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Tomographic brain imaging with nucleolar detail and automatic cell counting

Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we sh...

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Autores principales: Hieber, Simone E., Bikis, Christos, Khimchenko, Anna, Schweighauser, Gabriel, Hench, Jürgen, Chicherova, Natalia, Schulz, Georg, Müller, Bert
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007499/
https://www.ncbi.nlm.nih.gov/pubmed/27581254
http://dx.doi.org/10.1038/srep32156
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author Hieber, Simone E.
Bikis, Christos
Khimchenko, Anna
Schweighauser, Gabriel
Hench, Jürgen
Chicherova, Natalia
Schulz, Georg
Müller, Bert
author_facet Hieber, Simone E.
Bikis, Christos
Khimchenko, Anna
Schweighauser, Gabriel
Hench, Jürgen
Chicherova, Natalia
Schulz, Georg
Müller, Bert
author_sort Hieber, Simone E.
collection PubMed
description Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we show that hard X-ray phase tomography can visualise a volume of up to 43 mm(3) of human post mortem or biopsy brain samples, by demonstrating the method on the cerebellum. We automatically identified 5,000 Purkinje cells with an error of less than 5% at their layer and determined the local surface density to 165 cells per mm(2) on average. Moreover, we highlight that three-dimensional data allows for the segmentation of sub-cellular structures, including dendritic tree and Purkinje cell nucleoli, without dedicated staining. The method suggests that automatic cell feature quantification of human tissues is feasible in phase tomograms obtained with isotropic resolution in a label-free manner.
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spelling pubmed-50074992016-09-07 Tomographic brain imaging with nucleolar detail and automatic cell counting Hieber, Simone E. Bikis, Christos Khimchenko, Anna Schweighauser, Gabriel Hench, Jürgen Chicherova, Natalia Schulz, Georg Müller, Bert Sci Rep Article Brain tissue evaluation is essential for gaining in-depth insight into its diseases and disorders. Imaging the human brain in three dimensions has always been a challenge on the cell level. In vivo methods lack spatial resolution, and optical microscopy has a limited penetration depth. Herein, we show that hard X-ray phase tomography can visualise a volume of up to 43 mm(3) of human post mortem or biopsy brain samples, by demonstrating the method on the cerebellum. We automatically identified 5,000 Purkinje cells with an error of less than 5% at their layer and determined the local surface density to 165 cells per mm(2) on average. Moreover, we highlight that three-dimensional data allows for the segmentation of sub-cellular structures, including dendritic tree and Purkinje cell nucleoli, without dedicated staining. The method suggests that automatic cell feature quantification of human tissues is feasible in phase tomograms obtained with isotropic resolution in a label-free manner. Nature Publishing Group 2016-09-01 /pmc/articles/PMC5007499/ /pubmed/27581254 http://dx.doi.org/10.1038/srep32156 Text en Copyright © 2016, The Author(s) http://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/
spellingShingle Article
Hieber, Simone E.
Bikis, Christos
Khimchenko, Anna
Schweighauser, Gabriel
Hench, Jürgen
Chicherova, Natalia
Schulz, Georg
Müller, Bert
Tomographic brain imaging with nucleolar detail and automatic cell counting
title Tomographic brain imaging with nucleolar detail and automatic cell counting
title_full Tomographic brain imaging with nucleolar detail and automatic cell counting
title_fullStr Tomographic brain imaging with nucleolar detail and automatic cell counting
title_full_unstemmed Tomographic brain imaging with nucleolar detail and automatic cell counting
title_short Tomographic brain imaging with nucleolar detail and automatic cell counting
title_sort tomographic brain imaging with nucleolar detail and automatic cell counting
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5007499/
https://www.ncbi.nlm.nih.gov/pubmed/27581254
http://dx.doi.org/10.1038/srep32156
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