Mechanisms of cilia regeneration in Xenopus multiciliated epithelium in vivo.

Cilia regeneration is a physiological event, and while studied extensively in unicellular organisms, it remains poorly understood in vertebrates. In this study, using Xenopus multiciliated cells (MCCs) as a model, we demonstrate that, unlike unicellular organisms, deciliation removes the transition zone (TZ) along with the ciliary axoneme. While MCCs immediately begin the regeneration of the ciliary axoneme, surprisingly, the assembly of TZ was delayed. Instead, ciliary tip proteins, Sentan and Clamp, were the first to localize to regenerating cilia. Using cycloheximide (CHX) to block new protein synthesis, we show that the TZ protein B9d1 is not a component of the cilia precursor pool and requires new transcription/translation providing insights into the delayed repair of TZ. Moreover, CHX treatment led MCCs to assemble fewer (~ ten compared to ~150 in controls) but about wild-type length (78% of WT) cilia by gradually concentrating ciliogenesis proteins like IFT43 at a select few basal bodies, highlighting the exciting possibility of protein transport between basal bodies to facilitate faster regeneration in cells with multiple cilia. In summary, we demonstrate that MCCs begin regeneration with the assembly of ciliary tip and axoneme followed by TZ, questioning the importance of TZ in motile ciliogenesis.

A machine learning approach to predict cellular mechanical stresses in response to chemical perturbation.

Mechanical stresses generated at the cell-cell level and cell-substrate level have been suggested to be important in a host of physiological and pathological processes. However, the influence various chemical compounds have on the mechanical stresses mentioned above is poorly understood, hindering the discovery of novel therapeutics, and representing a barrier in the field. To overcome this barrier, we implemented two approaches: 1) monolayer boundary predictor and 2) discretized window predictor utilizing either stepwise linear regression or quadratic support vector machine machine learning model to predict the dose-dependent response of tractions and intercellular stresses to chemical perturbation. We used experimental traction and intercellular stress data gathered from samples subject to 0.2 or 2 µg/mL drug concentrations along with cell morphological properties extracted from the bright-field images as predictors to train our model. To demonstrate the predictive capability of our machine learning models, we predicted tractions and intercellular stresses in response to 0 and 1 µg/mL drug concentrations which were not utilized in the training sets. Results revealed the discretized window predictor trained just with four samples (292 images) to best predict both intercellular stresses and tractions using the quadratic support vector machine and stepwise linear regression models, respectively, for the unseen sample images.