Quantitative computerized analysis demonstrates strongly compartmentalized tissue deformation patterns underlying...

Area of Science:

• Developmental biology
• Computational biology
• Biophysics

Background:

• Quantitative analysis of cellular-resolution tissue deformation is crucial for understanding mammalian organogenesis.
• Existing methods face challenges in accurately tracking and modeling complex deformation patterns during development.

Purpose of the Study:

• To develop a novel computational workflow for extracting regional and temporal tissue deformation patterns.
• To apply this workflow to mouse cardiogenesis, providing insights into heart tube formation and ventricular development.

Main Methods:

• Developed a method to track tissue deformation directly from time-lapse raw microscopy images.
• Experimentally validated the tracking method against actual cell tracks.
• Utilized machine learning for temporal and spatial alignment of multiple specimens to create a statistical motion model, deducing strain, anisotropy, and growth maps.
• Implemented a virtual fate mapping tool for tracking cell positions from the cardiac primordium to the heart tube.

Main Results:

• Demonstrated predominant local cellular coherence during cardiac tissue deformation.
• Revealed strong compartmentalization of deformation patterns driving the transformation of the bilateral cardiac primordium into a 3D longitudinal heart tube.
• Described ventricular chamber formation as a hemi-barrel expansion constrained by arterial and venous pole harnessing belts.

Conclusions:

• The study provides a new computational approach for analyzing tissue deformation in organogenesis.
• A novel model for primitive heart tube formation is proposed, highlighting the role of constrained expansion.
• The findings offer a deeper understanding of the biomechanical forces shaping the developing heart.