Skip to main content

Thank you for visiting 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.

High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics


Time-dependent analysis of dynamic processes in single live cells is a revolutionary technique for the quantitative studies of signaling networks. Here we describe an experimental pipeline and associated protocol that incorporate microfluidic cell culture, precise stimulation of cells with signaling molecules or drugs, live-cell microscopy, computerized cell tracking, on-chip staining of key proteins and subsequent retrieval of cells for high-throughput gene expression analysis using microfluidic quantitative PCR (qPCR). Compared with traditional culture dish approaches, this pipeline enhances experimental precision and throughput by orders of magnitude and introduces much-desired new capabilities in cell and fluid handling, thus representing a major step forward in dynamic single-cell analysis. A combination of microfluidic membrane valves, automation and a streamlined protocol now enables a single researcher to generate 1 million data points on single-cell protein localization within 1 week, in various cell types and densities, under 48 predesigned experimental conditions selected from different signaling molecules or drugs, their doses, timings and combinations.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Single-cell live-imaging and gene expression analysis pipeline.
Figure 2: Photos of the microfluidic cell culture system setup.
Figure 3: Cell culture chip layout.
Figure 4: Cell culture chip control GUI.
Figure 5: Anticipated results.


  1. 1

    Fernandez-Suarez, M. & Ting, A.Y. Fluorescent probes for super-resolution imaging in living cells. Nat. Rev. Mol. Cell Biol. 9, 929–943 (2008).

    CAS  Article  Google Scholar 

  2. 2

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

    CAS  Article  Google Scholar 

  3. 3

    Albeck, J.G., Mills, G.B. & Brugge, J.S. Frequency-modulated pulses of ERK activity transmit quantitative proliferation signals. Mol. Cell 49, 249–261 (2013).

    CAS  Article  Google Scholar 

  4. 4

    You, X. et al. Intracellular protein interaction mapping with FRET hybrids. Proc. Natl. Acad. Sci. USA 103, 18458–18463 (2006).

    CAS  Article  Google Scholar 

  5. 5

    Reits, E.A. & Neefjes, J.J. From fixed to FRAP: measuring protein mobility and activity in living cells. Nat. Cell Biol. 3, E145–147 (2001).

    CAS  Article  Google Scholar 

  6. 6

    Kim, S.A., Heinze, K.G. & Schwille, P. Fluorescence correlation spectroscopy in living cells. Nat. Methods 4, 963–973 (2007).

    CAS  Article  Google Scholar 

  7. 7

    Muramoto, T. et al. Live imaging of nascent RNA dynamics reveals distinct types of transcriptional pulse regulation. Proc. Natl. Acad. Sci. USA 109, 7350–7355 (2012).

    CAS  Article  Google Scholar 

  8. 8

    Delebecque, C.J., Lindner, A.B., Silver, P.A. & Aldaye, F.A. Organization of intracellular reactions with rationally designed RNA assemblies. Science 333, 470–474 (2011).

    CAS  Article  Google Scholar 

  9. 9

    Yunger, S., Rosenfeld, L., Garini, Y. & Shav-Tal, Y. Quantifying the transcriptional output of single alleles in single living mammalian cells. Nat. Protoc. 8, 393–408 (2013).

    CAS  Article  Google Scholar 

  10. 10

    Puchner, E.M., Walter, J.M., Kasper, R., Huang, B. & Lim, W.A. Counting molecules in single organelles with superresolution microscopy allows tracking of the endosome maturation trajectory. Proc. Natl. Acad. Sci. USA 110, 16015–16020 (2013).

    CAS  Article  Google Scholar 

  11. 11

    Shim, S.H. et al. Super-resolution fluorescence imaging of organelles in live cells with photoswitchable membrane probes. Proc. Natl. Acad. Sci. USA 109, 13978–13983 (2012).

    CAS  Article  Google Scholar 

  12. 12

    Spiller, D.G., Wood, C.D., Rand, D.A. & White, M.R. Measurement of single-cell dynamics. Nature 465, 736–745 (2010).

    CAS  Article  Google Scholar 

  13. 13

    Germain, R.N., Robey, E.A. & Cahalan, M.D. A decade of imaging cellular motility and interaction dynamics in the immune system. Science 336, 1676–1681 (2012).

    CAS  Article  Google Scholar 

  14. 14

    Kulesa, P.M. & Fraser, S.E. Cell dynamics during somite boundary formation revealed by time-lapse analysis. Science 298, 991–995 (2002).

    CAS  Article  Google Scholar 

  15. 15

    Cai, L., Dalal, C.K. & Elowitz, M.B. Frequency-modulated nuclear localization bursts coordinate gene regulation. Nature 455, 485–490 (2008).

    CAS  Article  Google Scholar 

  16. 16

    Neumann, B. et al. High-throughput RNAi screening by time-lapse imaging of live human cells. Nat. Methods 3, 385–390 (2006).

    CAS  Article  Google Scholar 

  17. 17

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

    CAS  Article  Google Scholar 

  18. 18

    Levskaya, A., Weiner, O.D., Lim, W.A. & Voigt, C.A. Spatiotemporal control of cell signalling using a light-switchable protein interaction. Nature 461, 997–1001 (2009).

    CAS  Article  Google Scholar 

  19. 19

    Bugaj, L.J., Choksi, A.T., Mesuda, C.K., Kane, R.S. & Schaffer, D.V. Optogenetic protein clustering and signaling activation in mammalian cells. Nat. Methods 10, 249–252 (2013).

    CAS  Article  Google Scholar 

  20. 20

    Grier, D.G. A revolution in optical manipulation. Nature 424, 810–816 (2003).

    CAS  Article  Google Scholar 

  21. 21

    Milias-Argeitis, A. et al. In silico feedback for in vivo regulation of a gene expression circuit. Nat. Biotechnol. 29, 1114–1116 (2011).

    CAS  Article  Google Scholar 

  22. 22

    Gómez-Sjöberg, R., Leyrat, A.A., Pirone, D.M., Chen, C.S. & Quake, S.R. Versatile, fully automated, microfluidic cell culture system. Anal. Chem. 79, 8557–8563 (2007).

    Article  Google Scholar 

  23. 23

    Vedel, S., Tay, S., Johnston, D.M., Bruus, H. & Quake, S.R. Migration of cells in a social context. Proc. Natl. Acad. Sci. USA 110, 129–134 (2013).

    CAS  Article  Google Scholar 

  24. 24

    Sanchez-Freire, V. et al. Microfluidic single-cell real-time PCR for comparative analysis of gene expression patterns. Nat. Protoc. 7, 829–838 (2012).

    CAS  Article  Google Scholar 

  25. 25

    Cheong, R., Wang, C.J. & Levchenko, A. High-content cell screening in a microfluidic device. Mol. Cell. Proteom. 8, 433–442 (2009).

    CAS  Article  Google Scholar 

  26. 26

    Cheong, R., Wang, C.J. & Levchenko, A. Using a microfluidic device for high-content analysis of cell signaling. Sci. Signal. 2, pl2 (2009).

    Article  Google Scholar 

  27. 27

    Cheong, R., Rhee, A., Wang, C.J., Nemenman, I. & Levchenko, A. Information transduction capacity of noisy biochemical signaling networks. Science 334, 354–358 (2011).

    CAS  Article  Google Scholar 

  28. 28

    Frank, T. & Tay, S. Flow-switching allows independently programmable, extremely stable, high-throughput diffusion-based gradients. Lab. Chip 13, 1273–1281 (2013).

    CAS  Article  Google Scholar 

  29. 29

    Hung, P.J., Lee, P.J., Sabounchi, P., Lin, R. & Lee, L.P. Continuous perfusion microfluidic cell culture array for high-throughput cell-based assays. Biotechnol. Bioeng. 89, 1–8 (2005).

    CAS  Article  Google Scholar 

  30. 30

    Chung, K., Rivet, C.A., Kemp, M.L. & Lu, H. Imaging single-cell signaling dynamics with a deterministic high-density single-cell trap array. Anal. Chem. 83, 7044–7052 (2011).

    CAS  Article  Google Scholar 

  31. 31

    Faley, S.L. et al. Microfluidic single cell arrays to interrogate signalling dynamics of individual, patient-derived hematopoietic stem cells. Lab. Chip 9, 2659–2664 (2009).

    CAS  Article  Google Scholar 

  32. 32

    Roach, K.L. et al. High-throughput single-cell bioinformatics. Biotechnol. Prog. 25, 1772–1779 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Melin, J. & Quake, S.R. Microfluidic large-scale integration: the evolution of design rules for biological automation. Ann. Rev. Biophys. Biomol. Str. 36, 213–231 (2006).

    Article  Google Scholar 

  34. 34

    Vollmers, C., Sit, R.V., Weinstein, J.A., Dekker, C.L. & Quake, S.R. Genetic measurement of memory B-cell recall using antibody repertoire sequencing. Proc. Natl. Acad. Sci. USA 110, 13463–13468 (2013).

    CAS  Article  Google Scholar 

  35. 35

    Kalisky, T. & Quake, S.R. Single-cell genomics. Nat. Methods 8, 311–314 (2011).

    CAS  Article  Google Scholar 

  36. 36

    Ottesen, E.A., Hong, J.W., Quake, S.R. & Leadbetter, J.R. Microfluidic digital PCR enables multigene analysis of individual environmental bacteria. Science 314, 1464–1467 (2006).

    CAS  Article  Google Scholar 

  37. 37

    Warren, L., Bryder, D., Weissman, I.L. & Quake, S.R. Transcription factor profiling in individual hematopoietic progenitors by digital RT-PCR. Proc. Natl. Acad. Sci. USA 103, 17807–17812 (2006).

    CAS  Article  Google Scholar 

  38. 38

    Qin, D., Xia, Y. & Whitesides, G.M. Soft lithography for micro- and nanoscale patterning. Nat. Protoc. 5, 491–502 (2010).

    CAS  Article  Google Scholar 

  39. 39

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

    CAS  Article  Google Scholar 

  40. 40

    Unger, M.A., Chou, H.P., Thorsen, T., Scherer, A. & Quake, S.R. Monolithic microfabricated valves and pumps by multilayer soft lithography. Science 288, 113–116 (2000).

    CAS  Article  Google Scholar 

  41. 41

    Thorsen, T. Microfluidic large-scale integration. Science 298, 580–584 (2002).

    CAS  Article  Google Scholar 

  42. 42

    Berthier, E., Young, E.W. & Beebe, D. Engineers are from PDMS-land, biologists are from Polystyrenia. Lab. Chip 12, 1224–1237 (2012).

    CAS  Article  Google Scholar 

  43. 43

    Millet, L.J., Stewart, M.E., Sweedler, J.V., Nuzzo, R.G. & Gillette, M.U. Microfluidic devices for culturing primary mammalian neurons at low densities. Lab. Chip 7, 987–994 (2007).

    CAS  Article  Google Scholar 

  44. 44

    Kolnik, M., Tsimring, L.S. & Hasty, J. Vacuum-assisted cell loading enables shear-free mammalian microfluidic culture. Lab. Chip 12, 4732–4737 (2012).

    CAS  Article  Google Scholar 

  45. 45

    Landenberger, B., Hofemann, H., Wadle, S. & Rohrbach, A. Microfluidic sorting of arbitrary cells with dynamic optical tweezers. Lab. Chip 12, 3177–3183 (2012).

    CAS  Article  Google Scholar 

  46. 46

    Wall, E.A. et al. Suppression of LPS-induced TNF-α production in macrophages by cAMP is mediated by PKA-AKAP95-p105. Sci. Signal 2, ra28 (2009).

    Article  Google Scholar 

Download references


We acknowledge S. Quake for supervising earlier stages of this work. We thank M. Covert and his group for useful discussions and for making an earlier version of the image analysis software available, as well as for the gift of 3T3 cells with the p65-DsRed fusion protein. This work was supported by a European Research Council (ERC) starting grant and a Swiss National Science Foundation grant to S. Tay.

Author information




R.A.K. optimized the protocol and developed methods for cell retrieval and gene expression analysis, and wrote the manuscript. R.G-.S. and A.A.L. are the original developers of the microfluidic cell culture chip and software. S.T. optimized the protocol for cell signaling studies and supervised the signaling project. All authors edited the manuscript.

Corresponding author

Correspondence to Savaş Tay.

Ethics declarations

Competing interests

A.A.L. is an employee of Fluidigm Corporation. The remaining authors declare no competing financial interests.

Supplementary information

Supplementary Data

AutoCad DXF chip design file for cell culture chip molds (flow and control layers) (TXT 25643 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Kellogg, R., Gómez-Sjöberg, R., Leyrat, A. et al. High-throughput microfluidic single-cell analysis pipeline for studies of signaling dynamics. Nat Protoc 9, 1713–1726 (2014).

Download citation

Further reading


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.


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