Issue 12, 2023

Dispersive phase microscopy incorporated with droplet-based microfluidics for biofactory-on-a-chip

Abstract

Biomolecular imaging of intracellular structures of a single cell and subsequent screening of the cells are of high demand in metabolic engineering to develop strains with the desired phenotype. However, the capability of current methods is limited to population-scale identification of cell phenotyping. To address this challenge, we propose to utilize dispersive phase microscopy incorporated with a droplet-based microfluidic system that combines droplet volume-on-demand generation, biomolecular imaging, and droplet-on-demand sorting, to achieve high-throughput screening of cells with an identified phenotype. Particularly, cells are encapsulated in homogeneous environments with microfluidic droplet formation, and the biomolecule-induced dispersive phase can be investigated to indicate the biomass of a specific metabolite in a single cell. The retrieved biomass information consequently guides the on-chip droplet sorting unit to screen cells with the desired phenotype. To demonstrate the proof of concept, we showcase the method by promoting the evolution of the Haematococcus lacustris strain toward a high production of natural antioxidant astaxanthin. The validation of the proposed system with on-chip single-cell imaging and droplet manipulation functionalities reveals the high-throughput single-cell phenotyping and selection potential that applies to many other biofactory scenarios, such as biofuel production, critical quality attribute control in cell therapy, etc.

Graphical abstract: Dispersive phase microscopy incorporated with droplet-based microfluidics for biofactory-on-a-chip

Supplementary files

Article information

Article type
Paper
Submitted
14 Feb 2023
Accepted
05 May 2023
First published
05 May 2023

Lab Chip, 2023,23, 2766-2777

Dispersive phase microscopy incorporated with droplet-based microfluidics for biofactory-on-a-chip

Y. Luo, Y. Huang, Y. Li, X. Duan, Y. Jiang, C. Wang, J. Fang, L. Xi, N. Nguyen and C. Song, Lab Chip, 2023, 23, 2766 DOI: 10.1039/D3LC00127J

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