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

TY - JOUR

T1 - Time-Dependent Image Restoration of Low-SNR Live-Cell Ca2+ Fluorescence Microscopy Data

AU - Woelk, Lena-Marie

AU - Kannabiran, Sukanya A

AU - Brock, Valerie J

AU - Gee, Christine E

AU - Lohr, Christian

AU - Guse, Andreas H

AU - Diercks, Björn-Philipp

AU - Werner, René

PY - 2021/10/30

Y1 - 2021/10/30

N2 - 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 on the 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 datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the 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.

AB - 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 on the 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 datasets: synthetic data for in-depth evaluation of the algorithm behavior; two datasets acquired for analysis of initial Ca2+ microdomains in T-cells; finally, to illustrate the 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.

U2 - 10.3390/ijms222111792

DO - 10.3390/ijms222111792

M3 - SCORING: Journal article

C2 - 34769223

VL - 22

JO - INT J MOL SCI

JF - INT J MOL SCI

SN - 1661-6596

M1 - 11792

ER -