CellPyAbility: automated image analysis for high-throughput dose-response screening

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ID: 320647
2026
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Abstract
Abstract 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 one 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. Supplementary information Supplementary data are available at Bioinformatics online.
Reference Key
openalex_W7168034541 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors James L. Elia, Sam Friedman, Ranjit S. Bindra
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Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag513
URL
https://doi.org/10.1093/bioinformatics/btag513
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