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Integration of Droplet Microfluidic Tools for Single-cell Functional Metagenomics: An Engineering Head Start
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  • David Conchouso ,
  • Amani Al-Ma’abadi ,
  • Hayedeh Behzad ,
  • Mohammed Alarawi ,
  • Masahito Hosokawa ,
  • Yohei Nishikawa ,
  • Haruko Takeyama ,
  • Katsuhiko Mineta ,
  • Takashi Gojobori
David Conchouso
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Amani Al-Ma’abadi
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Hayedeh Behzad
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Mohammed Alarawi
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Masahito Hosokawa
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Yohei Nishikawa
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Haruko Takeyama
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Katsuhiko Mineta
King Abdullah University of Science and Technology

Corresponding Author:[email protected]

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Takashi Gojobori
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Droplet microfluidics techniques have shown promising results to study single-cells at high throughput. However, their adoption in laboratories studying “-omics” sciences is still irrelevant because of the field’s complex and multidisciplinary nature. To facilitate their use, here we provide engineering details and organized protocols for integrating three droplet-based microfluidic technologies into the metagenomic pipeline to enable functional screening of bioproducts at high throughput. First, a device encapsulating single-cells in droplets at a rate of ~ 250 Hz is described considering droplet size and cell growth. Then, we expand on previously reported fluorescent activated droplet sorting (FADS) systems to integrate the use of 4 independent fluorescence-exciting lasers (e.g., 405, 488, 561, 637 nm) in a single platform to make it compatible with different fluorescence-emitting biosensors. For this sorter, both hardware and software are provided and optimized for effortlessly sorting droplets at 60 Hz. Then, a passive droplet merger was also integrated into our method to enable adding new reagents to already made droplets at a rate of 200 Hz. Finally, we provide an optimized recipe for manufacturing these chips using silicon dry-etching tools. Because of the overall integration and the technical details presented here, our approach allows biologists to quickly use microfluidic technologies and achieve both single-cell resolution and high-throughput (> 50,000 cells/day) capabilities to mining and bioprospecting metagenomic data.
Jun 2021Published in Genomics, Proteomics & Bioinformatics volume 19 issue 3 on pages 504-518. 10.1016/j.gpb.2021.03.010