The complex three-dimensional organization of epithelial tissues

Gómez-Gálvez, Pedro; Vicente‐Munuera, Pablo; Anbari, Samira; Buceta, Javier; Escudero, Luis

doi:10.1242/dev.195669

Cited by 31 publications

(32 citation statements)

References 107 publications

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“…Note that we have only worked with apical surfaces of epithelial tissues. Another interesting direction is to adapt our analysis to 3D epithelium, which is nowadays a very active research field [33], as well as to other fields, such as material science [34].…”

Section: Discussionmentioning

confidence: 99%

Stable Topological Summaries for Analyzing the Organization of Cells in a Packed Tissue

2021

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We use topological data analysis tools for studying the inner organization of cells in segmented images of epithelial tissues. More specifically, for each segmented image, we compute different persistence barcodes, which codify the lifetime of homology classes (persistent homology) along different filtrations (increasing nested sequences of simplicial complexes) that are built from the regions representing the cells in the tissue. We use a complete and well-grounded set of numerical variables over those persistence barcodes, also known as topological summaries. A novel combination of normalization methods for both the set of input segmented images and the produced barcodes allows for the proven stability results for those variables with respect to small changes in the input, as well as invariance to image scale. Our study provides new insights to this problem, such as a possible novel indicator for the development of the drosophila wing disc tissue or the importance of centroids’ distribution to differentiate some tissues from their CVT-path counterpart (a mathematical model of epithelia based on Voronoi diagrams). We also show how the use of topological summaries may improve the classification accuracy of epithelial images using a Random Forest algorithm.

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Section: Discussionmentioning

confidence: 99%

Stable Topological Summaries for Analyzing the Organization of Cells in a Packed Tissue

2021

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“…The particular challenges to advance to 3D modelling mainly is the additional symmetry breaking via the apico-basal polarization of cells and the contact with the extracellular matrix. 150 One of the most advanced theoretical models for organoids 119 couples the 3D vertex model 142 with biochemical signaling based on a reaction-diffusion mechanism. 25 The chemo-mechanical coupling is implemented in the following way: the cell proliferation rate is linked to the concentration of an activator molecule.…”

Section: Vertex Modelsmentioning

confidence: 99%

Retina organoids: Window into the biophysics of neuronal systems

2022

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With a kind of magnetism, the human retina draws the eye of neuroscientist and physicist alike. It is attractive as a self-organizing system, which forms as a part of the central nervous system via biochemical and mechanical cues. The retina is also intriguing as an electro-optical device, converting photons into voltages to perform on-the-fly filtering before the signals are sent to our brain. Here, we consider how the advent of stem cell derived in vitro analogs of the retina, termed Retina Organoids, opens up an exploration of the interplay between optics, electrics and mechanics in a complex neuronal network, all in a Petri dish. This review presents state-of-the-art retina organoid protocols by emphasizing links to the biochemical and mechanical signals of in vivo retinogenesis. Electrophysiological recording of active signal processing becomes possible as retina organoids generate light sensitive and synaptically connected photoreceptors. Experimental biophysical tools provide data to steer the development of mathematical models operating at different levels of coarse-graining. In concert, they provide a means to study how mechanical factors guide retina self-assembly. In turn, this understanding informs the engineering of mechanical signals required to tailor the growth of neuronal network morphology. Tackling the complex developmental and computational processes in the retina requires an interdisciplinary endeavor combining experiment and theory, physics and biology. The reward is enticing: in the next few years, retina organoids could offer a glimpse inside the machinery of simultaneous cellular self-assembly and signal processing, all in an in vitro setting.

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“…Frequently their applications have been limited to segmentations in two dimensions due to the fact that three-dimensional data sets are difficult to obtain in some biological fields, where the availability of raw images is limited or where the segmentation is particularly difficult [29,30]. However, the necessity of studying biological tissues in a context of three-dimensionality has been proved, for example, to unveil the presence of a novel cellular shape necessary to pack efficiently monolayer epithelial tissues in curvature conditions: the scutoids [31], or to analyse the composition of other complex tissues, such as multilayer epithelia, where tetradecahedral cells predominate [32]. Consequently, some three-dimensional biological studies cannot progress as fast as they expect, and they still need to optimise the segmentation process manually or through classical approaches, which may cause significant delays.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster

et al. 2021

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Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine-tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues.

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The complex three-dimensional organization of epithelial tissues

Cited by 31 publications

References 107 publications

Stable Topological Summaries for Analyzing the Organization of Cells in a Packed Tissue

Stable Topological Summaries for Analyzing the Organization of Cells in a Packed Tissue

Retina organoids: Window into the biophysics of neuronal systems

Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster

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