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Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue

The following mini-review attempts to guide researchers in the quantification of fluorescently-labelled proteins within cultured thick or chromogenically-stained proteins within thin sections of brain tissue. It follows from our examination of the utility of Fiji ImageJ thresholding and binarization...

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Autores principales: Healy, Sinéad, McMahon, Jill, FitzGerald, Una
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Medknow Publications & Media Pvt Ltd 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126125/
https://www.ncbi.nlm.nih.gov/pubmed/30127105
http://dx.doi.org/10.4103/1673-5374.235222
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author Healy, Sinéad
McMahon, Jill
FitzGerald, Una
author_facet Healy, Sinéad
McMahon, Jill
FitzGerald, Una
author_sort Healy, Sinéad
collection PubMed
description The following mini-review attempts to guide researchers in the quantification of fluorescently-labelled proteins within cultured thick or chromogenically-stained proteins within thin sections of brain tissue. It follows from our examination of the utility of Fiji ImageJ thresholding and binarization algorithms. Describing how we identified the maximum intensity projection as the best of six tested for two dimensional (2D)-rendering of three-dimensional (3D) images derived from a series of z-stacked micrographs, the review summarises our comparison of 16 global and 9 local algorithms for their ability to accurately quantify the expression of astrocytic glial fibrillary acidic protein (GFAP), microglial ionized calcium binding adapter molecule 1 (IBA1) and oligodendrocyte lineage Olig2 within fixed cultured rat hippocampal brain slices. The application of these algorithms to chromogenically-stained GFAP and IBA1 within thin tissue sections, is also described. Fiji’s BioVoxxel plugin allowed categorisation of algorithms according to their sensitivity, specificity accuracy and relative quality. The Percentile algorithm was deemed best for quantifying levels of GFAP, the Li algorithm was best when quantifying IBA expression, while the Otsu algorithm was optimum for Olig2 staining, albeit with over-quantification of oligodendrocyte number when compared to a stereological approach. Also, GFAP and IBA expression in 3,3′-diaminobenzidine (DAB)/haematoxylin-stained cerebellar tissue was best quantified with Default, Isodata and Moments algorithms. The workflow presented in Figure 1 could help to improve the quality of research outcomes that are based on the quantification of protein with brain tissue.
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spelling pubmed-61261252018-09-12 Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue Healy, Sinéad McMahon, Jill FitzGerald, Una Neural Regen Res Review The following mini-review attempts to guide researchers in the quantification of fluorescently-labelled proteins within cultured thick or chromogenically-stained proteins within thin sections of brain tissue. It follows from our examination of the utility of Fiji ImageJ thresholding and binarization algorithms. Describing how we identified the maximum intensity projection as the best of six tested for two dimensional (2D)-rendering of three-dimensional (3D) images derived from a series of z-stacked micrographs, the review summarises our comparison of 16 global and 9 local algorithms for their ability to accurately quantify the expression of astrocytic glial fibrillary acidic protein (GFAP), microglial ionized calcium binding adapter molecule 1 (IBA1) and oligodendrocyte lineage Olig2 within fixed cultured rat hippocampal brain slices. The application of these algorithms to chromogenically-stained GFAP and IBA1 within thin tissue sections, is also described. Fiji’s BioVoxxel plugin allowed categorisation of algorithms according to their sensitivity, specificity accuracy and relative quality. The Percentile algorithm was deemed best for quantifying levels of GFAP, the Li algorithm was best when quantifying IBA expression, while the Otsu algorithm was optimum for Olig2 staining, albeit with over-quantification of oligodendrocyte number when compared to a stereological approach. Also, GFAP and IBA expression in 3,3′-diaminobenzidine (DAB)/haematoxylin-stained cerebellar tissue was best quantified with Default, Isodata and Moments algorithms. The workflow presented in Figure 1 could help to improve the quality of research outcomes that are based on the quantification of protein with brain tissue. Medknow Publications & Media Pvt Ltd 2018-09 /pmc/articles/PMC6126125/ /pubmed/30127105 http://dx.doi.org/10.4103/1673-5374.235222 Text en Copyright: © Neural Regeneration Research http://creativecommons.org/licenses/by-nc-sa/4.0 This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Review
Healy, Sinéad
McMahon, Jill
FitzGerald, Una
Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue
title Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue
title_full Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue
title_fullStr Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue
title_full_unstemmed Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue
title_short Seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue
title_sort seeing the wood for the trees: towards improved quantification of glial cells in central nervous system tissue
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6126125/
https://www.ncbi.nlm.nih.gov/pubmed/30127105
http://dx.doi.org/10.4103/1673-5374.235222
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