Hildebrand, Sven ; Schueth, Anna ; Herrler, Andreas ; Galuske, Ralf A. W. ; Roebroeck, Alard (2019)
Scalable Labeling for Cytoarchitectonic Characterization of Large Optically Cleared Human Neocortex Samples.
In: Scientific reports, 9 (1)
doi: 10.1038/s41598-019-47336-9
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
Optical clearing techniques and light sheet microscopy have transformed fluorescent imaging of rodent brains, and have provided a crucial alternative to traditional confocal or bright field techniques for thin sections. However, clearing and labeling human brain tissue through all cortical layers and significant portions of a cortical area, has so far remained extremely challenging, especially for formalin fixed adult cortical tissue. Here, we present MASH (Multiscale Architectonic Staining of Human cortex): a simple, fast and low-cost cytoarchitectonic labeling approach for optically cleared human cortex samples, which can be applied to large (up to 5 mm thick) formalin fixed adult brain samples. A suite of small-molecule fluorescent nuclear and cytoplasmic dye protocols in combination with new refractive index matching solutions allows deep volume imaging. This greatly reduces time and cost of imaging cytoarchitecture in thick samples and enables classification of cytoarchitectonic layers over the full cortical depth. We demonstrate application of MASH to large archival samples of human visual areas, characterizing cortical architecture in 3D from the scale of cortical areas to that of single cells. In combination with scalable light sheet imaging and data analysis, MASH could open the door to investigation of large human cortical systems at cellular resolution and in the context of their complex 3-dimensional geometry.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Hildebrand, Sven ; Schueth, Anna ; Herrler, Andreas ; Galuske, Ralf A. W. ; Roebroeck, Alard |
| Art des Eintrags: | Bibliographie |
| Titel: | Scalable Labeling for Cytoarchitectonic Characterization of Large Optically Cleared Human Neocortex Samples. |
| Sprache: | Englisch |
| Publikationsjahr: | 26 Juli 2019 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Scientific reports |
| Jahrgang/Volume einer Zeitschrift: | 9 |
| (Heft-)Nummer: | 1 |
| DOI: | 10.1038/s41598-019-47336-9 |
| Kurzbeschreibung (Abstract): | Optical clearing techniques and light sheet microscopy have transformed fluorescent imaging of rodent brains, and have provided a crucial alternative to traditional confocal or bright field techniques for thin sections. However, clearing and labeling human brain tissue through all cortical layers and significant portions of a cortical area, has so far remained extremely challenging, especially for formalin fixed adult cortical tissue. Here, we present MASH (Multiscale Architectonic Staining of Human cortex): a simple, fast and low-cost cytoarchitectonic labeling approach for optically cleared human cortex samples, which can be applied to large (up to 5 mm thick) formalin fixed adult brain samples. A suite of small-molecule fluorescent nuclear and cytoplasmic dye protocols in combination with new refractive index matching solutions allows deep volume imaging. This greatly reduces time and cost of imaging cytoarchitecture in thick samples and enables classification of cytoarchitectonic layers over the full cortical depth. We demonstrate application of MASH to large archival samples of human visual areas, characterizing cortical architecture in 3D from the scale of cortical areas to that of single cells. In combination with scalable light sheet imaging and data analysis, MASH could open the door to investigation of large human cortical systems at cellular resolution and in the context of their complex 3-dimensional geometry. |
| ID-Nummer: | pmid:31350519 |
| Fachbereich(e)/-gebiet(e): | 10 Fachbereich Biologie 10 Fachbereich Biologie > Systemische Neurophysiologie |
| Hinterlegungsdatum: | 30 Jul 2019 06:16 |
| Letzte Änderung: | 26 Aug 2019 09:44 |
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| Suche nach Titel in: | TUfind oder in Google |
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