Advertisement

Seq-Well: A Sample-Efficient, Portable Picowell Platform for Massively Parallel Single-Cell RNA Sequencing

  • Toby P. Aicher
  • Shaina Carroll
  • Gianmarco Raddi
  • Todd Gierahn
  • Marc H. WadsworthII
  • Travis K. Hughes
  • Chris Love
  • Alex K. Shalek
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1979)

Abstract

Seq-Well is a low-cost picowell platform that can be used to simultaneously profile the transcriptomes of thousands of cells from diverse, low input clinical samples. In Seq-Well, uniquely barcoded mRNA capture beads and cells are co-confined in picowells that are sealed using a semipermeable membrane, enabling efficient cell lysis and mRNA capture. The beads are subsequently removed and processed in parallel for sequencing, with each transcript’s cell of origin determined via the unique barcodes. Due to its simplicity and portability, Seq-Well can be performed almost anywhere.

Key words

Seq-Well Single-cell RNA sequencing Single-cell genomics Systems biology Transcriptomics RNA-Seq Picowells 

Notes

Acknowledgments

R.G. was supported by the Intramural Research Program of the Division of Intramural Research Z01AI000947, NIAID, NIH; the UCLA-Caltech MSTP, and the NIGMS T32 GM008042. A.K.S. was supported by the Searle Scholars Program, the Beckman Young Investigator Program, the Pew-Stewart Scholars, a Sloan Fellowship in Chemistry, NIH grants 1DP2OD020839, 2U19AI089992, 1U54CA217377, P01AI039671, 5U24AI118672, 2RM1HG006193, 1R33CA202820, 2R01HL095791, 1R01AI138546, 1R01HL126554, 1R01DA046277, 2R01HL095791, and Bill and Melinda Gates Foundation grants OPP1139972, OPP1137006, and OPP1116944. J.C.L. was supported by NIH grants DP3DK09768101, P01AI045757, R21AI106025, and R56AI104274, the W.M. Keck Foundation, Camille Dreyfus Teacher-Scholar program, and the US Army Research Office through the Institute for Soldier Nanotechnologies, under contract number W911NF-13-D-0001. This work was also supported in part by the Koch Institute Support (core) NIH Grant P30-CA14051 from the National Cancer Institute.

References

  1. 1.
    Kolodziejczyk AA, Lönnberg T (2018) Global and targeted approaches to single-cell transcriptome characterization. Brief Funct Genomics 17:209–219.  https://doi.org/10.1093/bfgp/elx025CrossRefPubMedGoogle Scholar
  2. 2.
    Svensson V, Vento-Tormo R, Teichmann SA (2018) Exponential scaling of single-cell RNA-seq in the past decade. Nat Protoc 13:599–604.  https://doi.org/10.1038/nprot.2017.149CrossRefPubMedGoogle Scholar
  3. 3.
    Kolodziejczyk AA, Kim JK, Svensson V et al (2015) The technology and biology of single-cell RNA sequencing. Mol Cell 58:610–620.  https://doi.org/10.1016/j.molcel.2015.04.005CrossRefPubMedGoogle Scholar
  4. 4.
    Ziegenhain C, Vieth B, Parekh S et al (2017) Comparative analysis of single-cell RNA sequencing methods. Mol Cell 65:631–643.e4.  https://doi.org/10.1016/j.molcel.2017.01.023CrossRefPubMedGoogle Scholar
  5. 5.
    Tang F, Barbacioru C, Wang Y et al (2009) mRNA-Seq whole-transcriptome analysis of a single cell. Nat Methods 6:377–382.  https://doi.org/10.1038/nmeth.1315CrossRefPubMedGoogle Scholar
  6. 6.
    Macaulay IC, Svensson V, Labalette C et al (2016) Single-cell RNA-sequencing reveals a continuous spectrum of differentiation in hematopoietic cells. Cell Rep 14:966–977.  https://doi.org/10.1016/j.celrep.2015.12.082CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Shalek AK, Satija R, Adiconis X et al (2013) Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498:236–240.  https://doi.org/10.1038/nature12172CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Klein AM, Mazutis L, Akartuna I et al (2015) Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells. Cell 161:1187–1201.  https://doi.org/10.1016/j.cell.2015.04.044CrossRefPubMedPubMedCentralGoogle Scholar
  9. 9.
    Macosko EZ, Basu A, Satija R et al (2015) Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–1214.  https://doi.org/10.1016/j.cell.2015.05.002CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Zheng GXY, Terry JM, Belgrader P et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049.  https://doi.org/10.1038/ncomms14049CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Gierahn TM, Ii MHW, Hughes TK et al (2017) Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput. Nat Methods 14:395–398.  https://doi.org/10.1038/nmeth.4179CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Bose S, Wan Z, Carr A et al (2015) Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol 16:120.  https://doi.org/10.1186/s13059-015-0684-3CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Kivioja T, Vähärautio A, Karlsson K et al (2012) Counting absolute numbers of molecules using unique molecular identifiers. Nat Methods 9:72–74.  https://doi.org/10.1038/nmeth.1778CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Toby P. Aicher
    • 1
    • 2
    • 3
  • Shaina Carroll
    • 1
    • 2
    • 3
  • Gianmarco Raddi
    • 4
    • 5
    • 6
    • 7
  • Todd Gierahn
    • 8
  • Marc H. WadsworthII
    • 1
    • 2
    • 3
  • Travis K. Hughes
    • 1
    • 2
    • 3
  • Chris Love
    • 1
    • 3
    • 8
    • 9
  • Alex K. Shalek
    • 1
    • 2
    • 3
    • 8
  1. 1.Ragon Institute of MGH, Harvard, and MITCambridgeUSA
  2. 2.Department of Chemistry, Institute for Medical Engineering and Sciences (IMES)MITCambridgeUSA
  3. 3.Broad Institute of MIT and HarvardCambridgeUSA
  4. 4.Wellcome Sanger InstituteWellcome Genome CampusCambridgeUK
  5. 5.University of CambridgeCambridgeUK
  6. 6.NIAIDNational Institutes of HealthBethesdaUSA
  7. 7.David Geffen School of Medicine at UCLALos AngelesUSA
  8. 8.Koch Institute for Integrative Cancer ResearchMITCambridgeUSA
  9. 9.Department of Chemical EngineeringMassachusetts Institute of TechnologyCambridgeUSA

Personalised recommendations