ForSys: Non-invasive stress inference from time-lapse microscopy

Area of Science:

• Biophysics
• Developmental Biology
• Computational Biology

Background:

• Mechanical forces are crucial for tissue development and regeneration.
• Measuring these forces in vivo is challenging.
• Existing stress inference algorithms struggle to incorporate tissue dynamics.

Purpose of the Study:

• Introduce ForSys, a novel Python-based software for inferring intercellular stresses and intracellular pressures.
• Enable dynamic stress inference from time-lapse microscopy data.
• Analyze spatiotemporal patterns of mechanical forces during tissue morphogenesis.

Main Methods:

• Developed ForSys, a versatile software tool.
• Validated ForSys using simulated and experimental data.
• Applied ForSys to zebrafish lateral-line primordium migration and neuromast development.
• Integrated ForSys with Fiji for a graphical user interface and accepted segmentation from EPySeg and Cellpose.

Main Results:

• ForSys successfully infers intercellular stresses and intracellular pressures from time-lapse microscopy.
• Revealed increased stress during cell rounding preceding mitosis in zebrafish.
• Accurately predicted the onset of epithelial rosettogenesis.
• Uncovered mechanical asymmetries in neuromast development linked to cell type-specific adhesion.

Conclusions:

• ForSys provides a powerful and versatile tool for analyzing mechanical forces in vivo.
• The software facilitates the study of how mechanical forces regulate tissue morphogenesis and regeneration.
• Enables dynamic stress inference, advancing the field of biophysical analysis in developmental biology.