Abstract
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 Herbaspirillum frisingense in a manner robust to carbon source variation and the presence of additional species. Broadly, kChip screening can identify multispecies consortia possessing any optically assayable function, including facilitation of biocontrol agents, suppression of pathogens, degradation of recalcitrant substrates, and robustness of these functions to perturbation, with many applications across basic and applied microbial ecology.
| Original language | American English |
|---|---|
| Pages (from-to) | 12804-12809 |
| Number of pages | 6 |
| Journal | Proceedings of the National Academy of Sciences of the United States of America |
| Volume | 116 |
| Issue number | 26 |
| DOIs | |
| State | Published - 2019 |
Bibliographical note
Funding Information:We thank Professor Otto Cordero and Professor Ahmad Khalil for early discussions about the use of droplet combinations to construct microbial communities. We also thank Prianca Tawde and Jameson Kief for assistance; members of the J.G. laboratory (MIT), Nadav Kashtan, and Mike Rothballer for samples; members of the laboratory of Otto Cordero (MIT), the S.K. laboratory (University of Illinois at Urbana–Cham-paign), the laboratory of Eric Alm (MIT), the J.G. laboratory (MIT), and the laboratory of Jim Collins (MIT, Broad Institute) for discussions; Alfonso Pérez-Escudero for reviewing the manuscript; and the Jupyter, numpy, scipy, scikit-image, scikit-learn, and pandas open-source development teams. This work was supported by National Science Foundation Graduate Research Fellowship Program [to J.K. (Fellow ID 2016220942) and A.K. (Fellow ID 2013164251)], an NIH grant [to C.M.A. (Grant F32CA236425)], the MIT Institute for Medical Engineering and Science Broshy Fellowship (to A.K.), a Career Award at the Scientific Interface from the Burroughs Wellcome Fund [to P.C.B. (Grant 1010240)], an MIT Deshpande Center Innovation Grant (to P.C.B.), a Scialog seed grant from the Gordon and Betty Moore Foundation and the Research Corporation for Science Advancement (to S.K. and P.C.B.), a grant from the Simons Foundation [to J.G. (Grant 542385)], a Bridge Project grant from the Dana Farber/Harvard Cancer Center and the Koch Institute for Integrative Cancer Research at MIT (to P.C.B.), a technology development seed grant from the Merkin Institute for Transformative Technologies in Healthcare at the Broad Institute (to P.C.B.), and a grant from the United States-Israel Binational Science Foundation [to J.F. (Grant 2017179)].
Funding Information:
ACKNOWLEDGMENTS. We thank Professor Otto Cordero and Professor Ahmad Khalil for early discussions about the use of droplet combinations to construct microbial communities. We also thank Prianca Tawde and Jameson Kief for assistance; members of the J.G. laboratory (MIT), Nadav Kashtan, and Mike Rothballer for samples; members of the laboratory of Otto Cordero (MIT), the S.K. laboratory (University of Illinois at Urbana–Champaign), the laboratory of Eric Alm (MIT), the J.G. laboratory (MIT), and the laboratory of Jim Collins (MIT, Broad Institute) for discussions; Alfonso Pérez-Escudero for reviewing the manuscript; and the Jupyter, numpy, scipy, scikit-image, scikit-learn, and pandas open-source development teams. This work was supported by National Science Foundation Graduate Research Fellowship Program [to J.K. (Fellow ID 2016220942) and A.K. (Fellow ID 2013164251)], an NIH grant [to C.M.A. (Grant F32CA236425)], the MIT Institute for Medical Engineering and Science Broshy Fellowship (to A.K.), a Career Award at the Scientific Interface from the Burroughs Wellcome Fund [to P.C.B. (Grant 1010240)], an MIT Deshpande Center Innovation Grant (to P.C.B.), a Scialog seed grant from the Gordon and Betty Moore Foundation and the Research Corporation for Science Advancement (to S.K. and P.C.B.), a grant from the Simons Foundation [to J.G. (Grant 542385)], a Bridge Project grant from the Dana Farber/Harvard Cancer Center and the Koch Institute for Integrative Cancer Research at MIT (to P.C.B.), a technology development seed grant from the Merkin Institute for Transformative Technologies in Healthcare at the Broad Institute (to P.C.B.), and a grant from the United States-Israel Binational Science Foundation [to J.F. (Grant 2017179)].
Publisher Copyright:
© 2019 National Academy of Sciences. All rights reserved.
Keywords
- Community assembly
- Droplet microfluidics
- High-throughput screening
- Microbial interactions
- Synthetic ecology