DARTS:
An open-source Python pipeline for Ca2+ microdomain analysis in live cell imaging data
General Information
DARTS is a pipeline for Ca2+ microdomain analysis in live cell imaging data written in Python 3 by Lena-Marie Woelk, Dejan Kovacevic, Hümeyra Husseini and Fritz Förster. For support, please open an issue or contact us directly.
This pipeline is part of the sfb1328 A02 project.
Program Structure
The pipeline can be roughly divided into three parts:
General postprocessing
These components include segmentation and cell tracking to identify regions of interest (ROI), image improvement by bleaching correction, deconvolution and denoising, background subtraction and ratiometric image handling, i.e. registration of two input channels and ratio computation.
Shape Normalization
This part normalizes arbitrary cell shapes (within certain convexity constraints) onto a circle with a well-defined radius to facilitate comparison of Ca2+ microdomains across multiple cells with differing morphologies.
Analysis
The normalized cells are then analyzed to detect Ca2+ hotspots and the results given as data tables and visualized either on an individual basis or across multiple cells on a dartboard shape, where hotspot frequency and location can be analyzed for different time frames.