We present an application of nonlinear image registration to align in microscopy time lapse sequences for every frame the cell outline and interior with the outline and interior of the same cell in a reference frame. The registration relies on a subcellular fiducial marker, a cell motion mask, and a topological regularization that enforces diffeomorphism on the registration without significant loss of granularity. This allows spatiotemporal analysis of extremely noisy and diffuse molecular processes across the entire cell. We validate the registration method for different fiducial markers by measuring the intensity differences between predicted and original time lapse sequences of Actin cytoskeleton images and by uncovering zones of spatially organized GEF- and GTPase signaling dynamics visualized by FRET-based activity biosensors in MDA-MB-231 cells. We then demonstrate applications of the registration method in conjunction with stochastic time-series analysis. We describe distinct zones of locally coherent dynamics of the cytoplasmic protein Profilin in U2OS cells. Further analysis of the Profilin dynamics revealed strong relationships with Actin cytoskeleton reorganization during cell symmetry-breaking and polarization. This study thus provides a framework for extracting information to explore functional interactions between cell morphodynamics, protein distributions, and signaling in cells undergoing continuous shape changes. Matlab code implementing the proposed registration method is available at https://github.com/DanuserLab/Mask-Regularized-Diffeomorphic-Cell-Registration.

We present an application of non-linear Image registration that allows spatiotemporal analysis of extremely noisy and diffuse molecular processes across the entire cell. To produce meaningful local tracking of the spatially coherent portion of diffuse protein dynamics, we improved upon existing non-linear image registration to compensate for cell movement and deformation. The registration relies on a subcellular fiducial marker, a cell motion mask, and a topological regularization that enforces diffeomorphism on the registration without significant loss of granularity. We demonstrate the potential of this approach in conjunction with stochastic time-series analysis through the discovery of distinct zones of coherent Profillin dynamics in symmetry-breaking U2OS cells. Further analysis of the resulting Profilin dynamics revealed strong relationships with the underlying actin organization. This study thus provides a framework for extracting functional interactions between cell morphodynamics, protein distributions, and signaling in cells undergoing continuous shape changes.Author SummaryBy adapting optical flow based nonlinear image registration we created a method specific for live cell movies that preserves the dynamics of a signal of interest by remapping using a separate measurable subcellular location fiducial. This is an extension as well on our lab’s previously published method of cell edge-based referencing of subcellular locations that was incapable of extracting interpretable subcellular time series more than a few microns away from the cell edge. We showed that our method overcomes this key limitation and allows sampling of subcellular time series from every subcellular location through our discovery of organized profilin dynamics in moving cell and that these profilin dynamics are related to actin dynamics due to their ability to bind growing actin structures likely through actin polymerizing factors. Most importantly, our method is applicable to discovering subcellular organization and coordination in a widely used form of live cell microscopy data that hitherto has been largely limited to anecdotal analysis.