An introduction to mass cytometry: fundamentals and applications (original) (raw)

Abstract

Mass cytometry addresses the analytical challenges of polychromatic flow cytometry by using metal atoms as tags rather than fluorophores and atomic mass spectrometry as the detector rather than photon optics. The many available enriched stable isotopes of the transition elements can provide up to 100 distinguishable reporting tags, which can be measured simultaneously because of the essential independence of detection provided by the mass spectrometer. We discuss the adaptation of traditional inductively coupled plasma mass spectrometry to cytometry applications. We focus on the generation of cytometry-compatible data and on approaches to unsupervised multivariate clustering analysis. Finally, we provide a high-level review of some recent benchmark reports that highlight the potential for massively multi-parameter mass cytometry.

Access this article

Log in via an institution

Subscribe and save

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Houk RS, Fassel VA, Flesch GD, Svec HJ, Gray AL, Taylor CE (1980) Inductively coupled argon plasma as an ion source for mass spectrometric determination of trace elements. Anal Chem 52:2283–2289
    Article CAS Google Scholar
  2. Lou XD, Zhang G, Herra I, Kinach R, Ornatsky O, Baranov V, Nitz M, Winnik MA (2007) Polymer-based elemental tags for sensitive bioassays. Angew Chem Int Ed Engl 46:6111–6114
    Article PubMed CAS Google Scholar
  3. Ornatsky O, Bandura D, Baranov V, Nitz M, Winnik MA, Tanner S (2010) Highly multiparametric analysis by mass cytometry. J Immunol Methods 361:1–20
    Article PubMed CAS Google Scholar
  4. Ornatsky OI, Lou X, Nitz M, Schafer S, Sheldrick WS, Baranov VI, Bandura DR, Tanner SD (2008) Study of cell antigens and intracellular DNA by identification of element-containing labels and metallointercalators using inductively coupled plasma mass spectrometry. Anal Chem 80:2539–2547
    Article PubMed CAS Google Scholar
  5. Majonis D, Herrera I, Ornatsky O, Schulze M, Lou X, Soleimani M, Nitz M, Winnik MA (2010) Synthesis of a functional metal-chelating polymer and steps toward quantitative mass cytometry bioassays. Anal Chem 82:8961–8969
    Article CAS Google Scholar
  6. Fienberg H, Simonds EF, Fantl WJ, Nolan GP, Bodenmiller B (2012) Platinum-based covalent viability reagent for single cell mass cytometry. Cytometry A 81A:467–475
    Article CAS Google Scholar
  7. Bandura DR, Baranov VI, Ornatsky OI, Antonov A, Kinach R, Lou X, Pavlov S, Vorobiev S, Dick JE, Tanner SD (2009) 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
    Article PubMed CAS Google Scholar
  8. Gillson GR, Douglas DJ, Fulford JE, Halligan KW, Tanner SD (1988) Non-spectroscopic interelement interferences in inductively coupled plasma mass spectrometry (ICP-MS). Anal Chem 60:1472–1474
    Article CAS Google Scholar
  9. Tanner SD (1992) Space charge in ICP-MS: calculation and implications. Spectrochimica Acta Part B 47B:809–823
    Article CAS Google Scholar
  10. Olesik JW, Gray PJ (2012) Considerations for measurement of individual nanoparticles or microparticles by ICP-MS: determination of the number of particles and the analyte mass in each particle. J Anal At Spectrom 27:1143–1155
    Article CAS Google Scholar
  11. Lugli E, Roederer M, Cossarizza A (2010) Data analysis in flow cytometry: the future just started. Cytometry A 77A:705–713
    Article CAS Google Scholar
  12. Balfoort HW, Snoek J, Smiths JRM, Breedveld LW, Hofstraat JW, Ringelberg J (1992) Automatic identification of algae: neural network analysis of flow cytometric data. J Plankton Res 14:575–589
    Article Google Scholar
  13. Zare H, Shooshtari P, Gupta A, Brinkman RR (2010) Data reduction for spectral clustering to analyze high throughput flow cytometry data. BMC Bioinform 11:403
    Article Google Scholar
  14. Pyne S, Hu X, Wang K, Rossin E, Lin TI, Maier LM, Baecher-Allan C, Mclachlan GJ, Tamayo P, Hafler DA, De Jager PL, Mesirov JP (2009) Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci 106(21):8519–8524
    Article PubMed CAS Google Scholar
  15. Lo K, Hahne F, Brinkman R, Gottardo R (2009) flowClust: a Bioconductor package for automated gating of flow cytometry data. BMC Bioinform 10:1–145
    Article Google Scholar
  16. Nima Aghaeepour N, Jalali A, O’Neill K, Chattopadhyay PK, Roederer M, Hoos HH, Brinkman RR (2012) RchyOptimyx: cellular hierarchy optimization for flow cytometry. Cytometry A. Published online 8 Oct 2012. doi:10.1002/cyto.a.22209
  17. Bendall SC, Simonds EF, Qiu P, Amir ED, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sach K, Pe’er D, Tanner SD, Nolan GP (2011) Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science 332:687–696
    Article PubMed CAS Google Scholar
  18. Qiu P, Simonds EF, Bendall SC, Gibbs KD, Bruggner RV, Linderman MD, Sachs K, Nolan GP, Plevritis SK (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29:886–891
    Article PubMed CAS Google Scholar
  19. Newell EW, Sigal N, Bendall SC, Nolan GP, Davis MM (2012) Cytometry by Time-of-Flight Shows Combinatorial Cytokine Expression and Virus-Specific Cell Niches within a Continuum of CD8+ T Cell Phenotypes. Immunity 36:142–152
    Article PubMed CAS Google Scholar
  20. Bodenmiller B, Zunder ER, Finck R, Chen TJ, Savig ES, Bruggner RV, Simonds EF, Bendall SC, Sachs K, Krutzik PO, Nolan GP (2012) Multiplexed mass cytometry profiling of cellular states perturbed by small-molecule regulators. Nat Biotechnol 30:858–867
    Article PubMed CAS Google Scholar

Download references

Acknowledgments

The University of Toronto investigators gratefully acknowledge receipt of financial support from the Ontario government through the Ontario Research Fund—Global Leadership in Genomics and Life Sciences (ORF-GL2-01-003). Scott Tanner further wishes to acknowledge previous enabling research funding from Genome Canada (Applied Human Health, and Technology Development) and ongoing support from the NIH-Office of AIDS Research.

Conflict of interest

The authors are employees of, and receive remuneration from, DVS Sciences Inc. Scott Tanner, Vladimir Baranov, Olga Ornatsky, and Dmitry Bandura are co-founders of and equity shareholders in DVS Sciences Inc. Scott Tanner is a member of the Board of Directors of DVS Sciences, Inc.

Author information

Authors and Affiliations

  1. Department of Chemistry, University of Toronto, 80 St. George Street, Toronto, ON, M5S 3H6, Canada
    Scott D. Tanner & Vladimir I. Baranov
  2. DVS Sciences Inc., #12-70 Esna Park Drive, Markham, ON, L3R 6E7, Canada
    Scott D. Tanner, Vladimir I. Baranov, Olga I. Ornatsky & Dmitry R. Bandura
  3. DVS Sciences, Inc., 639 North Pastoria Avenue, Sunnyvale, CA, 94085, USA
    Thaddeus C. George

Authors

  1. Scott D. Tanner
  2. Vladimir I. Baranov
  3. Olga I. Ornatsky
  4. Dmitry R. Bandura
  5. Thaddeus C. George

Corresponding author

Correspondence toScott D. Tanner.

Additional information

This paper is a Focussed Research Review based on a presentation given at the Tenth Annual Meeting of the Association for Cancer Immunotherapy (CIMT), held in Mainz, Germany, May 23–25, 2012. It is part of a CII series of Focussed Research Reviews and meeting report.

Rights and permissions

About this article

Cite this article

Tanner, S.D., Baranov, V.I., Ornatsky, O.I. et al. An introduction to mass cytometry: fundamentals and applications.Cancer Immunol Immunother 62, 955–965 (2013). https://doi.org/10.1007/s00262-013-1416-8

Download citation

Keywords