharmacology, The University of Tokyo, Tokyo 113-0033, Japan; Laboratory for Synthetic Biology, RIKEN BDR, Suita, Osaka 565-0871, Japan.
| | - Hans-Ulrich Dodt
- Department of Bioelectronics, FKE, Vienna University of Technology-TU Wien, Vienna, Austria; Section of Bioelectronics, Center for Brain Research, Medical University of Vienna, Vienna, Austria
| | - Pavel Osten
- Cold Spring Harbor Laboratories, Cold Spring Harbor, NY 11724, USA
| | - Michael N Economo
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | | | - Philipp J Keller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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191
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Jonkman J, Brown CM, Wright GD, Anderson KI, North AJ. Tutorial: guidance for quantitative confocal microscopy. Nat Protoc 2020. [PMID: 32235926 DOI: 10.1038/s41596-020-0313-319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
When used appropriately, a confocal fluorescence microscope is an excellent tool for making quantitative measurements in cells and tissues. The confocal microscope's ability to block out-of-focus light and thereby perform optical sectioning through a specimen allows the researcher to quantify fluorescence with very high spatial precision. However, generating meaningful data using confocal microscopy requires careful planning and a thorough understanding of the technique. In this tutorial, the researcher is guided through all aspects of acquiring quantitative confocal microscopy images, including optimizing sample preparation for fixed and live cells, choosing the most suitable microscope for a given application and configuring the microscope parameters. Suggestions are offered for planning unbiased and rigorous confocal microscope experiments. Common pitfalls such as photobleaching and cross-talk are addressed, as well as several troubling instrumentation problems that may prevent the acquisition of quantitative data. Finally, guidelines for analyzing and presenting confocal images in a way that maintains the quantitative nature of the data are presented, and statistical analysis is discussed. A visual summary of this tutorial is available as a poster (https://doi.org/10.1038/s41596-020-0307-7).
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Affiliation(s)
- James Jonkman
- Advanced Optical Microscopy Facility (AOMF), University Health Network, Toronto, Ontario, Canada.
| | - Claire M Brown
- Advanced BioImaging Facility (ABIF), McGill University, Montreal, Quebec, Canada
| | - Graham D Wright
- A*STAR Microscopy Platform (AMP), Skin Research Institute of Singapore, A*STAR, Singapore, Singapore
| | - Kurt I Anderson
- Crick Advanced Light Microscopy Facility (CALM), The Francis Crick Institute, London, UK
| | - Alison J North
- Bio-Imaging Resource Center, The Rockefeller University, New York, NY, USA
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192
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Yu CC(J, Barry NC, Wassie AT, Sinha A, Bhattacharya A, Asano S, Zhang C, Chen F, Hobert O, Goodman MB, Haspel G, Boyden ES. Expansion microscopy of C. elegans. eLife 2020; 9:e46249. [PMID: 32356725 PMCID: PMC7195193 DOI: 10.7554/elife.46249] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 03/30/2020] [Indexed: 12/20/2022] Open
Abstract
We recently developed expansion microscopy (ExM), which achieves nanoscale-precise imaging of specimens at ~70 nm resolution (with ~4.5x linear expansion) by isotropic swelling of chemically processed, hydrogel-embedded tissue. ExM of C. elegans is challenged by its cuticle, which is stiff and impermeable to antibodies. Here we present a strategy, expansion of C. elegans (ExCel), to expand fixed, intact C. elegans. ExCel enables simultaneous readout of fluorescent proteins, RNA, DNA location, and anatomical structures at resolutions of ~65-75 nm (3.3-3.8x linear expansion). We also developed epitope-preserving ExCel, which enables imaging of endogenous proteins stained by antibodies, and iterative ExCel, which enables imaging of fluorescent proteins after 20x linear expansion. We demonstrate the utility of the ExCel toolbox for mapping synaptic proteins, for identifying previously unreported proteins at cell junctions, and for gene expression analysis in multiple individual neurons of the same animal.
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Affiliation(s)
- Chih-Chieh (Jay) Yu
- Department of Biological Engineering, Massachusetts Institute of TechnologyCambridgeUnited States
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Nicholas C Barry
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Asmamaw T Wassie
- Department of Biological Engineering, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Anubhav Sinha
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
- Division of Health Sciences and Technology, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Abhishek Bhattacharya
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia UniversityNew YorkUnited States
| | - Shoh Asano
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Chi Zhang
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
| | - Fei Chen
- Broad Institute of MIT and HarvardCambridgeUnited States
| | - Oliver Hobert
- Department of Biological Sciences, Howard Hughes Medical Institute, Columbia UniversityNew YorkUnited States
| | - Miriam B Goodman
- Department of Molecular and Cellular Physiology, Stanford UniversityStanfordUnited States
| | - Gal Haspel
- Federated Department of Biological Sciences, New Jersey Institute of Technology and Rutgers University-NewarkNewarkUnited States
- The Brain Research Institute, New Jersey Institute of TechnologyNewarkUnited States
| | - Edward S Boyden
- Department of Biological Engineering, Massachusetts Institute of TechnologyCambridgeUnited States
- Media Lab, Massachusetts Institute of TechnologyCambridgeUnited States
- McGovern Institute, Massachusetts Institute of TechnologyCambridgeUnited States
- Koch Institute, Massachusetts Institute of TechnologyCambridgeUnited States
- Department of Brain and Cognitive Sciences, Massachusetts Institute of TechnologyCambridgeUnited States
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193
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Susaki EA, Shimizu C, Kuno A, Tainaka K, Li X, Nishi K, Morishima K, Ono H, Ode KL, Saeki Y, Miyamichi K, Isa K, Yokoyama C, Kitaura H, Ikemura M, Ushiku T, Shimizu Y, Saito T, Saido TC, Fukayama M, Onoe H, Touhara K, Isa T, Kakita A, Shibayama M, Ueda HR. Versatile whole-organ/body staining and imaging based on electrolyte-gel properties of biological tissues. Nat Commun 2020; 11:1982. [PMID: 32341345 PMCID: PMC7184626 DOI: 10.1038/s41467-020-15906-5] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 03/31/2020] [Indexed: 12/26/2022] Open
Abstract
Whole-organ/body three-dimensional (3D) staining and imaging have been enduring challenges in histology. By dissecting the complex physicochemical environment of the staining system, we developed a highly optimized 3D staining imaging pipeline based on CUBIC. Based on our precise characterization of biological tissues as an electrolyte gel, we experimentally evaluated broad 3D staining conditions by using an artificial tissue-mimicking material. The combination of optimized conditions allows a bottom-up design of a superior 3D staining protocol that can uniformly label whole adult mouse brains, an adult marmoset brain hemisphere, an ~1 cm3 tissue block of a postmortem adult human cerebellum, and an entire infant marmoset body with dozens of antibodies and cell-impermeant nuclear stains. The whole-organ 3D images collected by light-sheet microscopy are used for computational analyses and whole-organ comparison analysis between species. This pipeline, named CUBIC-HistoVIsion, thus offers advanced opportunities for organ- and organism-scale histological analysis of multicellular systems.
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Affiliation(s)
- Etsuo A Susaki
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, 1-3 Yamadaoka, Suita, Osaka, 565-5241, Japan.
| | - Chika Shimizu
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, 1-3 Yamadaoka, Suita, Osaka, 565-5241, Japan
| | - Akihiro Kuno
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
- Department of Anatomy and Embryology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, 305-8575, Japan
| | - Kazuki Tainaka
- Department of System Pathology for Neurological Disorders, Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku, Niigata, 951-8585, Japan
| | - Xiang Li
- Neutron Science Laboratory, The Institute for Solid State Physics, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8581, Japan
| | - Kengo Nishi
- Neutron Science Laboratory, The Institute for Solid State Physics, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8581, Japan
| | - Ken Morishima
- Neutron Science Laboratory, The Institute for Solid State Physics, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8581, Japan
| | - Hiroaki Ono
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Koji L Ode
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, 1-3 Yamadaoka, Suita, Osaka, 565-5241, Japan
| | - Yuki Saeki
- Faculty of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Kazunari Miyamichi
- Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
- ERATO Touhara Chemosensory Signal Project, Japan Science and Technology Agency, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
| | - Kaoru Isa
- Department of Neuroscience, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Yoshida-konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, 606-8501, Japan
| | - Chihiro Yokoyama
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe, Hyogo, 650-0047, Japan
| | - Hiroki Kitaura
- Department of Pathology, Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku, Niigata, 951-8585, Japan
| | - Masako Ikemura
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Tetsuo Ushiku
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Yoshihiro Shimizu
- Laboratory for Cell-Free Protein Synthesis, RIKEN Center for Biosystems Dynamics Research, 6-2-3, Furuedai, Suita, Osaka, 565-0874, Japan
| | - Takashi Saito
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
- Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University Graduate School of Medical Science, 1 Kawasumi, Mizuho-cho, Mizuho-ku, Nagoya, Aichi, 467-8601, Japan
| | - Takaomi C Saido
- Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, 2-1 Hirosawa, Wako, Saitama, 351-0198, Japan
| | - Masashi Fukayama
- Department of Pathology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Hirotaka Onoe
- Human Brain Research Center, Graduate School of Medicine, Kyoto University, 54 Shogoin-kawahara-cho, Sakyo-ku, Kyoto, 606-8507, Japan
| | - Kazushige Touhara
- Department of Applied Biological Chemistry, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
- ERATO Touhara Chemosensory Signal Project, Japan Science and Technology Agency, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo, 113-8657, Japan
- International Research Center for Neurointelligence (WPI-IRCN), UTIAS, The University of Tokyo, Tokyo, 113-0033, Japan
| | - Tadashi Isa
- Department of Neuroscience, Graduate School of Medicine and Faculty of Medicine, Kyoto University, Yoshida-konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan
- Institute for the Advanced Study of Human Biology (WPI-ASHBi), Kyoto University, Kyoto, 606-8501, Japan
| | - Akiyoshi Kakita
- Department of Pathology, Brain Research Institute, Niigata University, 1-757 Asahimachidori, Chuo-ku, Niigata, 951-8585, Japan
| | - Mitsuhiro Shibayama
- Neutron Science Laboratory, The Institute for Solid State Physics, The University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, Chiba, 277-8581, Japan
| | - Hiroki R Ueda
- Department of Systems Pharmacology, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan.
- Laboratory for Synthetic Biology, RIKEN Center for Biosystems Dynamics Research, 1-3 Yamadaoka, Suita, Osaka, 565-5241, Japan.
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194
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Josselyn SA, Tonegawa S. Memory engrams: Recalling the past and imagining the future. Science 2020; 367:367/6473/eaaw4325. [PMID: 31896692 DOI: 10.1126/science.aaw4325] [Citation(s) in RCA: 446] [Impact Index Per Article: 111.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
In 1904, Richard Semon introduced the term "engram" to describe the neural substrate for storing memories. An experience, Semon proposed, activates a subset of cells that undergo off-line, persistent chemical and/or physical changes to become an engram. Subsequent reactivation of this engram induces memory retrieval. Although Semon's contributions were largely ignored in his lifetime, new technologies that allow researchers to image and manipulate the brain at the level of individual neurons has reinvigorated engram research. We review recent progress in studying engrams, including an evaluation of evidence for the existence of engrams, the importance of intrinsic excitability and synaptic plasticity in engrams, and the lifetime of an engram. Together, these findings are beginning to define an engram as the basic unit of memory.
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Affiliation(s)
- Sheena A Josselyn
- Program in Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada. .,Department of Psychology, University of Toronto, Toronto, Ontario M5S 3G3, Canada.,Department of Physiology, University of Toronto, Toronto, Ontario M5G 1X8, Canada.,Institute of Medical Sciences, University of Toronto, Toronto, Ontario M5S 1A8, Canada.,Brain, Mind & Consciousness Program, Canadian Institute for Advanced Research (CIFAR), Toronto, Ontario M5G 1M1, Canada
| | - Susumu Tonegawa
- RIKEN-MIT Laboratory for Neural Circuit Genetics at the Picower Institute for Learning and Memory, Department of Biology and Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA. .,Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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195
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Jonkman J, Brown CM, Wright GD, Anderson KI, North AJ. Tutorial: guidance for quantitative confocal microscopy. Nat Protoc 2020; 15:1585-1611. [DOI: 10.1038/s41596-020-0313-9] [Citation(s) in RCA: 105] [Impact Index Per Article: 26.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2019] [Accepted: 02/10/2020] [Indexed: 01/04/2023]
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196
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Fluorescence microscopy tensor imaging representations for large-scale dataset analysis. Sci Rep 2020; 10:5632. [PMID: 32221334 PMCID: PMC7101442 DOI: 10.1038/s41598-020-62233-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/10/2020] [Indexed: 12/13/2022] Open
Abstract
Understanding complex biological systems requires the system-wide characterization of cellular and molecular features. Recent advances in optical imaging technologies and chemical tissue clearing have facilitated the acquisition of whole-organ imaging datasets, but automated tools for their quantitative analysis and visualization are still lacking. We have here developed a visualization technique capable of providing whole-organ tensor imaging representations of local regional descriptors based on fluorescence data acquisition. This method enables rapid, multiscale, analysis and virtualization of large-volume, high-resolution complex biological data while generating 3D tractographic representations. Using the murine heart as a model, our method allowed us to analyze and interrogate the cardiac microvasculature and the tissue resident macrophage distribution and better infer and delineate the underlying structural network in unprecedented detail.
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197
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Zhao S, Todorov MI, Cai R, -Maskari RA, Steinke H, Kemter E, Mai H, Rong Z, Warmer M, Stanic K, Schoppe O, Paetzold JC, Gesierich B, Wong MN, Huber TB, Duering M, Bruns OT, Menze B, Lipfert J, Puelles VG, Wolf E, Bechmann I, Ertürk A. Cellular and Molecular Probing of Intact Human Organs. Cell 2020; 180:796-812.e19. [PMID: 32059778 PMCID: PMC7557154 DOI: 10.1016/j.cell.2020.01.030] [Citation(s) in RCA: 151] [Impact Index Per Article: 37.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 12/04/2019] [Accepted: 01/22/2020] [Indexed: 12/16/2022]
Abstract
Optical tissue transparency permits scalable cellular and molecular investigation of complex tissues in 3D. Adult human organs are particularly challenging to render transparent because of the accumulation of dense and sturdy molecules in decades-aged tissues. To overcome these challenges, we developed SHANEL, a method based on a new tissue permeabilization approach to clear and label stiff human organs. We used SHANEL to render the intact adult human brain and kidney transparent and perform 3D histology with antibodies and dyes in centimeters-depth. Thereby, we revealed structural details of the intact human eye, human thyroid, human kidney, and transgenic pig pancreas at the cellular resolution. Furthermore, we developed a deep learning pipeline to analyze millions of cells in cleared human brain tissues within hours with standard lab computers. Overall, SHANEL is a robust and unbiased technology to chart the cellular and molecular architecture of large intact mammalian organs.
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Affiliation(s)
- Shan Zhao
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany; Munich Medical Research School (MMRS), 80336 Munich, Germany
| | - Mihail Ivilinov Todorov
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany; Graduate School of Neuroscience (GSN), 82152 Munich, Germany
| | - Ruiyao Cai
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany
| | - Rami Ai -Maskari
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany; Department of Computer Science, Technical University of Munich (TUM), 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 80798 Munich, Germany; Graduate School of Bioengineering, Technical University of Munich (TUM), 85748 Munich, Germany
| | - Hanno Steinke
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Elisabeth Kemter
- Institute of Molecular Animal Breeding and Biotechnology, Gene Center, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany; Center for Innovative Medical Models (CiMM), 85764 Oberschleißheim, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Hongcheng Mai
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany
| | - Zhouyi Rong
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany
| | - Martin Warmer
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Karen Stanic
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Oliver Schoppe
- Department of Computer Science, Technical University of Munich (TUM), 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 80798 Munich, Germany
| | - Johannes Christian Paetzold
- Department of Computer Science, Technical University of Munich (TUM), 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 80798 Munich, Germany; Graduate School of Bioengineering, Technical University of Munich (TUM), 85748 Munich, Germany
| | - Benno Gesierich
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany
| | - Milagros N Wong
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Tobias B Huber
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany
| | - Oliver Thomas Bruns
- Helmholtz Pioneer Campus, Helmholtz Zentrum München, 85764 Neuherberg, Germany
| | - Bjoern Menze
- Department of Computer Science, Technical University of Munich (TUM), 81675 Munich, Germany; Center for Translational Cancer Research (TranslaTUM) of the TUM, 80798 Munich, Germany; Graduate School of Bioengineering, Technical University of Munich (TUM), 85748 Munich, Germany
| | - Jan Lipfert
- Department of Physics and Center for Nanoscience, Ludwig Maximilian University of Munich (LMU), 80799 Munich, Germany
| | - Victor G Puelles
- III. Department of Medicine, University Medical Center Hamburg-Eppendorf, 20246 Hamburg, Germany; Department of Nephrology, Monash Health, and Center for Inflammatory Diseases, Monash University, Melbourne VIC 3168, Australia
| | - Eckhard Wolf
- Institute of Molecular Animal Breeding and Biotechnology, Gene Center, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany; Center for Innovative Medical Models (CiMM), 85764 Oberschleißheim, Germany; German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Ingo Bechmann
- Institute of Anatomy, University of Leipzig, 04109 Leipzig, Germany
| | - Ali Ertürk
- Insititute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Zentrum München, 85764 Neuherberg, Germany; Institute for Stroke and Dementia Research (ISD), University Hospital, Ludwig Maximilian University of Munich (LMU), 81377 Munich, Germany; Munich Cluster for Systems Neurology (SyNergy), 81377 Munich, Germany.
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198
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Ueda HR, Ertürk A, Chung K, Gradinaru V, Chédotal A, Tomancak P, Keller PJ. Tissue clearing and its applications in neuroscience. Nat Rev Neurosci 2020; 21:61-79. [PMID: 31896771 PMCID: PMC8121164 DOI: 10.1038/s41583-019-0250-1] [Citation(s) in RCA: 305] [Impact Index Per Article: 76.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 11/18/2019] [Indexed: 02/06/2023]
Abstract
State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.
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Affiliation(s)
- Hiroki R Ueda
- Department of Systems Pharmacology, University of Tokyo, Tokyo, Japan.
- Laboratory for Synthetic Biology, RIKEN BDR, Suita, Japan.
| | - Ali Ertürk
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilian University of Munich, Munich, Germany
- Institute of Tissue Engineering and Regenerative Medicine, Helmholtz Zentrum München, Neuherberg, Germany
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Kwanghun Chung
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA
- Picower Institute for Learning and Memory, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
- Eli & Edythe Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for NanoMedicine, Institute for Basic Science, Seoul, Republic of Korea
- Graduate Program of Nano Biomedical Engineering, Yonsei-IBS Institute, Yonsei University, Seoul, Republic of Korea
| | - Viviana Gradinaru
- Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA
| | - Alain Chédotal
- Institut de la Vision, Sorbonne Université, INSERM, CNRS, Paris, France
| | - Pavel Tomancak
- Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany
- IT4Innovations, Technical University of Ostrava, Ostrava, Czech Republic
| | - Philipp J Keller
- Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA
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199
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Porter DDL, Morton PD. Clearing techniques for visualizing the nervous system in development, injury, and disease. J Neurosci Methods 2020; 334:108594. [PMID: 31945400 PMCID: PMC10674098 DOI: 10.1016/j.jneumeth.2020.108594] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 01/09/2020] [Accepted: 01/10/2020] [Indexed: 01/05/2023]
Abstract
Modern clearing techniques enable high resolution visualization and 3D reconstruction of cell populations and their structural details throughout large biological samples, including intact organs and even entire organisms. In the past decade, these methods have become more tractable and are now being utilized to provide unforeseen insights into the complexities of the nervous system. While several iterations of optical clearing techniques have been developed, some are more suitable for specific applications than others depending on the type of specimen under study. Here we review findings from select studies utilizing clearing methods to visualize the developing, injured, and diseased nervous system within numerous model systems and species. We note trends and imbalances in the types of research questions being addressed with clearing methods across these fields in neuroscience. In addition, we discuss restrictions in applying optical clearing methods for postmortem tissue from humans and large animals and emphasize the lack in continuity between studies of these species. We aim for this review to serve as a key outline of available tissue clearing methods used successfully to address issues across neuronal development, injury/repair, and aging/disease.
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Affiliation(s)
- Demisha D L Porter
- Virginia Tech Graduate Program in Translational Biology, Medicine and Health, Virginia Polytechnic Institute and State University, Roanoke, VA, USA
| | - Paul D Morton
- Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
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200
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Dierssen M. Top ten discoveries of the year: Neurodevelopmental disorders. FREE NEUROPATHOLOGY 2020; 1:1-13. [PMID: 37283674 PMCID: PMC10209851 DOI: 10.17879/freeneuropathology-2020-2672] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 04/12/2020] [Indexed: 06/08/2023]
Abstract
Developmental brain disorders, a highly heterogeneous group of disorders with a prevalence of around 3% of worldwide population, represent a growing medical challenge. They are characterized by impaired neurodevelopmental processes leading to deficits in cognition, social interaction, behavior and motor functioning as a result of abnormal development of brain. This can include developmental brain dysfunction, which can manifest as neuropsychiatric problems or impaired motor function, learning, language or non-verbal communication. Several of these phenotypes can often co-exist in the same patient and characterize the same disorder. Here I discuss some contributions in 2019 that are shaking our basic understanding of the pathogenesis of neurodevelopmental disorders. Recent developments in sophisticated in-utero imaging diagnostic tools have raised the possibility of imaging the fetal human brain growth, providing insights into the developing anatomy and improving diagnostics but also allowing a better understanding of antenatal pathology. On the other hand, advances in our understanding of the pathogenetic mechanisms reveal a remarkably complex molecular neuropathology involving a myriad of genetic architectures and regulatory elements that will help establish more rigorous genotype-phenotype correlations.
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Affiliation(s)
- Mara Dierssen
- Centre for Genomic Regulation (CRG); The Barcelona Institute of Science and Technology, and Universitat Pompeu Fabra (UPF), 08003 Barcelona, Spain
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