Deriving the A/B Cells Policy as a Robust Multi-Object Cell Pipeline for Time-Lapse Microscopy.

Area of Science:

• Cell Biology
• Bioinformatics
• Regenerative Medicine

Background:

• Time-lapse microscopy of mesenchymal stem cells (MSCs) enables quantitative observation of self-renewal, proliferation, and differentiation.
• Comparing baseline (A) versus perturbed (B) conditions in MSCs is difficult due to single-cell heterogeneity in morphology, division timing, and migration.
• MSCs serve as an in vitro model for studying cell morphology and kinetics, crucial for assessing interventions like gene therapy and prime editing.

Purpose of the Study:

• To develop a robust, open-source Python package for analyzing and comparing MSC behavior under different conditions.
• To create a quantitative framework that bridges in vitro imaging data with in silico intervention strategy planning.
• To provide an interpretable, measurement-based system for analyzing single-cell morphological and dynamic heterogeneity.

Main Methods:

• Implementation of a modular, open-source Python package named A/B Cells Policy.
• Integration of a YOLO-based architecture for a two-stage cell assignment framework with recovery passes.
• Incorporation of robust cell tracking, re-identification of lost tracks, and lineage reconstruction capabilities.

Main Results:

• The A/B Cells Policy package enables the combination of static morphology with dynamic descriptors to generate weight profiles.
• These profiles highlight key morphological and behavioral dimensions driving cellular divergence between conditions.
• The framework facilitates the linking of descriptive statistics to a transferable system for further analysis.

Conclusions:

• A/B Cells Policy offers a robust solution for the quantitative comparison of MSCs under baseline and perturbed conditions.
• The software provides an interpretable bridge between in vitro imaging and in silico planning for interventions.
• This approach opens new avenues for regenerative medicine, pharmacology, and early translational research by addressing single-cell heterogeneity.