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Quantitative analysis of cell shape and the cytoskeleton in developmental biology

Advanced Review

Hannah G. Yevick, Adam C. Martin

Published Online: Aug 31 2018

DOI: 10.1002/wdev.333

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Computational approaches that enable quantification of microscopy data have revolutionized the field of developmental biology. Due to its inherent complexity, elucidating mechanisms of development requires sophisticated analysis of the structure, shape, and kinetics of cellular processes. This need has prompted the creation of numerous techniques to visualize, quantify, and merge microscopy data. These approaches have defined the order and structure of developmental events, thus, providing insight into the mechanisms that drive them. This review describes current computational approaches that are being used to answer developmental questions related to morphogenesis and describe how these approaches have impacted the field. Our intent is not to comprehensively review techniques, but to highlight examples of how different approaches have impacted our understanding of development. Specifically, we focus on methods to quantify cell shape and cytoskeleton structure and dynamics in developing tissues. Finally, we speculate on where the future of computational analysis in developmental biology might be headed. This article is categorized under: Technologies > Analysis of Cell, Tissue, and Animal Phenotypes Early Embryonic Development > Gastrulation and Neurulation Early Embryonic Development > Development to the Basic Body Plan

Analyzing complex cell shapes: (a) a wounded region in the Drosophila embryo expressing a green fluorescent protein fusion targeted to adherens junctions. The automated tracing algorithm, MEDUSA, employs an active contour algorithm where the delineation of the wound evolves to minimize its energy (green). (Reprinted with permission from Zulueta‐Coarasa, Tamada, Lee, & Fernandez‐Gonzalez (). Copyright 2014 The Company of Biologists) (b) Pavement cells in Arabidopsis thaliana exhibit complex shapes. (Reprinted with permission from Sanchez‐Corrales, Hartley, van Rooij, Maree, & Grieneisen (). Copyright 2018 The Company of Biologists) (c) Complex cell outlines can be approximated using elliptic Fourier analysis (LOCO‐EFA), which deconstructs cell shape into an infinite sum of elliptical modes. (Reprinted with permission from Sanchez‐Corrales et al. (). Copyright 2018 The Company of Biologists) Scale bar: 20 μm

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Image correlation analysis and measuring cytoskeletal flow. (a) Analysis on the Caenorhabditis elegans embryo illustrates how PIV can be used to detect protein movement during gastrulation. Images show PIV vectors tracking the movement of myosin clusters (green arrows) and the cell membrane (red arrows). Note that cell membrane movement becomes aligned with centripetal myosin movement at the late stages of gastrulation. (Reprinted with permission from Roh‐Johnson et al. (). Copyright 2012 American Association for the Advancement of Science) Scale bar: 5 μm. (b) Spatiotemporal image correlation spectroscopy analysis performed on a transmitted light video (left) quantifies the local velocities of yolk granules to measure cytoplasmic activity. (Reprinted with permission from Almonacid et al. (). Copyright 2015 Springer Nature) Scale bar: 15 μm

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Analyzing cytoskeletal dynamics. (a) Cross‐correlation between two signals identifies the time shift of one relative to the other. Here the time offset quantifies the delay between the time when the signal of interest peaks and the time when myosin is at local maximum. (b) To quantitatively identify a sequence of myosin pulses in the Drosophila ventral furrow, first cells were segmented and tracked. Myosin intensity was then quantified inside each cell and a multiple Gaussian fitting to the myosin intensity identified pulse sequences which were confirmed via manual curation. (Reprinted with permission from Xie & Martin (). Copyright 2015 Springer Nature)

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Analyzing cytoskeletal orientation at single time points. (a) Actin cables in fission yeast marked via GFP‐CHD. Actin is traced by SOAX analysis which also infers the topology of the network to identify network junctions (green). (Reprinted with permission from Xu et al. (). Copyright 2015 Springer Nature) (b) A Fourier transform is employed to calculate the local orientation of Dachs:GFP (D:GFP) in the Drosophila dorsal thorax. First, the 2D discrete Fourier transform is calculated on the original image (after multiplication by a square cosine function, “windowing,” to minimize edge effects). Pixels above the 80th percentile are retained and fitted by an ellipse. From the ellipse the anisotropy and orientation of the original image can be extracted. Green bars plotted over the tissue represent local D:GFP anisotropy. (Reprinted with permission from Bosveld et al. (). Copyright 2012 American Association for the Advancement of Science) Scale bar: 10 μm

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Analyzing the three‐dimensional (3D) shapes of cells in developing tissues. (a) 3D reconstruction of cell shape can be approximated by stacking the polygons obtained in each z frame from 2D cell segmentation algorithms. Tracking these stacks over time yields a four‐dimensional mapping of each cell in the tissue. (Reprinted with permission from Gelbart et al. (). Copyright 2012 National Academy of Sciences) (b) Reconstruction of cell shape using EDGE4D during mitotic divisions in the Drosophila embryo. 3D cell shape is followed before mitotic entry, at metaphase (yellow box, 0 s), during cytokinesis, and after abscission. (Reprinted with permission from Chanet, Sharan, Khan, & Martin (). Copyright 2017 Cell Press) (c) Multiangle acquisition of Arabidopsis thaliana flower expressing a flower‐specific GFP marker is fused into a single reconstruction. Each cell is automatically segmented and the full 3D organ reconstruction is represented in blue. By fusing acquisitions from different angles, the limitations of microscope resolution anisotropy on cell segmentation are avoided. (Reprinted with permission from Fernandez et al. (). Copyright 2010 Springer Nature) Scale bar: 25 μm. (d) Tissue cartography reduces the 3D shape of a tissue by identifying a surface of interest. The surface of Drosophila embryo is parameterized to map signal measured on its surface to a single plane for analysis. (Reprinted with permission from Heemskerk & Streichan (). Copyright 2015 eLife Sciences Publications)

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Analyzing how cell shape change and rearrangement contribute to tissue shape. (a) Simulation of one possible cellular scenario during tissue morphogenesis. Strain from cell shape changes counteract strain due to cell intercalations to result in no overall tissue shape change. Cumulative stretch ratios are plotted on a log scale versus time for vertical (solid line) and horizontal orientation (dotted line) for (b) tissue shape, (c) cell shape, and (d) cell intercalation. (Reprinted with permission from Blanchard et al. (). Copyright 2009 Springer Nature)

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