Surface-guided computing to analyze subcellular morphology and membrane-associated signals in 3D

Area of Science:

• Cell Biology
• Biophysics
• Imaging Science

Background:

• Cell signaling and function depend on the precise organization of membrane molecules.
• Current 3D microscopy lacks quantitative analysis of molecular signals across complex cell surfaces.
• Transient cell shapes hinder the measurement of molecular concentration, activity, and morphology correlations.

Approach:

• Introduced u-Unwrap3D, a framework to map complex 3D cell surfaces and signals into lower dimensions.
• Developed bidirectional mappings for versatile image processing and data representation.
• Enabled surface-guided computing for analyzing spatiotemporal dynamics on unconstrained geometries.

Key Points:

• Quantified Septin polymer recruitment during blebbing events by tracking 2D surface motifs.
• Measured actin enrichment within peripheral cell ruffles.
• Assessed the speed of cell ruffle movement across complex surface topographies.

Conclusions:

• u-Unwrap3D facilitates spatiotemporal analysis of cell biological parameters on complex 3D surfaces.
• The framework overcomes limitations in quantifying molecular signals on dynamic cell morphologies.
• Provides new quantitative insights into cell surface organization and signaling.