Accessible and accurate cytometry analysis of adherent cells using fluorescence microscopes

Abstract

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.