Automated Image-Based Profiling of Pluripotent Stem Cell Colonies.

ABSTRACT: Quantitative image analysis is essential for advancing stem cell biology, developmental studies, and drug discovery, yet most workflows still rely on manual or semi-quantitative scoring that is slow, subjective, and poorly scalable. A major challenge is converting complex colony morphologies into reproducible, high-dimensional datasets. To address this gap, we developed ColonyQuant, an open-source platform that integrates automated colony segmentation, alkaline phosphatase (AP) intensity quantification, morphometric profiling, and statistical classification into a single workflow. ColonyQuant computes per-colony functional readouts alongside comprehensive shape descriptors, capturing both staining intensity and structural features in a unified framework. Applied to embryonic stem ce

SUBMITTER: Geng R 

PROVIDER: S-EPMC12458935 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature