Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Brief Communication
  • Published:

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

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

Subjects

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.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

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

Similar content being viewed by others

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.

References

  1. Zheng, G. X. Y. et al. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8, 14049 (2017).

    Article  CAS  Google Scholar 

  2. Svensson, V., Vento-Tormo, R. & Teichmann, S. A. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13, 599–604 (2018).

    Article  CAS  Google Scholar 

  3. Han, X. et al. Mapping the Mouse Cell Atlas by Microwell-Seq. Cell 172, 1091–1107.e17 (2018).

    Article  CAS  Google Scholar 

  4. Cao, J et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).

    Article  CAS  Google Scholar 

  5. Stoeckius, M. et al. Cell hashing with barcoded antibodies enables multiplexing and doublet detection for single cell genomics. Genome Biol. 19, 224 (2018).

    Article  CAS  Google Scholar 

  6. Kang, H. M. et al. Multiplexed droplet single-cell RNA-sequencing using natural genetic variation. Nat. Biotechnol. 36, 89–94 (2018).

    Article  CAS  Google Scholar 

  7. McGinnis, C. S. et al. MULTI-seq: sample multiplexing for single-cell RNA sequencing using lipid-tagged indices. Nat. Methods 16, 619–626 (2019).

    Article  CAS  Google Scholar 

  8. Guo, C. et al. CellTag indexing: genetic barcode-based sample multiplexing for single-cell genomics. Genome Biol. 20, 90 (2019).

    Article  Google Scholar 

  9. Butler, A., Hoffman, P., Smibert, P., Papalexi, E. & Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat. Biotechnol. 36, 411–420 (2018).

    Article  CAS  Google Scholar 

  10. Suryawanshi, H. et al. A single-cell survey of the human first-trimester placenta and decidua. Sci. Adv. 4, eaau4788 (2018).

    Article  CAS  Google Scholar 

  11. Wolock, S. L., Lopez, R. & Klein, A. M. Scrublet: computational identification of cell doublets in single-cell transcriptomic data. Cell Syst. 8, 281–291.e9 (2019).

    Article  CAS  Google Scholar 

  12. Bond, A. M., Ming, G. & Song, H. Adult mammalian neural stem cells and neurogenesis: five decades later. Cell Stem Cell 17, 385–395 (2015).

    Article  CAS  Google Scholar 

  13. Imayoshi, I. et al. Oscillatory control of factors determining multipotency and fate in mouse neural progenitors. Science 342, 1203–1208 (2013).

    Article  CAS  Google Scholar 

  14. Hitoshi, S. et al. Mammalian Gcm genes induce Hes5 expression by active DNA demethylation and induce neural stem cells. Nat. Neurosci. 14, 957–964 (2011).

    Article  CAS  Google Scholar 

  15. Yuzwa, S. A. et al. Developmental emergence of adult neural stem cells as revealed by single-cell transcriptional profiling. Cell Rep. 21, 3970–3986 (2017).

    Article  CAS  Google Scholar 

  16. Janes, K. A. A Systems model of signaling identifies a molecular basis set for cytokine-induced apoptosis. Science 310, 1646–1653 (2005).

    Article  CAS  Google Scholar 

  17. Nelander, S. et al. Models from experiments: combinatorial drug perturbations of cancer cells. Mol. Syst. Biol. 4, 216 (2008).

    Article  Google Scholar 

  18. Sims, D. et al. High-throughput RNA interference screening using pooled shRNA libraries and next generation sequencing. Genome Biol. 12, R104 (2011).

    Article  CAS  Google Scholar 

  19. Lamb, J. et al. The connectivity map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    Article  CAS  Google Scholar 

  20. Datlinger, P. et al. Pooled CRISPR screening with single-cell transcriptome readout. Nat. Methods 14, 297–301 (2017).

    Article  CAS  Google Scholar 

  21. Hsiao, S. C. et al. Direct cell surface modification with DNA for the capture of primary cells and the investigation of myotube formation on defined patterns. Langmuir 25, 6985–6991 (2009).

    Article  CAS  Google Scholar 

  22. Peterson, V. M. et al. Multiplexed quantification of proteins and transcripts in single cells. Nat. Biotechnol. 35, 936–939 (2017).

    Article  CAS  Google Scholar 

  23. Melsted, P., Ntranos, V. & Pachter, L. The barcode, UMI, set format and BUStools. Bioinformatics 35(21), 4472–4473 (2019).

    Article  Google Scholar 

  24. Melsted, P. et al. Modular and efficient pre-processing of single-cell RNA-seq. Preprint at bioRxiv https://doi.org/10.1101/673285 (2019).

  25. Wolf, F. A., Angerer, P. & Theis, F. J. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 19, 15 (2018).

    Article  Google Scholar 

Download references

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.

Author information

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

Authors
  1. Jase Gehring
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jong Hwee Park
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sisi Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Matthew Thomson
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lior Pachter
    View author publications

    You can also search for this author in PubMed Google Scholar

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.

Ethics declarations

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).

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41587-019-0372-z

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research