Skip to main content

Use of Imaging Mass Cytometry in Studies of the Tissue Microenvironment

  • Chapter
  • First Online:
Biomarkers of the Tumor Microenvironment

Abstract

Techniques for analysis of tissues, such as immunofluorescence, immunohistochemistry and flow cytometry-based approaches for analysis of cell suspensions, have allowed the characterization of single cells within heterogeneous cell populations. However, the limitations in the number of parameters that can be simultaneously assessed have hampered advances in understanding complex tissue systems. The advent of single-cell mass cytometry, cytometry by time of flight (CyTOF), which uses metal-tagged antibodies, has made it possible to overcome these constraints as CyTOF allows the detection of a large number of cell markers in parallel. A more recently developed technique, imaging mass cytometry (IMC), has pushed the boundaries even further. By combining the transformational power of mass spectrometry with tissue-based approaches, the IMC allows for high-dimensional analysis of tissues with spatial resolution. However, different challenges must be faced to fully exploit the capabilities of IMC. Here, we provide an overview of IMC, covering the basic principles of the technology, the types of tissues used, marker selection, and antibody panel design. This technical discussion is followed by specific examples of applications of IMC to breast cancer tissues, paediatric brain tumours, and paraneoplastic cerebellar degeneration with a focus on our own research. Computational tools used to analyze the resulting multi-parametric data are also addressed.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
€32.70 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
EUR 29.95
Price includes VAT (Netherlands)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
EUR 74.89
Price includes VAT (Netherlands)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
EUR 98.09
Price includes VAT (Netherlands)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
EUR 141.69
Price includes VAT (Netherlands)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    MMTV-PyMT = mouse mammary tumour virus-polyoma middle tumour-antigen.

  2. 2.

    METABRIC cohort = Molecular Taxonomy of Breast Cancer International Consortium; ten subtypes or integrative clusters of breast cancer identified through an integrated analysis of genomic and transcriptomic data.

References

  1. Coons AH. The demonstration of pneumococcal antigen in tissues by the use of fluorescent antibody. J Immunol. 1942;45:159.

    CAS  Google Scholar 

  2. Coons AH, Creech HJ, Jones RN. Immunological properties of an antibody containing a fluorescent group. Proc Soc Exp Biol Med. 1941;47(2):200–2.

    Article  CAS  Google Scholar 

  3. Macrea ER. Immunology II: immunohistochemistry: roots and review. Lab Med. 1999;30(12):787–90.

    Article  Google Scholar 

  4. de Vries NL, et al. Unraveling the complexity of the cancer microenvironment with multidimensional genomic and cytometric technologies. Front Oncol. 2020;10:1254.

    Article  PubMed  PubMed Central  Google Scholar 

  5. O'Donnell EA, Ernst DN, Hingorani R. Multiparameter flow cytometry: advances in high resolution analysis. Immune Netw. 2013;13(2):43–54.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Futamura K, et al. Novel full-spectral flow cytometry with multiple spectrally-adjacent fluorescent proteins and fluorochromes and visualization of in vivo cellular movement. Cytometry A. 2015;87(9):830–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Bandura DR, et al. Mass cytometry: technique for real time single cell multitarget immunoassay based on inductively coupled plasma time-of-flight mass spectrometry. Anal Chem. 2009;81(16):6813–22.

    Article  CAS  PubMed  Google Scholar 

  8. Ornatsky O, et al. Highly multiparametric analysis by mass cytometry. J Immunol Methods. 2010;361(1–2):1–20.

    Article  CAS  PubMed  Google Scholar 

  9. Bendall SC, et al. Single-cell mass cytometry of differential immune and drug responses across a human hematopoietic continuum. Science. 2011;332(6030):687–96.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Bendall SC, et al. Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell. 2014;157(3):714–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Chang Q, et al. Single-cell measurement of the uptake, intratumoral distribution and cell cycle effects of cisplatin using mass cytometry. Int J Cancer. 2015;136(5):1202–9.

    Article  CAS  PubMed  Google Scholar 

  12. Chang Q, et al. Biodistribution of cisplatin revealed by imaging mass cytometry identifies extensive collagen binding in tumor and normal tissues. Sci Rep. 2016;6:36641.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Atkuri KR, Stevens JC, Neubert H. Mass cytometry: a highly multiplexed single-cell technology for advancing drug development. Drug Metab Dispos. 2015;43(2):227–33.

    Article  PubMed  CAS  Google Scholar 

  14. Giesen C, et al. Highly multiplexed imaging of tumor tissues with subcellular resolution by mass cytometry. Nat Methods. 2014;11(4):417–22.

    Article  CAS  PubMed  Google Scholar 

  15. Angelo M, et al. Multiplexed ion beam imaging of human breast tumors. Nat Med. 2014;20(4):436–42.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Keren L, et al. MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci Adv. 2019;5(10):eaax5851.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Davis AS, et al. Characterizing and diminishing autofluorescence in formalin-fixed paraffin-embedded human respiratory tissue. J Histochem Cytochem. 2014;62(6):405–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Schulz D, et al. Simultaneous multiplexed imaging of mRNA and proteins with subcellular resolution in breast cancer tissue samples by mass cytometry. Cell Syst. 2018;6(1):25–36.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bolognesi MM, et al. Multiplex staining by sequential immunostaining and antibody removal on routine tissue sections. J Histochem Cytochem. 2017;65(8):431–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gullaksen SE, et al. Titrating complex mass cytometry panels. Cytometry A. 2019;95(7):792–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Chang Q, Ornatsky O, Hedley D. Staining of frozen and formalin-fixed, paraffin-embedded tissues with metal-labeled antibodies for imaging mass cytometry analysis. Curr Protoc Cytom. 2017;82:12.47.1–8.

    CAS  Google Scholar 

  22. Ijsselsteijn ME, et al. A 40-marker panel for high dimensional characterization of cancer immune microenvironments by imaging mass cytometry. Front Immunol. 2019;10:2534.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Butovsky O, et al. Identification of a unique TGF-β-dependent molecular and functional signature in microglia. Nat Neurosci. 2014;17(1):131–43.

    Article  CAS  PubMed  Google Scholar 

  24. Lipman NS, et al. Monoclonal versus polyclonal antibodies: distinguishing characteristics, applications, and information resources. ILAR J. 2005;46(3):258–68.

    Article  CAS  PubMed  Google Scholar 

  25. Chevrier S, et al. Compensation of signal spillover in suspension and imaging mass cytometry. Cell Syst. 2018;6(5):612–620.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Bringeland GH, et al. Optimization of receptor occupancy assays in mass cytometry: standardization across channels with QSC beads. Cytometry A. 2019;95(3):314–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Takahashi C, et al. Mass cytometry panel optimization through the designed distribution of signal interference. Cytometry A. 2017;91(1):39–47.

    Article  CAS  PubMed  Google Scholar 

  28. Fernández-Zapata C, et al. The use and limitations of single-cell mass cytometry for studying human microglia function. Brain Pathol. 2020;30(6):1178–91.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  29. Ornatsky OI, et al. Development of analytical methods for multiplex bio-assay with inductively coupled plasma mass spectrometry. J Anal At Spectrom. 2008;23(4):463–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Böttcher C, et al. Human microglia regional heterogeneity and phenotypes determined by multiplexed single-cell mass cytometry. Nat Neurosci. 2019;22(1):78–90.

    Article  PubMed  CAS  Google Scholar 

  31. Junttila MR, de Sauvage FJ. Influence of tumour micro-environment heterogeneity on therapeutic response. Nature. 2013;501(7467):346–54.

    Article  CAS  PubMed  Google Scholar 

  32. Curtis C, et al. The genomic and transcriptomic architecture of 2,000 breast tumours reveals novel subgroups. Nature. 2012;486(7403):346–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15(2):81–94.

    Article  CAS  PubMed  Google Scholar 

  34. Anderson AR, et al. Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell. 2006;127(5):905–15.

    Article  CAS  PubMed  Google Scholar 

  35. Gerlinger M, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366(10):883–92.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Marusyk A, Almendro V, Polyak K. Intra-tumour heterogeneity: a looking glass for cancer? Nat Rev Cancer. 2012;12(5):323–34.

    Article  CAS  PubMed  Google Scholar 

  37. Rye IH, et al. Intratumor heterogeneity defines treatment-resistant HER2+ breast tumors. Mol Oncol. 2018;12(11):1838–55.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Yuan Y, Spatial heterogeneity in the tumor microenvironment. Cold Spring Harb Perspect Med. 2016;6(8).

    Google Scholar 

  39. Andor N, et al. Pan-cancer analysis of the extent and consequences of intratumor heterogeneity. Nat Med. 2016;22(1):105–13.

    Article  CAS  PubMed  Google Scholar 

  40. Gillies RJ, Verduzco D, Gatenby RA. Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nat Rev Cancer. 2012;12(7):487–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Allred DC, et al. Ductal carcinoma in situ and the emergence of diversity during breast cancer evolution. Clin Cancer Res. 2008;14(2):370–8.

    Article  CAS  PubMed  Google Scholar 

  42. Wu J, et al. Intratumoral spatial heterogeneity at perfusion MR imaging predicts recurrence-free survival in locally advanced breast cancer treated with neoadjuvant chemotherapy. Radiology. 2018;288(1):26–35.

    Article  PubMed  Google Scholar 

  43. Carmona-Bozo JC, et al. Hypoxia and perfusion in breast cancer: simultaneous assessment using PET/MR imaging. Eur Radiol. 2021;31(1):333–44.

    Article  PubMed  Google Scholar 

  44. Egeblad M, Nakasone ES, Werb Z. Tumors as organs: complex tissues that interface with the entire organism. Dev Cell. 2010;18(6):884–901.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Bergers G, Hanahan D. Modes of resistance to anti-angiogenic therapy. Nat Rev Cancer. 2008;8(8):592–603.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Chen Q, Zhang XH, Massagué J. Macrophage binding to receptor VCAM-1 transmits survival signals in breast cancer cells that invade the lungs. Cancer Cell. 2011;20(4):538–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Sahai E, et al. A framework for advancing our understanding of cancer-associated fibroblasts. Nat Rev Cancer. 2020;20(3):174–86.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Luga V, et al. Exosomes mediate stromal mobilization of autocrine Wnt-PCP signaling in breast cancer cell migration. Cell. 2012;151(7):1542–56.

    Article  CAS  PubMed  Google Scholar 

  49. Loges S, Schmidt T, Carmeliet P. Mechanisms of resistance to anti-angiogenic therapy and development of third-generation anti-angiogenic drug candidates. Genes Cancer. 2010;1(1):12–25.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Heindl A, Nawaz S, Yuan Y. Mapping spatial heterogeneity in the tumor microenvironment: a new era for digital pathology. Lab Investig. 2015;95(4):377–84.

    Article  PubMed  Google Scholar 

  51. Quail DF, Joyce JA. Microenvironmental regulation of tumor progression and metastasis. Nat Med. 2013;19(11):1423–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kalluri R. The biology and function of fibroblasts in cancer. Nat Rev Cancer. 2016;16(9):582–98.

    Article  CAS  PubMed  Google Scholar 

  53. Wagner J, et al. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell. 2019;177(5):1330–1345.e18.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Keren L, et al. A structured tumor-immune microenvironment in triple negative breast cancer revealed by multiplexed ion beam imaging. Cell. 2018;174(6):1373–1387.e19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Schapiro D, et al. histoCAT: analysis of cell phenotypes and interactions in multiplex image cytometry data. Nat Methods. 2017;14(9):873–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Carvajal-Hausdorf DE, et al. Multiplexed (18-plex) measurement of signaling targets and cytotoxic T cells in trastuzumab-treated patients using imaging mass cytometry. Clin Cancer Res. 2019;25(10):3054–62.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. McKeage K, Perry CM. Trastuzumab: a review of its use in the treatment of metastatic breast cancer overexpressing HER2. Drugs. 2002;62(1):209–43.

    Article  CAS  PubMed  Google Scholar 

  58. Carvajal-Hausdorf DE, et al. Measurement of domain-specific HER2 (ERBB2) expression may classify benefit from trastuzumab in breast cancer. J Natl Cancer Inst. 2015;107(8)

    Google Scholar 

  59. Ali HR, et al. Imaging mass cytometry and multiplatform genomics define the phenogenomic landscape of breast cancer. Nat Cancer. 2020;1(2):163–75.

    Article  CAS  PubMed  Google Scholar 

  60. Pàez-Ribes M, et al. Antiangiogenic therapy elicits malignant progression of tumors to increased local invasion and distant metastasis. Cancer Cell. 2009;15(3):220–31.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  61. Barrett RL, Puré E. Cancer-associated fibroblasts and their influence on tumor immunity and immunotherapy. elife. 2020;9

    Google Scholar 

  62. Taub DD, Longo DL, Murphy WJ. Human interferon-inducible protein-10 induces mononuclear cell infiltration in mice and promotes the migration of human T lymphocytes into the peripheral tissues and human peripheral blood lymphocytes-SCID mice. Blood. 1996;87(4):1423–31.

    Article  CAS  PubMed  Google Scholar 

  63. Jackson HW, et al. The single-cell pathology landscape of breast cancer. Nature. 2020;578(7796):615–20.

    Article  CAS  PubMed  Google Scholar 

  64. Georgopoulou D, et al. Landscapes of cellular phenotypic diversity in breast cancer xenografts and their impact on drug response. Nat Commun. 2021;12(1):1998.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Serganova I, et al. Tumor hypoxia imaging. Clin Cancer Res. 2006;12(18):5260–4.

    Article  PubMed  Google Scholar 

  66. Patil N, et al. Epidemiology of brainstem high-grade gliomas in children and adolescents in the United States, 2000–2017. Neuro Oncol. 2021;23(6):990–8.

    Google Scholar 

  67. Jones C, Baker SJ. Unique genetic and epigenetic mechanisms driving paediatric diffuse high-grade glioma. Nat Rev Cancer. 2014;14(10)

    Google Scholar 

  68. Jones C, Perryman L, Hargrave D. Paediatric and adult malignant glioma: close relatives or distant cousins? Nat Rev Clin Oncol. 2012;9(7):400–13.

    Article  CAS  PubMed  Google Scholar 

  69. Mackay A, et al. Integrated molecular meta-analysis of 1,000 pediatric high-grade and diffuse intrinsic pontine glioma. Cancer Cell. 2017;32(4):520–537.e5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Leach JL, et al. MR imaging features of diffuse intrinsic pontine glioma and relationship to overall survival: report from the international DIPG registry. Neuro-Oncology. 2020;22(11):1647–57.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Puget S, et al. Biopsy in a series of 130 pediatric diffuse intrinsic pontine gliomas. Childs Nerv Syst. 2015;31(10):1773–80.

    Article  PubMed  Google Scholar 

  72. Carai A, et al. Robot-assisted stereotactic biopsy of diffuse intrinsic pontine glioma: a single-center experience. World Neurosurg. 2017;101:584–8.

    Article  PubMed  Google Scholar 

  73. Broniscer A, et al. Prospective collection of tissue samples at autopsy in children with diffuse intrinsic pontine glioma. Cancer. 2010;116(19):4632–7.

    Article  PubMed  Google Scholar 

  74. Angelini P, et al. Post mortem examinations in diffuse intrinsic pontine glioma: challenges and chances. J Neuro-Oncol. 2011;101(1):75–81.

    Article  Google Scholar 

  75. Caretti V, et al. Implementation of a multi-institutional diffuse intrinsic pontine glioma autopsy protocol and characterization of a primary cell culture. Neuropathol Appl Neurobiol. 2013;39(4):426–36.

    Article  CAS  PubMed  Google Scholar 

  76. Brandon JC, et al. Emphysematous cholecystitis: pitfalls in its plain film diagnosis. Gastrointest Radiol. 1988;13(1):33–6.

    Article  CAS  PubMed  Google Scholar 

  77. Wu G, et al. Somatic histone H3 alterations in pediatric diffuse intrinsic pontine gliomas and non-brainstem glioblastomas. Nat Genet. 2012;44(3):251–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Schwartzentruber J, et al. Driver mutations in histone H3.3 and chromatin remodelling genes in paediatric glioblastoma. Nature. 2012;482(7384):226–31.

    Article  CAS  PubMed  Google Scholar 

  79. Taylor KR, et al. Recurrent activating ACVR1 mutations in diffuse intrinsic pontine glioma. Nat Genet. 2014;46(5):457–61.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Nikbakht H, et al. Spatial and temporal homogeneity of driver mutations in diffuse intrinsic pontine glioma. Nat Commun. 2016;7:11185.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Clarke M, et al. Infant high-grade gliomas comprise multiple subgroups characterized by novel targetable gene fusions and favorable outcomes. Cancer Discov. 2020;10(7):942–63.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Ceglie G, et al. Infantile/congenital high-grade gliomas: molecular features and therapeutic perspectives. Diagnostics (Basel). 2020;10(9)

    Google Scholar 

  83. Buczkowicz P, et al. Histopathological spectrum of paediatric diffuse intrinsic pontine glioma: diagnostic and therapeutic implications. Acta Neuropathol. 2014;128(4):573–81.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Vinci M, et al. Functional diversity and cooperativity between subclonal populations of pediatric glioblastoma and diffuse intrinsic pontine glioma cells. Nat Med. 2018;24(8):1204–15.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Robinson MH, et al. Subtype and grade-dependent spatial heterogeneity of T-cell infiltration in pediatric glioma. J Immunother Cancer. 2020;8(2)

    Google Scholar 

  86. Salloum R, et al. Characterizing temporal genomic heterogeneity in pediatric high-grade gliomas. Acta Neuropathol Commun. 2017;5(1):78.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  87. Hoffman M, et al. Intratumoral genetic and functional heterogeneity in pediatric glioblastoma. Cancer Res. 2019;79(9):2111–23.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Castel D, et al. Transcriptomic and epigenetic profiling of ‘diffuse midline gliomas, H3 K27M-mutant’ discriminate two subgroups based on the type of histone H3 mutated and not supratentorial or infratentorial location. Acta Neuropathol Commun. 2018;6(1):117.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  89. Pericoli G, et al. Integration of multiple platforms for the analysis of multifluorescent marking technology applied to pediatric GBM and DIPG. Int J Mol Sci. 2020;21(18)

    Google Scholar 

  90. Filbin MG, et al. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science. 2018;360(6386):331–5.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Chen CCL, et al. Histone H3.3G34-mutant interneuron progenitors co-opt PDGFRA for gliomagenesis. Cell. 2020;183(6):1617–1633.e22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  92. Galdieri L, et al. Defining phenotypic and functional heterogeneity of glioblastoma stem cells by mass cytometry. JCI Insight. 2021;6(4):e128456.

    Google Scholar 

  93. Mueller S, et al. Mass cytometry detects H3.3K27M-specific vaccine responses in diffuse midline glioma. J Clin Invest. 2020;130(12):6325–37.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Leelatian N, et al. Unsupervised machine learning reveals risk stratifying glioblastoma tumor cells. elife. 2020;9

    Google Scholar 

  95. Venkatesh HS, et al. Neuronal activity promotes glioma growth through Neuroligin-3 secretion. Cell. 2015;161(4):803–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Venkatesh HS, et al. Electrical and synaptic integration of glioma into neural circuits. Nature. 2019;573(7775):539–45.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Raspotnig M, et al. Cerebellar degeneration-related proteins 2 and 2-like are present in ovarian cancer in patients with and without Yo antibodies. Cancer Immunol Immunother. 2017;66(11):1463–71.

    Article  CAS  PubMed  Google Scholar 

  98. Herdlevær, I., et al., Paraneoplastic cerebellar degeneration: the importance of including CDR2L as a diagnostic marker. Neurol Neuroimmunol Neuroinflamm. 2021;8(2).

    Google Scholar 

  99. Peterson K, et al. Paraneoplastic cerebellar degeneration. I. a clinical analysis of 55 anti-Yo antibody-positive patients. Neurology. 1992;42(10):1931–7.

    Article  CAS  PubMed  Google Scholar 

  100. Kråkenes T, et al. CDR2L is the major Yo antibody target in paraneoplastic cerebellar degeneration. Ann Neurol. 2019;86(2):316–21.

    Article  PubMed  CAS  Google Scholar 

  101. Herdlevaer I, et al. Localization of CDR2L and CDR2 in paraneoplastic cerebellar degeneration. Annals Clin Transl Neurol. 2020;7(11):2231–42.

    Article  CAS  Google Scholar 

  102. Schubert M, et al. Paraneoplastic CDR2 and CDR2L antibodies affect Purkinje cell calcium homeostasis. Acta Neuropathol. 2014;128(6):835–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Greenlee JE, et al. Anti-Yo antibody uptake and interaction with its intracellular target antigen causes Purkinje cell death in rat cerebellar slice cultures: a possible mechanism for paraneoplastic cerebellar degeneration in humans with gynecological or breast cancers. PLoS One. 2015;10(4):e0123446.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  104. Storstein A, Krossnes BK, Vedeler CA. Morphological and immunohistochemical characterization of paraneoplastic cerebellar degeneration associated with Yo antibodies. Acta Neurol Scand. 2009;120(1):64–7.

    Article  CAS  PubMed  Google Scholar 

  105. Yshii L, et al. Neurons and T cells: understanding this interaction for inflammatory neurological diseases. Eur J Immunol. 2015;45(10):2712–20.

    Article  CAS  PubMed  Google Scholar 

  106. Darnell RB, Posner JB. Paraneoplastic syndromes involving the nervous system. N Engl J Med. 2003;349(16):1543–54.

    Article  CAS  PubMed  Google Scholar 

  107. Monstad SE, et al. Yo antibodies in ovarian and breast cancer patients detected by a sensitive immunoprecipitation technique. Clin Exp Immunol. 2006;144(1):53–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  108. Hickman S, et al. Microglia in neurodegeneration. Nat Neurosci. 2018;21(10):1359–69.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Tan Y-L, Yuan Y, Tian L. Microglial regional heterogeneity and its role in the brain. Mol Psychiatry. 2020;25(2):351–67.

    Article  PubMed  Google Scholar 

  110. Lawson LJ, et al. Heterogeneity in the distribution and morphology of microglia in the normal adult mouse brain. Neuroscience. 1990;39(1):151–70.

    Article  CAS  PubMed  Google Scholar 

  111. Stowell RD, et al. Cerebellar microglia are dynamically unique and survey Purkinje neurons in vivo. Dev Neurobiol. 2018;78(6):627–44.

    Article  PubMed  PubMed Central  Google Scholar 

  112. Tay TL, et al. A new fate mapping system reveals context-dependent random or clonal expansion of microglia. Nat Neurosci. 2017;20(6):793–803.

    Article  CAS  PubMed  Google Scholar 

  113. Soreq L, et al. Major shifts in glial regional identity are a transcriptional hallmark of human brain aging. Cell Rep. 2017;18(2):557–70.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Grabert K, et al. Microglial brain region-dependent diversity and selective regional sensitivities to aging. Nat Neurosci. 2016;19(3):504–16.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  115. Ransohoff RM, Cardona AE. The myeloid cells of the central nervous system parenchyma. Nature. 2010;468:253.

    Article  CAS  PubMed  Google Scholar 

  116. Vicar T, et al. Cell segmentation methods for label-free contrast microscopy: review and comprehensive comparison. BMC Bioinf. 2019;20(1):360.

    Article  Google Scholar 

  117. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation 2015. arXiv:1505.04597.

    Google Scholar 

  118. van der Maaten L, Hinton G. Viualizing data using t-SNE. J Mach Learn Res. 2008;9:2579–605.

    Google Scholar 

  119. McInnes L, Healy J, Melville J. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction. 2018. arXiv:1802.03426.

    Google Scholar 

  120. Carpenter AE, et al. CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol, 2006;7(10):R100.

    Google Scholar 

  121. Kuett L, Catena R, Özcan A, et al. Three-dimensional imaging mass cytometry for highly multiplexed molecular and cellular mapping of tissues and the tumor microenvironment. Nat Cancer. 2022;3:122–33.

    Google Scholar 

  122. Ptacek J, et al. Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types. Lab Invest. 2020;100(8):1111–23.

    Google Scholar 

  123. Goltsev Y, et al. Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell. 2018;174(4):968–81.e15.

    Google Scholar 

  124. Gut G, Herrmann MD, Pelkmans L. Multiplexed protein maps link subcellular organization to cellular states. Science. 2018;361(6401):468.

    Google Scholar 

Download references

Acknowledgements

MV is a Children with Cancer UK Fellow (grant 16-234) and acknowledges Children with Cancer UK and The Cure Starts Now, Fondazione Heal and Banca D´Italia for supporting this study. LLP acknowledges Fondazione AIRC. MV and LLP are grateful to Dr. Angela Mastronuzzi (Head of Neuro-Oncology Unit, OPBG), Dr. Andrea Carai (Oncological Neurosurgery Unit, OPBG), and Dr. Sabrina Rossi (Pathology Lab, OPBG) for clinical and pathological assessment of the DIPG case and the patient family. DB and FQ are funded by a CRUK ‘Grand Challenge’ grant (A24042), DB acknowledges the entire IMAXT project team and in particular the staff of the University of Cambridge Institute of Astronomy for the work performed on IMC data analysis and Dr. Bernd Bodenmiller and his laboratory, at the University of Zurich, for the many discussions and suggestions in implementing mass cytometry.

SG and IH would like to thank senior research technician, Bendik Nordanger, at the Department of Clinical Medicine, University of Bergen for his support in preparing PCD post-mortem TMA, and Head Engineer at the Core Facility for Flow Cytometry, Jørn Skavland, Department of Clinical Science, University of Bergen, for his technical support. Research grants from Torbjørg Hauges legacy supported this study. SG and IH are funded by NeuroSys-Med and Helse Vest.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sonia Gavasso .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Herdlevær, I., Petrilli, L.L., Qosaj, F., Vinci, M., Bressan, D., Gavasso, S. (2022). Use of Imaging Mass Cytometry in Studies of the Tissue Microenvironment. In: Akslen, L.A., Watnick, R.S. (eds) Biomarkers of the Tumor Microenvironment. Springer, Cham. https://doi.org/10.1007/978-3-030-98950-7_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-98950-7_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-98949-1

  • Online ISBN: 978-3-030-98950-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics