A Droplet Microfluidic Platform for Automating Genetic Engineering [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 574.5 [Unassigned]

Thông tin xuất bản: Washington, D.C. : Oak Ridge, Tenn. : United States. Dept. of Energy. Office of Science ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2016

Mô tả vật lý: Size: p. 426-433 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 260974

 We present a water-in-oil droplet microfluidic platform for transformation, culture and expression of recombinant proteins in multiple host organisms including bacteria, yeast and fungi. The platform consists of a hybrid digital microfluidic/channel-based droplet chip with integrated temperature control to allow complete automation and integration of plasmid addition, heat-shock transformation, addition of selection medium, culture, and protein expression. The microfluidic format permitted significant reduction in consumption (100-fold) of expensive reagents such as DNA and enzymes compared to the benchtop method. Furthermore, the chip contains a channel to continuously replenish oil to the culture chamber to provide a fresh supply of oxygen to the cells for long-term (~5 days) cell culture. The flow channel also replenished oil lost to evaporation and increased the number of droplets that could be processed and cultured. The platform was validated by transforming several plasmids into Escherichia coli including plasmids containing genes for fluorescent proteins GFP, BFP and RFP
  plasmids with selectable markers for ampicillin or kanamycin resistance
  and a Golden Gate DNA assembly reaction. We also demonstrate the applicability of this platform for transformation in widely used eukaryotic organisms such as Saccharomyces cerevisiae and Aspergillus Niger. Duration and temperatures of the microfluidic heat-shock procedures were optimized to yield transformation efficiencies comparable to those obtained by benchtop methods with a throughput up to 6 droplets/min. The platform we proposed offers potential for automation of molecular biology experiments significantly reducing cost, time and variability while improving throughput.
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