High‐Dimensional Fluorescence Cytometry

Thomas Myles Ashhurst1, Adrian Lloyd Smith2, Nicholas Jonathan Cole King3

1 Marie Bashir Institute for Emerging Infectious Disease (MBI), The University of Sydney, Sydney, 2 Ramaciotti Facility for Human Systems Biology (RFHSB), The University of Sydney and Centenary Institute, Sydney, 3 Australian Institute for Nanoscale Science and Technology, The University of Sydney, Sydney
Publication Name:  Current Protocols in Immunology
Unit Number:  Unit 5.8
DOI:  10.1002/cpim.37
Online Posting Date:  November, 2017
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The immune system consists of a complex network of cells, all expressing a wide range of surface and/or intracellular proteins. Using flow cytometry, these cells can be analyzed by labeling with fluorophore‐conjugated antibodies. The recent expansion of fluorescence flow cytometry technology, in conjunction with the ever‐expanding understanding of the complexity of the immune system, has led to the generation of larger high‐dimensional fluorescence flow cytometry panels. However, as panel size and complexity increases, so too does the difficulty involved in constructing high‐quality panels, in addition to the challenges of analyzing such high‐dimensional datasets. As such, this unit seeks to review the key principles involved in building high‐dimensional panels, as well as to guide users through the process of building and analyzing quality panels. Here, cytometer configuration, fluorophore brightness, spreading error, antigen density, choosing the best conjugates, titration, optimization, and data analysis will all be addressed. © 2017 by John Wiley & Sons, Inc.

Keywords: flow cytometry; fluorescence; high dimensional; panel design; polychromatic; spreading error

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Table of Contents

  • Introduction
  • Experimental Question and Hypothesis
  • Instrumentation
  • Reagent Selection and Characterization
  • Cellular Targets
  • Combining Antibodies with Fluorophores
  • Titration and Testing
  • Data Analysis
  • How Do Flow and Mass Cytometry Compare?
  • Conclusion
  • Literature Cited
  • Figures
  • Tables
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Literature Cited

  Aghaeepour, N., Finak, G., FlowCAP Consortium, DREAM Consortium, Hoos, H., Mosmann, T. R., … Scheuermann, R. H. (2013). Critical assessment of automated flow cytometry data analysis techniques. Nature Methods, 10(3)228–238. doi: 10.1038/nmeth.2365.
  Amir el, A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., … Pe'er, D. (2013). viSNE enables visualization of high dimensional single‐cell data and reveals phenotypic heterogeneity of leukemia. Nature Biotechnology, 31(6), 545–552. doi: 10.1038/nbt.2594.
  Bagwell, C. B. & Adams, E. G. (1993). Fluorescence spectral overlap compensation for any number of flow cytometry parameters. Annals of the New York Academy of Sciences, 677, 167–184. doi: 10.1111/j.1749‐6632.1993.tb38775.x.
  Bendall, S. C., Davis, K. L., Amir el, A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., … Pe'er, D. (2014). Single‐cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell, 157(3), 714–725. doi: 10.1016/j.cell.2014.04.005.
  Bendall, S. C., Simonds, E. F., Qiu, P., Amir el, A. D., Krutzik, P. O., Finck, R., … Nolan, G. P. (2011). Single‐cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science, 332(6030), 687–696. doi: 10.1126/science.1198704.
  Brinkman, R. R., Aghaeepour, N., Finak, G., Gottardo, R., Mosmann, T., & Scheuermann, R. H. (2016). Automated analysis of flow cytometry data comes of age. Cytometry. Part A, 89(1), 13–15. doi: 10.1002/cyto.a.22810.
  Chattopadhyay, P. K., Gaylord, B., Palmer, A., Jiang, N., Raven, M. A., Lewis, G., … Roederer, M. (2012). Brilliant violet fluorophores: A new class of ultrabright fluorescent compounds for immunofluorescence experiments. Cytometry. Part A, 81(6), 456–466. doi: 10.1002/cyto.a.22043.
  Chattopadhyay, P. K., Perfetto, S. P., Gaylord, B. et al. (2014). Toward 40+ Parameter Flow Cytometry. Paper presented at the CYTO 2014, Fort Lauderdale, Florida.
  Chattopadhyay, P. K., Price, D. A., Harper, T. F., Betts, M. R., Yu, J., Gostick, E., … Roederer, M. (2006). Quantum dot semiconductor nanocrystals for immunophenotyping by polychromatic flow cytometry. Nature Medicine, 12(8), 972–977. doi: 10.1038/nm1371.
  Chattopadhyay, P. K. & Roederer, M. (2015). A mine is a terrible thing to waste: High content, single cell technologies for comprehensive immune analysis. American Journal of Transplantation, 15(5), 1155–1161. doi: 10.1111/ajt.13193.
  Dittrich, W. & Gohde, W. (1969). [Impulse fluorometry of single cells in suspension]. Zeitschrift fur Naturforschung. Teil B, Chemie, Biochemie, Biophysik, Biologie und Verwandte Gebiete, 24(3), 360–361.
  Fulwyler, M. J. (1965). Electronic separation of biological cells by volume. Science, 150(3698), 910–911. doi: 10.1126/science.150.3698.910.
  Gaudilliere, B., Fragiadakis, G. K., Bruggner, R. V., Nicolau, M., Finck, R., Tingle, M., … Nolan, G. P. (2014). Clinical recovery from surgery correlates with single‐cell immune signatures. Science Translational Medicine, 6(255), 255ra131. doi: 10.1126/scitranslmed.3009701.
  Getts, D. R., Terry, R. L., Getts, M. T., Muller, M., Rana, S., Deffrasnes, C., … King, N. J. (2012). Targeted blockade in lethal West Nile virus encephalitis indicates a crucial role for very late antigen (VLA)‐4‐dependent recruitment of nitric oxide‐producing macrophages. Journal of Neuroinflammation, 9, 246. doi: 10.1186/1742‐2094‐9‐246.
  Hulett, H. R., Bonner, W. A., Barrett, J., & Herzenberg, L. A. (1969). Cell sorting: Automated separation of mammalian cells as a function of intracellular fluorescence. Science, 166(3906), 747–749. doi: 10.1126/science.166.3906.747.
  Hulspas, R. (2010). Titration of fluorochrome‐conjugated antibodies for labeling cell surface markers on live cells. Current Protocols in Cytometry, Chapter 6, UNIT 6 29. doi: 10.1002/0471142956.cy0629s54
  KuKuruga, M. (2012). Using the BD™ Cytometer Setup and Tracking (CS&T) System for Instrument Characterization and Performance Tracking. BD Webinar.
  Lee, P. Y., Wang, J.‐X., Parisini, E., Dascher, C. C., & Nigrovic, P. A. (2013). Ly6 family proteins in neutrophil biology. Journal of Leukocyte Biology, 94(4), 585–594. doi: 10.1189/jlb.0113014.
  Macaulay, I. C., Ponting, C. P., & Voet, T. (2017). Single‐Cell Multiomics: Multiple Measurements from Single Cells. Trends in Genetics, 33(2), 155–168. doi: 10.1016/j.tig.2016.12.003.
  Maecker, H. & Trotter, J. (2008). Selecting reagents for multicolor flow cytometry with BD™ LSR II and BD FACSCanto™ systems. Nature Methods, 5(Application note (advertising feature)).
  Maecker, H. T. & Trotter, J. (2006). Flow cytometry controls, instrument setup, and the determination of positivity. Cytometry. Part A, 69(9), 1037–1042. doi: 10.1002/cyto.a.20333.
  Mahnke, Y., Chattopadhyay, P., & Roederer, M. (2010). Publication of optimized multicolor immunofluorescence panels. Cytometry. Part A, 77(9), 814–818. doi: 10.1002/cyto.a.20916.
  Mahnke, Y. D. & Roederer, M. (2007). Optimizing a multicolor immunophenotyping assay. Clinics in Laboratory Medicine, 27(3), 469–485, v. doi: 10.1016/j.cll.2007.05.002.
  Meinelt, E., Reunanen, M., Edinger, M., Jaimes, M., Stall, A., Sasaki, D., & Trotter, J. (2012). Standardizing Application Setup Across Multiple Flow Cytometers Using BD FACSDiva™ Version 6 Software.
  Nguyen, R., Perfetto, S., Mahnke, Y. D., Chattopadhyay, P., & Roederer, M. (2013). Quantifying spillover spreading for comparing instrument performance and aiding in multicolor panel design. Cytometry. Part A, 83(3), 306–315. doi: 10.1002/cyto.a.22251.
  O'Neill, K., Aghaeepour, N., Parker, J., Hogge, D., Karsan, A., Dalal, B., & Brinkman, R. R. (2015). Deep profiling of multitube flow cytometry data. Bioinformatics, 31(10), 1623–1631. doi: 10.1093/bioinformatics/btv008.
  Parks, D. R., Roederer, M., & Moore, W. A. (2006). A new “Logicle” display method avoids deceptive effects of logarithmic scaling for low signals and compensated data. Cytometry. Part A, 69(6), 541–551. doi: 10.1002/cyto.a.20258.
  Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren‐Shaul, H., … Amit, I. (2015). Transcriptional heterogeneity and lineage commitment in myeloid progenitors. Cell, 163(7), 1663–1677. doi: 10.1016/j.cell.2015.11.013.
  Perfetto, S., Wood, J. C. S., Chattopadhyay, P., Hill, J. P., Nguyen, R., Ambrosak, D., & Roederer, M. (2015). The LED pulser: A New Device for Instrument Characterization and Panel Development. Paper presented at the CYTO2015, Glasgow, UK.
  Perfetto, S. P., Ambrozak, D., Nguyen, R., Chattopadhyay, P., & Roederer, M. (2006). Quality assurance for polychromatic flow cytometry. Nature Protocols, 1(3), 1522–1530. doi: 10.1038/nprot.2006.250.
  Perfetto, S. P., Ambrozak, D., Nguyen, R., Chattopadhyay, P. K., & Roederer, M. (2012). Quality assurance for polychromatic flow cytometry using a suite of calibration beads. Nature Protocols, 7(12), 2067–2079. doi: 10.1038/nprot.2012.126.
  Perfetto, S. P., Chattopadhyay, P. K., Lamoreaux, L., Nguyen, R., Ambrozak, D., Koup, R. A., & Roederer, M. (2010). Amine‐reactive dyes for dead cell discrimination in fixed samples. Current Protocols in Cytometry, Chapter 9, UNIT 9 34. doi: 10.1002/0471142956.cy0934s53
  Perfetto, S. P., Chattopadhyay, P. K., & Roederer, M. (2004). Seventeen‐colour flow cytometry: Unravelling the immune system. Nature Reviews. Immunology, 4(8), 648–655. doi: 10.1038/nri1416.
  Perfetto, S. P., Chattopadhyay, P. K., Wood, J., Nguyen, R., Ambrozak, D., Hill, J. P., & Roederer, M. (2014). Q and B values are critical measurements required for inter‐instrument standardization and development of multicolor flow cytometry staining panels. Cytometry. Part A, 85(12), 1037–1048. doi: 10.1002/cyto.a.22579.
  Perfetto, S. P., & Roederer, M. (2007). Increased immunofluorescence sensitivity using 532 nm laser excitation. Cytometry. Part A, 71(2), 73–79. doi: 10.1002/cyto.a.20358.
  Qiu, P., Simonds, E. F., Bendall, S. C., Gibbs, K. D., Jr., Bruggner, R. V., Linderman, M. D., … Plevritis, S. K. (2011). Extracting a cellular hierarchy from high‐dimensional cytometry data with SPADE. Nature Biotechnology, 29(10), 886–891. doi: 10.1038/nbt.1991.
  Roederer, M. (2001). Spectral compensation for flow cytometry: Visualization artifacts, limitations, and caveats. Cytometry, 45(3), 194–205. doi: 10.1002/1097‐0320(20011101)45:3%3c194::AID‐CYTO1163%3e3.0.CO;2‐C.
  Roederer, M. (2002). Compensation in flow cytometry. Current Protocols in Cytometry, Chapter 1, UNIT 1 14.
  Saeys, Y., Gassen, S. V., & Lambrecht, B. N. (2016). Computational flow cytometry: Helping to make sense of high‐dimensional immunology data. Nature Reviews. Immunology, 16(7), 449–462. doi: 10.1038/nri.2016.56.
  Schubert, S. M., Walter, S. R., Manesse, M., & Walt, D. R. (2016). Protein counting in single cancer cells. Analytical Chemistry, 88(5), 2952–2957. doi: 10.1021/acs.analchem.6b00146.
  Shekhar, K., Brodin, P., Davis, M. M., & Chakraborty, A. K. (2014). Automatic classification of cellular expression by nonlinear stochastic embedding (ACCENSE). Proceedings of the National Academy of Sciences of the United States of America, 111(1), 202–207. doi: 10.1073/pnas.1321405111.
  Smith, A. (2010). When Nine Lasers is Not Enough. Paper presented at the Australasian Flow Cytometry Group (AFCG) annual meeting, Sydney, Australia.
  Smith, A. (2011). When Nine Lasers is Not Enough. Paper presented at the CYTO 2011, Baltimore, ML.
  Stoeckius, M., Hafemeisterer, C., Stephenson, W., Houck‐Loomis, B., Swerdlow, H., Satija, R., & Smibert, P. (2017). Large‐scale simultaneous measurement of epitopes and transcriptomes in single cells. bioRxiv, Non‐peer reviewed pre‐print. doi: https://doi.org/10.1101/113068
  Telford, W. G., Babin, S. A., Khorev, S. V., & Rowe, S. H. (2009). Green fiber lasers: An alternative to traditional DPSS green lasers for flow cytometry. Cytometry. Part A, 75(12), 1031–1039. doi: 10.1002/cyto.a.20790.
  van der Maaten, L. (2014). Accelerating t‐SNE using tree‐based algorithms. Journal of Machine Learning Research, 15, 3221–3245.
  van der Maaten, L. & Hinto, G. (2008). Visualizing data using t‐SNE. Journal of Machine Learning Research, 9, 2579–2605.
Internet Resources
  Sydney Cytometry core facility website.
  Sydney Cytometry github.
  OMIP information page.
  BD Biosciences home page.
  FlowJo home page
  ACCENSE home page.
  cytofkit github page.
  Cytobank home page.
  Twitter, #tsne, #t‐sne.
  Website for the laboratory of Dana Pe'er.
  Github for bh‐tSNE.
  Github for Rtsne (R wrapper for C++ bh‐tSNE script).
  Inverse Hyperbolic Sine Transformations.
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