Context-explorer

Context-explorer quantifies spatially organized protein expression and colony-level spatial patterns to analyze how cellular microenvironments influence cell fate.

Key Features:

• Density-Based Clustering Algorithm: Uses a density-based clustering algorithm to automatically identify and classify cell colonies in micropatterned and unpatterned wells.
• Spatial Organization Analysis: Classifies cells by their relative positions within colonies to enable statistical analysis of spatial protein expression patterns.
• Application to Pluripotent Stem Cells: Demonstrated on human (hPSCs) and mouse pluripotent stem cells (mPSCs), revealing radial gradients in transcription factors SOX2 and OCT4.
• Versatility Across Colony Sizes and Shapes: Computes metrics for colonies of varying sizes and geometries to assess patterning fidelity of micropatterned plates.
• Integration with CellProfiler: Accepts or processes outputs from CellProfiler to extend downstream spatial analysis of single-cell protein expression.

Scientific Applications:

• Developmental Biology: Analyzes spatial heterogeneity and radial patterning relevant to cell differentiation and development.
• Regenerative Medicine: Characterizes context-dependent gene and protein expression relevant to stem cell–based regenerative strategies.
• Pharmacological Screening: Evaluates spatial heterogeneity within cell populations for drug development validation and screening.
• Fundamental Research on Microenvironmental Regulation: Supports studies of how cellular microenvironments regulate phenotype and functional responses.

Methodology:

Implements a density-based clustering algorithm; classifies cells by relative position within colonies; performs statistical analysis of spatial protein expression patterns; computes metrics for colony size and geometry; accepts outputs from CellProfiler for downstream analysis.