GliaMorph: A modular image analysis toolkit to quantify Müller glial cell morphology

Abstract

Cell morphology is critical for all cell functions. This is particularly true for glial cells as they rely on their complex shape to contact and support neurons. However, methods to quantify complex glial cell shape accurately and reproducibly are lacking. To address this gap in quantification approaches, we developed an analysis pipeline called "GliaMorph". GliaMorph is a modular image analysis toolkit developed to perform (i) image pre-processing, (ii) semi-automatic region-of-interest (ROI) selection, (iii) apicobasal texture analysis, (iv) glia segmentation, and (v) cell feature quantification. Mueller Glia (MG) are the principal retinal glial cell type with a stereotypic shape linked to their maturation and physiological status. We here characterized MG on three levels, including (a) global image-level, (b) apicobasal texture, and (c) apicobasal vertical-to-horizontal alignment. Using GliaMorph, we show structural changes occurring in the developing retina. Additionally, we study the loss of cadherin2 in the zebrafish retina, as well as a glaucoma mouse disease model. The GliaMorph toolkit enables an in-depth understanding of MG morphology in the developing and diseased retina.

Glia Cell Morphology Analysis Using the Fiji GliaMorph Toolkit

“…We do not discuss data acquisition and properties here, as this was addressed elsewhere (Kugler et al, 2023).…”

“…To address the current lack in terms of glia analysis workflows, we developed GliaMorph, a glia image analysis toolkit developed in the open‐source image analysis software Fiji (Schindelin et al., 2012) to analyze 3D glia morphology (Kugler et al, 2023). Briefly, GliaMorph covers: image pre‐processing : to improve image quality by understanding data in 3D and performing deconvolution; semi‐automatic region‐of‐interest (ROI) selection : to make images more comparable between samples and groups by semi‐automatic image rotation and 3D‐cropping; apicobasal intensity profile plots : to assess texture and complexity, which can be applied to original, segmented, and skeletonized data; MG segmentation : to binarize input data to background (non‐glia voxels) and foreground (glia voxels), which will be the foundation for extracting parameters such as volume and percentage volume coverage; and 3D feature quantification : which extracts (i) the surface to analyze surface, (ii) the Euclidean distance map to quantify thickness, and (iii) the skeleton to analyze skeleton length, number of junctions, and number of endpoints. …”