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Quantification of cell-bubble interactions in a 3D engineered tissue phantom.

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Cellular metabolism affects dissolved oxygen, influencing bubble nucleation during decompression. Higher cell density reduces bubble formation, but cell death increases post-decompression, highlighting the importance of 3D tissue models for studying in vivo bubble dynamics.

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Area of Science:

  • Biomedical Engineering
  • Cell Biology
  • Physiology

Background:

  • Cell-bubble interactions are critical for understanding pathologies like decompression sickness (DCS).
  • Bubbles can form in vivo due to medical procedures, surgery, or infections.
  • Investigating these interactions in biomimetic environments is essential for therapeutic and pathological insights.

Purpose of the Study:

  • To investigate cell-bubble interactions in a 3D engineered tissue model during decompression.
  • To determine the influence of cellular density and metabolism on bubble nucleation and growth.
  • To compare findings with 2D models for improved in vivo relevance.

Main Methods:

  • Utilized 3D engineered tissue phantoms with varying cellular densities.
  • Employed real-time oxygen monitoring to measure dissolved oxygen (DO) concentrations.
  • Conducted direct microscopic observation to analyze bubble nucleation, growth, and cell death.
  • Compared 3D results with existing 2D experimental data.

Main Results:

  • Increased cellular density significantly decreased DO concentrations (p=0.0003).
  • Higher cellular density led to a significant reduction in bubble nucleation (p=0.0024).
  • Bubble growth rate and maximum size were not significantly affected by cellular density (p=0.99 and 0.23).
  • Cell death significantly increased following bubble-forming decompression (p=0.0116).

Conclusions:

  • Bubble nucleation is primarily governed by dissolved oxygen levels influenced by cellular metabolism, not cell surface nucleation sites.
  • Bubble growth is influenced by DO concentration and competition for dissolved gases.
  • 3D biomimetic models provide more relevant data for understanding in vivo bubble dynamics compared to 2D models.