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

Authors :
Sinéad Healy
Jill McMahon
Una FitzGerald
Source :
Neural Regeneration Research, Vol 13, Iss 9, Pp 1520-1523 (2018)
Publication Year :
2018
Publisher :
Wolters Kluwer Medknow Publications, 2018.

Abstract

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.

Details

Language :
English
ISSN :
16735374 and 96374926
Volume :
13
Issue :
9
Database :
Directory of Open Access Journals
Journal :
Neural Regeneration Research
Publication Type :
Academic Journal
Accession number :
edsdoj.963749263ed4a0cb5140fa8305f53a1
Document Type :
article
Full Text :
https://doi.org/10.4103/1673-5374.235222