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 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. Open in a separate window Figure 1 Image analysis workflow. Z-stack images of immunofluorescently stained ex vivo brain slice cultures are acquired using a laser scanning confocal microscope. After post-processing (background subtraction and despeckling) the stacks are converted to maximum intensity projections and analysis of automatic threshold algorithms is then carried out using the Biovoxxel plug-in. Optimal projection and thresholding methods for each glial cell type are summarised in in the bottom pane and all steps are automated in a macro. TP: True positive; TN: true negative; FP: false positive; FNL false negative; OLs: oligodendrocytes.