High-throughput droplet microfluidics screening platform for selecting fast-growing and high lipid-producing microalgae from a mutant library.

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.