Accessible and accurate cytometry analysis using fluorescence microscopes

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 ₁₁ as a reporter.