ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

Dynamic cell seeding combines two complex phenomena: cellular adhesion and fluid
flow. Theoretical models can offer a better controlled and systematic approach for the
optimization of cell seeding process compared to the experimental approach. Olivares
and Lacroix [15] proposed a numerical method based on fluid particles to control the
cell seeding process under perfusion conditions. The models are checked with final
results obtained from the experiment and two principal limitations were found: (1) the
seeding process in time cannot be verified, and (2) final particles distribution was
compared with only the experimental cell distribution at the end of the seeding process.
For this study a combination of computational experimental methods are proposed to
optimize the cell seeding process. The general overview of this study is represented in
Fig. 1.
Fig. 1. Overview scheme represent the methods used in the study. (a) regular scaffold
(CAD from Biotek company), (b) Transparent micro-fluidic chamber.
3. MATERIALS AND METHODS
3.1 Computational approach
Rapid prototyped poly-caprolactone (PCL) scaffold of diameter 5 mm and height 1.5
mm (3D Biotek, USA) (Fig.1a) was used. A micro-fluidic chamber was designed and
fabricated to enable live visualization of the seeding process (Fig 1b). Three samples of
PCL scaffold were scanned using a μCT Skyscan1172 (Trabeculae, Spain) with a
resolution of 4.9 μm. The cross section of one sample is shown in Fig. 2a to show the
distribution of pores. Cross-sections were super-imposed using the software MIMICS
(Materialize) to form a three-dimensional reconstruction of the sample (Fig.2b). A
regular material distribution was showed through the μCT analysis.
Fig. 2. Micro CT scaffold reconstruction. (a) segmented image (b) 3D scaffold
reconstruction.