Discrimination of cell cycle phases in PCNA-immunolabeled cells

Area of Science:

• Cell Biology
• Molecular Biology
• Biophysics

Background:

• Cell cycle-dependent protein function is critical in eukaryotic cells.
• Nuclear protein localization, like Proliferating Cell Nuclear Antigen (PCNA), serves as a cell cycle progression marker.
• PCNA's role in DNA replication and cell cycle-dependent properties make it suitable for phase identification.

Purpose of the Study:

• To develop and validate a tool for automated cell cycle phase identification based on PCNA distribution patterns.
• To analyze and select optimal features for cell cycle classification.
• To compare classification performance between confocal and widefield microscopy images.

Main Methods:

• Acquisition of single time point images of PCNA-immunolabeled cells using confocal and widefield fluorescence microscopy.
• Development of an optimized image processing pipeline for feature extraction and classification.
• Evaluation of various classification algorithms and feature sets for discriminating cell cycle phases.

Main Results:

• The proposed processing pipeline successfully automates cell cycle phase classification from PCNA images, irrespective of acquisition technique.
• Classification accuracy was slightly higher for confocal microscopy images compared to widefield images.
• The tool effectively identifies cell cycle phases and sub-stages within the DNA replication (S-phase).

Conclusions:

• Automated identification of cell cycle phases, including S-phase sub-stages, using PCNA distribution patterns is achievable.
• The developed method is robust and applicable to both confocal and widefield microscopy image data.
• This tool provides a valuable method for cell cycle analysis in biological research.