seeding of tissue engineering scaffolds Andreea-Paula ROBU* L cr - - PowerPoint PPT Presentation

Computational study of the potential role

• f chemotaxis in enhancing the cell

seeding of tissue engineering scaffolds

Andreea-Paula ROBU*

Lăcrămioara STOICU-TIVADAR*

Adrian NEAGU**

* “Politehnica” University Timisoara ** University of Medicine and Pharmacy “Victor Babes” Timisoara

UNIVERSITY POLITEHNICA TIMISOARA, ROMANIA FACULTY OF AUTOMATION AND COMPUTERS DEPARTMENT OF AUTOMATION AND APPLIED INFORMATICS

Contents

v Considerations about Tissue Engineering

v The Differential Adhesion Hypothesis

v Goals v Motivation v The computational models of some multicellular

systems

v The simulation algorithm v The cell seeding simulations of a porous scaffold v A study of the influence of adhesion and chemotaxis for optimal cell seeding v Conclusions

Tissue Engineering

v A complex area of regenerative medicine v Studies in vitro development of living tissues and organs

v Cells are first expanded in conventional cell culture dishes v Then they are seeded on porous scaffolds v For weeks, the tissue construct is cultured in the laboratory v Finally, it is implanted and gradually restores tissue function

Methods of tissue engineering

Cell rearrangements described by DAH

Steinberg’s differential adhesion hypothesis (DAH) states that: v Cells move to form the largest number of strong connections with their neighbors v Cells tend to reach the minimum energy configuration v Cells use their motility to achieve the desired configuration

Goals

v Build computational models of tissue constructs v Study the interplay of adhesion and chemotaxis on cell seeding of porous scaffolds v Identify the optimal conditions that lead to an uniform and rapid distribution of cells in the scaffold

Motivation

v The importance of cell adhesion for cell migration and tissue integrity v The fact that morphogenesis also involves chemotactic cell movement v The emergence of solid free-form fabrication techniques for building scaffolds with desired microstructure that incorporate chemoattractants and growth factors with controllable gradients and release rates

Computational models

v Systems: cell suspension, respectively cell aggregate located near a porous scaffold, which contains a chemoattractant substance v We assumed that the chemoattractant is released at a steady rate, creating a constant concentration gradient v The 3D computational model is built on a cubic lattice: each site of the lattice is occupied by either a cell or medium or biomaterial

Computational models

v The cell aggregate is represented by a sphere: the elements

• f the sphere are lattice sites associated to individual cells

v Lattice sites associated to the cell suspension are occupied either by a cell, or by a medium particle v Lattice nodes associated to the scaffolds are occupied by biomaterial or cell culture medium (inside of the pores) v The porosity of the scaffold is achieved in the model by taking into account the radius of the pores as well as the radius of the circular orifices that connect the pores

Models visualization - VMD

Simulation alghoritm

v To study the interplay of adhesion and chemotaxis,

we simulated cell seeding using our SIMMMC software application, based on the Metropolis Monte Carlo method, extended to also take into account chemotaxis

Computational algorithm Metropolis Monte Carlo

v An elementary move = swap of a cell with a volume element of cell culture medium from its vicinity v A move is accepted with a probability : v A Monte Carlo step (MCS) is represented by the sequence

• f operations in which each cell is given the chance to make

a move

Total energy of the system Energy associated to chemotaxis :

• i labels cells (N is the total number of cells)
• Ci denote the chemoattractant's concentration in the vicinity
• f cell i
• K represents the chemotactic strength

Energy of adhesion

1 1 , t t ij ij is is i j i i j

E B B γ γ

− − = = <

= ⋅ + ⋅

∑ ∑

ij jj ii ij

ε ε ε γ − + = ) (

2 1 is ii is

ε ε γ − = 2

1

• The cell-substrate interfacial tension parameter:
• The cell-cell interfacial tension parameter:

v Input parameters:

Ø cohesion energy between cells Ø adhesion energy between cells and scaffold Ø radius of pores Ø radius of circular orifices that connect the pores Ø the minimum and maximum concentration of the chemoattractant Ø the chemotactic strength associated to cells

v Output parameter:

Ø centre of mass of seeded cells

Input and output parameters

Simulation I

v First we considered a cell aggregate on the surface of a scaffold Results of cell seeding obtained after running 4×105 MCS

Input parameters: εcc =1; εcs =1.4; R=5; r=2; C1=1; C2=10

• A. K=0; B. K=400; C. K=700; D. K=1000; visualized using VMD

Simulation II

v Second we considered a cell suspension on the surface of a scaffold

Results of cell seeding obtained after running 104 MCS

Input parameters: εcc =0; εcs =0.6; R=5; r=2; C1=1; C2=10

• A. K=0; B. K=200; C. K=500; D. K=1000; E. K=1300; F. K=1500;

visualized using VMD

The centre of mass of seeded cells - Simulation I

v Figure gives a quantitative assessment of the impact of chemotaxis on cell seeding v We observe that as K is larger the coordinate of the cells’ centre of mass decreases faster, indicating that more and more cells enter the scaffold v For K=1000 , within 4×105 MCS the Z coordinate of the cells’ centre of mass approaches 40, which is the Z coordinate of the scaffold’s centre of symmetry, indicating an uniform distribution of the cells within the scaffold

The centre of mass of seeded cells - Simulation II

v If chemotaxis is strong enough (K=1000), already after 10 000 MCS the cell distribution in the volume of the scaffold is uniform v If K is even larger, the cells increasingly tend to accumulate near the bottom of the scaffold v Prolonged evolution under the influence of chemotaxis leads to the accumulation of the cells in the lower layers of the scaffold

Conclusions

v We built three-dimensional models of a cell aggregate, respectively of a cell suspension, in the vicinity of a porous scaffold that contained a chemoattractant substance v We simulated cell seeding using an original application (SIMMMC) based on a Metropolis Monte Carlo algorithm, which was extended to also take into account chemotaxis v To identify the optimal conditions for a rapid and uniform cell seeding, we varied the chemotactic strength at constant energetic and geometric input parameters

v We analyzed cell seeding, by following the evolution of the Z coordinate of the cells’ centre of mass for different values of the chemotactic strengths v It can be seen that as the chemotactic strengths take higher values, the cells penetrate the scaffold more quickly and the distribution of the cells is uniform in the depth of the scaffold v Our study suggests that cell seeding of tissue engineering scaffolds may be enhanced by incorporating chemoattractants

• f controlled release rate into the scaffold

Conclusions

Thank you for your attention!

andreea.robu@aut.upt.ro