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.

  • Protocol
  • Published:

Linking single-cell transcriptomes with secretion using SEC-seq

Nature Protocols volume 20, pages 2034–2055 (2025)Cite this article

Subjects

Abstract

Cells secrete numerous proteins and other biomolecules into their surroundings to achieve critical functions—from communicating with other cells to blocking the activity of pathogens. Secretion of cytokines, growth factors, extracellular vesicles and even recombinant biologic drugs defines the therapeutic potency of many cell therapies. However, gene expression states that drive specific secretory phenotypes are largely unknown. We provide a protocol that enables the secretion amount of a target protein encoded (SEC) by oligonucleotide barcodes to be linked with transcriptional sequencing (seq) for thousands of single cells. SEC-seq leverages microscale hydrogel particles called Nanovials to isolate cells and capture their secretions in close proximity, oligonucleotide-labeled antibodies to tag secretions on Nanovials and flow cytometry and single-cell RNA-sequencing (scRNA-seq) platforms for readout. Cells on Nanovials can be sorted on the basis of viability, secretion amount or other surface markers without fixation or permeabilization, and cell- and secretion-containing Nanovials are directly introduced into microfluidic droplets-in-oil emulsions for single-cell barcoding of cell transcriptomes and secretions. We have used SEC-seq to link T cell receptor sequences to the relative amount of associated cytokine secretions, surface marker gene expression with a highly secreting and potential regenerative population of mesenchymal stromal cells and the transcriptome with high immunoglobulin secretion from plasma cells. Nanovial modification and cell loading takes <4 h, and once the desired incubation time is over, staining, cell sorting and emulsion generation for scRNA-seq can also be completed in <4 h. Compared to related techniques that link secretions to a cell’s surface, SEC-seq provides a general solution across any secretion target because of the ease with which biotinylated Nanovials can be modified. By linking gene expression and secretory strength, SEC-seq can expand our understanding of cell secretion, how it is regulated and how it can be engineered to make better therapies.

Key points

  • Secretion encoded single-cell sequencing (SEC-seq) enables secretions from a single cell to be spatially associated with it in a downstream transcriptomic workflow. Microscale hydrogel particles called Nanovials isolate cells and capture secretions, which are tagged by an oligo barcode-labeled antibody for subsequent single-cell RNA-sequencing.

  • Only a few methods link secretion and transcriptomes of single cells at scale, and SEC-seq provides a broader flexibility in secretion incubation time, multiplexing and cell type compatibility.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to the full article PDF.

USD 39.95

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

Fig. 1: A summary of the SEC-seq workflow.
Fig. 2: SEC-seq demonstrated with three distinct cell types and secretions.
Fig. 3: Detailed SEC-seq protocol.
Fig. 4: Gating strategies for sorting single-cell-loaded Nanovials for sequencing and analysis.
Fig. 5: Workflow for geting started and troubleshooting for SEC-seq.

Similar content being viewed by others

Data availability

Sequencing data from SEC-seq studies can be found on the Gene Expression Omnibus with the accession number GSE223550 (ref. 4), GSE229042 (ref. 3), and GSE252830 (ref. 5).

References

  1. Uhlén, M. et al. Tissue-based map of the human proteome. Science 347, 1260419 (2015).

    Article  Google Scholar 

  2. Vogel, C. & Marcotte, E. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat. Rev. Genet. 13, 227–232 (2012).

    Article  CAS  PubMed Central  Google Scholar 

  3. Cheng, R. Y.-H. et al. SEC-seq: association of molecular signatures with antibody secretion in thousands of single human plasma cells. Nat. Commun. 14, 3567 (2023).

    Article  CAS  PubMed Central  Google Scholar 

  4. Udani, S. et al. Associating growth factor secretions and transcriptomes of single cells in nanovials using SEC-seq. Nat. Nanotechnol. 19, 354–363 (2024).

    Article  CAS  Google Scholar 

  5. Koo, D. et al. Defining T cell receptor repertoires using nanovial-based binding and functional screening. Proc. Natl Acad. Sci. USA 121, e2320442121 (2024).

    Article  CAS  PubMed Central  Google Scholar 

  6. de Rutte, J. et al. Suspendable hydrogel nanovials for massively parallel single-cell functional analysis and sorting. ACS Nano 16, 7242–7257 (2022).

    Article  PubMed Central  Google Scholar 

  7. Lee, S., de Rutte, J., Dimatteo, R., Koo, D. & Di Carlo, D. Scalable fabrication and use of 3D structured microparticles spatially functionalized with biomolecules. ACS Nano 16, 38–49 (2022).

    Article  CAS  Google Scholar 

  8. Ghosh, R. et al. Lab on a particle technologies. Anal. Chem. 96, 7817–7839 (2024).

    Article  CAS  PubMed Central  Google Scholar 

  9. Stoeckius, M. et al. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14, 865–868 (2017).

    Article  CAS  PubMed Central  Google Scholar 

  10. Koo, D. et al. Optimizing cell therapy by sorting cells with high extracellular vesicle secretion. Nat. Commun. 15, 4870 (2024).

    Article  CAS  PubMed Central  Google Scholar 

  11. Dixit, A. et al. Perturb-seq: dissecting molecular circuits with scalable single cell RNA profiling of pooled genetic screens. Cell 167, 1853–1866 (2016).

    Article  CAS  PubMed Central  Google Scholar 

  12. Miwa, H., Dimatteo, R., de Rutte, J., Ghosh, R. & Di Carlo, D. Single-cell sorting based on secreted products for functionally defined cell therapies. Microsyst. Nanoeng. 8, 84 (2022).

    Article  CAS  PubMed Central  Google Scholar 

  13. Healy, Z., Weinhold, K. & Murdoch, D. Transcriptional profiling of CD8+ CMV-specific T cell functional subsets obtained using a modified method for isolating high-quality RNA from fixed and permeabilized cells. Front. Immunol. 11, 1859 (2020).

    Article  CAS  PubMed Central  Google Scholar 

  14. Cecil, C., Nilsson, L.-A., Nygren, H., Ouchterlony, O. & Tarkowski, A. A solid-phase enzyme-linked immunospot (ELISPOT) assay for enumeration of specific antibody-secreting cells. J. Immunol. Methods 65, 109–121 (1983).

    Article  Google Scholar 

  15. Ansaryan, S. et al. High-throughput spatiotemporal monitoring of single-cell secretions via plasmonic microwell arrays. Nat. Biomed. Eng. 7, 943–958 (2023).

    Article  CAS  PubMed Central  Google Scholar 

  16. Wu, T., Womersley, H. J., Jiehao Ray, W., Scolnick, J. & Lih Feng, C. Time-resolved assessment of single-cell protein secretion by sequencing. Nat. Methods 20, 723–734 (2023).

    Article  CAS  Google Scholar 

  17. Xu, Y. et al. Single-cell secretion analysis via microfluidic cell membrane immunosorbent assay for immune profiling. Anal. Chem. 96, 49–58 (2024).

    Article  Google Scholar 

  18. Dimatteo, R. & Di Carlo, D. IL-2 secretion-based sorting of single T cells using high-throughput microfluidic on-cell cytokine capture. Lab Chip 22, 1576–1583 (2022).

    Article  CAS  PubMed Central  Google Scholar 

  19. Rosenberg, A. et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360, 176–182 (2018).

    Article  CAS  PubMed Central  Google Scholar 

  20. Sasagawa, Y. et al. Quartz-Seq2: a high-throughput single-cell RNA-sequencing method that effectively uses limited sequence reads. Genome Biol. 19, 29 (2018).

    Article  PubMed Central  Google Scholar 

  21. Macosko, E. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed Central  Google Scholar 

  22. de Rutte, J., Dimatteo, R., Zhu, S., Archang, M. M. & Di Carlo, D. Sorting single-cell microcarriers using commercial flow cytometers. SLAS Technol. 27, 150–159 (2022).

    Article  Google Scholar 

  23. Koo, D., Dimatteo, R., Lee, S., de Rutte, J. & Di Carlo, D. Sorting single T cells based on secreted cytokines and surface markers using hydrogel nanovials. Preprint at https://www.biorxiv.org/content/10.1101/2022.04.28.489940v1 (2022).

  24. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    Article  CAS  Google Scholar 

  25. Luecken, M. & Theis, F. Current best practices in single‐cell RNA‐seq analysis: a tutorial. Mol. Syst. Biol. 15, e8746 (2019).

    Article  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank S. Udani for key contributions to developing the SEC-seq methodology and feedback on the manuscript. We thank D. Koo for developing the original protocol for the TCR SEC-seq workflow and for providing figures showing expected results. This project has been made possible in part by grant 2023-332386 from the Chan Zuckerberg Initiative Donor Advised Fund (CZI DAF), an advised fund of the Silicon Valley Community Foundation.

Author information

Author notes

  1. These authors contributed equally: Justin Langerman, Sevana Baghdasarian.

Authors and Affiliations

  1. Department of Biological Chemistry, David Geffen School of Medicine at the University of California Los Angeles, Los Angeles, CA, USA

    Justin Langerman & Kathrin Plath

  2. Department of Chemical and Biomolecular Engineering, University of California Los Angeles, Los Angeles, CA, USA

    Sevana Baghdasarian

  3. Center of Immunotherapy and Immunity, Seattle Children’s Research Institute, Seattle, WA, USA

    Rene Yu-Hong Cheng & Richard G. James

  4. Molecular Engineering and Science Institute, University of Washington, Seattle, WA, USA

    Rene Yu-Hong Cheng & Richard G. James

  5. Department of Lab Medicine and Pathology, University of Washington, Seattle, WA, USA

    Richard G. James

  6. Department of Pediatrics, University of Washington, Seattle, WA, USA

    Richard G. James

  7. Department of Pharmacology, University of Washington, Seattle, WA, USA

    Richard G. James

  8. Brotman-Baty Institute for Precision Medicine, Seattle, WA, USA

    Richard G. James

  9. Molecular Biology Institute, Jonsson Comprehensive Cancer Center, Brain Research Institute, Graduate Program in the Biosciences, Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California Los Angeles, Los Angeles, CA, USA

    Kathrin Plath

  10. Partillion Bioscience Corporation, Pasadena, CA, USA

    Dino Di Carlo

  11. Department of Bioengineering, University of California Los Angeles, Los Angeles, CA, USA

    Dino Di Carlo

  12. Department of Mechanical and Aerospace Engineering, University of California Los Angeles, Los Angeles, CA, USA

    Dino Di Carlo

  13. California NanoSystems Institute (CNSI), University of California Los Angeles, Los Angeles, CA, USA

    Dino Di Carlo

Authors
  1. Justin Langerman
    View author publications

    Search author on:PubMed Google Scholar

  2. Sevana Baghdasarian
    View author publications

    Search author on:PubMed Google Scholar

  3. Rene Yu-Hong Cheng
    View author publications

    Search author on:PubMed Google Scholar

  4. Richard G. James
    View author publications

    Search author on:PubMed Google Scholar

  5. Kathrin Plath
    View author publications

    Search author on:PubMed Google Scholar

  6. Dino Di Carlo
    View author publications

    Search author on:PubMed Google Scholar

Contributions

J.L., S.B. and R.Y.-H.C. developed the methodology and wrote the manuscript. J.L. and S.B. contributed writing to all sections, constructed the figures and edited the manuscript. K.P. and R.G.J. contributed guidance for the writing and edited the manuscript. D.D.C. contributed writing to all sections, edited the manuscript and helped design figures.

Corresponding author

Correspondence to Dino Di Carlo.

Ethics declarations

Competing interests

D.D.C. and the Regents of the University of California have financial interests in Partillion Bioscience, which sells Nanovials.

Peer review

Peer review information

Nature Protocols thanks Chia-Hung Chen and Tian Tian for their contribution to the peer review of this work.

Additional information

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

Key references

Cheng, R. Y.-H. et al. Nat. Commun. 14, 3567 (2023): https://doi.org/10.1038/s41467-023-39367-8

Udani, S. et al. Nat. Nanotechnol. 19, 354–363 (2024): https://doi.org/10.1038/s41565-023-01560-7

Koo, D. et al. Proc. Natl Acad. Sci. USA 121, e2320442121 (2024): https://doi.org/10.1073/pnas.2320442121

de Rutte, J. et al. ACS Nano 16, 7242–7257 (2022): https://doi.org/10.1021/acsnano.1c11420

Lee, S. et al. ACS Nano 16, 38–49 (2022): https://doi.org/10.1021/acsnano.1c05857

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Langerman, J., Baghdasarian, S., Cheng, R.YH. et al. Linking single-cell transcriptomes with secretion using SEC-seq. Nat Protoc 20, 2034–2055 (2025). https://doi.org/10.1038/s41596-024-01112-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41596-024-01112-w

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing