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

Area of Science:

• Cell Biology
• Biophysics
• Imaging Science

Background:

• Signal transduction and cell function depend on the spatiotemporal organization of membrane-associated molecules.
• Current 3D light microscopy lacks quantitative understanding of molecular signal regulation at the whole cell scale, especially with complex cell surface morphologies.
• Challenges include complete sampling of cell geometry, membrane-associated molecular concentration/activity, and calculating morphology-signal cofluctuations.

Approach:

• Introduces u-Unwrap3D, a computational framework to remap complex 3D cell surfaces and associated signals into lower dimensional representations.
• The bidirectional mapping allows for optimized image processing in suitable dimensions and results presentation in any representation, including the original 3D surface.
• Employs a surface-guided computing paradigm for analyses.

Key Points:

• Quantifies Septin polymer recruitment during blebbing events by tracking segmented surface motifs in 2D.
• Measures actin enrichment in peripheral ruffles and calculates ruffle movement speed along complex cell surface topographies.
• Enables spatiotemporal analyses of cell biological parameters on unconstrained 3D surface geometries and signals.

Conclusions:

• u-Unwrap3D overcomes limitations in analyzing molecular signals on dynamic and complex 3D cell surfaces.
• Provides a powerful tool for quantitative, spatiotemporal analysis in cell biology.
• Facilitates deeper understanding of cell function and signal transduction through advanced imaging and computational methods.