About

Information about the Project

The Project  

During our work with live-cell imaging–based spheroid assays, we frequently encountered the challenge of handling large datasets from which only basic features could be extracted at a limited number of time points, largely due to the amount of required manual working time. As a result, much of the datasets’ potential remained untapped.

To address this issue, we implemented computational methods that have been continuously refined to incorporate increasingly advanced analysis methods. In this context, we introduce a Python-based tool specifically designed to streamline the analysis of spheroid cancer cell cultures. Its primary goal is to enhance research efficiency and support high-throughput analysis of experimental data. The tool facilitates the extraction of quantitative parameters from imaging data by automating processes that are traditionally labor-intensive, thereby making complex biological data more accessible, interpretable, and reproducible.

Adhering to the principles of open science, all tools developed within this project will be published as open-source software. Users are encouraged to adapt and extend the tools for their own scientific inquiries. Contributions to the codebase are welcomed. The source code, comprehensive documentation, and contribution guidelines will be accessible via the project’s GitHub.

This project has been supported by the Mildred-Scheel-Fellowship of the German Cancer Aid. It is part of the Group Signal Transduction in Cancer at the Institute for Biochemistry & Signaltransduction at the University Medical Center Hamburg-Eppendorf (UKE) led by Prof. Manfred Jücker.