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High-throughput cultivation and identification of bacteria from the plant root microbiota

Nature Protocols volume 16, pages 988–1012 (2021)Cite this article

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Abstract

Cultivating native bacteria from roots of plants grown in a given environment is essential for dissecting the functions of the root microbiota for plant growth and health with strain-specific resolution. In this study, we established a straightforward protocol for high-throughput bacterial isolation from fresh root samples using limiting dilution to ensure that most cultured bacteria originated from only one microorganism. This is followed by strain characterization using a two-sided barcode polymerase chain reaction system to identify pure and heterogeneous bacterial cultures. Our approach overcomes multiple difficulties of traditional bacterial isolation and identification methods, such as obtaining bacteria with diverse growth rates while greatly increasing throughput. To facilitate data processing, we developed an easy-to-use bioinformatic pipeline called ‘Culturome’ (https://github.com/YongxinLiu/Culturome) and a graphical user interface web server (http://bailab.genetics.ac.cn/culturome/). This protocol allows any research group (two or three lab members without expertise in bioinformatics) to systematically cultivate root-associated bacteria within 8–9 weeks.

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Fig. 1
Fig. 2: High-throughput bacterial isolation from root microbiota samples.
Fig. 3: Bacterial identification using the two-sided barcode system.
Fig. 4: Workflow of bioinformatic analysis to identify cultivated bacteria.
Fig. 5: Preservation of cultivated bacteria.
Fig. 6: Anticipated results for bacterial cultivation from the O. sativa L. roots.

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Data availability

The raw sequence data used in this paper have been deposited in the Genome Sequence Archive57 in the BIG Data Center58 under accession number CRA002517. The authors declare that all data supporting the findings of this study are available in the original paper12. Because many labs might not frequently perform systematic bacterial isolation, to reduce the experimental cost we are happy to share the full set of barcoded primers with any scientists who want to use this protocol. Correspondence and requests for materials and computational scripts should be addressed to Y.B.

Code availability

The computational scripts of the data analysis pipeline (Culturome v1.0) are freely available at GitHub (https://github.com/YongxinLiu/Culturome) under a GNU General Public License v2 or later. The Culturome web server is freely available at http://bailab.genetics.ac.cn/culturome and continuously maintained by the authors.

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Acknowledgements

We thank T. Chen, Y. Liu and J. Wang at EHBIO Gene Technology (Beijing, China) for their help on the construction of the web server. This work was supported by grants from the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA24020104 to Y.B.), the Key Research Program of Frontier Sciences of the Chinese Academy of Sciences (QYZDB-SSW-SMC021 to Y.B.), the National Natural Science Foundation of China (31772400 and 31761143017 to Y.B.; 31801945 to J.Z.; and 31701997 to X.G.) and the Chinese Academy of Sciences Youth Innovation Promotion Association (2020101 to J.Z.). This work was supported by the Max Planck Society (R.G.-O. and P.S.-L.), the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) in the Priority Programme SPP 2125 DECRyPT (R.G.-O. and P.S.-L.) and under Germany’s Excellence Strategy–EXC-Number 2048/1–project 390686111 (R.G.-O. and P.S.-L.).

Author information

Author notes

  1. These authors contributed equally: Jingying Zhang, Yong-Xin Liu.

Authors and Affiliations

  1. State Key Laboratory of Plant Genomics, Institute of Genetics and Developmental Biology, Innovation Academy for Seed Design, Chinese Academy of Sciences, Beijing, China

    Jingying Zhang, Yong-Xin Liu, Xiaoxuan Guo, Yuan Qin & Yang Bai

  2. CAS Center for Excellence in Biotic Interactions, University of Chinese Academy of Sciences, Beijing, China

    Jingying Zhang, Yong-Xin Liu, Xiaoxuan Guo, Yuan Qin & Yang Bai

  3. CAS-JIC Centre of Excellence for Plant and Microbial Science, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Beijing, China

    Jingying Zhang, Yong-Xin Liu, Xiaoxuan Guo, Yuan Qin & Yang Bai

  4. College of Advanced Agricultural Sciences, University of Chinese Academy of Sciences, Beijing, China

    Yuan Qin & Yang Bai

  5. Max Planck Institute for Plant Breeding Research, Cologne, Germany

    Ruben Garrido-Oter & Paul Schulze-Lefert

  6. Cluster of Excellence on Plant Sciences (CEPLAS), Max Planck Institute for Plant Breeding Research, Cologne, Germany

    Ruben Garrido-Oter & Paul Schulze-Lefert

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Contributions

J.Z. and X.G. performed the experiments. Y.-X.L. and Y.Q. performed the analysis. Y.Q., J.Z., Y.-X.L. and Y.B. designed the figures. R.G.-O., P.S.-L. and Y.B. supervised the project. J.Z., Y.-X.L., R.G.-O., P.S.-L. and Y.B. wrote the manuscript.

Corresponding authors

Correspondence to Ruben Garrido-Oter, Paul Schulze-Lefert or Yang Bai.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Paul Dennis, Kiwamu Minamisawa and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Key references using this protocol

Zhang, J. et al. Nat. Biotechnol. 37, 676–684 (2019): https://doi.org/10.1038/s41587-019-0104-4

Huang, A. C. et al. Science 364, eaau6389 (2019): https://doi.org/10.1126/science.aau6389

Bai, Y. et al. Nature 528, 364–369 (2015): https://doi.org/10.1038/nature16192

Supplementary information

Reporting Summary

Supplementary Table 1

Primer sequences used for bacterial identification. 16S rRNA gene primers 799F and 1193R are in gray. Well and plate barcodes are in red and blue, respectively. Illumina adapters (P5 and Read1, P7, Index and Read2) are in gray.

Supplementary Table 2

Mapping file.

Supplementary Table 3

Taxonomy and sequences of ASVs.

Supplementary Table 4

Read counts of ASVs in each well (ASV table).

Supplementary Table 5

Five best candidate wells corresponding to each ASV.

Supplementary Table 6

Data to generate anticipated results in Fig. 6.

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Zhang, J., Liu, YX., Guo, X. et al. High-throughput cultivation and identification of bacteria from the plant root microbiota. Nat Protoc 16, 988–1012 (2021). https://doi.org/10.1038/s41596-020-00444-7

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