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Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins

Nature Biotechnology volume 38, pages 35–38 (2020)Cite this article

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Abstract

We describe a universal sample multiplexing method for single-cell RNA sequencing in which fixed cells are chemically labeled by attaching identifying DNA oligonucleotides to cellular proteins. Analysis of a 96-plex perturbation experiment revealed changes in cell population structure and transcriptional states that cannot be discerned from bulk measurements, establishing an efficient method for surveying cell populations from large experiments or clinical samples with the depth and resolution of single-cell RNA sequencing.

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Fig. 1: 96-plex scRNA-seq experiment.
Fig. 2: Perturbation responses at single-cell resolution.

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Data availability

Sequencing data from these experiments can be obtained from CaltechDATA at https://doi.org/10.22002/D1.1311.

Code availability

Code and tutorials for the kITE demultiplexing workflow can be found at https://www.kallistobus.tools/kite_tutorial.html. Python notebooks used to process data and generate figures are available on GitHub at https://github.com/pachterlab/GPCTP_2019. The same GitHub repository also contains a fully reproducible reanalysis using ‘kallisto | bustools’ transcript alignments and a Google Colab notebook.

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Acknowledgements

We thank Z. Gartner and C. McGinnis for helpful feedback regarding the ClickTag protocol and V. Svensson for suggestions regarding analysis of multiplexed datasets. Thanks to P. Melsted and S. Booeshaghi for developing the ‘kallisto | bustools’ functions used in the preprocessing workflow and to P. Rivaud for assistance with 10x data processing. Additional support was provided by the the Caltech Bioinformatics Resource Center and the Single Cell Profiling and Engineering Center (SPEC) in the Beckman Institute at Caltech.

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Authors and Affiliations

  1. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

    Jase Gehring

  2. Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA, USA

    Jase Gehring, Jong Hwee Park, Sisi Chen, Matthew Thomson & Lior Pachter

  3. Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, USA

    Lior Pachter

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  1. Jase Gehring
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Contributions

J.G. conceived and developed the ClickTag multiplexing strategy. J.G., J.H.P. and S.C. designed the scRNA-seq experiments and J.G. and J.H.P. performed the experiments. J.H.P. performed all tissue culture operations and J.G. developed the kITE demultiplexing workflow and analyzed the scRNA-seq data. J.G., J.H.P., S.C, M.T. and L.P. contributed to the interpretation of the results and writing of the manuscript.

Corresponding author

Correspondence to Lior Pachter.

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Competing interests

J.G., L.P., S.C. and J.H.P. are listed as co-inventors on a patent application related to this work (US patent application 16/296,075).

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Supplementary information

Supplementary Materials

Supplementary Figs. 1–19 and Supplementary Tables 1–2.

Reporting Summary

Supplementary Table 3

Ninety-six-sample multiplex Louvain marker genes. The top 100 marker genes from each cluster for n=21,223 cells in the 96-sample multiplexed experiment. Clusters used for differential expression were determined by Louvain community detection with resolution = 2.2. Differentially expressed genes were identified with the Wilcoxon test and the ScanPy ‘rank_genes_groups’ function.

Supplementary Table 4

Primer sequences used in this study.

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Gehring, J., Hwee Park, J., Chen, S. et al. Highly multiplexed single-cell RNA-seq by DNA oligonucleotide tagging of cellular proteins. Nat Biotechnol 38, 35–38 (2020). https://doi.org/10.1038/s41587-019-0372-z

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