The Role of Mechanical Interactions between Cells and the Extracellular Matrix on Mineralization in a Stem Cell-Seeded Scaffold: a Multi-Scale Approach

Fracture healing is a complex phenomenon, which is not always successful, especially in elderly osteoporotic bones with a diminished capacity to bear load. Tissue engineering with stem cells may offer an approach to augment the healing rate in difficult-to-heal cases. Cell-collagen gel constructs have been recently used in several studies to promote mineralization. However, the role of mechanical factors to accelerate fracture repair has not been optimized nor is fully understood. The goal of this study was to investigate the role of mechanical environment, at tissue (macro) and cellular (micro) level, on tissue differentiation within a stem cell-seeded collagen construct. At the macro-scale, it was demonstrated that bone healing is influenced by fixation stability, aging, and bone quality. The studies performed at the tissue level provided insight into the potential of combination of stem cell-seeded constructs and mechanical pre-stimulation to accelerate fracture healing in elderly/osteoporotic bones. Our computational predictions were compared against experimental studies, and the “overall” tissue patterns matched relatively well with experimental observations. Experiments and imaging techniques were conducted to characterize the structure of collagen fibril network, cellular attachment patterns, and structure of initial mineralized structures in collagen-I gels seeded with murine embryonic stem cells. This data was used to develop a model representing the heterogeneous nature of the cell-collagen gel construct and verify our micro-scale computational simulations. An image-based micro-scale model containing the collagen fibril network, cells, and non-fibrous tissue was developed. A multi-scale approach was adopted to study the load transfer from tissue to cellular level, and formation of initial nodules of mineralization was predicted in a cell-collagen gel construct. Each macro-scale model was linked to a micro-scale model using a fully automated algorithm developed in MATLAB combined with python scripts and user-defined FORTRAN subroutines (UMAT, ORIENT, and USDFLD) for ABAQUS simulations. Not only the strain magnitudes were different at the macro-scale compared to those from the micro-scale, but also the strain field was found to be highly non-homogeneous at the cellular level. Local micro-environment affected cellular responses and differentiation patterns. Similar to our experimental observations, mineralization started from cell boundary, enlarged in size, and propagated along the collagen fibrils. Overall, a multi-scale approach can be a better representative of the heterogeneous nature of stem cell-seeded collagen gels and may lead to predictions that are more realistic. The proposed model seems to be a promising tool to predict cellular responses and control the cell fate in a cell-collagen gel construct under different loading regimes. This knowledge can be used to identify the optimal conditions to accelerate bone formation and facilitate development of an efficient cell-based therapy.

Name thesis:degree_name

Doctor of Philosophy (PhD)

Discipline thesis:degree_discipline

Mechanical and Manufacturing Engineering

Grantor dc:publisher.institution

Graduate Studies

Year dc:date.issued

2017

Author dc:creator

• Nasr, Saghar

Advisor dc:contributor.advisor

• Duncan, Neil A.

Committee members dc:contributor.committeemember

• Matyas, John R
• Boyd, Steven K.

• Engineering--Biomedical
• Engineering--Mechanical

Statement dc:rights

• University of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.

Language dc:language.iso

eng

DOI

http://dx.doi.org/10.11575/PRISM/28332

OAI identifier oai:identifier

oai:ucalgary.scholaris.ca:11023/3923