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High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding

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Abstract

Microbial split-pool ligation transcriptomics (microSPLiT) is a high-throughput single-cell RNA sequencing method for bacteria. With four combinatorial barcoding rounds, microSPLiT can profile transcriptional states in hundreds of thousands of Gram-negative and Gram-positive bacteria in a single experiment without specialized equipment. As bacterial samples are fixed and permeabilized before barcoding, they can be collected and stored ahead of time. During the first barcoding round, the fixed and permeabilized bacteria are distributed into a 96-well plate, where their transcripts are reverse transcribed into cDNA and labeled with the first well-specific barcode inside the cells. The cells are mixed and redistributed two more times into new 96-well plates, where the second and third barcodes are appended to the cDNA via in-cell ligation reactions. Finally, the cells are mixed and divided into aliquot sub-libraries, which can be stored until future use or prepared for sequencing with the addition of a fourth barcode. It takes 4 days to generate sequencing-ready libraries, including 1 day for collection and overnight fixation of samples. The standard plate setup enables single-cell transcriptional profiling of up to 1 million bacterial cells and up to 96 samples in a single barcoding experiment, with the possibility of expansion by adding barcoding rounds. The protocol requires experience in basic molecular biology techniques, handling of bacterial samples and preparation of DNA libraries for next-generation sequencing. It can be performed by experienced undergraduate or graduate students. Data analysis requires access to computing resources, familiarity with Unix command line and basic experience with Python or R.

Key points

  • Through four rounds of combinatorial barcoding, this low-cost and high-throughput single-cell RNA sequencing method enables transcriptomic analysis of individual bacterial cells and the detection of phenotypically distinct subpopulations.

  • Conditions for cell wall digestion, membrane permeabilization and the barcoding procedure are optimized to profile tens of thousands of bacterial cells in a single bench-based barcoding experiment without the need for a dedicated instrument.

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Fig. 1: MicroSPLiT in-cell cDNA barcoding scheme.
Fig. 2: MicroSPLiT sequencing library preparation.
Fig. 3: Suggested cell numbers at different stages of microSPLiT.
Fig. 4: Representative B. subtilis cells throughout the course of a successful microSPLiT experiment.
Fig. 5: Library quantification by using Agilent 4200 TapeStation Screentapes.

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Data availability

The main data discussed in this protocol are available in the supporting primary research publication (https://doi.org/10.1126/science.aba5257). The raw sequencing files are available at the Sequence Read Archive: GSM4594094, GSM4594095 and GSM4594096. Processed data were submitted to Gene Expression Omnibus, with accession number GSE151940.

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Acknowledgements

We thank Nikolay Burnaevskiy for help with setting up the STARsolo workflow. A.K. and G.S. acknowledge support from the Department of Energy Office of Science, Biological and Environmental Research (BER) Program, Grant DE-SC0023091. A.K. is supported by the National Institute of Dental and Craniofacial Research of the National Institutes of Health (NIH) Grant R21DE032890 and from the National Institute of General Medical Sciences of the NIH Grant R35GM150994.

Author information

Author notes

  1. These authors contributed equally: Karl D. Gaisser, Sophie N. Skloss.

Authors and Affiliations

  1. Institute for Systems Biology, Seattle, WA, USA

    Karl D. Gaisser, Sophie N. Skloss & Anna Kuchina

  2. Biodesign Institute Center for Mechanisms of Evolution, Arizona State University, Tempe, AZ, USA

    Leandra M. Brettner

  3. Department of Genome Sciences, University of Washington, Seattle, WA, USA

    Luana Paleologu

  4. Parse Biosciences, Seattle, WA, USA

    Charles M. Roco & Alexander B. Rosenberg

  5. Department of Electrical and Computer Engineering, University of Washington, Seattle, WA, USA

    Matthew Hirano, Georg Seelig & Anna Kuchina

  6. Center for Microbiome Sciences and Therapeutics, School of Medicine, University of Washington, Seattle, WA, USA

    R. William DePaolo

  7. Department of Medicine, Division of Gastroenterology, School of Medicine, University of Washington, Seattle, WA, USA

    R. William DePaolo

  8. Molecular Engineering and Sciences Institute, University of Washington, Seattle, WA, USA

    Georg Seelig & Anna Kuchina

  9. Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, WA, USA

    Georg Seelig

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Contributions

K.D.G., S.N.S. and A.K. tested and formalized the microSPLiT protocol and produced the presented figures. L.M.B., L.P., R.W.D., G.S. and A.K. primarily developed the microSPLiT method. K.D.G., S.N.S., C.M.R., A.B.R and M.H. optimized sequencing library preparation. K.D.G. and A.K. implemented the sequencing data analysis workflow. K.D.G., S.N.S. and A.K. wrote the manuscript. A.K. supervised the work.

Corresponding author

Correspondence to Anna Kuchina.

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Competing interests

A.K., L.M.B., R.W.D. and G.S. are inventors on a patent application for microSPLiT filed by the University of Washington. C.M.R., A.B.R and G.S. are cofounders and shareholders of Parse Biosciences, an scRNA-seq company.

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Peer review information

Nature Protocols thanks Anne-Kristin Kaster and Lars Barquist for their contribution to the peer review of this work.

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Key reference using this protocol

Kuchina, A. et al. Science 371, eaba5257 (2020): https://doi.org/10.1126/science.aba5257

Extended data

Extended Data Fig. 1 Schematic of a microSPLiT sequencing construct.

Only the polyA-primed construct is shown as an example. The random hexamer-primed construct would harbor six random nucleotides in place of the dT15 sequence.

Extended Data Fig. 2 Example data before filtering, shown as a barcode-rank plot.

A red line indicates a chosen threshold for filtering out low-quality cells and debris. a, A high-resolution experiment; data were filtered at 400 total UMIs/cell. Before filtering: 884,736 barcode combinations; after filtering: 60,831 combinations, corresponding to cells. b, A poor-resolution experiment; data were filtered at 350 total UMIs/cell. Before filtering: 884,736 barcode combinations; after filtering: 644 combinations.

Supplementary information

Reporting Summary

Supplementary Tables 1–4

Supplementary Table 1: cost estimate of microSPLiT. Supplementary Table 2: cost estimate and comparison of high-throughput bacterial scRNA-seq methods. Supplementary Table 3: sequences of oligonucleotides and source barcode plates used in microSPLiT. Supplementary Table 4: barcode sequence files for STARsolo analysis.

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Gaisser, K.D., Skloss, S.N., Brettner, L.M. et al. High-throughput single-cell transcriptomics of bacteria using combinatorial barcoding. Nat Protoc (2024). https://doi.org/10.1038/s41596-024-01007-w

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