Ultra-high molar mass linear polyacrylamide (LPA) networks for microchannel electrophoresis [electronic resource] : tuning LPA matrix dynamics and rheological responses to optimally impact DNA migration mechanism

Since the 2003 completion of the human genome by means of the Sanger-based DNA sequencing strategy and capillary array electrophoresis, the scientific and medical community has worked to make personalized medicine a reality. Next-generation DNA sequencing technologies with astoundingly high throughput are now in use and have provided exciting new capabilities. However, next-generation instruments are currently incapable of providing highly accurate, long sequencing read lengths, have difficulty sequencing through repetitive sequences, and have yet to "scale down" well for point-of-care diagnostics. The 600 to 700 base read lengths at 98% accuracy that are provided by capillary electrophoresis and the Sanger method are still the gold standard for medical sequencing. In recent years, there has been a natural progression from CAE to microchip electrophoresis. Microchip electrophoresis provides highly accurate sequencing at a small compromise to read length (~ 500 to 600 bases) while offering the potential for a significant increase in throughput due to the reduced separation times and the possibility to create a lab-on-a-chip (LOC) device that integrates upstream sample preparation steps. These advantages render CAE and microchip electrophoresis platforms desirable for clinical diagnostic tests since they offer highly accurate sequencing needed to analyze medically relevant genes and precisely genotype forensic samples. Therefore, this work focuses on understanding variables that influence the migration mechanism of single-stranded DNA (ssDNA) through physically- entangled polymer matrices used in CAE and microchip electrophoresis in an effort to increase throughput and bridge the gap for clinical adoption. First, I discuss the application of the biased reptation model (BRM) to CAE sequencing separations. In this study, I report the synthesis and characterization of an ultra-high molar mass linear polyacrylamide (LPA) to be used as a replaceable, physically-entangled polymer matrix and the migration mechanisms of ssDNA through the LPA matrices are evaluated using reptation plots. Properties of the LPA matrices (i.e. average pore size and viscosity) is tuned by the overall polymer concentration as well as mixing ultra-high and low molar mass LPA. In the end, a blended LPA solution generates a dynamically responsive polymer matrix that increases sequencing read lengths by delaying the onset of oriented biased reptation. The near-optimal blended LPA matrix which extends average read lengths to 830 bases in ~1 hour on a high-throughput CAE instrument. This represents a performance that is substantially better than the best commercial instrument/matrix CAE system available, which takes 2 hours to produce 830 bases. This was achieved by matrix optimization only. Second, I discuss transient effects on the DNA sequencing read length caused by shear-induced LPA stretching during the pressure-induced capillary loading of the polymer matrix which results in entanglement inhomogeneity. The network inhomogeneity, in turn, increases peak widths due to mobility variation among ssDNA fragments in the radial direction of the microchannel. The contributions of the matrix rheological properties, microchannel dimensions, and loading pressure to the shear forces placed on the matrix during loading are thoroughly examined. Finally, tests were carried out on microfluidic chips to reduced polymer network disruption. In the end, the loading conditions were optimized to enable loading and unloading of a sequencing matrix in less than 1 minute that also provides sequencing read lengths on the order of 550 to 600 bases in ~ 8 minutes. These results provide universal parameters for high-throughput loading of polymer matrices into capillaries and microchips used in electrophoretic genotyping assays and newly invented LOC platforms, such that the performance of the matrix will be optimal.

Keywords: Chemical Engineering
Advisor: Barron Annelise E. primary advisor. Thesis advisor
Shaqfeh Eric S. G. (Eric Stefan Garrido) primary advisor. Thesis advisor
Fuller Gerald G.