Microfluidics-Based Enrichment and Whole-Genome Amplification Enable Strain-Level Resolution for Airway Metagenomics

Author:

Shi Xing12,Shao Changjun1,Luo Chunxiong34,Chu Yanan1,Wang Jian1,Meng Qingren1,Yu Jun1,Gao Zhancheng2,Kang Yu1

Affiliation:

1. CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, People’s Republic of China

2. Department of Respiratory and Critical Care Medicine, Peking University People’s Hospital, Beijing, People’s Republic of China

3. The State Key Laboratory for Artificial Microstructures and Mesoscopic Physics, School of Physics, Peking University, Beijing, People’s Republic of China

4. Center for Quantitative Biology, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, People’s Republic of China

Abstract

The airway microbial community, which takes important pathogenic roles for respiratory diseases, is far from clear in terms of taxonomy and gene functions. One of the critical reasons is the heavy contamination of host cell/DNA in airway samples, which hinders the subsequent sequencing of the whole genomic contents of the microbial community—the metagenome. Here, we describe a protocol for airway sample preparation which couples a microbe enrichment microfluidic device and a DNA amplification method performed in numerous droplets. When evaluated with mock and clinical sputum samples, the microfluidics-based enrichment device and emulsion-based whole-genome amplification (MEEA) procedure efficiently removes host cells, amplifies the microbial genome, and shows no obvious bias among microbes. The efficiency of MEEA makes it a promising method in research of respiratory microbial communities and their roles in diseases.

Funder

National Key Research and Development Program of China

the National Scientific Foundation of China

Key research program of frontier sciences, CAS

Programs of Beijing Municipal Science and Technology Project

Publisher

American Society for Microbiology

Subject

Computer Science Applications,Genetics,Molecular Biology,Modeling and Simulation,Ecology, Evolution, Behavior and Systematics,Biochemistry,Physiology,Microbiology

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