Immune Monitoring of Cancer Patients by Multi-color Flow Cytometry

  • Shi Yong Neo
  • Aine O’Reilly
  • Yago Pico de Coaña
Part of the Methods in Molecular Biology book series (MIMB, volume 1913)


The irruption of immune-activating therapies to treat cancer has created a need for evaluating both the response and possible adverse events related to these novel treatments. Multicolor flow cytometry is a powerful tool that enables tumor immunologists to characterize the immune system of patients before and in response to immunotherapy. We present here a protocol for purifying human peripheral blood mononuclear cells and staining them with a set of six multicolor panels that allow for a thorough characterization of the immune system of healthy donors as well as patients that are undergoing treatments that may modify the immune system.

Key words

Flow cytometry Peripheral blood mononuclear cells Immune monitoring Immunotherapy Tumor immunology 


  1. 1.
    Whiteside TL, Demaria S, Rodriguez-Ruiz ME, Zarour HM, Melero I (2016) Emerging opportunities and challenges in cancer immunotherapy. Clin Cancer Res 22(8):1845–1855. Scholar
  2. 2.
    Lim WA, June CH (2017) The principles of engineering immune cells to treat cancer. Cell 168(4):724–740. Scholar
  3. 3.
    Pico de Coana Y, Choudhury A, Kiessling R (2015) Checkpoint blockade for cancer therapy: revitalizing a suppressed immune system. Trends Mol Med 21(8):482–491. Scholar
  4. 4.
    Weiden J, Tel J, Figdor CG (2018) Synthetic immune niches for cancer immunotherapy. Nat Rev Immunol 18(3):212–219. Scholar
  5. 5.
    Michot JM, Bigenwald C, Champiat S, Collins M, Carbonnel F, Postel-Vinay S et al (2016) Immune-related adverse events with immune checkpoint blockade: a comprehensive review. Eur J Cancer 54:139–148. Scholar
  6. 6.
    Finak G, Langweiler M, Jaimes M, Malek M, Taghiyar J, Korin Y et al (2016) Standardizing flow Cytometry Immunophenotyping analysis from the human immunophenotyping consortium. Sci Rep 6:20686. Scholar
  7. 7.
    Adan A, Alizada G, Kiraz Y, Baran Y, Nalbant A (2017) Flow cytometry: basic principles and applications. Crit Rev Biotechnol 37(2):163–176. Scholar
  8. 8.
    Poschke I, De Boniface J, Mao Y, Kiessling R (2012) Tumor-induced changes in the phenotype of blood-derived and tumor-associated T cells of early stage breast cancer patients. Int J Cancer 131(7):1611–1620. Scholar
  9. 9.
    Pico de Coana Y, Poschke I, Gentilcore G, Mao Y, Nystrom M, Hansson J et al (2013) Ipilimumab treatment results in an early decrease in the frequency of circulating granulocytic myeloid-derived suppressor cells as well as their Arginase1 production. Cancer Immunol Res 1(3):158–162. Scholar
  10. 10.
    Veluchamy JP, Delso-Vallejo M, Kok N, Bohme F, Seggewiss-Bernhardt R, van der Vliet HJ et al (2017) Standardized and flexible eight colour flow cytometry panels harmonized between different laboratories to study human NK cell phenotype and function. Sci Rep 7:43873. Scholar
  11. 11.
    Elisa M, Del ZG, Della CM, Cristina MM, Alessandro M, De MA et al (2013) Human NK cell receptors/markers: a tool to analyze NK cell development, subsets and function. Cytometry A 83(8):702–713. Scholar
  12. 12.
    Erick TK, Brossay L (2016) Phenotype and functions of conventional and non-conventional NK cells. Curr Opin Immunol 38:67–74. Scholar
  13. 13.
    Poschke I, Kiessling R (2012) On the armament and appearances of human myeloid-derived suppressor cells. Clin Immunol 144(3):250–268. Scholar
  14. 14.
    Pico de Coana Y, Wolodarski M, Poschke I, Yoshimoto Y, Yang Y, Nystrom M et al (2017) Ipilimumab treatment decreases monocytic MDSCs and increases CD8 effector memory T cells in long-term survivors with advanced melanoma. Oncotarget 8(13):21539–21553. Scholar
  15. 15.
    Lazarus AH, Ellis J, Blanchette V, Freedman J, Sheng-Tanner X (1998) Permeabilization and fixation conditions for intracellular flow cytometric detection of the T-cell receptor zeta chain and other intracellular proteins in lymphocyte subpopulations. Cytometry 32(3):206–213CrossRefGoogle Scholar
  16. 16.
    Yang R, Lirussi D, Thornton TM, Jelley-Gibbs DM, Diehl SA, Case LK et al (2015) Mitochondrial Ca(2+) and membrane potential, an alternative pathway for interleukin 6 to regulate CD4 cell effector function. Elife 4:e06376. Scholar
  17. 17.
    MacIver NJ, Michalek RD, Rathmell JC (2013) Metabolic regulation of T lymphocytes. Annu Rev Immunol 31:259–283. Scholar
  18. 18.
    Palmer CS, Duette GA, Wagner MCE, Henstridge DC, Saleh S, Pereira C et al (2017) Metabolically active CD4+ T cells expressing Glut1 and OX40 preferentially harbor HIV during in vitro infection. FEBS Lett 591(20):3319–3332. Scholar
  19. 19.
    Schurich A, Pallett Laura J, Jajbhay D, Wijngaarden J, Otano I, Gill Upkar S et al (2016) Distinct metabolic requirements of exhausted and functional virus-specific CD8 T cells in the same host. Cell Rep 16(5):1243–1252. Scholar
  20. 20.
    Meinelt E, Reunanen M, Edinger M, Jaimes M, Stall A, Sasaki D et al. (2012) Standardizing application setup across multiple flow cytometers using BD FACSDiva™ Version 6 Software. Technical document. BD biosciences.

Copyright information

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

Authors and Affiliations

  • Shi Yong Neo
    • 1
  • Aine O’Reilly
    • 1
  • Yago Pico de Coaña
    • 1
  1. 1.Department of Oncology-PathologyKarolinska InstituteStockholmSweden

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