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:

Single-cell barcoding and sequencing using droplet microfluidics

Nature Protocols volume 12, pages 44–73 (2017)Cite this article

Subjects

Abstract

Single-cell RNA sequencing has recently emerged as a powerful tool for mapping cellular heterogeneity in diseased and healthy tissues, yet high-throughput methods are needed for capturing the unbiased diversity of cells. Droplet microfluidics is among the most promising candidates for capturing and processing thousands of individual cells for whole-transcriptome or genomic analysis in a massively parallel manner with minimal reagent use. We recently established a method called inDrops, which has the capability to index >15,000 cells in an hour. A suspension of cells is first encapsulated into nanoliter droplets with hydrogel beads (HBs) bearing barcoding DNA primers. Cells are then lysed and mRNA is barcoded (indexed) by a reverse transcription (RT) reaction. Here we provide details for (i) establishing an inDrops platform (1 d); (ii) performing hydrogel bead synthesis (4 d); (iii) encapsulating and barcoding cells (1 d); and (iv) RNA-seq library preparation (2 d). inDrops is a robust and scalable platform, and it is unique in its ability to capture and profile >75% of cells in even very small samples, on a scale of thousands or tens of thousands of cells.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Figure 1: Single-cell transcriptome barcoding in drops.
Figure 2: Synthesis of barcoding hydrogel beads.
Figure 3: Library preparation.
Figure 4: The inDrops platform.
Figure 5: Design of the microfluidic devices.
Figure 6: Quality control of barcoded hydrogel bead synthesis.
Figure 7: Setting the qPCR threshold.
Figure 8: BioAnalyzer electropherograms of amplified RNA (Step 145) and sequencing-ready DNA libraries (Step 164).
Figure 9: Results of sequencing a library of 2,000 immune cells.

Similar content being viewed by others

References

  1. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  2. Bedard, P.L. et al. Tumour heterogeneity in the clinic. Nature 501, 355–364 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Levine, J.H. et al. Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162, 184–197 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Ramsköld, D. et al. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30, 777–782 (2012).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Patel, A.P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. 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 

  7. Bendall, S.C. et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157, 714–725 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Trapnell, C. et al. The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol. 32, 381–386 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Buettner, F. et al. Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol. 33, 155–160 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Kolodziejczyk, A.A. et al. The technology and biology of single-cell RNA sequencing. Mol. Cell 58, 610–620 (2015).

    Article  CAS  PubMed  Google Scholar 

  11. Deng, Q. et al. Single-cell RNA-seq reveals dynamic, random monoallelic gene expression in mammalian cells. Science 343, 193–196 (2014).

    Article  CAS  PubMed  Google Scholar 

  12. Stubbington, M.J. et al. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13, 329–332 2016.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  13. Miyamoto, D.T. et al. RNA-Seq of single prostate CTCs implicates noncanonical Wnt signaling in antiandrogen resistance. Science 349, 1351–1356 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Sandberg, R. Entering the era of single-cell transcriptomics in biology and medicine. Nat. Methods 11, 22–24 (2014).

    Article  CAS  PubMed  Google Scholar 

  15. Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).

    Article  CAS  PubMed  Google Scholar 

  16. 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 

  17. Sasagawa, Y. et al. Quartz-Seq: a highly reproducible and sensitive single-cell RNA sequencing method, reveals non-genetic gene-expression heterogeneity. Genome Biol. 14, R31 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  18. Stoler, D.L., Stewart, C.C. & Stomper, P.C. Breast epithelium procurement from stereotactic core biopsy washings: flow cytometry-sorted cell count analysis. Clin. Cancer Res 8, 428–432 (2002).

    PubMed  Google Scholar 

  19. Tay, S. et al. Single-cell NF-kappaB dynamics reveal digital activation and analogue information processing. Nature 466, 267–271 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Junkin, M. & Tay, S. Microfluidic single-cell analysis for systems immunology. Lab Chip 14, 1246–1260 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Kellogg, R.A. & Tay, S. Noise facilitates transcriptional control under dynamic inputs. Cell 160, 381–392 (2015).

    Article  CAS  PubMed  Google Scholar 

  22. Weinstein, J.A. et al. High-throughput sequencing of the zebrafish antibody repertoire. Science 324, 807–810 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Fan, H.C. et al. Whole-genome molecular haplotyping of single cells. Nat. Biotechnol. 29, 51–57 (2011).

    Article  CAS  PubMed  Google Scholar 

  24. Lecault, V. et al. High-throughput analysis of single hematopoietic stem cell proliferation in microfluidic cell culture arrays. Nat. Methods 8, 581–586 (2011).

    Article  CAS  PubMed  Google Scholar 

  25. Streets, A.M. et al. Microfluidic single-cell whole-transcriptome sequencing. Proc. Natl. Acad. Sci. USA 111, 7048–7053 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wu, A.R. et al. Quantitative assessment of single-cell RNA-sequencing methods. Nat. Methods 11, 41–46 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Shalek, A.K. et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature 498, 236–240 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Usoskin, D. et al. Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing. Nat. Neurosci. 18, 145–153 (2015).

    Article  CAS  PubMed  Google Scholar 

  29. Treutlein, B. et al. Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq. Nature 509, 371–375 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kumar, R.M. et al. Deconstructing transcriptionalheterogeneity in pluripotent stem cells. Nature 516, 56–61 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Shalek, A.K. et al. Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature 510, 363–369 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. 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  PubMed Central  Google Scholar 

  33. Fan, H.C., Fu, G.K. & Fodor, S.P. Expression profiling. Combinatorial labeling of single cells for gene expression cytometry. Science 347, 1258367 (2015).

    Article  PubMed  CAS  Google Scholar 

  34. Bose, S. et al. Scalable microfluidics for single-cell RNA printing and sequencing. Genome Biol. 16, 120 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  35. Guo, M.T. et al. Droplet microfluidics for high-throughput biological assays. Lab Chip 12, 2146–2155 (2012).

    Article  CAS  PubMed  Google Scholar 

  36. Price, A.K. & Paegel, B.M. Discovery in droplets. Anal. Chem. 88, 339–353 (2016).

    Article  CAS  PubMed  Google Scholar 

  37. Joensson, H.N. & Andersson Svahn, H. Droplet microfluidics--a tool for single-cell analysis. Angew. Chem. Int. Ed. Engl. 51, 12176–12192 (2012).

    Article  CAS  PubMed  Google Scholar 

  38. Theberge, A.B. et al. Microdroplets in microfluidics: an evolving platform for discoveries in chemistry and biology. Angew. Chem. Int. Ed. Engl. 49, 5846–5868 (2010).

    Article  CAS  PubMed  Google Scholar 

  39. Thorsen, T. et al. Dynamic pattern formation in a vesicle-generating microfluidic device. Phys. Rev. Lett. 86, 4163–4166 (2001).

    Article  CAS  PubMed  Google Scholar 

  40. Anna, S.L., Bontoux, N. & Stone, H.A. Formation of dispersions using 'flow focusing' in microchannels. Appl. Phys. Lett. 82, 364–366 (2003).

    Article  CAS  Google Scholar 

  41. Nisisako, T., Ando, T. & Hatsuzawa, T. High-volume production of single and compound emulsions in a microfluidic parallelization arrangement coupled with coaxial annular world-to-chip interfaces. Lab Chip 12, 3426–3435 (2012).

    Article  CAS  PubMed  Google Scholar 

  42. Christopher, G.F. & Anna, S.L. Microfluidic methods for generating continuous droplet streams. J. Phys. D: Appl. Phys. 40, R319–R336 (2007).

    Article  CAS  Google Scholar 

  43. Garstecki, P. et al. Formation of droplets and bubbles in a microfluidic T-junction - scaling and mechanism of break-up. Lab Chip 6, 437–446 (2006).

    Article  CAS  PubMed  Google Scholar 

  44. Mazutis, L. & Griffiths, A.D. Selective droplet coalescence using microfluidic systems. Lab Chip 12, 1800–1806 (2012).

    Article  CAS  PubMed  Google Scholar 

  45. Ahn, K. et al. Electrocoalescence of drops synchronized by size-dependent flow in microfluidic channels. Appl. Phys. Lett. 88, 264105 (2006).

    Article  CAS  Google Scholar 

  46. Chabert, M., Dorfman, K.D. & Viovy, J.L. Droplet fusion by alternating current (AC) field electrocoalescence in microchannels. Electrophoresis 26, 3706–3715 (2005).

    Article  CAS  PubMed  Google Scholar 

  47. Priest, C., Herminghaus, S. & Seemann, R. Controlled electrocoalescence in microfluidics: targeting a single lamella. Appl. Phys. Lett. 89, 134101 (2006).

    Article  CAS  Google Scholar 

  48. Abate, A.R. et al. High-throughput injection with microfluidics using picoinjectors. Proc. Natl. Acad. Sci. USA 107, 19163–19166 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Rotem, A. et al. High-throughput single-cell labeling (Hi-SCL) for RNA-Seq using drop-based microfluidics. PLoS One 10, e0116328 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Link, D.R. et al. Geometrically mediated breakup of drops in microfluidic devices. Phys. Rev. Lett. 92, 054503 (2004).

    Article  CAS  PubMed  Google Scholar 

  51. Hatch, A.C. et al. 1-Million droplet array with wide-field fluorescence imaging for digital PCR. Lab Chip 11, 3838–3845 (2011).

    Article  CAS  PubMed  Google Scholar 

  52. Ahn, K. et al. Dielectrophoretic manipulation of drops for high-speed microfluidic sorting devices. Appl. Phys. Lett. 88, 024104 (2006).

    Article  CAS  Google Scholar 

  53. Franke, T. et al. Surface acoustic wave (SAW) directed droplet flow in microfluidics for PDMS devices. Lab Chip 9, 2625–2627 (2009).

    Article  CAS  PubMed  Google Scholar 

  54. Baret, J.C. et al. Fluorescence-activated droplet sorting (FADS): efficient microfluidic cell sorting based on enzymatic activity. Lab Chip 9, 1850–1858 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Granieri, L. et al. High-throughput screening of enzymes by retroviral display using droplet-based microfluidics. Chem. Biol. 17, 229–235 (2010).

    Article  CAS  PubMed  Google Scholar 

  56. Debs, B.E. et al. Functional single-cell hybridoma screening using droplet-based microfluidics. Proc. Natl. Acad. Sci. USA 109, 11570–11575 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  57. Mazutis, L. et al. Single-cell analysis and sorting using droplet-based microfluidics. Nat. Protoc. 8, 870–91 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Mazutis, L. & Griffiths, A.D. Preparation of monodisperse emulsions by hydrodynamic size fractionation. Appl. Phys. Lett. 95, 204103 (2009).

  59. Sciambi, A. & Abate, A.R. Accurate microfluidic sorting of droplets at 30 kHz. Lab Chip 15, 47–51 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Clausell-Tormos, J. et al. Droplet-based microfluidic platforms for the encapsulation and screening of mammalian cells and multicellular organisms. Chem. Biol. 15, 427–437 (2008).

    Article  CAS  PubMed  Google Scholar 

  61. Koster, S. et al. Drop-based microfluidic devices for encapsulation of single cells. Lab Chip 8, 1110–1115 (2008).

    Article  CAS  PubMed  Google Scholar 

  62. Brouzes, E. et al. Droplet microfluidic technology for single-cell high-throughput screening. Proc. Natl. Acad. Sci. USA 106, 14195–14200 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Liu, W. et al. Isolation, incubation, and parallel functional testing and identification by FISH of rare microbial single-copy cells from multi-species mixtures using the combination of chemistrode and stochastic confinement. Lab Chip 9, 2153–2162 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Zeng, Y. et al. High-performance single cell genetic analysis using microfluidic emulsion generator arrays. Anal. Chem. 82, 3183–3190 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Huebner, A. et al. Development of quantitative cell-based enzyme assays in microdroplets. Anal. Chem. 80, 3890–3896 (2008).

    Article  CAS  PubMed  Google Scholar 

  66. Chokkalingam, V. et al. Probing cellular heterogeneity in cytokine-secreting immune cells using droplet-based microfluidics. Lab Chip 13, 4740–4744 (2013).

    Article  CAS  PubMed  Google Scholar 

  67. Martino, C. et al. Intracellular protein determination using droplet-based immunoassays. Anal. Chem. 83, 5361–5368 (2011).

    Article  CAS  PubMed  Google Scholar 

  68. Sjostrom, S.L. et al. High-throughput screening for industrial enzyme production hosts by droplet microfluidics. Lab Chip 14, 806–813 (2014).

    Article  CAS  PubMed  Google Scholar 

  69. Del Ben, F. et al. A method for detecting circulating tumor cells based on the measurement of single-cell metabolism in droplet-based microfluidics. Angew. Chem. Int. Ed. Engl. 55, 8581–8584 (2016).

    Article  CAS  PubMed  Google Scholar 

  70. DeKosky, B.J. et al. In-depth determination and analysis of the human paired heavy- and light-chain antibody repertoire. Nat. Med. 21, 86–91 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Kintses, B. et al. Picoliter cell lysate assays in microfluidic droplet compartments for directed enzyme evolution. Chem. Biol. 19, 1001–1009 (2012).

    Article  CAS  PubMed  Google Scholar 

  72. Eastburn, D.J., Sciambi, A. & Abate, A.R. Ultrahigh-throughput mammalian single-cell reverse-transcriptase polymerase chain reaction in microfluidic drops. Anal. Chem. 85, 8016–8021 (2013).

    Article  CAS  PubMed  Google Scholar 

  73. 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  PubMed Central  CAS  Google Scholar 

  74. Rane, T.D. et al. Droplet microfluidics for amplification-free genetic detection of single cells. Lab Chip 12, 3341–3347 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Novak, R. et al. Single-cell multiplex gene detection and sequencing with microfluidically generated agarose emulsions. Angew. Chem. Int. Ed. Engl. 50, 390–395 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Agresti, J.J. et al. Ultrahigh-throughput screening in drop-based microfluidics for directed evolution. Proc. Natl. Acad. Sci. USA 107, 4004–4009 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Abate, A.R. et al. Beating Poisson encapsulation statistics using close-packed ordering. Lab Chip 9, 2628–2631 (2009).

    Article  CAS  PubMed  Google Scholar 

  78. Wang, Q. et al. Tagmentation-based whole-genome bisulfite sequencing. Nat. Protoc. 8, 2022–2032 (2013).

    Article  CAS  PubMed  Google Scholar 

  79. Islam, S. et al. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11, 163–166 (2014).

    Article  CAS  PubMed  Google Scholar 

  80. White, A.K. et al. High-throughput microfluidic single-cell RT-qPCR. Proc. Natl. Acad. Sci. USA 108, 13999–14004 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Hashimshony, T. et al. CEL-Seq: single-cell RNA-Seq by multiplexed linear amplification. Cell Rep. 2, 666–673 (2012).

    Article  CAS  PubMed  Google Scholar 

  82. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Lithuanian-Swiss Research and Development Program (grant no. CH-3-SMM-01/03) and an Edward J Mallinckrodt Foundation Grant (to A.M.K.). L.M. holds a Marie Curie Individual Fellowship (705791). A.M.K. holds a Burroughs Wellcome Fund CASI Award. A.V. is supported by the HSCI Medical Scientist Training Fellowship and the Harvard Presidential Scholars Fund. Liquid-handling robotics was carried out at the ICCB-Longwood Screening Facility at Harvard Medical School (HMS); microfabrication was carried out at the HMS Microfabrication/Microfluidics core facility.

Author information

Authors and Affiliations

  1. Institute of Biotechnology,

    Rapolas Zilionis, Juozas Nainys & Linas Mazutis

  2. Vilnius University,, Vilnius, Lithuania

    Rapolas Zilionis, Juozas Nainys & Linas Mazutis

  3. Department of Systems Biology, Harvard Medical School, Boston, Massachusetts, USA

    Rapolas Zilionis, Adrian Veres, Virginia Savova & Allon M Klein

  4. Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, Massachusetts, USA

    Adrian Veres

  5. Harvard Stem Cell Institute, Harvard University, Cambridge, Massachusetts, USA

    Adrian Veres

  6. Division of Immunology, Department of Microbiology and Immunobiology, Harvard Medical School, Boston, Massachusetts, USA

    David Zemmour

Authors
  1. Rapolas Zilionis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Juozas Nainys
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Adrian Veres
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Virginia Savova
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David Zemmour
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Allon M Klein
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Linas Mazutis
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.M.K. and L.M. developed the original inDrops method. A.M.K., A.V., and V.S. developed the bioinformatic pipeline for raw sequencing data analysis. R.Z., A.M.K., and L.M. analyzed data provided in the Anticipated Results. D.Z. designed library PCR primers and custom sequencing primers. R.Z., J.N., A.M.K., and L.M. wrote the manuscript.

Corresponding authors

Correspondence to Allon M Klein or Linas Mazutis.

Ethics declarations

Competing interests

L.M. and A.M.K. are inventors on a patent application (PCT/US2015/026443) that includes some of the ideas described in this article. A.M.K. is a cofounder and science advisory board member of 1CellBio. L.M. is affiliated with Droplet Genomics. The rest of the authors declare no competing financial interests.

Integrated supplementary information

Supplementary information

Supplementary Text and Figures

Supplementary Methods 1 and 2, Supplementary Table 1, and Supplementary Figure 1 (PDF 280 kb)

Supplementary Table 2

Supplementary Tables 2 (XLSX 41 kb)

Supplementary Table 3

Supplementary Tables 3 (XLSX 45 kb)

Supplementary Table 4

Supplementary Tables 4 (XLSX 44 kb)

Supplementary Data 1

Designs for microfluidic chips. (ZIP 442 kb)

Supplementary Data 2

Hamilton Microlab STAR-let liquid handler method files. (ZIP 38 kb)

Supplementary Software

Python script for sequencing data processing. (ZIP 27 kb)

Coencapsulation of cells, RT/lysis reagents, and barcoding hydrogel beads (slowed down 23×). (MOV 1264 kb)

Transfer of reagents to a syringe for injection into the microfluidic device. (MOV 10824 kb)

Hydrogel droplet production (slowed down 166×). (MP4 2832 kb)

Preparation of the syringe with hydrogel beads. (MOV 10829 kb)

Exposure of emulsion to UV light to release photocleavable primers. (MOV 5549 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zilionis, R., Nainys, J., Veres, A. et al. Single-cell barcoding and sequencing using droplet microfluidics. Nat Protoc 12, 44–73 (2017). https://doi.org/10.1038/nprot.2016.154

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nprot.2016.154

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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