Nellie

Nellie automates segmentation, tracking, and hierarchical feature extraction of intracellular structures in 2D/3D live-cell microscopy to enable quantitative analysis of organelle morphology and motility.

Key Features:

• Hierarchical Segmentation: Resolves sub-organellar regions through adaptive segmentation based on image metadata.
• Radius-Adaptive Pattern Matching: Enables precise motion tracking of organelles with varying sizes and dynamics.
• Organelle Unmixing: Distinguishes multiple organelle types from single-channel microscopy data.
• Graph Autoencoders: Quantifies mitochondrial responses to stimuli such as ionomycin.
• Network Characterization: Analyzes endoplasmic reticulum network architecture across cell types and time points.

Scientific Applications:

• Organelle Dynamics Analysis: Quantifies morphological changes and dynamic interactions of cellular organelles fundamental to cell homeostasis, stress responses, and disease progression.
• Mitochondrial Response Quantification: Measures mitochondrial morphological responses to ionomycin treatment using graph autoencoders.
• Endoplasmic Reticulum Network Analysis: Characterizes ER network architecture across different cell types and experimental time points.
• Multi-Organelle Unmixing: Segments and tracks multiple distinct organelles from single-channel fluorescence microscopy data.

Methodology:

Nellie employs hierarchical segmentation to resolve sub-organellar regions and uses radius-adaptive pattern matching for precise motion tracking. The pipeline adapts to image metadata for automated analysis.