Processing Calcium Signaling Fluorescence Microscopy Image Stacks
Calcium signalling plays an important role in the functioning of the cells in salivary glands. Recent advances enable real-time in vivo microscopy imaging of fully intact cells. With fluorescence microscopy each resultant image is a 2D cut-plane through the sample and these are generally accumulated over time. Images can also be acquired at levels of increasing depth to provide 3D structural detail. The image stacks from a single experiment can easily consume over 1GB of storage and, in the past, analysis of the data from a single experiment took several weeks.
In this presentation we show details of a collection of python based jupyter notebooks that we created for pre-processing and analysing our image stacks as well as for post-processing the results to extract additional information. Initial analysis of an experiment can now be done in under twenty minutes. The notebooks are used by lab staff in a “cookbook” like fashion with very little training required.
In the interest of reproducibility across different labs as well as open science, which our funding agency encourages, the code for our notebooks is hosted on GitHub.
ABOUT THE AUTHOR(S)
Dr John Rugis
John is currently a Scientific Programmer in the Department of Mathematics at the University of Auckland. His primary research interest is in computer based 3D modelling and visualisation.
Professor James Sneyd
James is a Professor in the Department of Mathematics at the University of Auckland. He specialises in the numerical modelling of calcium signalling in biological cells and has authored many papers and several books on the topic.
Professor David Yule
David holds a number of roles at the University of Rochester Medical Center in New York and heads his own lab there. He specialises in experimental cell biology with an emphasis in a range of aspects of calcium signalling and has published numerous papers on the topic. His research work is funded by the National Institutes of Health.