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Flow perfusion rate modulates cell deposition onto scaffold substrate during cell seeding
A Campos Marín1, M Brunelli1, D Lacroix2
1Department of Mechanical Engineering, Insigneo Institute for in Silico Medicine, The University of Sheffield, Pam Liversidge Building, Mappin Street, Sheffield, S1 3JD, UK.
Biomechanics and Modeling in Mechanobiology
|December 1, 2017
Summary
This study reveals gravity and secondary flow are key to cell deposition in scaffolds. A computational model aids in optimizing cell seeding strategies for better efficiency in perfusion bioreactors.
Area of Science:
- Biomaterials Engineering
- Cell Biology
- Computational Modeling
Background:
- Perfusion bioreactors combined with porous scaffolds enhance cell transport during seeding.
- Cell penetration into scaffolds doesn't guarantee attachment.
- Existing in vitro models lack detailed understanding of flow rate effects on cell deposition.
Purpose of the Study:
- To identify mechanisms of cell transport and deposition onto scaffolds from suspension flow.
- To investigate the impact of flow rate and gravity on cell seeding efficiency.
- To develop and validate a computational model for optimizing cell seeding strategies.
Main Methods:
- Development of an in vitro perfusion system to study cell seeding.
- Creation of a computational model simulating in vitro conditions.
- Investigation of static and dynamic cell seeding with varying flow rates (12, 120, 600 [Formula: see text]) and gravity.
Main Results:
- Gravity and secondary flow significantly influence cell deposition onto scaffolds.
- In vitro and in silico seeding efficiencies showed similar trends with flow rate.
- Static seeding yielded higher efficiency but irregular cell distribution compared to dynamic seeding.
- A flow rate of 120 [Formula: see text] provided optimal dynamic seeding results.
- Perfusion seeding showed low efficiency for the tested scaffold, leading to cell waste.
Conclusions:
- Gravity and secondary flow are primary drivers for cell-scaffold deposition.
- The developed in silico model can predict and optimize hydrodynamic-based cell seeding.
- Further optimization is needed to improve low seeding efficiencies in perfusion systems.
