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Open Access Publications from the University of California

Microfluidics for investigating single-cell biodynamics

  • Author(s): Cookson, Scott Warren
  • et al.
Abstract

Progress in synthetic biology requires the development of novel techniques for investigating long-term dynamics in single cells. Here, we demonstrate the utility of microfluidics for investigating single-cell biodynamics within tightly-controlled environments in the model organisms Saccharomyces cerevisiae and Escherichia coli. First, we develop a microfluidic chemostat for monitoring single-cell gene expression within large populations of S. cerevisiae over many cellular generations. We overcome typical difficulties in tracking individual cells throughout long sequences of time-lapse microscopy images by constraining colony growth to a monolayer. Second, we construct a variant of this device to elucidate a new mechanism of cellular ordering within dense E. coli populations. By comparing colony growth and alignment inside a long and narrow microfluidic channel with continuum models and discrete element simulations, we conclude that the observed dynamic transition from an isotropic disordered phase to a nematic ordered phase is caused by biomechanical interactions arising from cellular growth and division. Finally, we use microfluidics and time-lapse microscopy to characterize an engineered genetic oscillator in E. coli. We find the circuit to exhibit fast and robust oscillatory periods that can be tuned by altering inducer levels, temperature, and the growth medium. Together, these studies illustrate the role of novel measurement techniques in advancing the goals of synthetic biology. Specifically, the ability to generate single-cell expression profiles for a large number of cells is essential to understanding the roles of regulatory motifs within native and synthetic gene networks. Through such an understanding, we aim to develop an engineering-based approach to building gene-regulatory circuits, where design specifications generated from computational modeling drive the construction of regulatory networks with desired properties

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