High‐throughput droplet microfluidics screening platform for selecting fast‐growing and high lipid‐producing microalgae from a mutant library Academic Article uri icon

abstract

  • Biofuels derived from microalgal lipids have demonstrated a promising potential as future renewable bioenergy. However, the production costs for microalgae-based biofuels are not economically competitive, and one strategy to overcome this limitation is to develop better-performing microalgal strains that have faster growth and higher lipid content through genetic screening and metabolic engineering. In this work, we present a high-throughput droplet microfluidics-based screening platform capable of analyzing growth and lipid content in populations derived from single cells of a randomly mutated microalgal library to identify and sort variants that exhibit the desired traits such as higher growth rate and increased lipid content. By encapsulating single cells into water-in-oil emulsion droplets, each variant was separately cultured inside an individual droplet that functioned as an independent bioreactor. In conjunction with an on-chip fluorescent lipid staining process within droplets, microalgal growth and lipid content were characterized by measuring chlorophyll and BODIPY fluorescence intensities through an integrated optical detection system in a flow-through manner. Droplets containing cells with higher growth and lipid content were selectively retrieved and further analyzed off-chip. The growth and lipid content screening capabilities of the developed platform were successfully demonstrated by first carrying out proof-of-concept screening using known Chlamydomonas reinhardtii mutants. The platform was then utilized to screen an ethyl methanesulfonate (EMS)-mutated C. reinhardtii population, where eight potential mutants showing faster growth and higher lipid content were selected from 200,000 examined samples, demonstrating the capability of the platform as a high-throughput screening tool for microalgal biofuel development.

altmetric score

  • 121.594

author list (cited authors)

  • Kim, H. S., Hsu, S., Han, S., Thapa, H. R., Guzman, A. R., Browne, D. R., ... Han, A.

citation count

  • 28

publication date

  • September 2017

publisher