Position and dynamics of cells are essential pieces of information for the study of embryonic development. Unfortunately, this information is lost in many cell gene expression analysis processes, such as single cell RNA sequencing. Being able to predict the physical positions and the temporal dynamics of cells from gene expression data is therefore a major challenge. After motivating our study with data from C. elegans development, we first review current methods based on optimal transport that aim at either predicting the spatial position of cells from transcriptomic data or interpolating differentiation trajectories from time series of transcriptomic data. However, they are not designed to capture simple temporal transformations of spatial data such as a rotation, we propose an extension of the framework proposed by Nitzan et al. [8] including a temporal regularization for the inference of the optimal transport plan. This new framework is tested on artificial data using a combination of the Sinkhorn algorithm and gradient descent. We show that we can successfully learn simple dynamic transformations from very high dimensional data.