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High-throughput identification and quantification of bacterial cells in the microbiota based on 16S rRNA sequencing with single-base accuracy using BarBIQ

Abstract

Bacteria often function as a community, called the microbiota, consisting of many different bacterial species. The accurate identification of bacterial types and the simultaneous quantification of the cells of each bacterial type will advance our understanding of microbiota; however, this cannot be performed by conventional 16S rRNA sequencing methods as they only identify and quantify genes, which do not always represent cells. Here, we present a protocol for our developed method, barcoding bacteria for identification and quantification (BarBIQ). In BarBIQ, the 16S rRNA genes of single bacterial cells are amplified and attached to a unique cellular barcode in a droplet. Sequencing the tandemly linked cellular barcodes and 16S rRNA genes from many droplets (representing many cells with unique cellular barcodes) and clustering the sequences using the barcodes determines both the bacterial type for each cell based on 16S rRNA gene and the number of cells for each bacterial type based on the quantity of barcode types sequenced. Single-base accuracy for 16S rRNA sequencing is achieved via the barcodes and by avoiding chimera formation from 16S rRNA genes of different bacteria using droplets. For data processing, an easy-to-use bioinformatic pipeline is available (https://github.com/Shiroguchi-Lab/BarBIQ_Pipeline_V1_2_0). This protocol allows researchers with experience in molecular biology but without bioinformatics experience to perform the process in ~2 weeks. We show the application of BarBIQ in mouse gut microbiota analysis as an example; however, this method is also applicable to other microbiota samples, including those from the mouth and skin, marine environments, soil and plants, as well as those from other terrestrial environments.

Key points

  • This protocol describes barcoding bacteria for identification and quantification, a method for identifying and quantifying bacteria in microbiota samples.

  • Barcoding bacteria for identification and quantification uses a barcoding and single-step droplet-based amplification strategy that enables it to more precisely determine bacterial species than conventional 16S rRNA sequencing methods do.

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Fig. 1: The BarBIQ workflow. BarBIQ identifies and quantifies bacterial cells in microbiota samples based on droplet-based barcoding and amplification of 16S rRNA genes from single bacterial cells.
Fig. 2: Typical result of the gel purification procedure.
Fig. 3: Typical result of the quality check of libraries in Step 98.
Fig. 4: Typical result of the quality check of spike-in control mixture in Step 163.
Fig. 5: Results of BarBIQ for a mock community and murine cecal microbiota.

Data availability

The sequencing data generated using the protocol in the relevant study are available from the National Center for Biotechnology Information Sequence Read Archive under accessions PRJNA639639, PRJNA639647, PRJNA780331, and PRJNA780361. A demonstration dataset is available at github (https://github.com/Shiroguchi-Lab/BarBIQ_Pipeline_V1_2_0).

Code availability

The pipeline (v1.2.0) for BarBIQ is available at GitHub (https://github.com/Shiroguchi-Lab/BarBIQ_Pipeline_V1_2_0).

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Acknowledgements

We thank K. Fukuhara for helping technical development in library preparation and sequencing. This work was supported by JST/PRESTO JPMJPR15F3 (K.S.), the Nakatani Foundation (K.S.), the Japan Society for the Promotion of Science KAKENHI grant numbers 17K19364 (K.S.) and 26115719 (K.S.), and Incentive Research Project from RIKEN (J.J.). J.J. was supported by the Japan Society for the Promotion of Science Postdoctoral Fellowships for Foreign Researchers in Japan (P17389).

Author information

Authors and Affiliations

  1. State Key Laboratory of Integrated Management of Pest Insects and Rodents, Institute of Zoology, Chinese Academy of Sciences, Beijing, P.R. China

    Jianshi Jin

  2. Laboratory for Prediction of Cell Systems Dynamics, RIKEN Center for Biosystems Dynamics Research (BDR), Osaka, Japan

    Jianshi Jin, Reiko Yamamoto & Katsuyuki Shiroguchi

Authors

  1. Jianshi Jin
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  2. Reiko Yamamoto
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  3. Katsuyuki Shiroguchi
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Contributions

K.S. developed the initial version of the library preparation and sequencing system for BarBIQ. J.J. developed the pipeline for BarBIQ and the final version of whole BarBIQ. R.Y. developed some steps of the ecDNA separation and gel purification for BarBIQ. J.J., R.Y. and K.S. wrote the manuscript.

Corresponding authors

Correspondence to
Jianshi Jin or Katsuyuki Shiroguchi.

Ethics declarations

Competing interests

K.S., J.J. and R.Y. are co-inventors on a patent application based on this work filed by RIKEN.

Peer review

Peer review information

Nature Protocols thanks Wataru Suda and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Related links

Key references using this protocol

Jin, J. et al. Nat. Commun. 13, 863 (2022): https://doi.org/10.1038/s41467-022-28426-1

Ogawa, T. et al. Sci. Rep. 7, 13576 (2017): https://doi.org/10.1038/s41598-017-13529-3

Supplementary information

Supplementary Information

Supplementary Methods 1 and 2.

Reporting Summary

Supplementary Table 1

Information of oligomers and barcodes.

Supplementary Table 2

16S rRNA sequences of bacteria in mock community.

Supplementary Table 3

Quantification of bacteria in mock community.

Supplementary Table 4

16S rRNA sequences of bacteria in cecal samples.

Supplementary Table 5

Quantification of bacteria in cecal samples.

Supplementary Data 1

Example of ‘sample sheet’ for a MiSeq run.

Supplementary Data 2

16S rRNA sequences of cecal samples in fasta format with predicted taxonomies.

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Jin, J., Yamamoto, R. & Shiroguchi, K. High-throughput identification and quantification of bacterial cells in the microbiota based on 16S rRNA sequencing with single-base accuracy using BarBIQ.
Nat Protoc (2023). https://doi.org/10.1038/s41596-023-00906-8

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  • Received: 01 December 2022

  • Accepted: 24 August 2023

  • Published: 27 November 2023

  • DOI: https://doi.org/10.1038/s41596-023-00906-8

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