# Accessible and accurate cytometry analysis of adherent cells using fluorescence microscopes

## TL;DR

This paper introduces a new method using microscopes and Python tools to analyze adherent cells like flow cytometry, enabling efficient single-cell fluorescence quantification.

## Contribution

A novel Python-based tool and method for generating flow-cytometry-like data from adherent cells using accessible microscopes.

## Key findings

- The method efficiently quantifies single-cell fluorescence using generalist cell segmentation algorithms.
- The approach was successfully demonstrated by screening 88 prime editing conditions with mNeonGreen211 as a reporter.

## 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.