Using CellX to quantify intracellular events.

Area of Science:

• Cell Biology
• Computational Biology
• Microscopy Image Analysis

Background:

• Quantitative analysis of cellular features using light microscopy is crucial in modern biology.
• Existing computational tools for image analysis are often limited to specific cell types or microscopy techniques, hindering broader application.
• There is a need for versatile software capable of analyzing diverse microscopy image data.

Purpose of the Study:

• To introduce CellX, an open-source software package designed for robust cell segmentation, intensity quantification, and cell tracking.
• To provide a unified protocol for utilizing CellX across various microscopy image datasets, regardless of cell shape or image density.
• To enable advanced downstream analysis through versatile output formats.

Main Methods:

• CellX software implementation for automated cell segmentation, independent of cell morphology.
• Application of CellX to images with high cell density.
• Development of a standardized protocol for cell segmentation and quantification using CellX.
• Integration of cell tracking capabilities within the CellX workflow for time-series image analysis.

Main Results:

• CellX demonstrates effective cell segmentation even in crowded images and with diverse cell shapes.
• The software provides a consistent protocol for both static image analysis and time-lapse cell tracking.
• Output includes visual validation controls, statistical data files, and MATLAB objects for further analysis.

Conclusions:

• CellX offers a flexible and powerful solution for quantitative analysis of microscopy images.
• Its open-source nature and broad applicability facilitate wider adoption in biological research.
• The software enhances the efficiency and depth of cellular feature quantification and tracking.