1
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Weiss KR, Huisken J, Khanjani N, Bakalov V, Engle ML, Krzyzanowski MC, Madden T, Maiese DR, Waterfield JR, Williams DN, Wood L, Wu X, Hamilton CM, Huggins W. T-CLEARE: a pilot community-driven tissue clearing protocol repository. Front Bioeng Biotechnol 2024; 12:1304622. [PMID: 39351064 PMCID: PMC11439823 DOI: 10.3389/fbioe.2024.1304622] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 06/18/2024] [Indexed: 10/04/2024] Open
Abstract
Selecting and implementing a tissue clearing protocol is challenging. Established more than 100 years ago, tissue clearing is still a rapidly evolving field of research. There are currently many published protocols to choose from, and each performs better or worse across a range of key evaluation factors (e.g., speed, cost, tissue stability, fluorescence quenching). Additionally, tissue clearing protocols are often optimized for specific experimental contexts, and applying an existing protocol to a new problem can require a lengthy period of adaptation by trial and error. Although the primary literature and review articles provide a useful starting point for optimization, there is growing recognition that results can vary dramatically with changes to tissue type or antibody used. To help address this issue, we have developed a novel, freely available repository of tissue clearing protocols named T-CLEARE (Tissue CLEAring protocol REpository; https://doryworkspace.org/doryviz). T-CLEARE incorporates community responses to an open survey designed to capture details not commonly found in the scientific literature, including modifications to published protocols required for specific use cases and instances when tissue clearing protocols did not perform well (negative results). The goal of T-CLEARE is to help the community share evaluations and modifications of tissue clearing protocols for various tissue types and potentially identify best-in-class methods for a given application.
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Affiliation(s)
- Kurt R. Weiss
- Morgridge Institute for Research, Madison, WI, United States
| | - Jan Huisken
- Morgridge Institute for Research, Madison, WI, United States
| | - Neda Khanjani
- Mark and Mary Stevens Neuroimaging and Informatics Institute, Laboratory of Neuro Imaging, Keck School of Medicine of University of Southern California, Los Angeles, CA, United States
| | - Vesselina Bakalov
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - Michelle L. Engle
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | | | - Tom Madden
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - Deborah R. Maiese
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - Justin R. Waterfield
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - David N. Williams
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - Lauren Wood
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - Xin Wu
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - Carol M. Hamilton
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
| | - Wayne Huggins
- Bioinformatics and Computational Biology Program, RTI International, Durham, NC, United States
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2
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Meo DD, Sorelli M, Ramazzotti J, Cheli F, Bradley S, Perego L, Lorenzon B, Mazzamuto G, Emmi A, Porzionato A, Caro RD, Garbelli R, Biancheri D, Pelorosso C, Conti V, Guerrini R, Pavone FS, Costantini I. Quantitative cytoarchitectural phenotyping of deparaffinized human brain tissues. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.10.612232. [PMID: 39314456 PMCID: PMC11419081 DOI: 10.1101/2024.09.10.612232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 09/25/2024]
Abstract
Advanced 3D imaging techniques and image segmentation and classification methods can profoundly transform biomedical research by offering deep insights into the cytoarchitecture of the human brain in relation to pathological conditions. Here, we propose a comprehensive pipeline for performing 3D imaging and automated quantitative cellular phenotyping on Formalin-Fixed Paraffin-Embedded (FFPE) human brain specimens, a valuable yet underutilized resource. We exploited the versatility of our method by applying it to different human specimens from both adult and pediatric, normal and abnormal brain regions. Quantitative data on neuronal volume, ellipticity, local density, and spatial clustering level were obtained from a machine learning-based analysis of the 3D cytoarchitectural organization of cells identified by different molecular markers in two subjects with malformations of cortical development (MCD). This approach will grant access to a wide range of physiological and pathological paraffin-embedded clinical specimens, allowing for volumetric imaging and quantitative analysis of human brain samples at cellular resolution. Possible genotype-phenotype correlations can be unveiled, providing new insights into the pathogenesis of various brain diseases and enlarging treatment opportunities.
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Affiliation(s)
- Danila Di Meo
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
| | - Michele Sorelli
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
| | - Josephine Ramazzotti
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
| | - Franco Cheli
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
| | - Samuel Bradley
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
| | - Laura Perego
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
| | - Beatrice Lorenzon
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
| | - Giacomo Mazzamuto
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
| | - Aron Emmi
- Institute of Human Anatomy, Department of Neuroscience, University of Padova, Italy
| | - Andrea Porzionato
- Institute of Human Anatomy, Department of Neuroscience, University of Padova, Italy
| | - Raffaele De Caro
- Institute of Human Anatomy, Department of Neuroscience, University of Padova, Italy
| | - Rita Garbelli
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta
| | - Dalila Biancheri
- Epilepsy Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta
| | - Cristiana Pelorosso
- Department of Neuroscience and Medical Genetics, Meyer Children's Hospital IRCCS, Florence, Italy
| | - Valerio Conti
- Department of Neuroscience and Medical Genetics, Meyer Children's Hospital IRCCS, Florence, Italy
| | - Renzo Guerrini
- Department of Neuroscience and Medical Genetics, Meyer Children's Hospital IRCCS, Florence, Italy
- Department of Neuroscience, Psychology, Drug Research and Child Health, University of Florence, Florence, Italy
| | - Francesco S Pavone
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino, Italy
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
| | - Irene Costantini
- European Laboratory for Non-linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino, Italy
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
- Department of Biology, University of Florence, Italy
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3
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Zeng X, Puonti O, Sayeed A, Herisse R, Mora J, Evancic K, Varadarajan D, Balbastre Y, Costantini I, Scardigli M, Ramazzotti J, DiMeo D, Mazzamuto G, Pesce L, Brady N, Cheli F, Saverio Pavone F, Hof PR, Frost R, Augustinack J, van der Kouwe A, Eugenio Iglesias J, Fischl B. Segmentation of supragranular and infragranular layers in ultra-high-resolution 7T ex vivo MRI of the human cerebral cortex. Cereb Cortex 2024; 34:bhae362. [PMID: 39264753 PMCID: PMC11391621 DOI: 10.1093/cercor/bhae362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 08/06/2024] [Accepted: 08/18/2024] [Indexed: 09/14/2024] Open
Abstract
Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Building on recent advancements in ultra-high-resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 $\mu $m, we propose a Multi-resolution U-Nets framework that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.
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Affiliation(s)
- Xiangrui Zeng
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Oula Puonti
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Blegdamsvej 9, 2100 København, Denmark
| | - Areej Sayeed
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Rogeny Herisse
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Jocelyn Mora
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Kathryn Evancic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Divya Varadarajan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Yael Balbastre
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Irene Costantini
- National Institute of Optics (CNR-INO), National Research Council, Largo Enrico Fermi, 6, 50125 Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
- Department of Biology, University of Florence, P.za di San Marco, 4, 50121 Firenze FI, Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
| | - Josephine Ramazzotti
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
| | - Danila DiMeo
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
| | - Giacomo Mazzamuto
- National Institute of Optics (CNR-INO), National Research Council, Largo Enrico Fermi, 6, 50125 Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, P.za di San Marco, 4, 50121 Firenze FI, Italy
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
| | - Niamh Brady
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
| | - Franco Cheli
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
| | - Francesco Saverio Pavone
- National Institute of Optics (CNR-INO), National Research Council, Largo Enrico Fermi, 6, 50125 Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Via Nello Carrara, 1, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, P.za di San Marco, 4, 50121 Firenze FI, Italy
| | - Patrick R Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY 10029, USA
| | - Robert Frost
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Jean Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - André van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13th St, Boston, MA 02129, USA
- Department of Radiology, Harvard Medical School, 25 Shattuck Street, Boston, MA 02115, USA
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4
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Checcucci C, Wicinski B, Mazzamuto G, Scardigli M, Ramazzotti J, Brady N, Pavone FS, Hof PR, Costantini I, Frasconi P. Deep learning-based localization algorithms on fluorescence human brain 3D reconstruction: a comparative study using stereology as a reference. Sci Rep 2024; 14:14629. [PMID: 38918523 PMCID: PMC11199592 DOI: 10.1038/s41598-024-65092-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Accepted: 06/17/2024] [Indexed: 06/27/2024] Open
Abstract
3D reconstruction of human brain volumes at high resolution is now possible thanks to advancements in tissue clearing methods and fluorescence microscopy techniques. Analyzing the massive data produced with these approaches requires automatic methods able to perform fast and accurate cell counting and localization. Recent advances in deep learning have enabled the development of various tools for cell segmentation. However, accurate quantification of neurons in the human brain presents specific challenges, such as high pixel intensity variability, autofluorescence, non-specific fluorescence and very large size of data. In this paper, we provide a thorough empirical evaluation of three techniques based on deep learning (StarDist, CellPose and BCFind-v2, an updated version of BCFind) using a recently introduced three-dimensional stereological design as a reference for large-scale insights. As a representative problem in human brain analysis, we focus on a 4 -cm 3 portion of the Broca's area. We aim at helping users in selecting appropriate techniques depending on their research objectives. To this end, we compare methods along various dimensions of analysis, including correctness of the predicted density and localization, computational efficiency, and human annotation effort. Our results suggest that deep learning approaches are very effective, have a high throughput providing each cell 3D location, and obtain results comparable to the estimates of the adopted stereological design.
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Affiliation(s)
- Curzio Checcucci
- Department of Information Engineering, University of Florence, 50100, Firenze, FI, Italy.
| | - Bridget Wicinski
- Nash Family Department of Neuroscience, Friedman Brain Institute and Center for Discovery and Innovation, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Giacomo Mazzamuto
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, FI, Italy
- National Research Council, National Institute of Optics (CNR-INO), 50019, Sesto Fiorentino, FI, Italy
- Department of Physics, University of Florence, 50019, Sesto Fiorentino, FI, Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, FI, Italy
- Department of Experimental and Clinical Medicine, University of Florence, 50100, Firenze, FI, Italy
| | - Josephine Ramazzotti
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, FI, Italy
| | - Niamh Brady
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, FI, Italy
| | - Francesco S Pavone
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, FI, Italy
- National Research Council, National Institute of Optics (CNR-INO), 50019, Sesto Fiorentino, FI, Italy
- Department of Physics, University of Florence, 50019, Sesto Fiorentino, FI, Italy
| | - Patrick R Hof
- Nash Family Department of Neuroscience, Friedman Brain Institute and Center for Discovery and Innovation, Icahn School of Medicine at Mount Sinai, New York, NY, 10019, USA
| | - Irene Costantini
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, FI, Italy
- National Research Council, National Institute of Optics (CNR-INO), 50019, Sesto Fiorentino, FI, Italy
- Department of Biology, University of Florence, 50019, Sesto Fiorentino, FI, Italy
| | - Paolo Frasconi
- Department of Information Engineering, University of Florence, 50100, Firenze, FI, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, FI, Italy
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5
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Lindhout FW, Krienen FM, Pollard KS, Lancaster MA. A molecular and cellular perspective on human brain evolution and tempo. Nature 2024; 630:596-608. [PMID: 38898293 DOI: 10.1038/s41586-024-07521-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 04/29/2024] [Indexed: 06/21/2024]
Abstract
The evolution of the modern human brain was accompanied by distinct molecular and cellular specializations, which underpin our diverse cognitive abilities but also increase our susceptibility to neurological diseases. These features, some specific to humans and others shared with related species, manifest during different stages of brain development. In this multi-stage process, neural stem cells proliferate to produce a large and diverse progenitor pool, giving rise to excitatory or inhibitory neurons that integrate into circuits during further maturation. This process unfolds over varying time scales across species and has progressively become slower in the human lineage, with differences in tempo correlating with differences in brain size, cell number and diversity, and connectivity. Here we introduce the terms 'bradychrony' and 'tachycrony' to describe slowed and accelerated developmental tempos, respectively. We review how recent technical advances across disciplines, including advanced engineering of in vitro models, functional comparative genetics and high-throughput single-cell profiling, are leading to a deeper understanding of how specializations of the human brain arise during bradychronic neurodevelopment. Emerging insights point to a central role for genetics, gene-regulatory networks, cellular innovations and developmental tempo, which together contribute to the establishment of human specializations during various stages of neurodevelopment and at different points in evolution.
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Affiliation(s)
- Feline W Lindhout
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK.
| | - Fenna M Krienen
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Katherine S Pollard
- Gladstone Institutes, San Francisco, CA, USA
- Chan Zuckerberg Biohub, San Francisco, CA, USA
- Department of Epidemiology & Biostatistics, Institute for Computational Health Sciences, and Institute for Human Genetics, University of California San Francisco, San Francisco, CA, USA
| | - Madeline A Lancaster
- MRC Laboratory of Molecular Biology, Cambridge Biomedical Campus, Cambridge, UK.
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6
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Liu Z, Feng Z, Liu G, Li A, Gong H, Yang X, Li X. A complementary approach for neocortical cytoarchitecture inspection with cellular resolution imaging at whole brain scale. Front Neuroanat 2024; 18:1388084. [PMID: 38846539 PMCID: PMC11153794 DOI: 10.3389/fnana.2024.1388084] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Accepted: 04/26/2024] [Indexed: 06/09/2024] Open
Abstract
Cytoarchitecture, the organization of cells within organs and tissues, serves as a crucial anatomical foundation for the delineation of various regions. It enables the segmentation of the cortex into distinct areas with unique structural and functional characteristics. While traditional 2D atlases have focused on cytoarchitectonic mapping of cortical regions through individual sections, the intricate cortical gyri and sulci demands a 3D perspective for unambiguous interpretation. In this study, we employed fluorescent micro-optical sectioning tomography to acquire architectural datasets of the entire macaque brain at a resolution of 0.65 μm × 0.65 μm × 3 μm. With these volumetric data, the cortical laminar textures were remarkably presented in appropriate view planes. Additionally, we established a stereo coordinate system to represent the cytoarchitectonic information as surface-based tomograms. Utilizing these cytoarchitectonic features, we were able to three-dimensionally parcel the macaque cortex into multiple regions exhibiting contrasting architectural patterns. The whole-brain analysis was also conducted on mice that clearly revealed the presence of barrel cortex and reflected biological reasonability of this method. Leveraging these high-resolution continuous datasets, our method offers a robust tool for exploring the organizational logic and pathological mechanisms of the brain's 3D anatomical structure.
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Affiliation(s)
- Zhixiang Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
| | - Zhao Feng
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Guangcai Liu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
| | - Anan Li
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Hui Gong
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Xiaoquan Yang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
| | - Xiangning Li
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Research Unit of Multimodal Cross Scale Neural Signal Detection and Imaging, Chinese Academy of Medical Sciences, HUST-Suzhou Institute for Brainsmatics, JITRI, Suzhou, China
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7
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Zeng X, Puonti O, Sayeed A, Herisse R, Mora J, Evancic K, Varadarajan D, Balbastre Y, Costantini I, Scardigli M, Ramazzotti J, DiMeo D, Mazzamuto G, Pesce L, Brady N, Cheli F, Pavone FS, Hof PR, Frost R, Augustinack J, van der Kouwe A, Iglesias JE, Fischl B. Segmentation of supragranular and infragranular layers in ultra-high resolution 7T ex vivo MRI of the human cerebral cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.06.570416. [PMID: 38106176 PMCID: PMC10723438 DOI: 10.1101/2023.12.06.570416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Accurate labeling of specific layers in the human cerebral cortex is crucial for advancing our understanding of neurodevelopmental and neurodegenerative disorders. Leveraging recent advancements in ultra-high resolution ex vivo MRI, we present a novel semi-supervised segmentation model capable of identifying supragranular and infragranular layers in ex vivo MRI with unprecedented precision. On a dataset consisting of 17 whole-hemisphere ex vivo scans at 120 μm, we propose a multi-resolution U-Nets framework (MUS) that integrates global and local structural information, achieving reliable segmentation maps of the entire hemisphere, with Dice scores over 0.8 for supra- and infragranular layers. This enables surface modeling, atlas construction, anomaly detection in disease states, and cross-modality validation, while also paving the way for finer layer segmentation. Our approach offers a powerful tool for comprehensive neuroanatomical investigations and holds promise for advancing our mechanistic understanding of progression of neurodegenerative diseases.
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Affiliation(s)
- Xiangrui Zeng
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Oula Puonti
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Areej Sayeed
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Rogeny Herisse
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Jocelyn Mora
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Kathryn Evancic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Divya Varadarajan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Yael Balbastre
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Irene Costantini
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
- Department of Biology, University of Florence, Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | | | - Danila DiMeo
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Giacomo Mazzamuto
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Italy
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Niamh Brady
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Franco Cheli
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
| | - Francesco Saverio Pavone
- National Research Council - National Institute of Optics (CNR-INO), Sesto Fiorentino, Italy
- European Laboratory for Non-Linear Spectroscopy (LENS), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Italy
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Frost
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Jean Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - André van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Harvard Medical School, Department of Radiology, Boston, MA, USA
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8
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Costantini I, Morgan L, Yang J, Balbastre Y, Varadarajan D, Pesce L, Scardigli M, Mazzamuto G, Gavryusev V, Castelli FM, Roffilli M, Silvestri L, Laffey J, Raia S, Varghese M, Wicinski B, Chang S, Chen IA, Wang H, Cordero D, Vera M, Nolan J, Nestor K, Mora J, Iglesias JE, Garcia Pallares E, Evancic K, Augustinack JC, Fogarty M, Dalca AV, Frosch MP, Magnain C, Frost R, van der Kouwe A, Chen SC, Boas DA, Pavone FS, Fischl B, Hof PR. A cellular resolution atlas of Broca's area. SCIENCE ADVANCES 2023; 9:eadg3844. [PMID: 37824623 PMCID: PMC10569704 DOI: 10.1126/sciadv.adg3844] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/03/2023] [Indexed: 10/14/2023]
Abstract
Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with a digital stereological approach on the 3D reconstruction at cellular resolution from a custom-made inverted confocal light-sheet fluorescence microscope (LSFM). Mesoscale optical coherence tomography enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system. The outcome is an integrated high-resolution cellular census of Broca's area in a human postmortem specimen, within a whole-brain reference space atlas.
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Affiliation(s)
- Irene Costantini
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Biology, University of Florence, Florence, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
| | - Leah Morgan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jiarui Yang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yael Balbastre
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Divya Varadarajan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Division of Physiology, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Giacomo Mazzamuto
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Vladislav Gavryusev
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Bioretics srl, Cesena, Italy
| | | | - Ludovico Silvestri
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Jessie Laffey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sophia Raia
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Merina Varghese
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bridget Wicinski
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shuaibin Chang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | | | - Hui Wang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Devani Cordero
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Matthew Vera
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jackson Nolan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kimberly Nestor
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jocelyn Mora
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Erendira Garcia Pallares
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kathryn Evancic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Morgan Fogarty
- Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Adrian V. Dalca
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew P. Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Caroline Magnain
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Robert Frost
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Andre van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Shih-Chi Chen
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - David A. Boas
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Francesco Saverio Pavone
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- HST, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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9
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Pugliese LA, De Lorenzi V, Bernardi M, Ghignoli S, Tesi M, Marchetti P, Pesce L, Cardarelli F. Unveiling nanoscale optical signatures of cytokine-induced β-cell dysfunction. Sci Rep 2023; 13:13342. [PMID: 37587148 PMCID: PMC10432522 DOI: 10.1038/s41598-023-40272-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 08/08/2023] [Indexed: 08/18/2023] Open
Abstract
Pro-inflammatory cytokines contribute to β-cell failure in both Type-1 and Type-2 Diabetes. Data collected so far allowed to dissect the genomic, transcriptomic, proteomic and biochemical landscape underlying cytokine-induced β-cell progression through dysfunction. Yet, no report thus far complemented such molecular information with the direct optical nanoscopy of the β-cell subcellular environment. Here we tackle this issue in Insulinoma 1E (INS-1E) β-cells by label-free fluorescence lifetime imaging microscopy (FLIM) and fluorescence-based super resolution imaging by expansion microscopy (ExM). It is found that 24-h exposure to IL-1β and IFN-γ is associated with a neat modification of the FLIM signature of cell autofluorescence due to the increase of either enzyme-bound NAD(P)H molecules and of oxidized lipid species. At the same time, ExM-based direct imaging unveils neat alteration of mitochondrial morphology (i.e. ~ 80% increase of mitochondrial circularity), marked degranulation (i.e. ~ 40% loss of insulin granules, with mis-localization of the surviving pool), appearance of F-actin-positive membrane blebs and an hitherto unknown extensive fragmentation of the microtubules network (e.g. ~ 37% reduction in the number of branches). Reported observations provide an optical-microscopy framework to interpret the amount of molecular information collected so far on β-cell dysfunction and pave the way to future ex-vivo and in-vivo investigations.
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Affiliation(s)
- Licia Anna Pugliese
- NEST Laboratory - Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, Italy.
| | - Valentina De Lorenzi
- NEST Laboratory - Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, Italy
| | - Mario Bernardi
- NEST Laboratory - Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, Italy
| | - Samuele Ghignoli
- NEST Laboratory - Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, Italy
| | - Marta Tesi
- Department of Clinical and Experimental Medicine, Islet Cell Laboratory, University of Pisa, Pisa, Italy
| | - Piero Marchetti
- Department of Clinical and Experimental Medicine, Islet Cell Laboratory, University of Pisa, Pisa, Italy
| | - Luca Pesce
- NEST Laboratory - Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, Italy.
| | - Francesco Cardarelli
- NEST Laboratory - Scuola Normale Superiore, Piazza San Silvestro 12, Pisa, Italy.
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10
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Ghose S, Ju Y, McDonough E, Ho J, Karunamurthy A, Chadwick C, Cho S, Rose R, Corwin A, Surrette C, Martinez J, Williams E, Sood A, Al-Kofahi Y, Falo LD, Börner K, Ginty F. 3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging. Commun Biol 2023; 6:718. [PMID: 37468758 PMCID: PMC10356782 DOI: 10.1038/s42003-023-04991-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 05/11/2023] [Indexed: 07/21/2023] Open
Abstract
Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a workflow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fixed skin (stained with 18 biomarkers) from 12 donors aged between 32-72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show significantly shorter distances between immune cells and vascular endothelial cells (56 µm in 3D vs 108 µm in 2D). We also show 10-70% more T cells (total) within 30 µm of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.
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Affiliation(s)
- Soumya Ghose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yingnan Ju
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA
| | | | - Jonhan Ho
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | | | | | - Sanghee Cho
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Rachel Rose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Alex Corwin
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | | | - Jessica Martinez
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Eric Williams
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Anup Sood
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yousef Al-Kofahi
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Louis D Falo
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | - Katy Börner
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA.
| | - Fiona Ginty
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA.
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11
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Ushakov DS, Finke S. Tissue optical clearing and 3D imaging of virus infections. Adv Virus Res 2023; 116:89-121. [PMID: 37524483 DOI: 10.1016/bs.aivir.2023.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
Imaging pathogens within 3D environment of biological tissues provides spatial information about their localization and interactions with the host. Technological advances in fluorescence microscopy and 3D image analysis now permit visualization and quantification of pathogens directly in large tissue volumes and in great detail. In recent years large volume imaging became an important tool in virology research helping to understand the properties of viruses and the host response to infection. In this chapter we give a review of fluorescence microscopy modalities and tissue optical clearing methods used for large volume tissue imaging. A summary of recent applications for virus research is provided with particular emphasis on studies using light sheet fluorescence microscopy. We describe the challenges and approaches for volumetric image analysis. Practical examples of volumetric imaging implemented in virology laboratories and addressing specialized research questions, such as virus tropism and immune host response are described. We conclude with an overview of the emerging technologies and their potential for virus research.
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Affiliation(s)
- Dmitry S Ushakov
- Institute for Molecular Virology and Cell Biology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany.
| | - Stefan Finke
- Institute for Molecular Virology and Cell Biology, Friedrich-Loeffler-Institut, Greifswald-Insel Riems, Germany
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12
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Siwczak F, Hiller C, Pfannkuche H, Schneider MR. Culture of vibrating microtome tissue slices as a 3D model in biomedical research. J Biol Eng 2023; 17:36. [PMID: 37264444 DOI: 10.1186/s13036-023-00357-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/21/2023] [Indexed: 06/03/2023] Open
Abstract
The basic idea behind the use of 3-dimensional (3D) tools in biomedical research is the assumption that the structures under study will perform at the best in vitro if cultivated in an environment that is as similar as possible to their natural in vivo embedding. Tissue slicing fulfills this premise optimally: it is an accessible, unexpensive, imaging-friendly, and technically rather simple procedure which largely preserves the extracellular matrix and includes all or at least most supportive cell types in the correct tissue architecture with little cellular damage. Vibrating microtomes (vibratomes) can further improve the quality of the generated slices because of the lateral, saw-like movement of the blade, which significantly reduces tissue pulling or tearing compared to a straight cut. In spite of its obvious advantages, vibrating microtome slices are rather underrepresented in the current discussion on 3D tools, which is dominated by methods as organoids, organ-on-chip and bioprinting. Here, we review the development of vibrating microtome tissue slices, the major technical features underlying its application, as well as its current use and potential advances, such as a combination with novel microfluidic culture chambers. Once fully integrated into the 3D toolbox, tissue slices may significantly contribute to decrease the use of laboratory animals and is likely to have a strong impact on basic and translational research as well as drug screening.
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Affiliation(s)
- Fatina Siwczak
- Institute of Veterinary Physiology, University of Leipzig, An den Tierkliniken 7, 04103, Leipzig, Germany
| | - Charlotte Hiller
- Institute of Veterinary Physiology, University of Leipzig, An den Tierkliniken 7, 04103, Leipzig, Germany
| | - Helga Pfannkuche
- Institute of Veterinary Physiology, University of Leipzig, An den Tierkliniken 7, 04103, Leipzig, Germany
| | - Marlon R Schneider
- Institute of Veterinary Physiology, University of Leipzig, An den Tierkliniken 7, 04103, Leipzig, Germany.
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13
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Laurino A, Franceschini A, Pesce L, Cinci L, Montalbano A, Mazzamuto G, Sancataldo G, Nesi G, Costantini I, Silvestri L, Pavone FS. A Guide to Perform 3D Histology of Biological Tissues with Fluorescence Microscopy. Int J Mol Sci 2023; 24:ijms24076747. [PMID: 37047724 PMCID: PMC10094801 DOI: 10.3390/ijms24076747] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 03/27/2023] [Accepted: 03/29/2023] [Indexed: 04/09/2023] Open
Abstract
The analysis of histological alterations in all types of tissue is of primary importance in pathology for highly accurate and robust diagnosis. Recent advances in tissue clearing and fluorescence microscopy made the study of the anatomy of biological tissue possible in three dimensions. The combination of these techniques with classical hematoxylin and eosin (H&E) staining has led to the birth of three-dimensional (3D) histology. Here, we present an overview of the state-of-the-art methods, highlighting the optimal combinations of different clearing methods and advanced fluorescence microscopy techniques for the investigation of all types of biological tissues. We employed fluorescence nuclear and eosin Y staining that enabled us to obtain hematoxylin and eosin pseudo-coloring comparable with the gold standard H&E analysis. The computational reconstructions obtained with 3D optical imaging can be analyzed by a pathologist without any specific training in volumetric microscopy, paving the way for new biomedical applications in clinical pathology.
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Affiliation(s)
- Annunziatina Laurino
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Alessandra Franceschini
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Luca Pesce
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Lorenzo Cinci
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, Careggi University Hospital, 50134 Florence, Italy
| | - Alberto Montalbano
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Neurofarba Section of Pharmacology and Toxicology, University of Florence, 50139 Florence, Italy
| | - Giacomo Mazzamuto
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
| | - Giuseppe Sancataldo
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
| | - Gabriella Nesi
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Irene Costantini
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
- Department of Biology, University of Florence, 50019 Florence, Italy
| | - Ludovico Silvestri
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
| | - Francesco Saverio Pavone
- European Laboratory for Non-linear Spectroscopy, LENS, 50019 Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, 50019 Florence, Italy
- National Research Council—National Institute of Optics (CNR-INO), 50125 Sesto Fiorentino, Italy
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14
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Sorelli M, Costantini I, Bocchi L, Axer M, Pavone FS, Mazzamuto G. Fiber enhancement and 3D orientation analysis in label-free two-photon fluorescence microscopy. Sci Rep 2023; 13:4160. [PMID: 36914673 PMCID: PMC10011555 DOI: 10.1038/s41598-023-30953-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 03/03/2023] [Indexed: 03/16/2023] Open
Abstract
Fluorescence microscopy can be exploited for evaluating the brain's fiber architecture with unsurpassed spatial resolution in combination with different tissue preparation and staining protocols. Differently from state-of-the-art polarimetry-based neuroimaging modalities, the quantification of fiber tract orientations from fluorescence microscopy volume images entails the application of specific image processing techniques, such as Fourier or structure tensor analysis. These, however, may lead to unreliable outcomes as they do not isolate myelinated fibers from the surrounding tissue. In this work, we describe a novel image processing pipeline that enables the computation of accurate 3D fiber orientation maps from both grey and white matter regions, exploiting the selective multiscale enhancement of tubular structures of varying diameters provided by a 3D implementation of the Frangi filter. The developed software tool can efficiently generate orientation distribution function maps at arbitrary spatial scales which may support the histological validation of modern diffusion-weighted magnetic resonance imaging tractography. Despite being tested here on two-photon scanning fluorescence microscopy images, acquired from tissue samples treated with a label-free technique enhancing the autofluorescence of myelinated fibers, the presented pipeline was developed to be employed on all types of 3D fluorescence images and fiber staining.
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Affiliation(s)
- Michele Sorelli
- Department of Physics and Astronomy, University of Florence, 50019, Sesto Fiorentino, Italy.,European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, Italy
| | - Irene Costantini
- European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, Italy. .,Department of Biology, University of Florence, 50019, Sesto Fiorentino, Italy. .,National Research Council, National Institute of Optics (CNR-INO), 50019, Sesto Fiorentino, Italy.
| | - Leonardo Bocchi
- Department of Information Engineering, University of Florence, 50039, Florence, Italy
| | - Markus Axer
- Research Centre Jülich, Institute of Neuroscience and Medicine, 52428, Jülich, Germany
| | - Francesco Saverio Pavone
- Department of Physics and Astronomy, University of Florence, 50019, Sesto Fiorentino, Italy.,European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, Italy.,National Research Council, National Institute of Optics (CNR-INO), 50019, Sesto Fiorentino, Italy
| | - Giacomo Mazzamuto
- Department of Physics and Astronomy, University of Florence, 50019, Sesto Fiorentino, Italy.,European Laboratory for Non-Linear Spectroscopy (LENS), 50019, Sesto Fiorentino, Italy.,National Research Council, National Institute of Optics (CNR-INO), 50019, Sesto Fiorentino, Italy
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15
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Efficient 3D light-sheet imaging of very large-scale optically cleared human brain and prostate tissue samples. Commun Biol 2023; 6:170. [PMID: 36781939 PMCID: PMC9925784 DOI: 10.1038/s42003-023-04536-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Accepted: 01/26/2023] [Indexed: 02/15/2023] Open
Abstract
The ability to image human tissue samples in 3D, with both cellular resolution and a large field of view (FOV), can improve fundamental and clinical investigations. Here, we demonstrate the feasibility of light-sheet imaging of ~5 cm3 sized formalin fixed human brain and up to ~7 cm3 sized formalin fixed paraffin embedded (FFPE) prostate cancer samples, processed with the FFPE-MASH protocol. We present a light-sheet microscopy prototype, the cleared-tissue dual view Selective Plane Illumination Microscope (ct-dSPIM), capable of fast 3D high-resolution acquisitions of cm3 scale cleared tissue. We used mosaic scans for fast 3D overviews of entire tissue samples or higher resolution overviews of large ROIs with various speeds: (a) Mosaic 16 (16.4 µm isotropic resolution, ~1.7 h/cm3), (b) Mosaic 4 (4.1 µm isotropic resolution, ~ 5 h/cm3) and (c) Mosaic 0.5 (0.5 µm near isotropic resolution, ~15.8 h/cm3). We could visualise cortical layers and neurons around the border of human brain areas V1&V2, and could demonstrate suitable imaging quality for Gleason score grading in thick prostate cancer samples. We show that ct-dSPIM imaging is an excellent technique to quantitatively assess entire MASH prepared large-scale human tissue samples in 3D, with considerable future clinical potential.
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Wang T, Chen Y, Wang B, Wu M. Recent progress of second near-infrared (NIR-II) fluorescence microscopy in bioimaging. Front Physiol 2023; 14:1126805. [PMID: 36895633 PMCID: PMC9990761 DOI: 10.3389/fphys.2023.1126805] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 02/09/2023] [Indexed: 02/25/2023] Open
Abstract
Visualizing biological tissues in vivo at a cellular or subcellular resolution to explore molecular signaling and cell behaviors is a crucial direction for research into biological processes. In vivo imaging can provide quantitative and dynamic visualization/mapping in biology and immunology. New microscopy techniques combined with near-infrared region fluorophores provide additional avenues for further progress in vivo bioimaging. Based on the development of chemical materials and physical optoelectronics, new NIR-II microscopy techniques are emerging, such as confocal and multiphoton microscopy, light-sheet fluorescence microscopy (LSFM), and wide-field microscopy. In this review, we introduce the characteristics of in vivo imaging using NIR-II fluorescence microscopy. We also cover the recent advances in NIR-II fluorescence microscopy techniques in bioimaging and the potential for overcoming current challenges.
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Affiliation(s)
- Tian Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yingying Chen
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Bo Wang
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Mingfu Wu
- Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Capitini C, Pesce L, Fani G, Mazzamuto G, Genovese M, Franceschini A, Paoli P, Pieraccini G, Zasloff M, Chiti F, Pavone FS, Calamai M. Studying the trafficking of labeled trodusquemine and its application as nerve marker for light-sheet and expansion microscopy. FASEB J 2022; 36:e22655. [PMID: 36421008 PMCID: PMC9827910 DOI: 10.1096/fj.202201276r] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 10/27/2022] [Accepted: 11/01/2022] [Indexed: 11/25/2022]
Abstract
Trodusquemine is an aminosterol with a variety of biological and pharmacological functions, such as acting as an antimicrobial, stimulating body weight loss and interfering with the toxicity of proteins involved in the development of Alzheimer's and Parkinson's diseases. The mechanisms of interaction of aminosterols with cells are, however, still largely uncharacterized. Here, by using fluorescently labeled trodusquemine (TRO-A594 and TRO-ATTO565), we show that trodusquemine binds initially to the plasma membrane of living cells, that the binding affinity is dependent on cholesterol, and that trodusquemine is then internalized and mainly targeted to lysosomes after internalization. We also found that TRO-A594 is able to strongly and selectively bind to myelinated fibers in fixed mouse brain slices, and that it is a marker compatible with tissue clearing and light-sheet fluorescence microscopy or expansion microscopy. In conclusion, this work contributes to further characterize the biology of aminosterols and provides a new tool for nerve labeling suitable for the most advanced microscopy techniques.
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Affiliation(s)
- Claudia Capitini
- European Laboratory for Non‐Linear Spectroscopy (LENS)University of FlorenceSesto FiorentinoItaly,Department of PhysicsUniversity of FlorenceSesto FiorentinoItaly
| | - Luca Pesce
- European Laboratory for Non‐Linear Spectroscopy (LENS)University of FlorenceSesto FiorentinoItaly,Department of PhysicsUniversity of FlorenceSesto FiorentinoItaly
| | - Giulia Fani
- Department of Experimental and Clinical Biomedical Sciences, Section of BiochemistryUniversity of FlorenceFlorenceItaly
| | - Giacomo Mazzamuto
- European Laboratory for Non‐Linear Spectroscopy (LENS)University of FlorenceSesto FiorentinoItaly,Department of PhysicsUniversity of FlorenceSesto FiorentinoItaly,National Institute of Optics – National Research Council (CNR‐INO)Sesto FiorentinoItaly
| | - Massimo Genovese
- Department of Experimental and Clinical Biomedical Sciences, Section of BiochemistryUniversity of FlorenceFlorenceItaly
| | - Alessandra Franceschini
- European Laboratory for Non‐Linear Spectroscopy (LENS)University of FlorenceSesto FiorentinoItaly,Department of PhysicsUniversity of FlorenceSesto FiorentinoItaly
| | - Paolo Paoli
- Department of Experimental and Clinical Biomedical Sciences, Section of BiochemistryUniversity of FlorenceFlorenceItaly
| | | | - Michael Zasloff
- Enterin Inc.PhiladelphiaPennsylvaniaUSA,MedStar‐Georgetown Transplant InstituteGeorgetown University School of MedicineWashingtonDCUSA
| | - Fabrizio Chiti
- Department of Experimental and Clinical Biomedical Sciences, Section of BiochemistryUniversity of FlorenceFlorenceItaly
| | - Francesco S. Pavone
- European Laboratory for Non‐Linear Spectroscopy (LENS)University of FlorenceSesto FiorentinoItaly,Department of PhysicsUniversity of FlorenceSesto FiorentinoItaly,National Institute of Optics – National Research Council (CNR‐INO)Sesto FiorentinoItaly
| | - Martino Calamai
- European Laboratory for Non‐Linear Spectroscopy (LENS)University of FlorenceSesto FiorentinoItaly,National Institute of Optics – National Research Council (CNR‐INO)Sesto FiorentinoItaly
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Meklef RA, Siemers F, Rein S. Development of a 3D-immunofluorescence analysis for sensory nerve endings in human ligaments. J Neurosci Methods 2022; 382:109724. [PMID: 36207004 DOI: 10.1016/j.jneumeth.2022.109724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2022] [Revised: 09/29/2022] [Accepted: 10/01/2022] [Indexed: 11/05/2022]
Abstract
BACKGROUND The analysis of ligamentous mechanoreceptors is difficult due to a high amount of unclassifiable mechanoreceptors, which result from incomplete visualization through limited microscopic techniques. NEW METHOD The method was developed using dorsal intercarpal ligaments and dorsal regions of the scapholunate interosseous ligament from human cadaver wrists. Consecutive 70 µm thick cryosections were stained with immunofluorescence markers for protein S100B, neurotrophin receptor p75 (p75), protein gene product 9.5 (PGP 9.5) and 4',6-diamidino-2-phenylindole (DAPI). 3D images of sensory nerve endings were obtained using a confocal laser scanning microscope. Experimental point spread functions (PSF) were used to deconvolve images. Sensory nerve endings were localised in each section plane and classified according to Freeman and Wyke. Finally, confocal data was visualized as 3D-images. RESULTS The method produced excellent image quality, revealing detailed three-dimensional structures. The created 3D-model of sensory nerve endings could be analyzed in all three dimensions, augmenting visualization of the form and immunoreactive pattern of sensory nerve endings. Deconvolution with experimentally measured PSFs aided in enhancing image quality. COMPARISON WITH EXISTING METHODS Using a triple immunofluorescent staining method allows to visualize the structure of sensory nerve endings more precisely than techniques with serial analysis of different monostaining of neural markers. Imaging in three dimensions enhances morphologic details, which are limited in 2D-microscopy. CONCLUSION 3D-triple immunofluorescence produces high quality visualization of mechanoreceptors, thereby improving their analysis. As an elaborate technique, it is ideal for defined research questions concerning the microstructure of sensory nerve endings.
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Affiliation(s)
- Rami Al Meklef
- Department of Plastic and Hand Surgery, Burn Unit, Klinikum Sankt Georg, Delitzscher Straße 141, 04129 Leipzig, Germany; Martin-Luther-University Halle-Wittenberg, Germany
| | - Frank Siemers
- Martin-Luther-University Halle-Wittenberg, Germany; Department of Plastic and Hand Surgery with Burn Unit, Trauma Center Bergmannstrost, 06112 Halle, Germany
| | - Susanne Rein
- Department of Plastic and Hand Surgery, Burn Unit, Klinikum Sankt Georg, Delitzscher Straße 141, 04129 Leipzig, Germany; Martin-Luther-University Halle-Wittenberg, Germany.
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Costantini I, Axer M, Magnain C, Hof PR. Editorial: The human brain multiscale imaging challenge. Front Neuroanat 2022; 16:1060405. [DOI: 10.3389/fnana.2022.1060405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 10/14/2022] [Indexed: 11/13/2022] Open
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