Work

CellPyAbility v1.0.0

Year: 2026

Type: software

Abstract: 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.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20693744

Authors: Elia, James, Friedman, Sam, Bindra, Ranjit

Institution: Yale University

Language: English

Topic: Cell Image Analysis Techniques

Subfield: Biophysics

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

OpenAlex ID: W7164732753

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