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Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators

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Abstract

Mass cytometry facilitates high-dimensional, quantitative analysis of the effects of bioactive molecules on human samples at single-cell resolution, but instruments process only one sample at a time. Here we describe mass-tag cellular barcoding (MCB), which increases mass cytometry throughput by using n metal ion tags to multiplex up to 2n samples. We used seven tags to multiplex an entire 96-well plate, and applied MCB to characterize human peripheral blood mononuclear cell (PBMC) signaling dynamics and cell-to-cell communication, signaling variability between PBMCs from eight human donors, and the effects of 27 inhibitors on this system. For each inhibitor, we measured 14 phosphorylation sites in 14 PBMC types at 96 conditions, resulting in 18,816 quantified phosphorylation levels from each multiplexed sample. This high-dimensional, systems-level inquiry allowed analysis across cell-type and signaling space, reclassified inhibitors and revealed off-target effects. High-content, high-throughput screening with MCB should be useful for drug discovery, preclinical testing and mechanistic investigation of human disease.

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Figure 1: Mass-tag cell barcoding.
Figure 2: PBMC signaling time-course experiment.
Figure 3: Signaling response comparison of PBMCs from eight donors.
Figure 4: Analysis of PBMC response to kinase inhibition.
Figure 5: Overview of inhibitor impact.
Figure 6: Principal component analysis of cell type and drug response.

Change history

  • 23 August 2012

    In the version of this article initially published online, in the legend for Figure 2e, LPS stimulation was said to be by “NFκB, STAT3 and STAT1,” instead of by “NFκB, STAT3 and BTK/ITK.” The error has been corrected for the print, PDF and HTML versions of this article.

References

  1. 1

    Paul, S.M. et al. How to improve R&D productivity: the pharmaceutical industry's grand challenge. Nat. Rev. Drug Discov. 9, 203–214 (2010).

    CAS  Article  Google Scholar 

  2. 2

    Nolan, G.P. What's wrong with drug screening today. Nat. Chem. Biol. 3, 187–191 (2007).

    CAS  Article  Google Scholar 

  3. 3

    Kola, I. & Landis, J. Can the pharmaceutical industry reduce attrition rates? Nat. Rev. Drug Discov. 3, 711–715 (2004).

    CAS  Article  Google Scholar 

  4. 4

    Kramer, J.A., Sagartz, J.E. & Morris, D.L. The application of discovery toxicology and pathology towards the design of safer pharmaceutical lead candidates. Nat. Rev. Drug Discov. 6, 636–649 (2007).

    CAS  Article  Google Scholar 

  5. 5

    Kolch, W. & Pitt, A. Functional proteomics to dissect tyrosine kinase signalling pathways in cancer. Nat. Rev. Cancer 10, 618–629 (2010).

    CAS  Article  Google Scholar 

  6. 6

    Bodenmiller, B. et al. Phosphoproteomic analysis reveals interconnected system-wide responses to perturbations of kinases and phosphatases in yeast. Sci. Signal. 3, rs4 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7

    Zhang, J., Yang, P.L. & Gray, N.S. Targeting cancer with small molecule kinase inhibitors. Nat. Rev. Cancer 9, 28–39 (2009).

    Article  Google Scholar 

  8. 8

    Hynes, N.E. & Lane, H.A. ERBB receptors and cancer: the complexity of targeted inhibitors. Nat. Rev. Cancer 5, 341–354 (2005).

    CAS  Article  Google Scholar 

  9. 9

    Irish, J.M., Kotecha, N. & Nolan, G.P. Mapping normal and cancer cell signalling networks: towards single-cell proteomics. Nat. Rev. Cancer 6, 146–155 (2006).

    CAS  Article  Google Scholar 

  10. 10

    Knight, Z.A., Lin, H. & Shokat, K.M. Targeting the cancer kinome through polypharmacology. Nat. Rev. Cancer 10, 130–137 (2010).

    CAS  Article  Google Scholar 

  11. 11

    Arrell, D.K. & Terzic, A. Network systems biology for drug discovery. Clin. Pharmacol. Ther. 88, 120–125 (2010).

    CAS  Article  Google Scholar 

  12. 12

    Irish, J.M. et al. Single cell profiling of potentiated phospho-protein networks in cancer cells. Cell 118, 217–228 (2004).

    CAS  Article  Google Scholar 

  13. 13

    Fabian, M.A. et al. A small molecule-kinase interaction map for clinical kinase inhibitors. Nat. Biotechnol. 23, 329–336 (2005).

    CAS  Article  Google Scholar 

  14. 14

    Karaman, M.W. et al. A quantitative analysis of kinase inhibitor selectivity. Nat. Biotechnol. 26, 127–132 (2008).

    CAS  Article  Google Scholar 

  15. 15

    Bamborough, P., Drewry, D., Harper, G., Smith, G.K. & Schneider, K. Assessment of chemical coverage of kinome space and its implications for kinase drug discovery. J. Med. Chem. 51, 7898–7914 (2008).

    CAS  Article  Google Scholar 

  16. 16

    Anastassiadis, T., Deacon, S.W., Devarajan, K., Ma, H. & Peterson, J.R. Comprehensive assay of kinase catalytic activity reveals features of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1039–1045 (2011).

    CAS  Article  Google Scholar 

  17. 17

    Davis, M.I. et al. Comprehensive analysis of kinase inhibitor selectivity. Nat. Biotechnol. 29, 1046–1051 (2011).

    CAS  Article  Google Scholar 

  18. 18

    Melnick, J.S. et al. An efficient rapid system for profiling the cellular activities of molecular libraries. Proc. Natl. Acad. Sci. USA 103, 3153–3158 (2006).

    CAS  Article  Google Scholar 

  19. 19

    Evans, W.E. & Relling, M.V. Pharmacogenomics: translating functional genomics into rational therapeutics. Science 286, 487–491 (1999).

    CAS  Article  Google Scholar 

  20. 20

    Kramer, R. & Cohen, D. Functional genomics to new drug targets. Nat. Rev. Drug Discov. 3, 965–972 (2004).

    CAS  Article  Google Scholar 

  21. 21

    Bantscheff, M. et al. Quantitative chemical proteomics reveals mechanisms of action of clinical ABL kinase inhibitors. Nat. Biotechnol. 25, 1035–1044 (2007).

    CAS  Article  Google Scholar 

  22. 22

    Bantscheff, M., Scholten, A. & Heck, A.J. Revealing promiscuous drug-target interactions by chemical proteomics. Drug Discov. Today 14, 1021–1029 (2009).

    CAS  Article  Google Scholar 

  23. 23

    Perlman, Z.E. et al. Multidimensional drug profiling by automated microscopy. Science 306, 1194–1198 (2004).

    CAS  Article  Google Scholar 

  24. 24

    Singh, D.K. et al. Patterns of basal signaling heterogeneity can distinguish cellular populations with different drug sensitivities. Mol. Syst. Biol. 6, 369 (2010).

    Article  Google Scholar 

  25. 25

    Geva-Zatorsky, N. et al. Protein dynamics in drug combinations: a linear superposition of individual-drug responses. Cell 140, 643–651 (2010).

    CAS  Article  Google Scholar 

  26. 26

    Hulett, H.R., Bonner, W.A., Barrett, J. & Herzenberg, L.A. Cell sorting: automated separation of mammalian cells as a function of intracellular fluorescence. Science 166, 747–749 (1969).

    CAS  Article  Google Scholar 

  27. 27

    Perfetto, S.P., Chattopadhyay, P.K. & Roederer, M. Seventeen-colour flow cytometry: unravelling the immune system. Nat. Rev. Immunol. 4, 648–655 (2004).

    CAS  Article  Google Scholar 

  28. 28

    Chattopadhyay, P.K. et al. Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometry. Nat. Med. 12, 972–977 (2006).

    CAS  Article  Google Scholar 

  29. 29

    Perez, O.D. & Nolan, G.P. Simultaneous measurement of multiple active kinase states using polychromatic flow cytometry. Nat. Biotechnol. 20, 155–162 (2002).

    CAS  Article  Google Scholar 

  30. 30

    Young, S.M. et al. High-throughput screening with HyperCyt flow cytometry to detect small molecule formylpeptide receptor ligands. J. Biomol. Screen. 10, 374–382 (2005).

    CAS  Article  Google Scholar 

  31. 31

    Bartsch, J.W. et al. An investigation of liquid carryover and sample residual for a high-throughput flow cytometer sample delivery system. Anal. Chem. 76, 3810–3817 (2004).

    CAS  Article  Google Scholar 

  32. 32

    Krutzik, P.O., Crane, J.M., Clutter, M.R. & Nolan, G.P. High-content single-cell drug screening with phosphospecific flow cytometry. Nat. Chem. Biol. 4, 132–142 (2008).

    CAS  Article  Google Scholar 

  33. 33

    Krutzik, P.O. & Nolan, G.P. Fluorescent cell barcoding in flow cytometry allows high-throughput drug screening and signaling profiling. Nat. Methods 3, 361–368 (2006).

    CAS  Article  Google Scholar 

  34. 34

    Bandura, D.R. et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal. Chem. 81, 6813–6822 (2009).

    CAS  Article  Google Scholar 

  35. 35

    Bendall, S.C. et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332, 687–696 (2011).

    CAS  Article  Google Scholar 

  36. 36

    Lou, X. et al. Polymer-based elemental tags for sensitive bioassays. Angew. Chem. Int. Edn. Engl. 46, 6111–6114 (2007).

    CAS  Article  Google Scholar 

  37. 37

    Qiu, P. et al. Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat. Biotechnol. 29, 886–891 (2011).

    CAS  Article  Google Scholar 

  38. 38

    Shuai, K. & Liu, B. Regulation of JAK-STAT signalling in the immune system. Nat. Rev. Immunol. 3, 900–911 (2003).

    CAS  Article  Google Scholar 

  39. 39

    Rawlings, J.S., Rosler, K.M. & Harrison, D.A. The JAK/STAT signaling pathway. J. Cell Sci. 117, 1281–1283 (2004).

    CAS  Article  Google Scholar 

  40. 40

    Platanias, L.C. Mechanisms of type-I- and type-II-interferon-mediated signalling. Nat. Rev. Immunol. 5, 375–386 (2005).

    CAS  Article  Google Scholar 

  41. 41

    Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296–309 (2011).

    CAS  Article  Google Scholar 

  42. 42

    Akira, S. & Takeda, K. Toll-like receptor signalling. Nat. Rev. Immunol. 4, 499–511 (2004).

    CAS  Article  Google Scholar 

  43. 43

    Andersson, U. & Matsuda, T. Human interleukin 6 and tumor necrosis factor alpha production studied at a single-cell level. Eur. J. Immunol. 19, 1157–1160 (1989).

    CAS  Article  Google Scholar 

  44. 44

    Perez-Oliva, A.B. et al. Epitope mapping, expression and post-translational modifications of two isoforms of CD33 (CD33M and CD33m) on lymphoid and myeloid human cells. Glycobiology 21, 757–770 (2011).

    CAS  Article  Google Scholar 

  45. 45

    Deisseroth, A. et al. U.s. Food and drug administration approval: ruxolitinib for the treatment of patients with intermediate and high-risk myelofibrosis. Clin. Cancer Res. 18, 3212–3217 (2012).

    CAS  Article  Google Scholar 

  46. 46

    Knight, Z.A. & Shokat, K.M. Features of selective kinase inhibitors. Chem. Biol. 12, 621–637 (2005).

    CAS  Article  Google Scholar 

  47. 47

    Lown, J.W. The mechanism of action of quinone antibiotics. Mol. Cell. Biochem. 55, 17–40 (1983).

    CAS  Article  Google Scholar 

  48. 48

    Pardanani, A. JAK2 inhibitor therapy in myeloproliferative disorders: rationale, preclinical studies and ongoing clinical trials. Leukemia 22, 23–30 (2008).

    CAS  Article  Google Scholar 

  49. 49

    Brandman, O., Ferrell, J.E. Jr, Li, R. & Meyer, T. Interlinked fast and slow positive feedback loops drive reliable cell decisions. Science 310, 496–498 (2005).

    CAS  Article  Google Scholar 

  50. 50

    Jorgensen, C. et al. Cell-specific information processing in segregating populations of Eph receptor ephrin-expressing cells. Science 326, 1502–1509 (2009).

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We would like to thank A. Trejo, M. Clutter, K. Gibbs and G. Behbahani for their experimental support and discussions, and D. Pe'er and El-ad D. Amir for their feedback on data analysis. B.B. was supported by fellowships of the Swiss National Science Foundation (SNF), the European Molecular Biology Organization (EMBO), and the Marie Curie IOF. E.R.Z. is supported by a fellowship from National Institute of General Medical Sciences (F32GM093508). T.J.C. is supported by the Department of Defense (DoD) through the National Defense Science & Engineering Graduate Fellowship (NDSEG) Program, and the Stanford Graduate Fellowship in Science and Engineering. S.C.B. is supported by the Damon Runyon Cancer Research Foundation Fellowship (DRG-2017-09). G.P.N. is supported by the Rachford and Carlota A. Harris Endowed Professorship and grants from U19 AI057229, P01 CA034233, HHSN272200700038C, 1R01CA130826, CIRM DR1-01477 and RB2-01592, NCI RFA CA 09-011, NHLBI-HV-10-05(2), European Commission HEALTH.2010.1.2-1, and the Bill and Melinda Gates Foundation (GF12141-137101).

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B.B. conceived and designed the experiments, performed all PBMC experiments, MCB multiplexing and mass cytometry analysis, analyzed the data and wrote the manuscript. E.R.Z. conceived and designed the experiments, developed the mDOTA reagents, performed all PBMC experiments, MCB multiplexing and mass cytometry analysis, analyzed the data and wrote the manuscript. R.F. designed and implemented algorithms and software tools for barcode deconvolution, semi-automatic cell-type gating and dose-response analysis. T.J.C. performed PCA and assisted with data analysis. E.S.S. designed and implemented high-density data visualization. R.V.B. designed and implemented scripts for FCS file processing and assisted with SPADE analysis. E.F.S. assisted with SPADE analysis, designed the 96-well barcoding scheme. S.C.B. assisted with antibody labeling and running the mass cytometer, and high-dimensional analysis of human immune cell populations. K.S. helped with bioinformatic data analysis. P.O.K. conceived cell barcoding for mass cytometry. G.P.N. conceived and designed the experiments, wrote the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Garry P Nolan.

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Competing interests

G.P.N. has personal financial interest in the companies Nodality, DVS Sciences and Becton Dickinson, the manufacturers that produce the reagents or instrumentation used in this manuscript.

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Bodenmiller, B., Zunder, E., Finck, R. et al. Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat Biotechnol 30, 858–867 (2012). https://doi.org/10.1038/nbt.2317

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