Pluri-IQ

Pluri-IQ automates quantification of pluripotency from large, low-magnification images to evaluate colony states in stem cell experiments.

Key Features:

• Automated quantification: Performs automated evaluation and quantification of pluripotency in large images.
• Automated segmentation and classification: Employs an automated segmentation algorithm combined with a supervised machine-learning platform to classify cell colonies as pluripotent, mixed, or differentiated.
• Model system basis: Classification and analysis are based on mouse embryonic stem cells (mESC) as a model system.
• Assay support: Supports image-based assays including alkaline phosphatase staining, immunocytochemistry, and fluorescent reporters such as OCT4-GFP.
• Comparative analysis: Enables automatic comparison of different culture conditions to evaluate their effects on pluripotency.

Scientific Applications:

• Pluripotency assessment: Quantifies colony-level pluripotency for stem cell differentiation and maintenance studies.
• High-throughput image analysis: Processes large low-magnification image datasets to compare experimental conditions efficiently.
• Marker-based evaluation: Analyzes alkaline phosphatase staining, immunocytochemistry, and OCT4-GFP expression for marker-driven pluripotency assessment.

Methodology:

Automated image segmentation followed by a supervised machine-learning classifier labels colonies as pluripotent, mixed, or differentiated, with classification based on analysis of mouse embryonic stem cells (mESC).