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Physical constraints in cell fate specification. A case in point: Microgravity and phenotypes differentiation
Maria Grazia Masiello1, Roberto Verna2, Alessandra Cucina3
1Department of Experimental Medicine, Sapienza University of Rome, viale Regina Elena 324, 00161 Rome, Italy; Department of Surgery "PietroValdoni", Sapienza University of Rome, via A. Scarpa 14, 00161 Rome, Italy.
Progress in Biophysics and Molecular Biology
|January 9, 2018
Summary
Cellular development in microgravity reveals that gravity shapes cell phenotypes, challenging current molecular models. Upon return to gravity, these distinct cell types merge, demonstrating macro-scale influences on biological systems.
Area of Science:
- Cell Biology
- Biophysics
- Developmental Biology
Background:
- Current molecular models inadequately explain cell fate specification.
- Gravity acts as a significant physical constraint on cellular systems.
- Understanding cell behavior requires considering macro-scale environmental factors.
Purpose of the Study:
- To investigate cell fate specification in the absence of gravity.
- To determine the role of gravity in shaping cellular phenotypes.
- To explore the relationship between macro-scale physical forces and cellular behavior.
Main Methods:
- Studying mammalian cell populations in microgravity conditions.
- Observing cell allocation into distinct phenotypes.
- Analyzing the influence of gravity on cell phenotype stability upon return to 1g.
Main Results:
- Cells in microgravity spontaneously differentiate into two distinct phenotypes.
- These phenotypes are thermodynamically and functionally compatible with microgravity.
- Upon return to 1g, the two phenotypes collapse into a single type.
- Gravity shapes cell phenotypes de novo via cytoskeleton modification.
Conclusions:
- Cell fate specification is influenced by macro-scale parameters like gravity, beyond current molecular explanations.
- Gravity imposes top-down control, modifying cellular structures and limiting phenotype possibilities.
- Macro-scale control parameters are essential for understanding biological systems and setting boundary conditions for lower-scale models.
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