Accessible and accurate cytometry analysis using fluorescence microscopes.

Area of Science:

• Biotechnology
• Cell Biology
• Microscopy

Background:

• Flow cytometry is a standard technique for cell analysis.
• Accessible microscopy methods are needed for broader research applications.

Purpose of the Study:

• To develop an accessible, cost-effective method for generating flow cytometry-like data.
• To enable sensitive quantification of fluorescent intensity in cells using simple microscopes.

Main Methods:

• Developed a novel imaging and analysis method using accessible microscopes.
• Integrated generalist algorithms for robust cell segmentation of various cell types.
• Utilized mNeonGreen11 as a reporter for screening prime editing conditions.

Main Results:

• The method produces flow cytometry-like data with high sensitivity and comparable speed.
• Successfully segmented semi-adherent and suspended cells for accurate fluorescent intensity quantification.
• Demonstrated utility by screening 88 prime editing conditions.

Conclusions:

• This approach provides a sensitive and accessible alternative to traditional flow cytometry.
• The Python-based tool facilitates cell analysis and screening in diverse research settings.
• Enables efficient evaluation of genetic editing strategies using fluorescence reporters.