Fully automated attenuation measurement and motion correction in FLIP image sequences

Non-Rigid Contour-Based Registration of Cell Nuclei in 2-D Live Cell Microscopy Images Using a Dynamic Elasticity Model

The analysis of the pure motion of subnuclear structures without influence of the cell nucleus motion and deformation is essential in live cell imaging. In this paper, we propose a 2-D contour-based image registration approach for compensation of nucleus motion and deformation in fluorescence microscopy time-lapse sequences. The proposed approach extends our previous approach, which uses a static elasticity model to register cell images. Compared with that scheme, the new approach employs a dynamic elasticity model for the forward simulation of nucleus motion and deformation based on the motion of its contours. The contour matching process is embedded as a constraint into the system of equations describing the elastic behavior of the nucleus. This results in better performance in terms of the registration accuracy. Our approach was successfully applied to real live cell microscopy image sequences of different types of cells including image data that was specifically designed and acquired for evaluation of cell image registration methods. An experimental comparison with the existing contour-based registration methods and an intensity-based registration method has been performed. We also studied the dependence of the results on the choice of method parameters.

Diffeomorphic Multi-Frame Non-Rigid Registration of Cell Nuclei in 2D and 3D Live Cell Images

To gain a better understanding of cellular and molecular processes, it is important to quantitatively analyze the motion of subcellular particles in live cell microscopy image sequences. Since, generally, the subcellular particles move and cell nuclei move as well as deform, it is important to decouple the movement of particles from that of the cell nuclei using non-rigid registration methods. We have developed a diffeomorphic multi-frame approach for non-rigid registration of cell nuclei in 2D and 3D live cell fluorescence microscopy images. Our non-rigid registration approach is based on local optic flow estimation, exploits information from multiple consecutive image frames, and determines diffeomorphic transformations in the log-domain, which allows efficient computation of the inverse transformations. To register single images of an image sequence to a reference image, we use a temporally weighted mean image, which is constructed based on inverse transformations and multiple consecutive frames. Using multiple consecutive frames improves the registration accuracy compared to pairwise registration, and using a temporally weighted mean image significantly reduces the computation time compared with previous work. In addition, we use a flow boundary preserving method for regularization of computed deformation vector fields, which prevents from over-smoothing compared to standard Gaussian filtering. Our approach has been successfully applied to 2D and 3D synthetic as well as real live cell microscopy image sequences, and an experimental comparison with non-rigid pairwise, multi-frame, and temporal groupwise registration has been carried out.

Quantitative fluorescence loss in photobleaching for analysis of protein transport and aggregation

Background

Fluorescence loss in photobleaching (FLIP) is a widely used imaging technique, which provides information about protein dynamics in various cellular regions. In FLIP, a small cellular region is repeatedly illuminated by an intense laser pulse, while images are taken with reduced laser power with a time lag between the bleaches. Despite its popularity, tools are lacking for quantitative analysis of FLIP experiments. Typically, the user defines regions of interest (ROIs) for further analysis which is subjective and does not allow for comparing different cells and experimental settings.

Results

We present two complementary methods to detect and quantify protein transport and aggregation in living cells from FLIP image series. In the first approach, a stretched exponential (StrExp) function is fitted to fluorescence loss (FL) inside and outside the bleached region. We show by reaction–diffusion simulations, that the StrExp function can describe both, binding/barrier–limited and diffusion-limited FL kinetics. By pixel-wise regression of that function to FL kinetics of enhanced green fluorescent protein (eGFP), we determined in a user-unbiased manner from which cellular regions eGFP can be replenished in the bleached area. Spatial variation in the parameters calculated from the StrExp function allow for detecting diffusion barriers for eGFP in the nucleus and cytoplasm of living cells. Polyglutamine (polyQ) disease proteins like mutant huntingtin (mtHtt) can form large aggregates called inclusion bodies (IB’s). The second method combines single particle tracking with multi-compartment modelling of FL kinetics in moving IB’s to determine exchange rates of eGFP-tagged mtHtt protein (eGFP-mtHtt) between aggregates and the cytoplasm. This method is self-calibrating since it relates the FL inside and outside the bleached regions. It makes it therefore possible to compare release kinetics of eGFP-mtHtt between different cells and experiments.

Conclusions

We present two complementary methods for quantitative analysis of FLIP experiments in living cells. They provide spatial maps of exchange dynamics and absolute binding parameters of fluorescent molecules to moving intracellular entities, respectively. Our methods should be of great value for quantitative studies of intracellular transport.