A computational platform for robotized fluorescence microscopy (II): DNA damage, replication, checkpoint activation,...

Area of Science:

• Cell Biology
• Computational Biology
• Biotechnology

Background:

• Analyzing complex molecular networks in rare cell populations is challenging due to technological limitations.
• Current methods struggle with simultaneous quantification, high-resolution localization, and robust multi-parameter analysis.

Purpose of the Study:

• To develop a novel computational platform for quantitative image analysis of microscopy data.
• To apply this platform to study DNA damage response and checkpoint activation in mammary cells.

Main Methods:

• Development of the Automated Microscopy for Image CytOmetry (A.M.I.CO) computational platform.
• Quantitative image analysis of confocal/widefield microscopy data.
• Correlation of cell-cycle profiles with proliferation (Ki67), DNA damage response (γH2AX, 53BP1), and checkpoint activation (p53, p21) markers.

Main Results:

• Demonstrated cell-cycle modulated mechanisms in DNA damage response (DDR).
• Identified distinct roles for γH2AX and 53BP1 in DDR.
• Showcased differential functions of p53 and p21 in checkpoint activation and quiescence regulation.
• Established correlation between protein expression and molecular interactions using Proximity Ligation Analysis.

Conclusions:

• A.M.I.CO facilitates statistically robust analysis of complex molecular networks in rare cell populations.
• The study elucidates novel insights into cell-cycle dependent DNA damage response pathways.
• This technology enables unprecedented statistical relevance in correlating molecular interactions with protein expression.