Abstract
A common feature of morphogenesis is the formation of three-dimensional structures from the folding of two-dimensional epithelial sheets, aided by cell shape changes at the cellular-level. Changes in cell shape must be studied in the context of cell-polarised biomechanical processes within the epithelial sheet. In epithelia with highly curved surfaces, finding single-cell alignment along a biological axis can be difficult to automate in silico. We present 'Origami', a MATLAB-based image analysis pipeline to compute direction-variant cell shape features along the epithelial apico-basal axis. Our automated method accurately computed direction vectors denoting the apico-basal axis in regions with opposing curvature in synthetic epithelia and fluorescence images of zebrafish embryos. As proof of concept, we identified different cell shape signatures in the developing zebrafish inner ear, where the epithelium deforms in opposite orientations to form different structures. Origami is designed to be user-friendly and is generally applicable to fluorescence images of curved epithelia.
Highlights
Complex morphologies across taxa and tissue types are generated through the deformation of epithelial sheets [1,2,3]
The genetic and biomechanical processes driving epithelial folding can be polarised in the epithelium, leading to asymmetric shape changes at the single cell level
Defects in such epithelial shaping have been linked to many developmental anomalies and diseases
Summary
Complex morphologies across taxa and tissue types are generated through the deformation of epithelial sheets [1,2,3]. Fluorescence imaging techniques have made it possible to follow such shape changes at cellular resolution, in vivo and in real-time [6,7,8] These imaging advances have driven the development of tools to quantify epithelial dynamics, especially cell shape changes. Many image analysis tools measuring cell shape change have been limited to two-dimensional [9,10,11,12] or quasi-3D fluorescence microscopy data [13] Extending these measurements to 3D has been aided by the development of membrane-based 3D segmentation methods such as ACME [14], RACE [15], 3DMMS [16], CellProfiler 3.0 [17], and more recently, deep-learning-based methods [18,19,20,21]. Finding the position of 3D-segmented cells along biologically relevant axes to quantify directionally variant shape features is still a challenging problem that has so far not seen a generalised solution
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