Issue 15, 2021

High-throughput optofluidic screening for improved microbial cell factories via real-time micron-scale productivity monitoring

Abstract

The industrial synthetic biology sector has made huge investments to achieve relevant miniaturized screening systems for scalable fermentation. Here we present the first example of a high-throughput (>103 genotypes per week) perfusion-based screening system to improve small-molecule secretion from microbial strains. Using the Berkeley Lights Beacon® system, the productivity of each strain could be directly monitored in real time during continuous culture, yielding phenotypes that correlated strongly (r2 > 0.8, p < 0.0005) with behavior in industrially relevant bioreactor processes. This method allows a much closer approximation of a typical fed-batch fermentation than conventional batch-like droplet or microplate culture models, in addition to rich time-dependent data on growth and productivity. We demonstrate these advantages by application to the improvement of high-productivity strains using whole-genome random mutagenesis, yielding mutants with substantially improved (by up to 85%) peak specific productivities in bioreactors. Each screen of ∼5 × 103 mutants could be completed in under 8 days (including 5 days involving user intervention), saving ∼50–75% of the time required for conventional microplate-based screening methods.

Graphical abstract: High-throughput optofluidic screening for improved microbial cell factories via real-time micron-scale productivity monitoring

Supplementary files

Article information

Article type
Paper
Submitted
04 Maijs 2021
Accepted
09 Jūn. 2021
First published
23 Jūn. 2021
This article is Open Access
Creative Commons BY-NC license

Lab Chip, 2021,21, 2901-2912

High-throughput optofluidic screening for improved microbial cell factories via real-time micron-scale productivity monitoring

M. Rienzo, K. Lin, K. C. Mobilia, E. K. Sackmann, V. Kurz, A. H. Navidi, J. King, R. M. Onorato, L. K. Chao, T. Wu, H. Jiang, J. K. Valley, T. A. Lionberger and M. D. Leavell, Lab Chip, 2021, 21, 2901 DOI: 10.1039/D1LC00389E

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