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Title: Surface acoustic wave-induced dielectrophoresis : Lab on a Chip cell sorting technology for autologous cell therapies
Author: Smith, Alban Josiah
Awarding Body: University of Leeds
Current Institution: University of Leeds
Date of Award: 2017
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The ability to isolate specific cells from a heterogeneous population using label-free methods, and subsequently extract these cells at high throughput, is a key step in the advancement of autologous cell-based therapies in the field of regenerative medicine. Surface acoustic wave-based dielectrophoresis (SAW-DEP) utilises a standing shear horizontal SAW to provide a non-uniform electric field in which cells can be separated by dielectrophoresis. Due to the absence of electrodes in contact with the solution, this method overcomes many disadvantages of current dielectrophoretic technologies, allowing cell separation at high continuous throughput (< 500 cells/second) and in high conductivity solutions (< 0.4 S/m). This thesis describes the design and application of several iterations of SAW-DEP devices. After demonstrating the alignment of latex beads (1 - 10 μm) by negative DEP, the force exerted on latex beads is studied by particle tracking, and is shown to closely match a theoretical model for dielectrophoretic trapping. Following this, the separation of viable from non-viable yeast cells and mammalian cells is demonstrated. To ascertain whether SAW-DEP has any adverse effect on cells, primary human mesenchymal stromal cells (dental pulp stromal cells) are tested for viability and differentiation capability following exposure to the SAW-DEP electric field, with the results showing that the electric field had no effect, compared to controls where the cells were not exposed to the electric field. Finally, the ability to tune the liquid conductivity is investigated as a means to separate live from dead mammalian cells to high fidelity (live and dead cells were enriched into lines of 97% and 99% purity, respectively), as well as separating live mammalian cells of different lineages.
Supervisor: Wood, Christopher ; Wälti, Christoph ; Kirkham, Jennifer Sponsor: EPSRC ; Wellcome Trust
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available