生化工程
大规模并行测序
巨量平行
生物
计算生物学
计算机科学
工程类
遗传学
基因组
基因
并行计算
作者
Jared Kehe,Anthony Kulesa,Anthony Ortiz Lopez,Cheri M. Ackerman,Sri Gowtham Thakku,Daniel Sellers,Seppe Kuehn,Jeff Gore,Jonathan Friedman,Paul C. Blainey
标识
DOI:10.1073/pnas.1900102116
摘要
Microbial communities have numerous potential applications in biotechnology, agriculture, and medicine. Nevertheless, the limited accuracy with which we can predict interspecies interactions and environmental dependencies hinders efforts to rationally engineer beneficial consortia. Empirical screening is a complementary approach wherein synthetic communities are combinatorially constructed and assayed in high throughput. However, assembling many combinations of microbes is logistically complex and difficult to achieve on a timescale commensurate with microbial growth. Here, we introduce the kChip, a droplets-based platform that performs rapid, massively parallel, bottom-up construction and screening of synthetic microbial communities. We first show that the kChip enables phenotypic characterization of microbes across environmental conditions. Next, in a screen of ∼100,000 multispecies communities comprising up to 19 soil isolates, we identified sets that promote the growth of the model plant symbiont
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