Morphogenesis requires highly coordinated, complex interactions between cellular processes: proliferation, migration, and apoptosis, along with physical tissue interactions. How these cellular and tissue dynamics drive morphogenesis remains elusive. Three dimensional (3D) microscopic imaging poses great promise, and generates beautiful images. However, generating even moderate through-put quantified images is challenging for many reasons. As a result, the association between morphogenesis and cellular processes in 3D developing tissues has not been fully explored. To address this critical gap, we have developed an imaging and image analysis pipeline to enable 3D quantification of cellular dynamics along with 3D morphology for the same individual embryo. Specifically, we focus on how 3D distribution of proliferation relates to morphogenesis during mouse facial development. Our method involves imaging with light-sheet microscopy, automated segmentation of cells and tissues using machine learning-based tools, and quantification of external morphology via geometric morphometrics. Applying this framework, we show that changes in proliferation are tightly correlated to changes in morphology over the course of facial morphogenesis. These analyses illustrate the potential of this pipeline to investigate mechanistic relationships between cellular dynamics and morphogenesis during embryonic development.
A variety of genetic mutations affect cell proliferation during organism development, leading to structural birth defects. However, the mechanisms by which these alterations influence the development of the face remain unclear. Cell proliferation and its relation to shape variation can be studied using Light-Sheet Microscopy (LSM) imaging across a range of developmental time points using mouse models. The aim of this work was to develop and evaluate accurate automatic methods based on convolutional neural networks (CNNs) for: (i) tissue segmentation (neural ectoderm and mesenchyme), (ii) cell segmentation in nuclear-stained images, and (iii) segmentation of proliferating cells in phospho-Histone H3 (pHH3)-stained LSM images of mouse embryos. For training and evaluation of the CNN models, 155 to 176 slices from 10 mouse embryo LSM images with corresponding manual segmentations were available depending on the segmentation task. Three U-net CNN models were trained optimizing their loss functions, among other hyper-parameters, depending on the segmentation task. The tissue segmentation achieved a macro-average F-score of 0.84, whereas the inter-observer value was 0.89. The cell segmentation achieved a Dice score of 0.57 and 0.56 for nuclear-stained and pHH3-stained images, respectively, whereas the corresponding inter-observer Dice scores were 0.39 and 0.45, respectively. The proposed pipeline using the U-net CNN architecture can accelerate LSM image analysis and together with the annotated datasets can serve as a reference for comparison of more advanced LSM image segmentation methods in future.
One common phenotype observed in response to many developmental perturbations is a change in proliferation or apoptosis. Further, it is often predicted that small changes in proliferation or apoptosis can explain the development of a structural birth defect. One flaw with this logic is that little is known about the relationships between proliferation and morphology in the face. Does proliferation actually play a role in the normal directional outgrowth and morphological changes which pattern the developing face? Here, we set out to understand the spatial distribution of proliferation in the developing mouse face and relate regional proliferation to the growth of the face over a small span of developmental time (E10‐E11). We use light sheet microscopy to capture total and proliferating nuclei in 30 E10.5 to 11.5 mouse embryo heads. Cells are quantified using a convolutional neural network methodology that has similar accuracy in cell identification to the between observer error. From these images, we then generate an atlas using linear and non‐linear transformation and perform analysis of embryo morphology and distribution of proliferation relative to total cells. Models of proliferation and its ability to alter morphology are generated in PhysiCell (http://www.physicell.org). We identify regions where there is both change in proliferation and morphology that relates to changes in the number of tail somites. We also use the spatial data gathered from these to inform a model of growth of the maxillary prominence to determine how much proliferation is likely to contribute to the directional growth of the maxilla.
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