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<p>We have developed a method along with a Python-based analysis tool to capture images and produce flow-cytometry-like data for adherent cell culture utilizing simple accessible microscopes. Leveraging the recently developed generalist algorithms for cell segmentation, our approach efficiently quantifies single-cell fluorescence signals. We demonstrated the utility of this method by screening a set of 88 prime editing conditions using the integration of mNeonGreen2<sub>11</sub> as a reporter.</p>

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Accessible and accurate cytometry analysis of adherent cells using fluorescence microscopes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月28日

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  • Daniel Foyt,
  • Yiming Kuang,
  • Samma Rehem,
  • Klaus Yserentant,
  • Bo Huang

摘要

We have developed a method along with a Python-based analysis tool to capture images and produce flow-cytometry-like data for adherent cell culture utilizing simple accessible microscopes. Leveraging the recently developed generalist algorithms for cell segmentation, our approach efficiently quantifies single-cell fluorescence signals. We demonstrated the utility of this method by screening a set of 88 prime editing conditions using the integration of mNeonGreen211 as a reporter.