Celldetective, an AI-enhanced image analysis tool for unraveling dynamic cell interactions

Area of Science:

• Bioimaging
• Immunology
• Computational Biology

Background:

• Analyzing dynamic cell interactions in bioimaging is challenging, particularly for immunology and immunotherapy.
• Existing tools may not fully address the complexity of multimodal and multidimensional image data.

Purpose of the Study:

• Introduce Celldetective, an open-source Python software for high-performance, end-to-end analysis of in vitro immune and immunotherapy assays.
• Provide a tool for analyzing 2D multi-channel time-lapse microscopy of mixed cell populations across multiple conditions.

Main Methods:

• Celldetective integrates AI-based segmentation, tracking, and automated single-cell event detection.
• The software features an intuitive graphical interface with interactive visualization, annotation, and training capabilities.
• Utilizes multicondition, 2D multi-channel time-lapse microscopy data.

Main Results:

• Demonstrated Celldetective's capabilities using datasets of immune effector cell interactions with activating surfaces and antibody-dependent cell cytotoxicity events.
• Successfully performed end-to-end analysis of complex cellular interactions.
• Validated AI-driven segmentation, tracking, and event detection.

Conclusions:

• Celldetective offers a powerful, broadly applicable solution for analyzing interacting cell populations in biological systems.
• The software streamlines complex image-based assay analysis, particularly in immunology and immunotherapy research.
• Facilitates high-performance analysis within an accessible graphical user interface.