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:

FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics

Nature Protocols volume 19, pages 3191–3218 (2024)Cite this article

Subjects

Abstract

Rare cells have an important role in development and disease, and methods for isolating and studying cell subsets are therefore an essential part of biology research. Such methods traditionally rely on labeled antibodies targeted to cell surface proteins, but large public databases and sophisticated computational approaches increasingly define cell subsets on the basis of genomic, epigenomic and transcriptomic sequencing data. Methods for isolating cells on the basis of nucleic acid sequences powerfully complement these approaches by providing experimental access to cell subsets discovered in cell atlases, as well as those that cannot be otherwise isolated, including cells infected with pathogens, with specific DNA mutations or with unique transcriptional or splicing signatures. We recently developed a nucleic acid cytometry platform called ‘focused interrogation of cells by nucleic acid detection and sequencing’ (FIND-seq), capable of isolating rare cells on the basis of RNA or DNA markers, followed by bulk or single-cell transcriptomic analysis. This platform has previously been used to characterize the splicing-dependent activation of the transcription factor XBP1 in astrocytes and HIV persistence in memory CD4 T cells from people on long-term antiretroviral therapy. Here, we outline the molecular and microfluidic steps involved in performing FIND-seq, including protocol updates that allow detection and whole transcriptome sequencing of rare HIV-infected cells that harbor genetically intact virus genomes. FIND-seq requires knowledge of microfluidics, optics and molecular biology. We expect that FIND-seq, and this comprehensive protocol, will enable mechanistic studies of rare HIV+ cells, as well as other cell subsets that were previously difficult to recover and sequence.

Key points

  • FIND-seq is a nucleic acid cytometry platform for isolating rare cells on the basis of RNA or DNA markers. This protocol describes the fabrication of three microfluidic devices and their use, with associated molecular steps, followed by the preparation of samples for bulk or single-cell transcriptomic analysis.

  • This method for isolating cells on the basis of nucleic acid sequences complements techniques that rely on labeled antibodies targeted to cell surface proteins.

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: Major stages in the FIND-seq workflow.
Fig. 2: Operation of three sequential microfluidic devices for cell lysis, nucleic acid detection and sorting.
Fig. 3: Multiplexed detection of cells containing HIV provirus.
Fig. 4: Accurate sorting of rare Ψ+ env+ cells.

Similar content being viewed by others

Data availability

The design files for all microfluidic chips are provided in Supplementary Data 1–3. Source data are provided with this paper.

References

  1. Siliciano, J. D. et al. Long-term follow-up studies confirm the stability of the latent reservoir for HIV-1 in resting CD4+ T cells. Nat. Med. 9, 727–728 (2003).

    Article  CAS  PubMed  Google Scholar 

  2. Siliciano, J. M. & Siliciano, R. F. The remarkable stability of the latent reservoir for HIV-1 in resting memory CD4+ T cells. J. Infect. Dis. 212, 1345–1347 (2015).

    Article  PubMed  Google Scholar 

  3. Smajic, S. et al. Single-cell sequencing of human midbrain reveals glial activation and a Parkinson-specific neuronal state. Brain 145, 964–978 (2022).

    Article  PubMed  Google Scholar 

  4. Xu, J. et al. Human striatal glia differentially contribute to AD- and PD-specific neurodegeneration. Nat. Aging 3, 346–365 (2023).

    CAS  PubMed  Google Scholar 

  5. Bhat, S., Viswanathan, P., Chandanala, S., Prasanna, S. J. & Seetharam, R. N. Expansion and characterization of bone marrow derived human mesenchymal stromal cells in serum-free conditions. Sci. Rep. 11, 3403 (2021).

    Article  CAS  PubMed  Google Scholar 

  6. Pittenger, M. F. et al. Mesenchymal stem cell perspective: cell biology to clinical progress. NPJ Regen. Med. 4, 22 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Ng, Y. Y., Baert, M. R., de Haas, E. F., Pike-Overzet, K. & Staal, F. J. Isolation of human and mouse hematopoietic stem cells. Methods Mol. Biol. 506, 13–21 (2009).

    Article  CAS  PubMed  Google Scholar 

  8. Darcis, G., Berkhout, B. & Pasternak, A. O. The quest for cellular markers of HIV reservoirs: any color you like. Front. Immunol. 10, 2251 (2019).

    Article  CAS  PubMed  Google Scholar 

  9. Neidleman, J. et al. Phenotypic analysis of the unstimulated in vivo HIV CD4 T cell reservoir. eLife 9, e60933 (2020).

    Article  CAS  PubMed  Google Scholar 

  10. Lee, H. C., Chathuranga, K. & Lee, J. S. Intracellular sensing of viral genomes and viral evasion. Exp. Mol. Med. 51, 1–13 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Harold, D. et al. Genome-wide association study identifies variants at CLU and PICALM associated with Alzheimer’s disease. Nat. Genet. 41, 1088–1093 (2009).

    Article  CAS  PubMed  Google Scholar 

  12. Gisler, F. M., von Kanel, T., Kraemer, R., Schaller, A. & Gallati, S. Identification of SNPs in the cystic fibrosis interactome influencing pulmonary progression in cystic fibrosis. Eur. J. Hum. Genet. 21, 397–403 (2013).

    Article  CAS  PubMed  Google Scholar 

  13. Tiscornia, G. & Mahadevan, M. S. Myotonic dystrophy: the role of the CUG triplet repeats in splicing of a novel DMPK exon and altered cytoplasmic DMPK mRNA isoform ratios. Mol. Cell 5, 959–967 (2000).

    Article  CAS  PubMed  Google Scholar 

  14. Ait Benichou, S. et al. Antisense oligonucleotides as a potential treatment for brain deficits observed in myotonic dystrophy type 1. Gene Ther. 29, 698–709 (2022).

    Article  CAS  PubMed  Google Scholar 

  15. Gierahn, T. M. et al. Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat. Methods 14, 395–398 (2017).

    Article  CAS  PubMed  Google Scholar 

  16. Cao, J. et al. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357, 661–667 (2017).

    Article  CAS  PubMed  Google Scholar 

  17. Rosenberg, A. B. 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  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  20. Klein, A. M. et al. Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161, 1187–1201 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Clark, I. C. et al. Microfluidics-free single-cell genomics with templated emulsification. Nat. Biotechnol. 41, 1557–1566 (2023).

    Article  CAS  PubMed  Google Scholar 

  22. Aldridge, S. & Teichmann, S. A. Single cell transcriptomics comes of age. Nat. Commun. 11, 4307 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Clarke, Z. A. et al. Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods. Nat. Protoc. 16, 2749–2764 (2021).

    Article  CAS  PubMed  Google Scholar 

  24. Schreibing, F. & Kramann, R. Mapping the human kidney using single-cell genomics. Nat. Rev. Nephrol. 18, 347–360 (2022).

    Article  PubMed  Google Scholar 

  25. 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  PubMed  Google Scholar 

  26. Mincarelli, L., Lister, A., Lipscombe, J. & Macaulay, I. C. Defining cell identity with single-cell omics. Proteomics 18, e1700312 (2018).

    Article  PubMed  Google Scholar 

  27. Sun, D. et al. Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nat. Biotechnol. 40, 527–538 (2022).

    Article  CAS  PubMed  Google Scholar 

  28. Kharchenko, P. V. The triumphs and limitations of computational methods for scRNA-seq. Nat. Methods 18, 723–732 (2021).

    Article  CAS  PubMed  Google Scholar 

  29. Clark, I. C. et al. Identification of astrocyte regulators by nucleic acid cytometry. Nature 614, 326–333 (2023).

    Article  CAS  PubMed  Google Scholar 

  30. Clark, I. C. et al. HIV silencing and cell survival signatures in infected T cell reservoirs. Nature 614, 318–325 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643.e4 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  33. Picelli, S. et al. Smart-seq2 for sensitive full-length transcriptome profiling in single cells. Nat. Methods 10, 1096–1098 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Hagemann-Jensen, M., Ziegenhain, C. & Sandberg, R. Scalable single-cell RNA sequencing from full transcripts with Smart-seq3xpress. Nat. Biotechnol. 40, 1452–1457 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Hagemann-Jensen, M. et al. Single-cell RNA counting at allele and isoform resolution using Smart-seq3. Nat. Biotechnol. 38, 708–714 (2020).

    Article  CAS  PubMed  Google Scholar 

  36. Jaitin, D. A. et al. Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types. Science 343, 776–779 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Shao, X. et al. scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network. Nucleic Acids Res. 49, e122 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Fa, B. et al. GapClust is a light-weight approach distinguishing rare cells from voluminous single cell expression profiles. Nat. Commun. 12, 4197 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Bej, S., Galow, A. M., David, R., Wolfien, M. & Wolkenhauer, O. Automated annotation of rare-cell types from single-cell RNA-sequencing data through synthetic oversampling. BMC Bioinformatics 22, 557 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Amamoto, R. et al. Probe-Seq enables transcriptional profiling of specific cell types from heterogeneous tissue by RNA-based isolation. eLife 8, e51452 (2019).

    Article  CAS  PubMed  Google Scholar 

  41. Arrigucci, R. et al. FISH-Flow, a protocol for the concurrent detection of mRNA and protein in single cells using fluorescence in situ hybridization and flow cytometry. Nat. Protoc. 12, 1245–1260 (2017).

    Article  CAS  PubMed  Google Scholar 

  42. Eastburn, D. J., Sciambi, A. & Abate, A. R. Identification and genetic analysis of cancer cells with PCR-activated cell sorting. Nucleic Acids Res. 42, e128 (2014).

    Article  PubMed  Google Scholar 

  43. Clark, I. C., Thakur, R. & Abate, A. R. Concentric electrodes improve microfluidic droplet sorting. Lab Chip 18, 710–713 (2018).

    Article  CAS  PubMed  Google Scholar 

  44. Wan, L. et al. A digital microfluidic system for loop-mediated isothermal amplification and sequence specific pathogen detection. Sci. Rep. 7, 14586 (2017).

    Article  PubMed  Google Scholar 

  45. Choi, J. W., Seo, W. H., Kang, T., Kang, T. & Chung, B. G. Droplet digital recombinase polymerase amplification for multiplexed detection of human coronavirus. Lab Chip 23, 2389–2398 (2023).

    Article  CAS  PubMed  Google Scholar 

  46. Sukovich, D. J., Lance, S. T. & Abate, A. R. Sequence specific sorting of DNA molecules with FACS using 3dPCR. Sci. Rep. 7, 39385 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Hahaut, V. et al. Fast and highly sensitive full-length single-cell RNA sequencing using FLASH-seq. Nat. Biotechnol. 40, 1447–1451 (2022).

    Article  CAS  PubMed  Google Scholar 

  48. Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–891 (2013).

    Article  CAS  PubMed  Google Scholar 

  49. Panwar, J., Autour, A. & Merten, C. A. Design and construction of a microfluidics workstation for high-throughput multi-wavelength fluorescence and transmittance activated droplet analysis and sorting. Nat. Protoc. 18, 1090–1136 (2023).

    Article  CAS  PubMed  Google Scholar 

  50. Lorenz, H. et al. High-aspect-ratio, ultrathick, negative-tone near-UV photoresist and its applications for MEMS. Sens. Actuator A Phys. 64, 33–39 (1998).

    Article  CAS  Google Scholar 

  51. Mata, A., Fleischman, A. J. & Roy, S. Fabrication of multi-layer SU-8 microstructures. J. Micromech. Microeng. 16, 276 (2006).

    Article  Google Scholar 

  52. Duffy, D. C., McDonald, J. C., Schueller, O. J. & Whitesides, G. M. Rapid prototyping of microfluidic systems in poly(dimethylsiloxane). Anal. Chem. 70, 4974–4984 (1998).

    Article  CAS  PubMed  Google Scholar 

  53. Yan, Z., Clark, I. C. & Abate, A. R. Rapid encapsulation of cell and polymer solutions with bubble-triggered droplet generation. Macromol. Chem. Phys. 218, 1600297 (2017).

    Article  Google Scholar 

  54. Bruner, K. M. et al. A quantitative approach for measuring the reservoir of latent HIV-1 proviruses. Nature 566, 120–125 (2019).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank members of the Clark laboratory for helpful discussions related to the development of this protocol. I.C.C. and S.W.S. were supported by K22AI152644, 1R01DA059551-01 and U54 AI170856 from the NIH.

Author information

Authors and Affiliations

  1. Department of Bioengineering, College of Engineering, California Institute for Quantitative Biosciences (QB3), University of California Berkeley, Berkeley, CA, USA

    Seung Won Shin & Iain C. Clark

  2. Virus Persistence and Dynamics Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Prakriti Mudvari & Eli A. Boritz

  3. Ann Romney Center for Neurologic Diseases, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Shravan Thaploo, Michael A. Wheeler & Francisco J. Quintana

  4. Human Immunology Section, Vaccine Research Center, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Daniel C. Douek

  5. Department of Bioengineering and Therapeutic Sciences, School of Pharmacy, University of California San Francisco, San Francisco, CA, USA

    Adam R. Abate

Authors
  1. Seung Won Shin
    View author publications

    Search author on:PubMed Google Scholar

  2. Prakriti Mudvari
    View author publications

    Search author on:PubMed Google Scholar

  3. Shravan Thaploo
    View author publications

    Search author on:PubMed Google Scholar

  4. Michael A. Wheeler
    View author publications

    Search author on:PubMed Google Scholar

  5. Daniel C. Douek
    View author publications

    Search author on:PubMed Google Scholar

  6. Francisco J. Quintana
    View author publications

    Search author on:PubMed Google Scholar

  7. Eli A. Boritz
    View author publications

    Search author on:PubMed Google Scholar

  8. Adam R. Abate
    View author publications

    Search author on:PubMed Google Scholar

  9. Iain C. Clark
    View author publications

    Search author on:PubMed Google Scholar

Contributions

S.W.S., P.M., S.T., M.A.W. and I.C.C. designed and performed experiments or analyzed data. D.C.D., F.J.Q., E.A.B. and A.R.A. supervised the original development of FIND-seq. I.C.C. supervised the work presented in this protocol. S.W.S. and I.C.C. wrote the paper with input from their co-authors.

Corresponding author

Correspondence to Iain C. Clark.

Ethics declarations

Competing interests

I.C.C., E.A.B. and A.R.A. have filed a patent related to FIND-seq.

Peer review

Peer review information

Nature Protocols thanks Linas Mazutis and the other, anonymous, reviewer(s) 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.

Related links

Key references using this protocol

Clark, I. C. et al. Nature 614, 326–333 (2023): https://doi.org/10.1038/s41586-022-05613-0

Clark, I. C. et al. Nature 614, 318–325 (2023): https://doi.org/10.1038/s41586-022-05556-6

Supplementary information

Supplementary Information

Supplementary Figs. 1–7

Supplementary Data 1

Computer-aided design (CAD) file for the bubble-triggered microfluidic device

Supplementary Data 2

CAD file for the re-injector device

Supplementary Data 3

CAD file for the droplet sorter device

Source data

Source Data Fig. 3

Raw data for the detection efficiency and correlation curves for sample spike-in experiments

Source Data Fig. 4

Raw data for gag TaqMan assay qPCR analysis of HIV-1 provirus from sorted droplets

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

Shin, S.W., Mudvari, P., Thaploo, S. et al. FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics. Nat Protoc 19, 3191–3218 (2024). https://doi.org/10.1038/s41596-024-01021-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Version of record:

  • Issue date:

  • DOI: https://doi.org/10.1038/s41596-024-01021-y

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