Deep learning-based reconstruction of embryonic cell-division cycle from label-free microscopy time-series of evolutionarily diverse nematodes

Microscopy of cellular dynamics during embryogenesis of non-model organisms can be tech- nically challenging due to limitations of molecular labelling methods. Label-free differential interference contrast (DIC) microscopy of the first embryonic cell division of nematodes related to Caenorhabditis elegans has been successfully employed to examine the constraints and divergence of intra-cellular mechanisms during this asymmetric cell division. However, identifying stages of the cell division cycle were performed interactively, pointing to a need to automate of cell stage identification from DIC microscopy. To this end, we have trained deep convolutional neural networks (CNNs), both pre-existing such as ResNet, VGGNet and EfficientNet, and a customized shallow network, EvoCellNet, to automatically classify first-embryonic division into the stages: (i) pro-nuclear migration and (ii) centration and rotation, (iii) spindle elongation and (iv) cytokinesis, with all networks performing with 91% or greater accuracy. The activations of the networks superimposed on the images result in segmentation-free detection of intracellular features such as pro-nuclei, spindle and spindle- poles in case of the shallow EvoCellNet, while ResNet, VGGNet and and EfficientNet detect large-scale, features that are less biologically meaningful. The UMAP space representation combined with support vector machines (SVM) allows for stage boundary identification and recovers a cyclical map connecting the states (i) to (iv) of the division. This approach could be used to automate quantification of cell division stages and sub-cellular dynamics without explicit labelling in label-free microscopy. SummaryWe have trained multiple convolutional neural networks (CNNs) to classify the stages of cell division from the first embryonic division of diverse nematodes, evolutionarily related to Caenorhabditis elegans. We find two classifiers, VggNet and a customized EvoCellNet, can detect intracellular features and a UMAP representation can reconstruct the cyclical progression of first embryonic division from related species.