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Updated: Jul 13, 2026
Multiparametric Tumor Organoid Drug Screening Using Widefield Live-Cell Imaging for Bulk and Single-Organoid Analysis
Published on: December 23, 2022
CellPyAbility: automated image analysis for high-throughput dose-response screening
James L Elia1, Sam Friedman2, Ranjit S Bindra1
1Department of Pathology, Yale School of Medicine, New Haven, CT 06510, United States.
Summary:
Nuclei counting provides a low-cost, metabolic-independent alternative to ATP- or tetrazolium-based cell viability assays. However, the fragmentation of image processing, normalization, and statistical modeling across multiple software platforms hinders high-throughput adoption. We present CellPyAbility, a Python-based suite that automates image processing, dose-response fitting, and synergy analysis. It converts unedited whole-well images into publication-ready graphics in under 1 minute per 96-well plate on standard desktop hardware.
Availability And Implementation:
CellPyAbility is open-source (MIT License) and available as a Python package via PyPI and Bioconda, or as a code-free application for macOS and Windows. Source code and documentation are available at https://github.com/bindralab/cellpyability and on Zenodo at https://doi.org/10.5281/zenodo.20693745.
