Foyt Daniel, Kuang Yiming, Rehem Samma, Yserentant Klaus, Huang Bo
UCSF-UC Berkeley Joint Graduate Program in Bioengineering, University of California San Francisco, San Francisco, California, 94143, United States of America.
Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, 94143, United States of America.
bioRxiv. 2025 Jan 24:2025.01.22.634380. doi: 10.1101/2025.01.22.634380.
We have developed a method along with a python-based analysis tool to capture images and produce flow cytometry like data utilizing simple accessible microscopes. Utilizing the recently developed generalist algorithms for cell segmentation, our approach easily segments semi-adherent or suspended cells facilitating quantification of fluorescent intensity similar to flow cytometry. We have shown that our approach exhibits similar speed and enhanced sensitivity when compared to typical flow cytometry. The utility of our approach is demonstrated by screening a set of 88 prime editing conditions utilizing the integration of mNeonGreen as a reporter.
我们开发了一种方法以及一个基于Python的分析工具，利用简单易用的显微镜来捕获图像并生成类似流式细胞术的数据。利用最近开发的用于细胞分割的通用算法，我们的方法能够轻松分割半贴壁或悬浮细胞，便于对荧光强度进行定量，类似于流式细胞术。我们已经表明，与典型的流式细胞术相比，我们的方法具有相似的速度和更高的灵敏度。通过使用mNeonGreen作为报告基因的整合来筛选一组88个碱基编辑条件，证明了我们方法的实用性。
