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Mass cytometry defines distinct immune profile in germinal center B-cell lymphomas


Tumor-associated macrophage and T-cell subsets are implicated in the pathogenesis of diffuse large B-cell lymphoma, follicular lymphoma, and classical Hodgkin lymphoma. Macrophages provide essential mechanisms of tumor immune evasion through checkpoint ligand expression and secretion of suppressive cytokines. However, normal and tumor-associated macrophage phenotypes are less well characterized than those of tumor-infiltrating T-cell subsets, and it would be especially valuable to know whether the polarization state of macrophages differs across lymphoma tumor microenvironments. Here, an established mass cytometry panel designed to characterize myeloid-derived suppressor cells and known macrophage maturation and polarization states was applied to characterize B-lymphoma tumors and non-malignant human tissue. High-dimensional single-cell analyses were performed using dimensionality reduction and clustering tools. Phenotypically distinct intra-tumor macrophage subsets were identified based on abnormal marker expression profiles that were associated with lymphoma tumor types. While it had been proposed that measurement of CD163 and CD68 might be sufficient to reveal macrophage subsets in tumors, results here indicated that S100A9, CCR2, CD36, Slan, and CD32 should also be measured to effectively characterize lymphoma-specific tumor macrophages. Additionally, the presence of phenotypically distinct, abnormal macrophage populations was closely linked to the phenotype of intra-tumor T-cell populations, including PD-1 expressing T cells. These results further support the close links between macrophage polarization and T-cell functional state, as well as the rationale for targeting tumor-associated macrophages in cancer immunotherapies.

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Bovine serum albumin


Classical dendritic cells


Central memory


Cytometry by time-of-flight


Dendritic cell


Diffuse large B-cell lymphoma


Effector memory


Effector memory CD45RApos


Fluorescein isothiocyanate


Follicular lymphoma


Granulocyte-colony stimulating factor


Granulocyte macrophage-colony stimulating factor


Hodgkin lymphoma


Indoleamine 2,3-dioxygenase


Macrophage polarized by IL-10


Macrophage polarized by IL-4


Macrophage polarized by TPP


Macrophage-colony stimulating factor


Myeloid-derived suppressor cells


Multiplex immunohistochemistry




Phosphate-buffered saline


Programmed cell death protein 1


Programmed death-ligand 1


Plasmacytoid dendritic cell






Reactive lymphoid hyperplasia


S100 calcium-binding protein A


6-Sulfo LacNAc


Spanning-tree progression analysis of density-normalized events


T-distributed stochastic neighbor embedding


Tumor-associated macrophage


Tumor microenvironment


Cocktail including TNFα, Pam3CSK4, and prostaglandin E2


Regulatory T cell


Visualization of t-distributed stochastic neighbor embedding


  1. 1.

    Scott DW, Gascoyne RD (2014) The tumour microenvironment in B cell lymphomas. Nat Rev Cancer 14:517–534.

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Galati D, Corazzelli G, De Filippi R, Pinto A (2016) Dendritic cells in hematological malignancies. Crit Rev Oncol Hematol 108:86–96.

    Article  PubMed  Google Scholar 

  3. 3.

    Tudor CS, Bruns H, Daniel C et al (2014) Macrophages and dendritic cells as actors in the immune reaction of classical Hodgkin lymphoma. PLoS One 9:e114345.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. 4.

    Chang KC, Huang GC, Jones D, Lin YH (2007) Distribution patterns of dendritic cells and T cells in diffuse large B-cell lymphomas correlate with prognoses. Clin Cancer Res 13:6666–6672.

    CAS  Article  PubMed  Google Scholar 

  5. 5.

    Mantovani A, Marchesi F, Malesci A et al (2017) Tumour-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol 14:399–416.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. 6.

    Xue J, Schmidt SV, Sander J et al (2014) Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity 40:274–288.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Marini O, Spina C, Mimiola E et al (2016) Identification of granulocytic myeloid-derived suppressor cells (G-MDSCs) in the peripheral blood of Hodgkin and non-Hodgkin lymphoma patients. Oncotarget 7:27676–27688.

    Article  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Azzaoui I, Uhel F, Rossille D et al (2016) T-cell defect in diffuse large B-cell lymphomas involves expansion of myeloid-derived suppressor cells. Blood 128:1081–1092.

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Kumar V, Patel S, Tcyganov E, Gabrilovich DI (2016) The nature of myeloid-derived suppressor cells in the tumor microenvironment. Trends Immunol 37:208–220.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Ugel S, De Sanctis F, Mandruzzato S, Bronte V (2015) Tumor-induced myeloid deviation: when myeloid-derived suppressor cells meet tumor-associated macrophages. J Clin Investig 125:3365–3376.

    Article  PubMed  Google Scholar 

  11. 11.

    Chevrier S, Levine JH, Zanotelli VRT et al (2017) An immune atlas of clear cell renal cell carcinoma. Cell 169:736–738.e18.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  12. 12.

    Wagner J, Rapsomaniki MA, Chevrier S et al (2019) A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell.

    Article  PubMed  PubMed Central  Google Scholar 

  13. 13.

    Lavin Y, Kobayashi S, Leader A et al (2017) Innate Immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169:750–757.e15.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Riihijarvi S, Fiskvik I, Taskinen M et al (2015) Prognostic influence of macrophages in patients with diffuse large B-cell lymphoma: a correlative study from a nordic phase II trial. Haematologica 100:238–245.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hasselblom S, Hansson U, Sigurdardottir M et al (2008) Expression of CD68 tumor-associated macrophages in patients with diffuse large B-cell lymphoma and its relation to prognosis. Pathol Int 58:529–532.

    Article  PubMed  Google Scholar 

  16. 16.

    Shen L, Li H, Shi Y et al (2016) M2 tumour-associated macrophages contribute to tumour progression via legumain remodelling the extracellular matrix in diffuse large B cell lymphoma. Sci Rep 6:30347.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. 17.

    Aldinucci D, Celegato M, Casagrande N (2016) Microenvironmental interactions in classical Hodgkin lymphoma and their role in promoting tumor growth, immune escape and drug resistance. Cancer Lett 380:243–252.

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Greaves P, Clear A, Owen A et al (2013) Defining characteristics of classical Hodgkin lymphoma microenvironment T-helper cells. Blood 122:2856–2863.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Steidl C, Lee T, Shah SP et al (2010) Tumor-associated macrophages and survival in classic Hodgkin's lymphoma. N Engl J Med 362:875–885.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Azambuja D, Natkunam Y, Biasoli I et al (2012) Lack of association of tumor-associated macrophages with clinical outcome in patients with classical Hodgkin's lymphoma. Ann Oncol 23:736–742.

    CAS  Article  PubMed  Google Scholar 

  21. 21.

    Kridel R, Steidl C, Gascoyne RD (2015) Tumor-associated macrophages in diffuse large B-cell lymphoma. Haematologica 100:143–145.

    Article  PubMed  PubMed Central  Google Scholar 

  22. 22.

    Roussel M, Ferrell PB, Greenplate AR et al (2017) Mass cytometry deep phenotyping of human mononuclear phagocytes and myeloid-derived suppressor cells from human blood and bone marrow. J Leukoc Biol 102:437–447.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  23. 23.

    Fienberg HG, Simonds EF, Fantl WJ et al (2012) A platinum-based covalent viability reagent for single-cell mass cytometry. Cytom A 81:467–475.

    CAS  Article  Google Scholar 

  24. 24.

    Finck R, Simonds EF, Jager A et al (2013) Normalization of mass cytometry data with bead standards. Cytom A 83:483–494.

    CAS  Article  Google Scholar 

  25. 25.

    Diggins KE, Ferrell PB, Irish JM (2015) Methods for discovery and characterization of cell subsets in high dimensional mass cytometry data. Methods 82:55–63.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Roussel M, Bartkowiak T, Irish JM (2019) Picturing polarized myeloid phagocytes and regulatory cells by mass cytometry. Methods Mol Biol 1989:217–226.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. 27.

    Kotecha N, Krutzik PO, Irish JM (2010) Web-based analysis and publication of flow cytometry experiments. Curr Protoc Cytom.

    Article  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Gravelle P, Péricart S, Tosolini M et al (2018) EBV infection determines the immune hallmarks of plasmablastic lymphoma. Oncoimmunology 7:e1486950.

    Article  PubMed  PubMed Central  Google Scholar 

  29. 29.

    Vermi W, Micheletti A, Finotti G et al (2018) slan+ monocytes and macrophages mediate CD20-dependent B-cell lymphoma elimination via ADCC and ADCP. Can Res 78:3544–3559.

    CAS  Article  Google Scholar 

  30. 30.

    Bronte V, Brandau S, Chen S-H et al (2016) Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun 7:12150.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Feng P-H, Lee K-Y, Chang Y-L et al (2012) CD14(+)S100A9(+) monocytic myeloid-derived suppressor cells and their clinical relevance in non-small cell lung cancer. Am J Respir Crit Care Med 186:1025–1036.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  32. 32.

    Zhao F, Hoechst B, Duffy A et al (2012) S100A9 a new marker for monocytic human myeloid-derived suppressor cells. Immunology 136:176–183.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  33. 33.

    Chen X, Eksioglu EA, Zhou J et al (2013) Induction of myelodysplasia by myeloid-derived suppressor cells. J Clin Investig 123:4595–4611.

    CAS  Article  PubMed  Google Scholar 

  34. 34.

    Feng P-H, Yu C-T, Chen K-Y et al (2018) S100A9+ MDSC and TAM-mediated EGFR-TKI resistance in lung adenocarcinoma: the role of RELB. Oncotarget 9:7631–7643.

    Article  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Vari F, Arpon D, Keane C et al (2018) Immune evasion via PD-1/PD-L1 on NK cells and monocyte/macrophages is more prominent in Hodgkin lymphoma than DLBCL. Blood 131:1809–1819.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. 36.

    McCord R, Bolen CR, Koeppen H et al (2019) PD-L1 and tumor-associated macrophages in de novo DLBCL. Blood Adv 3:531–540.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Carey CD, Gusenleitner D, Lipschitz M et al (2017) Topological analysis reveals a PD-L1-associated microenvironmental niche for Reed-Sternberg cells in Hodgkin lymphoma. Blood 130:2420–2430.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. 38.

    Cader FZ, Schackmann RCJ, Hu X et al (2018) Mass cytometry of Hodgkin lymphoma reveals a CD4+ regulatory T-cell-rich and exhausted T-effector microenvironment. Blood 132:825–836.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Yang Z-Z, Kim HJ, Villasboas JC et al (2019) Mass cytometry analysis reveals that specific intratumoral CD4+ T cell subsets correlate with patient survival in follicular lymphoma. Cell Rep 26:2178–2193.e3.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Wogsland CE, Greenplate AR, Kolstad A et al (2017) Mass cytometry of follicular lymphoma tumors reveals intrinsic heterogeneity in proteins including HLA-DR and a deficit in nonmalignant plasmablast and germinal center B-cell populations. Cytom B Clin Cytom 92:79–87.

    CAS  Article  Google Scholar 

  41. 41.

    Nissen MD, Kusakabe M, Wang X et al (2019) Single cell phenotypic profiling of 27 DLBCL cases reveals marked intertumoral and intratumoral heterogeneity. Cytom A 9:2579.

    Article  Google Scholar 

  42. 42.

    Leelatian N, Doxie DB, Greenplate AR et al (2017) Single cell analysis of human tissues and solid tumors with mass cytometry. Cytom B Clin Cytom 92:68–78.

    CAS  Article  Google Scholar 

  43. 43.

    Mistry AM, Greenplate AR, Ihrie RA, Irish JM (2018) Beyond the message: advantages of snapshot proteomics with single-cell mass cytometry in solid tumors. FEBS J.

    Article  Google Scholar 

  44. 44.

    Giesen C, Wang HAO, Schapiro D et al (2014) Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods 11:417–422.

    CAS  Article  PubMed  Google Scholar 

  45. 45.

    Chang Q, Ornatsky OI, Siddiqui I et al (2017) Imaging mass cytometry. Cytom A 91:160–169.

    Article  Google Scholar 

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We are indebted to the clinicians of the BREHAT (Bretagne Réseau Expertise Hématologie) network and the CeVi collection from the Carnot/CALYM Institute ( funded by the ANR (Agence Nationale de la Recherche) for providing samples. The authors acknowledge the Centre de Ressources Biologiques (CRB) of Rennes (BB-0033-00056, [Celine Pangault] and the CeVi network for managing samples.


This work was supported by research grants: National Institutes of Health/National Cancer Institute (NIH/NCI R00 CA143231, R01 CA226833, U54 CA217450, U01 AI125056), and the Vanderbilt-Ingram Cancer Center (VICC, P30 CA68485) [to Jonathan M. Irish]; Comité pour la recherche translationnelle (CORECT) from the University hospital at Rennes (Grant no. 2015) [to Faustine Lhomme]; and the CeVi collection from the Carnot/CALYM Institute (ANR) [to Camille Laurent and Mikael Roussel]. Mikael Roussel is recipient of a fellowship from the Nuovo-Soldati Fundation (Switzerland). Pauline Gravelle is supported by the CeVi collection from the Carnot/CALYM Institute.

Author information




MR and JMI conceived and designed the experiments, analyzed data, and wrote the manuscript; TB and TF analyzed data; MR, FL, CER, PG, and CL performed experiments. All authors revised the manuscript.

Corresponding authors

Correspondence to Mikael Roussel or Jonathan M. Irish.

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Conflict of interest

Jonathan M. Irish was a co-founder and was a board member of Cytobank Inc. and received research support from Incyte Corp, Janssen, and Pharmacyclics. The authors declare that there are no other conflicts of interest.

Research sites

Sample collection was performed in France (Rennes [all samples except HL #1, #2, #3, and #4] and through the CeVi_collection [HL #1, #2, #3, and #4]). CyTOF analysis was performed in Nashville, TN, USA by Mikael Roussel during a postdoctoral position in Jonathan Irish’s Lab at Vanderbilt University. Data analysis were performed in both sites (Rennes and Nashville). Multiplex IHC was performed in Toulouse (France).

Ethical approval and ethical standards

Samples were obtained under French legal guidelines and fulfilled the requirements of the University Hospital of Rennes institutional ethics committee for samples collected in Rennes (CRB) [approval number DC-2008-630 and DC-2016-2565] and of the Comité de Protection des Personnes for samples collected through the Cevi collection [approval number DC-2013-1783].

Informed consent

Tissue from patients was acquired with informed consent in accordance with local institutional review and the Declaration of Helsinki. A written consent was obtained from patients before qualification for research in the CRB or the CeVI collection. The consent was for the use of their specimens and data for research and for publication.

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Roussel, M., Lhomme, F., Roe, C.E. et al. Mass cytometry defines distinct immune profile in germinal center B-cell lymphomas. Cancer Immunol Immunother 69, 407–420 (2020).

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  • Germinal center
  • Lymphoma
  • Tumor-associated macrophages
  • Mass cytometry