Evidence map›Paper›PMID 34952209›Full record

ArticleGenomics, proteomics & bioinformatics2021

Integration of Droplet Microfluidic Tools for Single-Cell Functional Metagenomics: An Engineering Head Start.

David Conchouso, Amani Al-Ma'abadi, Hayedeh Behzad, Mohammed Alarawi, Masahito Hosokawa, Yohei Nishikawa, Haruko Takeyama, Katsuhiko Mineta, Takashi Gojobori

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Genomics, proteomics & bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Review
  2. Cell-Free Production Systems in Droplet Microfluidics.Advances in biochemical engineering/biotechnology · 2023
    Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

David ConchousoDepartment of Industrial and Mechanical Engineering, Universidad de las Américas Puebla, Puebla 72810, Mexico; Computational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia; Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Amani Al-Ma'abadiComputational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia; Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Hayedeh BehzadComputational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia; Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Mohammed AlarawiComputational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia; Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia.
Masahito HosokawaResearch Organization for Nano & Life Innovation, Waseda University, Tokyo 162-0041, Japan; Department of Life Science and Medical Bioscience, Waseda University, Tokyo 162-8480, Japan; Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan.
Yohei NishikawaResearch Organization for Nano & Life Innovation, Waseda University, Tokyo 162-0041, Japan; Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Tokyo 169-0072, Japan.
Haruko TakeyamaResearch Organization for Nano & Life Innovation, Waseda University, Tokyo 162-0041, Japan; Department of Life Science and Medical Bioscience, Waseda University, Tokyo 162-8480, Japan; Institute for Advanced Research of Biosystem Dynamics, Waseda Research Institute for Science and Engineering, Waseda University, Tokyo 169-8555, Japan; Computational Bio Big-Data Open Innovation Laboratory, AIST-Waseda University, Tokyo 169-0072, Japan.
Katsuhiko MinetaComputational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia; Computer, Electrical, and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. Electronic address: katsuhiko.mineta@kaust.edu.sa.
Takashi GojoboriComputational Bioscience Research Center, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia; Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Saudi Arabia. Electronic address: takashi.gojobori@kaust.edu.sa.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Droplet microfluidic techniques have shown promising outcome to study single cells at high throughput. However, their adoption in laboratories studying "-omics" sciences is still irrelevant due to the complex and multidisciplinary nature of the field. 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 fluorescence-activated droplet sorting systems to integrate the use of 4 independent fluorescence-exciting lasers (i.e., 405, 488, 561, and 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 is also integrated into our pipeline 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 capability (>50,000 cells/day) for mining and bioprospecting metagenomic data.

Indexed as

MetagenomicsMicrofluidicsBiotechnologyDroplet microfluidicsDroplet sorterMetagenomicsSingle cell

Identifiers

PMID34952209
PMCPMC8864243

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.