Advertisement

Mass Cytometry pp 281-294 | Cite as

Analysis of High-Dimensional Phenotype Data Generated by Mass Cytometry or High-Dimensional Flow Cytometry

  • Branko Cirovic
  • Natalie Katzmarski
  • Andreas SchlitzerEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 1989)

Abstract

Recent advances in single cell multi-omics methodologies significantly expand our understanding of cellular heterogeneity, particularly in the field of immunology. Today’s state-of-the-art flow and mass cytometers can detect up to 50 parameters to comprehensively characterize the identity and function of individual cells within a heterogeneous population. As a consequence, the increasing number of parameters that can be detected simultaneously also introduces substantial complexity for the experimental setup and downstream data processing. However, this challenge in data analysis fostered the development of novel bioinformatic tools to fully exploit the high-dimensional data. These tools will eventually replace cumbersome serial, manual gating in the two-dimensional space driven by a priori knowledge, which still represents the gold standard in flow cytometric analysis, to meet the needs of the today’s immunologist. To this end, we provide guidelines for a high-dimensional cytometry workflow including experimental setup, panel design, fluorescent spillover compensation, and data analysis.

Key words

Mass cytometry Flow cytometry High-dimensional cytometry Data analysis 

References

  1. 1.
    Chen H, Lau MC, Wong MT et al (2016) Cytofkit: a bioconductor package for an integrated mass cytometry data analysis pipeline. PLoS Comput Biol 12(9):e1005112.  https://doi.org/10.1371/journal.pcbi.1005112CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Sander J, Schmidt SV, Cirovic B et al (2017) Cellular differentiation of human monocytes is regulated by time-dependent interleukin-4 signaling and the transcriptional regulator NCOR2. Immunity 47(6):1051–1066. e1012.  https://doi.org/10.1016/j.immuni.2017.11.024CrossRefPubMedPubMedCentralGoogle Scholar
  3. 3.
    Finak G, Perez JM, Weng A et al (2010) Optimizing transformations for automated, high throughput analysis of flow cytometry data. BMC Bioinformatics 11:546.  https://doi.org/10.1186/1471-2105-11-546CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Becher B, Schlitzer A, Chen J et al (2014) High-dimensional analysis of the murine myeloid cell system. Nat Immunol 15(12):1181–1189.  https://doi.org/10.1038/ni.3006CrossRefPubMedGoogle Scholar
  5. 5.
    Levine JH, Simonds EF, Bendall SC et al (2015) Data-driven phenotypic dissection of AML reveals progenitor-like cells that correlate with prognosis. Cell 162(1):184–197.  https://doi.org/10.1016/j.cell.2015.05.047CrossRefPubMedPubMedCentralGoogle Scholar
  6. 6.
    Weber LM, Robinson MD (2016) Comparison of clustering methods for high-dimensional single-cell flow and mass cytometry data. Cytometry A 89(12):1084–1096.  https://doi.org/10.1002/cyto.a.23030CrossRefPubMedGoogle Scholar
  7. 7.
    Amir el AD, Davis KL, Tadmor MD et al (2013) viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat Biotechnol 31(6):545–552.  https://doi.org/10.1038/nbt.2594CrossRefGoogle Scholar
  8. 8.
    van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605Google Scholar
  9. 9.
    Haghverdi L, Buettner F, Theis FJ (2015) Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics 31(18):2989–2998.  https://doi.org/10.1093/bioinformatics/btv325CrossRefPubMedGoogle Scholar
  10. 10.
    Tenenbaum JB, de Silva V, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. Science 290(5500):2319–2323.  https://doi.org/10.1126/science.290.5500.2319CrossRefPubMedGoogle Scholar
  11. 11.
    Haghverdi L, Buttner M, Wolf FA et al (2016) Diffusion pseudotime robustly reconstructs lineage branching. Nat Methods 13:845.  https://doi.org/10.1038/nmeth.3971CrossRefPubMedGoogle Scholar
  12. 12.
    Nguyen R, Perfetto S, Mahnke YD et al (2013) Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry A 83(3):306–315.  https://doi.org/10.1002/cyto.a.22251CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Parks D (2004) Presented at the XXII congress of the International Society for Analytical Cytology. Montpellier, FranceGoogle Scholar
  14. 14.
    Fletez-Brant K, Spidlen J, Brinkman RR et al (2016) flowClean: automated identification and removal of fluorescence anomalies in flow cytometry data. Cytometry A 89(5):461–471.  https://doi.org/10.1002/cyto.a.22837CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Le Meur N, Rossini A, Gasparetto M et al (2007) Data quality assessment of ungated flow cytometry data in high throughput experiments. Cytometry A 71(6):393–403.  https://doi.org/10.1002/cyto.a.20396CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Hahne F, LeMeur N, Brinkman RR et al (2009) flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinformatics 10:106.  https://doi.org/10.1186/1471-2105-10-106CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Finak G, Frelinger J, Jiang W et al (2014) OpenCyto: an open source infrastructure for scalable, robust, reproducible, and automated, end-to-end flow cytometry data analysis. PLoS Comput Biol 10(8):e1003806.  https://doi.org/10.1371/journal.pcbi.1003806CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Malek M, Taghiyar MJ, Chong L et al (2015) flowDensity: reproducing manual gating of flow cytometry data by automated density-based cell population identification. Bioinformatics 31(4):606–607.  https://doi.org/10.1093/bioinformatics/btu677CrossRefPubMedGoogle Scholar
  19. 19.
    Qiu P, Simonds EF, Bendall SC et al (2011) Extracting a cellular hierarchy from high-dimensional cytometry data with SPADE. Nat Biotechnol 29(10):886–891.  https://doi.org/10.1038/nbt.1991CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Van Gassen S, Callebaut B, Van Helden MJ et al (2015) FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data. Cytometry A 87(7):636–645.  https://doi.org/10.1002/cyto.a.22625CrossRefPubMedGoogle Scholar
  21. 21.
    Bruggner RV, Bodenmiller B, Dill DL et al (2014) Automated identification of stratifying signatures in cellular subpopulations. Proc Natl Acad Sci U S A 111(26):E2770–E2777.  https://doi.org/10.1073/pnas.1408792111CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    O'Neill K, Jalali A, Aghaeepour N et al (2014) Enhanced flowType/RchyOptimyx: a BioConductor pipeline for discovery in high-dimensional cytometry data. Bioinformatics 30(9):1329–1330.  https://doi.org/10.1093/bioinformatics/btt770CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Bendall SC, Davis KL, Amir el AD et al (2014) Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell 157(3):714–725.  https://doi.org/10.1016/j.cell.2014.04.005CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Pyne S, Hu X, Wang K et al (2009) Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci U S A 106(21):8519–8524.  https://doi.org/10.1073/pnas.0903028106CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Aghaeepour N, Nikolic R, Hoos HH et al (2011) Rapid cell population identification in flow cytometry data. Cytometry A 79(1):6–13.  https://doi.org/10.1002/cyto.a.21007CrossRefPubMedPubMedCentralGoogle Scholar
  26. 26.
    Qian Y, Wei C, Eun-Hyung Lee F et al (2010) Elucidation of seventeen human peripheral blood B-cell subsets and quantification of the tetanus response using a density-based method for the automated identification of cell populations in multidimensional flow cytometry data. Cytometry B Clin Cytom 78(Suppl 1):S69–S82.  https://doi.org/10.1002/cyto.b.20554CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Monaco G, Chen H, Poidinger M et al (2016) flowAI: automatic and interactive anomaly discerning tools for flow cytometry data. Bioinformatics 32(16):2473–2480.  https://doi.org/10.1093/bioinformatics/btw191CrossRefPubMedGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Branko Cirovic
    • 1
  • Natalie Katzmarski
    • 1
  • Andreas Schlitzer
    • 1
    Email author
  1. 1.Myeloid Cell Biology, Life and Medical Sciences InstituteUniversity of BonnBonnGermany

Personalised recommendations