Scholarlink

<p>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 cells (ESCs) treated with a selective KDM4 histone-demethylase inhibitor, ColonyQuant revealed dose-dependent reductions in colony area and integrated AP signal, together with systematic remodeling of morphometric metrics. Multivariate analyses robustly stratified treatment groups and identified intensity and solidity as principal features capturing dose-dependent colony responses. By transforming subjective scoring into objective, scalable, and biologically interpretable phenotyping, ColonyQuant provides a reproducible platform for stem cell research and high-content screening.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

ColonyQuant: automated quantification and morphometric analysis of pluripotent stem cell colonies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月19日

查看全文
  • Rui Geng,
  • Benjamin L. Kidder

摘要

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 cells (ESCs) treated with a selective KDM4 histone-demethylase inhibitor, ColonyQuant revealed dose-dependent reductions in colony area and integrated AP signal, together with systematic remodeling of morphometric metrics. Multivariate analyses robustly stratified treatment groups and identified intensity and solidity as principal features capturing dose-dependent colony responses. By transforming subjective scoring into objective, scalable, and biologically interpretable phenotyping, ColonyQuant provides a reproducible platform for stem cell research and high-content screening.