Cell Shape Classifier

Cell Shape Classifier predicts mesenchymal stem cell (MSC) fate from phase-contrast time-lapse microscopy-derived cellular morphology to assess differentiation pathways.

Key Features:

• Morphological analysis: Seven-dimensional feature vectors are derived from segmented phase-contrast time-lapse microscopy images to capture cell shape and structure.
• Custom image-processing pipeline: A tailored pipeline performs segmentation of individual cells and prepares morphological data for analysis.
• Statistical classification methodology: A tree-like classification method analyzes high-dimensional morphological feature vectors to identify patterns correlated with differentiation.
• Specific informative features: Eccentricity and filopodia (referred to as "fingers") are identified as particularly informative for distinguishing myogenic differentiation from other pathways.

Scientific Applications:

• MSC fate prediction: Predicts mesenchymal stem cell differentiation outcomes based on morphological trajectories.
• Lineage forecasting: Supports forecasting of differentiation into adipogenic, osteogenic, chondrogenic, and myogenic progenitors.
• Early non-invasive prediction: Enables prediction of lineage commitment prior to marker gene expression or histological confirmation using time-lapse morphology.
• Assessment of variability effects: Facilitates analysis of how cell density, proliferation rates, and donor-specific variation influence morphology-based classification performance.

Methodology:

Segmented phase-contrast time-lapse images are processed by a custom image-processing pipeline to extract seven morphological features per cell (forming seven-dimensional feature vectors), which are analyzed using a tree-like statistical classification method.