Time-Dependent Image Restoration of Low-SNR Live Cell Ca2+ Fluorescence Microscopy Data

Abstract

Live cell Ca2+ fluorescence microscopy is a cornerstone of cellular signaling analysis and imaging. The demand for high spatial and temporal imaging resolution is, however, intrinsically linked to a low signal-to-noise ratio (SNR) of the acquired spatio-temporal image data, which impedes subsequent image analysis. Advanced deconvolution and image restoration algorithms can partly mitigate the corresponding problems, but are usually defined only for static images. Frame-by-frame application to spatio-temporal image data neglects inter-frame contextual relationships and temporal consistency of the imaged biological processes. Here, we propose a variational approach to time-dependent image restoration built on entropy-based regularization specifically suited to process low- and lowest-SNR fluorescence microscopy data. The advantage of the presented approach is demonstrated by means of four data sets: synthetic data for in-depth evaluation of the algorithm behavior; two data sets acquired for analysis of initial Ca2+ microdomains in T cells; and, to illustrate transferability of the methodical concept to different applications, one dataset depicting spontaneous Ca2+ signaling in jGCaMP7b-expressing astrocytes. To foster re-use and reproducibility, the source code is made publicly available.