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High-throughput microfluidics for the screening of yeast libraries
KTH, School of Biotechnology (BIO), Proteomics and Nanobiotechnology. Novo Nordisk Foundation Center for Biosustainability.ORCID iD: 0000-0002-7195-2629
2018 (English)In: Synthetic Metabolic Pathways: Methods and Protocols, Humana Press, 2018, Vol. 1671, p. 307-317Chapter in book (Refereed)
Abstract [en]

Cell factory development is critically important for efficient biological production of chemicals, biofuels, and pharmaceuticals. Many rounds of the Design–Build–Test–Learn cycles may be required before an engineered strain meeting specific metrics required for industrial application. The bioindustry prefer products in secreted form (secreted products or extracellular metabolites) as it can lower the cost of downstream processing, reduce metabolic burden to cell hosts, and allow necessary modification on the final products, such as biopharmaceuticals. Yet, products in secreted form result in the disconnection of phenotype from genotype, which may have limited throughput in the Test step for identification of desired variants from large libraries of mutant strains. In droplet microfluidic screening, single cells are encapsulated in individual droplet and enable high-throughput processing and sorting of single cells or clones. Encapsulation in droplets allows this technology to overcome the throughput limitations present in traditional methods for screening by extracellular phenotypes. In this chapter, we describe a protocol/guideline for high-throughput droplet microfluidics screening of yeast libraries for higher protein secretion. This protocol can be adapted to screening by a range of other extracellular products from yeast or other hosts.

Place, publisher, year, edition, pages
Humana Press, 2018. Vol. 1671, p. 307-317
Series
Methods in Molecular Biology, ISSN 1064-3745 ; 1671
Keywords [en]
Droplet microfluidics, High-throughput screening, Protein secretion, Random mutagenesis, Systems biology, Yeast cell factories
National Category
Biological Sciences
Identifiers
URN: urn:nbn:se:kth:diva-219635DOI: 10.1007/978-1-4939-7295-1_19Scopus ID: 2-s2.0-85035111151ISBN: 978-1-4939-7294-4 (print)OAI: oai:DiVA.org:kth-219635DiVA, id: diva2:1164577
Note

QC 20171211

Available from: 2017-12-11 Created: 2017-12-11 Last updated: 2017-12-11Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
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Language
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Output format
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