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. Müller 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.
Highlights
Glial morphology is complex, making it challenging to accurately quantify 3D cell shape.
We developed the GliaMorph toolkit for image pre-processing, glial segmentation, and quantification of Müller glial cells.
Müller glia elaborate their morphology and rearrange subcellular features during embryonic development.
GliaMorph accurately identifies subcellular changes in models with disrupted glia cells, including zebrafish cadherin2 loss of function and a mouse glaucoma model.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Conflict of Interest. The authors declare that no conflict of interest exists.
Funding: This project was funded by a Moorfields Eye Charity Springboard award (GR001208) funding ECK and a Biotechnology and Biological Sciences David Phillips Fellowship (BB/S010386/1) awarded to RBM. SM and BMS were funded by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement no. 681808). PM acknowledges funding from CIHR PJT 166032 and CIHR PJT 166074. The funders had no role in the study design, data collection, and analysis, decision to publish, or preparation of the manuscript.