Monitoring of significant changes over time in fluorescence microscopy imaging of living cells

Abstract

The question whether structural changes in time-resolved images are of statistical significance, and therefore of scientific interest, or merely emerge from random noise is of great relevance in many practical applications such as live cell uorescence microscopy, where intracellular diffusion processes are investigated. In this paper the statistical recovery of such time-resolved images from fluorescence microscopy of living cells is discussed, based on which a bootstrap method is introduced that allows to both monitor and visualize statistically significant structural changes between individual frames over time. The method can be adopted for use in other imaging systems. It yields a criterion to assess time-resolved small scale structural changes e. g. in the nanometer range. The proposed bootstrap method is based on data reconstruction with a regularization technique as well as new theoretical results on uniform confidence bands for the function of interest in a two-dimensional heteroscedastic nonparametric convolution-type inverse regression model of Poisson-type. Moreover, a data-driven selection method for the regularization parameter based on statistical multiscale methods is discussed. The method can be used for an automatic, data-driven data analysis. The theoretical results are demonstrated in a simulation study and are used to analyze data of fluorescently labeled intracellular transport compartments in living cells.