cellPLATO

cellPLATO analyzes large-scale time-lapse microscopy data to quantify single-cell morphology and motility and to identify dynamic behavioral states in time-resolved cell trajectories.

Key Features:

• Implementation: Python-based software for computational analysis of cell trajectory datasets.
• Unsupervised analysis: Employs an unsupervised approach to identify and classify cell behaviors within heterogeneous cell trajectory datasets.
• Feature extraction and clustering: Extracts morphological and motility features from segmented and tracked cells and uses dimensionality reduction and clustering algorithms to define behavioral subtypes.
• Behavioral ID assignment: Assigns a behavioral ID to each cell track at every timepoint based on extracted features.
• Trajectory grouping and analysis: Groups similar sequences of behavioral IDs into discrete trajectories, each with an assigned ID for temporal pattern analysis.

Scientific Applications:

• Cell migration and morphology analysis: Quantifies changes in cell migration and shape to study cellular responses to stimuli or conditions.
• NK cell migration under IL-15: Applied to analyze effects of IL-15 on human natural killer (NK) cell migration dynamics on integrin ligands ICAM-1 and VCAM-1.

Methodology:

Extracts morphological and motility features from segmented and tracked cells, applies dimensionality reduction and unsupervised clustering to define behavioral subsets, assigns per-timepoint behavioral IDs, and groups sequences of these IDs into discrete trajectories.