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Cancer Cell Biology pp 147–168Cite as

An End-to-End Workflow for Interrogating Tumor-Infiltrating Myeloid Cells Using Mass Cytometry

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Part of the Methods in Molecular Biology book series (MIMB,volume 2508)

Abstract

Myeloid cells are a highly heterogeneous group of innate immune cells which include a diverse collection of cell types and cell states. Distinct subsets can impact tumor progression differently, with conventional type 1 DCs important in protective anti-tumor immune responses, while immunosuppressive tumor-associated macrophages and myeloid-derived suppressive cells (MDSCs) play tumor-promoting roles. Deep phenotyping of myeloid cells using single-cell technologies such as mass cytometry provides the unprecedented opportunity to comprehensively characterize the underlying heterogeneity of myeloid cells. Here we provide a detailed end-to-end workflow including both experimental and computational protocols enabling deep phenotyping of tumor-infiltrating myeloid cells using mass cytometry. A protocol that facilitates interrogation of phosphoproteins in circulating and tumor-infiltrating myeloid cells has been provided together with detailed scripts for Phenograph analysis of tumor-infiltrating myeloid cells.

Key words

  • Mass cytometry
  • CyTOF
  • Myeloid cells
  • Deep phenotyping
  • Phenograph
  • Monocytes
  • Macrophages
  • Dendritic cells
  • Myeloid-derived suppressor cells

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  • DOI: 10.1007/978-1-0716-2376-3_12
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References

  1. Bassler K, Schulte-Schrepping J, Warnat-Herresthal S, Aschenbrenner AC, Schultze JL (2019) The myeloid cell compartment-cell by cell. Annu Rev Immunol 37:269–293. https://doi.org/10.1146/annurev-immunol-042718-041728

    CrossRef  CAS  PubMed  Google Scholar 

  2. Becher B, Schlitzer A, Chen J, Mair F, Sumatoh HR, Teng KW, Low D, Ruedl C, Riccardi-Castagnoli P, Poidinger M, Greter M, Ginhoux F, Newell EW (2014) High-dimensional analysis of the murine myeloid cell system. Nat Immunol 15(12):1181–1189. https://doi.org/10.1038/ni.3006

    CrossRef  CAS  PubMed  Google Scholar 

  3. Geissmann F, Manz MG, Jung S, Sieweke MH, Merad M, Ley K (2010) Development of monocytes, macrophages, and dendritic cells. Science 327(5966):656–661. https://doi.org/10.1126/science.1178331

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  4. Guilliams M, Mildner A, Yona S (2018) Developmental and functional heterogeneity of monocytes. Immunity 49(4):595–613. https://doi.org/10.1016/j.immuni.2018.10.005

    CrossRef  CAS  PubMed  Google Scholar 

  5. Guilliams M, Thierry GR, Bonnardel J, Bajenoff M (2020) Establishment and maintenance of the macrophage niche. Immunity 52(3):434–451. https://doi.org/10.1016/j.immuni.2020.02.015

    CrossRef  CAS  PubMed  Google Scholar 

  6. Villani AC, Satija R, Reynolds G, Sarkizova S, Shekhar K, Fletcher J, Griesbeck M, Butler A, Zheng S, Lazo S, Jardine L, Dixon D, Stephenson E, Nilsson E, Grundberg I, McDonald D, Filby A, Li W, De Jager PL, Rozenblatt-Rosen O, Lane AA, Haniffa M, Regev A, Hacohen N (2017) Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science 356(6335). https://doi.org/10.1126/science.aah4573

  7. Bendall SC, Nolan GP, Roederer M, Chattopadhyay PK (2012) A deep profiler’s guide to cytometry. Trends Immunol 33(7):323–332. https://doi.org/10.1016/j.it.2012.02.010

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  8. Bendall SC, Simonds EF, Qiu P, Amir el AD, Krutzik PO, Finck R, Bruggner RV, Melamed R, Trejo A, Ornatsky OI, Balderas RS, Plevritis SK, Sachs 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(6030):687–696. https://doi.org/10.1126/science.1198704

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  9. Amir el AD, Davis KL, Tadmor MD, Simonds EF, Levine JH, Bendall SC, Shenfeld DK, Krishnaswamy S, Nolan GP, Pe'er D (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.2594

    CrossRef  CAS  Google Scholar 

  10. Levine JH, Simonds EF, Bendall SC, Davis KL, Amir el AD, Tadmor MD, Litvin O, Fienberg HG, Jager A, Zunder ER, Finck R, Gedman AL, Radtke I, Downing JR, Pe'er D, Nolan GP (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.2015.05.047

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  11. 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.23030

    CrossRef  CAS  PubMed  Google Scholar 

  12. Veglia F, Sanseviero E, Gabrilovich DI (2021) Myeloid-derived suppressor cells in the era of increasing myeloid cell diversity. Nat Rev Immunol. https://doi.org/10.1038/s41577-020-00490-y

  13. Bronte V, Brandau S, Chen SH, Colombo MP, Frey AB, Greten TF, Mandruzzato S, Murray PJ, Ochoa A, Ostrand-Rosenberg S, Rodriguez PC, Sica A, Umansky V, Vonderheide RH, Gabrilovich DI (2016) Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun 7:12150. https://doi.org/10.1038/ncomms12150

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  14. Hanahan D, Coussens LM (2012) Accessories to the crime: functions of cells recruited to the tumor microenvironment. Cancer Cell 21(3):309–322. https://doi.org/10.1016/j.ccr.2012.02.022

    CrossRef  CAS  PubMed  Google Scholar 

  15. Gonzalez H, Hagerling C, Werb Z (2018) Roles of the immune system in cancer: from tumor initiation to metastatic progression. Genes Dev 32(19–20):1267–1284. https://doi.org/10.1101/gad.314617.118

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  16. Hiam-Galvez KJ, Allen BM, Spitzer MH (2021) Systemic immunity in cancer. Nat Rev Cancer. https://doi.org/10.1038/s41568-021-00347-z

  17. Kiss M, Caro AA, Raes G, Laoui D (2020) Systemic reprogramming of monocytes in cancer. Front Oncol 10:1399. https://doi.org/10.3389/fonc.2020.01399

    CrossRef  PubMed  PubMed Central  Google Scholar 

  18. Hanna RN, Cekic C, Sag D, Tacke R, Thomas GD, Nowyhed H, Herrley E, Rasquinha N, McArdle S, Wu R, Peluso E, Metzger D, Ichinose H, Shaked I, Chodaczek G, Biswas SK, Hedrick CC (2015) Patrolling monocytes control tumor metastasis to the lung. Science 350(6263):985–990. https://doi.org/10.1126/science.aac9407

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  19. Galon J, Bruni D (2019) Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov 18(3):197–218. https://doi.org/10.1038/s41573-018-0007-y

    CrossRef  CAS  PubMed  Google Scholar 

  20. Wei SC, Levine JH, Cogdill AP, Zhao Y, Anang NAS, Andrews MC, Sharma P, Wang J, Wargo JA, Pe’er D, Allison JP (2017) Distinct cellular mechanisms underlie anti-CTLA-4 and anti-PD-1 checkpoint blockade. Cell 170(6):1120–1133 e1117. https://doi.org/10.1016/j.cell.2017.07.024

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  21. Gubin MM, Esaulova E, Ward JP, Malkova ON, Runci D, Wong P, Noguchi T, Arthur CD, Meng W, Alspach E, Medrano RFV, Fronick C, Fehlings M, Newell EW, Fulton RS, Sheehan KCF, Oh ST, Schreiber RD, Artyomov MN (2018) High-dimensional analysis delineates myeloid and lymphoid compartment remodeling during successful immune-checkpoint cancer therapy. Cell 175(5):1443. https://doi.org/10.1016/j.cell.2018.11.003

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  22. Krieg C, Nowicka M, Guglietta S, Schindler S, Hartmann FJ, Weber LM, Dummer R, Robinson MD, Levesque MP, Becher B (2018) High-dimensional single-cell analysis predicts response to anti-PD-1 immunotherapy. Nat Med 24(2):144–153. https://doi.org/10.1038/nm.4466

    CrossRef  CAS  PubMed  Google Scholar 

  23. Allen BM, Hiam KJ, Burnett CE, Venida A, DeBarge R, Tenvooren I, Marquez DM, Cho NW, Carmi Y, Spitzer MH (2020) Systemic dysfunction and plasticity of the immune macroenvironment in cancer models. Nat Med 26(7):1125–1134. https://doi.org/10.1038/s41591-020-0892-6

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  24. Nixon AB, Schalper KA, Jacobs I, Potluri S, Wang IM, Fleener C (2019) Peripheral immune-based biomarkers in cancer immunotherapy: can we realize their predictive potential? J Immunother Cancer 7(1):325. https://doi.org/10.1186/s40425-019-0799-2

    CrossRef  PubMed  PubMed Central  Google Scholar 

  25. Leelatian N, Sinnaeve J, Mistry AM, Barone SM, Brockman AA, Diggins KE, Greenplate AR, Weaver KD, Thompson RC, Chambless LB, Mobley BC, Ihrie RA, Irish JM (2020) Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells. elife 9. https://doi.org/10.7554/eLife.56879

  26. Gaudilliere B, Fragiadakis GK, Bruggner RV, Nicolau M, Finck R, Tingle M, Silva J, Ganio EA, Yeh CG, Maloney WJ, Huddleston JI, Goodman SB, Davis MM, Bendall SC, Fantl WJ, Angst MS, Nolan GP (2014) Clinical recovery from surgery correlates with single-cell immune signatures. Sci Transl Med 6(255):255ra131. https://doi.org/10.1126/scitranslmed.3009701

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  27. Arunachalam PS, Wimmers F, Mok CKP, Perera R, Scott M, Hagan T, Sigal N, Feng Y, Bristow L, Tak-Yin Tsang O, Wagh D, Coller J, Pellegrini KL, Kazmin D, Alaaeddine G, Leung WS, Chan JMC, Chik TSH, Choi CYC, Huerta C, Paine McCullough M, Lv H, Anderson E, Edupuganti S, Upadhyay AA, Bosinger SE, Maecker HT, Khatri P, Rouphael N, Peiris M, Pulendran B (2020) Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science 369(6508):1210–1220. https://doi.org/10.1126/science.abc6261

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

  28. Roberts ME, Barvalia M, Silva J, Cederberg RA, Chu W, Wong A, Tai DC, Chen S, Matos I, Priatel JJ, Cullis PR, Harder KW (2020) Deep phenotyping by mass cytometry and single-cell RNA-sequencing reveals LYN-regulated signaling profiles underlying monocyte subset heterogeneity and lifespan. Circ Res 126(10):e61–e79. https://doi.org/10.1161/CIRCRESAHA.119.315708

    CrossRef  CAS  PubMed  Google Scholar 

  29. Wall EM, Milne K, Martin ML, Watson PH, Theiss P, Nelson BH (2007) Spontaneous mammary tumors differ widely in their inherent sensitivity to adoptively transferred T cells. Cancer Res 67(13):6442–6450. https://doi.org/10.1158/0008-5472.CAN-07-0622

    CrossRef  CAS  PubMed  Google Scholar 

  30. Hahne F, LeMeur N, Brinkman RR, Ellis B, Haaland P, Sarkar D, Spidlen J, Strain E, Gentleman R (2009) flowCore: a Bioconductor package for high throughput flow cytometry. BMC Bioinform 10:106. https://doi.org/10.1186/1471-2105-10-106

    CrossRef  CAS  Google Scholar 

  31. Pedersen CB, Olsen LR (2019) Analysis of mass cytometry data. Methods Mol Biol 1989:267–279. https://doi.org/10.1007/978-1-4939-9454-0_17

    CrossRef  CAS  PubMed  Google Scholar 

  32. Chen H, Lau MC, Wong MT, Newell EW, Poidinger M, Chen J (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.1005112

    CrossRef  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgments

We would like to thank Dr. Morgan Roberts for generating the original data presented in Figs. 1 and 2 and Israel Matos for generating the original data presented in Fig. 3. We would like to extend our gratitude to Jessica Silva for her thoughtful critique of this chapter.

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Authors and Affiliations

  1. Life Science Institute, Department of Microbiology and Immunology, University of British Columbia, Vancouver, BC, Canada

    Maunish Barvalia & Kenneth W. Harder

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  1. Maunish Barvalia
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  2. Kenneth W. Harder
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Correspondence to Kenneth W. Harder .

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Editors and Affiliations

  1. Department of Biochemistry, Memorial University of Newfoundland, St. John's, NL, Canada

    Dr. Sherri L. Christian

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Barvalia, M., Harder, K.W. (2022). An End-to-End Workflow for Interrogating Tumor-Infiltrating Myeloid Cells Using Mass Cytometry. In: Christian, S.L. (eds) Cancer Cell Biology. Methods in Molecular Biology, vol 2508. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2376-3_12

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  • DOI: https://doi.org/10.1007/978-1-0716-2376-3_12

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  • Print ISBN: 978-1-0716-2375-6

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