Cecelia: a multifunctional image analysis toolbox for decoding spatial cellular interactions and behaviour

Area of Science:

• Microscopy and Computational Biology
• Bioimage Analysis
• Quantitative Imaging

Background:

• Increasing complexity of microscopy necessitates advanced computational workflows for image analysis.
• Current solutions often lack flexibility, interactivity, and intuitive interfaces for non-specialists.
• There is a significant need for integrated software for analyzing complex 3D and live cell imaging data.

Purpose of the Study:

• To present Cecelia, a toolbox designed to make quantitative multidimensional image analysis accessible.
• To integrate various open-source packages into a coherent data management suite.
• To provide an unbiased approach for distinguishing dynamic cell behaviors in live imaging data.

Main Methods:

• Integration of multiple open-source software packages into a unified toolbox.
• Development of a user-friendly interface (Shiny app) for data management and analysis.
• Application of the toolbox to analyze immunologically relevant scenarios and live cell imaging data.

Main Results:

• Cecelia provides a coherent data management suite for quantitative multidimensional image analysis.
• The toolbox successfully integrates various open-source packages, enhancing accessibility for non-specialists.
• An unbiased method was developed to distinguish dynamic cell behaviors from live imaging data.

Conclusions:

• Cecelia offers a valuable framework for researchers needing accessible quantitative image analysis.
• The toolbox addresses the need for integrated solutions for complex 3D and live cell imaging.
• The developed approaches are expected to benefit a broad range of biological researchers.