Machine learning based methodology to identify cell shape phenotypes associated with microenvironmental cues.

Area of Science:

• Biomaterials Science
• Cell Biology
• Computational Biology

Background:

• Cell morphology is a key indicator of stem cell behavior in response to biomaterials.
• Analyzing cell shape is challenging due to heterogeneous cell populations and microenvironments.
• Existing methods struggle with multi-parametric cell morphology definitions.

Purpose of the Study:

• To develop a robust shape phenotyping framework for associating cell morphology with biomaterial interactions.
• To identify key cell shape metrics that predict microenvironment-induced morphological changes.
• To establish methods for analyzing heterogeneous cell populations and microenvironments.

Main Methods:

• Developed a shape phenotyping framework utilizing support vector machines.
• Implemented a feature selection procedure to identify significant cell shape metrics.
• Employed a 'supercell' method averaging shape measurements from small cell groups to handle heterogeneity.
• Utilized a subsampling validation algorithm to determine optimal sample and supercell sizes.

Main Results:

• The framework accurately identifies and predicts microenvironment-driven morphological differences in heterogeneous cell populations.
• A minimum of 57 cells, grouped into supercells of size 4, are required for stable phenotyping.
• Minor axis length, solidity, and mean negative curvature were identified as critical early indicators of cell response.

Conclusions:

• The developed framework provides an accurate and stable method for analyzing cell morphology in biomaterial research.
• The 'supercell' approach effectively accounts for cellular and microenvironmental heterogeneity.
• Specific shape metrics can serve as early indicators of stem cell responses to different biomaterial microenvironments.