Development of Tools for the Selective Visualization and Quantification of TLS-Immune Cells on Tissue Sections

  • Christophe Klein
  • Priyanka Devi-Marulkar
  • Marie-Caroline Dieu-Nosjean
  • Claire GermainEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1845)


Tertiary lymphoid structures (TLS) are considered as genuine markers of inflammation. Their presence within inflamed tissues or within the tumor microenvironment has been associated with the local development of an active immune response. While high densities of TLS are correlated with disease severity in autoimmune diseases or during graft rejection, it has been associated with longer patient survival in many cancer types. Their efficient visualization and quantification within human tissues may represent new tools for helping clinicians in adjusting their therapeutic strategy. Some immunohistochemistry (IHC) protocols are already used in the clinic to appreciate the level of immune infiltration in formalin-fixed, paraffin-embedded (FFPE) tissues. However, the use of two or more markers may sometimes be useful to better characterize this immune infiltrate, especially in the case of TLS. Besides the growing development of multiplex labeling approaches, imaging can also be used to overcome some technical difficulties encountered during the immunolabeling of tissues with several markers.

This chapter describes IHC methods to visualize in a human tissue (tumoral or not) the presence of TLS. These methods are based on the immunostaining of four TLS-associated immune cell populations, namely follicular B cells, follicular dendritic cells (FDCs), mature dendritic cells (mDCs), and follicular helper T cells (TFH), together with non-TFH T cells. Methodologies for subsequent quantification of TLS density are also proposed, as well as a virtual multiplexing method based on image registration using the open-source software ImageJ (IJ), aiming at co-localizing several immune cell populations from different IHC stainings performed on serial tissue sections.

Key words

Tertiary lymphoid structure Double immunohistochemistry Virtual multiplexing Image registration CD20 DC-LAMP PD-1 B-cell follicle Mature dendritic cell Follicular helper T cell Open-source software ImageJ 



Activation-induced cytidine deaminase


Alkaline phosphatase


Alkaline phosphatase anti-alkaline phosphatase


B-cell lymphoma 6


Class-switch recombination


Follicular dendritic cell


Formalin-fixed, paraffin-embedded


Germinal center


Horseradish peroxidase


Human serum








Mature dendritic cell


Non-small cell lung cancer


Rheumatoid arthritis


Region of analysis


Region of interest


Somatic hypermutation


Scale invariant feature transform


Secondary lymphoid organ


Follicular helper T cell


Tertiary lymphoid structure


Whole-slide image



This work was supported by the “Institut National de la Santé et de la Recherche Médicale (INSERM), Sorbonne University, Paris-Descartes University, the Labex Immuno-Oncology (LAXE62_9UMRS872 Fridman), CARPEM (Cancer Research for PErsonalized Medicine), Fondation ARC pour la Recherche sur le Cancer. Priyanka Devi-Marulkar was supported by a grant from the Fondation ARC pour la Recherche sur le Cancer. Claire Germain was supported by a grant from MedImmune LLC.


  1. 1.
    Dieu-Nosjean M-C, Antoine M, Danel C et al (2008) Long-term survival for patients with non–small-cell lung cancer with intratumoral lymphoid structures. J Clin Oncol 26:4410–4417CrossRefPubMedGoogle Scholar
  2. 2.
    Germain C, Gnjatic S, Tamzalit F et al (2014) Presence of B cells in tertiary lymphoid structures is associated with a protective immunity in patients with lung cancer. Am J Respir Crit Care Med 189:832–844CrossRefPubMedGoogle Scholar
  3. 3.
    Thaunat O, Patey N, Morelon E et al (2006) Lymphoid neogenesis in chronic rejection: the murderer is in the house. Curr Opin Immunol 18:576–579CrossRefPubMedGoogle Scholar
  4. 4.
    Cipponi A, Mercier M, Seremet T et al (2012) Neogenesis of lymphoid structures and antibody responses occur in human melanoma metastases. Cancer Res 72:3997–4007CrossRefPubMedGoogle Scholar
  5. 5.
    Gottlin EB, Bentley RC, Campa MJ et al (2011) The Association of Intratumoral Germinal Centers with early-stage non-small cell lung cancer. J Thorac Oncol Off Publ Int Assoc Study Lung Cancer 6:1687–1690Google Scholar
  6. 6.
    Goc J, Germain C, Vo-Bourgais TKD et al (2014) Dendritic cells in tumor-associated tertiary lymphoid structures signal a Th1 cytotoxic immune contexture and license the positive prognostic value of infiltrating CD8+ T cells. Cancer Res 74:705–715CrossRefPubMedGoogle Scholar
  7. 7.
    Teillaud J-L, Dieu-Nosjean M-C (2017) Tertiary lymphoid structures: an anti-tumor school for adaptive immune cells and an antibody factory to fight cancer? Front Immunol 8:830CrossRefPubMedPubMedCentralGoogle Scholar
  8. 8.
    Drayton DL, Liao S, Mounzer RH et al (2006) Lymphoid organ development: from ontogeny to neogenesis. Nat Immunol 7:344–353CrossRefPubMedGoogle Scholar
  9. 9.
    Aloisi F, Pujol-Borrell R (2006) Lymphoid neogenesis in chronic inflammatory diseases. Nat Rev Immunol 6:205–217CrossRefGoogle Scholar
  10. 10.
    Dieu-Nosjean M-C, Goc J, Giraldo NA et al (2014) Tertiary lymphoid structures in cancer and beyond. Trends Immunol 35:571–580CrossRefPubMedGoogle Scholar
  11. 11.
    Neyt K, Perros F, GeurtsvanKessel CH et al (2012) Tertiary lymphoid organs in infection and autoimmunity. Trends Immunol 33:297–305CrossRefPubMedGoogle Scholar
  12. 12.
    Baddoura FK, Nasr IW, Wrobel B et al (2005) Lymphoid neogenesis in murine cardiac allografts undergoing chronic rejection. Am J Transplant Off J Am Soc Transplant Am Soc Transpl Surg 5:510–516CrossRefGoogle Scholar
  13. 13.
    Kerjaschki D, Regele HM, Moosberger I et al (2004) Lymphatic neoangiogenesis in human kidney transplants is associated with immunologically active lymphocytic infiltrates. J Am Soc Nephrol JASN 15:603–612CrossRefPubMedGoogle Scholar
  14. 14.
    Thaunat O, Patey N, Caligiuri G et al (2010) Chronic rejection triggers the development of an aggressive intragraft immune response through recapitulation of lymphoid organogenesis. J Immunol Baltim Md 1950 185:717–728Google Scholar
  15. 15.
    Thaunat O, Field A-C, Dai J et al (2005) Lymphoid neogenesis in chronic rejection: evidence for a local humoral alloimmune response. Proc Natl Acad Sci U S A 102:14,723–14,728CrossRefGoogle Scholar
  16. 16.
    Moyron-Quiroz JE, Rangel-Moreno J, Kusser K et al (2004) Role of inducible bronchus associated lymphoid tissue (iBALT) in respiratory immunity. Nat Med 10:927–934CrossRefPubMedGoogle Scholar
  17. 17.
    Messina JL, Fenstermacher DA, Eschrich S et al (2012) 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep 2:765CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Coppola D, Nebozhyn M, Khalil F et al (2011) Unique ectopic lymph node-like structures present in human primary colorectal carcinoma are identified by immune gene array profiling. Am J Pathol 179:37–45CrossRefPubMedPubMedCentralGoogle Scholar
  19. 19.
    Gu-Trantien C, Loi S, Garaud S et al (2013) CD4+ follicular helper T cell infiltration predicts breast cancer survival. J Clin Invest 123:2873–2892CrossRefPubMedPubMedCentralGoogle Scholar
  20. 20.
    Wirsing AM, Rikardsen OG, Steigen SE et al (2014) Characterisation and prognostic value of tertiary lymphoid structures in oral squamous cell carcinoma. BMC Clin Pathol 14:38CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Sautès-Fridman C, Lawand M, Giraldo NA et al (2016) Tertiary lymphoid structures in cancers: prognostic value, regulation, and manipulation for therapeutic intervention. Front Immunol 7:407CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    Glass G, Papin JA, Mandell JW (2009) SIMPLE: a sequential immunoperoxidase labeling and erasing method. J Histochem Cytochem Off J Histochem Soc 57:899–905CrossRefGoogle Scholar
  23. 23.
    Potts S, Johnson T, Voelker F, et al (2014) Methods for feature analysis on consecutive tissue sections,
  24. 24.
    Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Schindelin J, Arganda-Carreras I, Frise E et al (2012) Fiji: an open-source platform for biological-image analysis. Nat Methods 9:676–682CrossRefPubMedGoogle Scholar
  26. 26.
    Linkert M, Rueden CT, Allan C et al (2010) Metadata matters: access to image data in the real world. J Cell Biol 189:777–782CrossRefPubMedPubMedCentralGoogle Scholar
  27. 27.
    Deroulers C, Ameisen D, Badoual M et al (2013) Analyzing huge pathology images with open source software. Diagn Pathol 8:92CrossRefPubMedPubMedCentralGoogle Scholar
  28. 28.
    Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60:91–110CrossRefGoogle Scholar
  29. 29.
    Thévenaz P, Ruttimann UE, Unser M (1998) A pyramid approach to subpixel registration based on intensity. IEEE Trans Image Process Publ IEEE Signal Process Soc 7:27–41CrossRefGoogle Scholar
  30. 30.
    Arganda-Carreras I, Sorzano COS, Marabini R et al (2006) Consistent and elastic registration of histological sections using vector-spline regularization. In: Beichel RR, Sonka M (eds) Computer vision approaches to medical image analysis. Springer, Berlin, Heidelberg, pp 85–95CrossRefGoogle Scholar
  31. 31.
    Mueller D, Vossen D, Hulsken B (2011) Real-time deformable registration of multi-modal whole slides for digital pathology. Comput Med Imaging Graph Off J Comput Med Imaging Soc 35:542–556CrossRefGoogle Scholar
  32. 32.
    Moles Lopez X, Barbot P, Van Eycke Y-R et al (2015) Registration of whole immunohistochemical slide images: an efficient way to characterize biomarker colocalization. J Am Med Inform Assoc 22:86–99CrossRefPubMedGoogle Scholar
  33. 33.
    Obando DFG, Frafjord A, Øynebråten I, et al (2017) Multi-staining registration of large histology images, In: 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp. 345–348Google Scholar
  34. 34.
    de CF, Dallongeville S, Chenouard N et al (2012) Icy: an open bioimage informatics platform for extended reproducible research. Nat Methods 9:690–696CrossRefGoogle Scholar
  35. 35.
    Trahearn N, Epstein D, Cree I et al (2017) Hyper-stain inspector: a framework for robust registration and localised co-expression analysis of multiple whole-slide images of serial histology sections. Sci Rep 7:5641CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Christophe Klein
    • 1
    • 2
    • 3
  • Priyanka Devi-Marulkar
    • 2
    • 3
    • 4
    • 5
  • Marie-Caroline Dieu-Nosjean
    • 2
    • 3
    • 4
  • Claire Germain
    • 2
    • 3
    • 4
    • 6
    Email author
  1. 1.Center of Cellular Imaging and CytometryInstitut National de la Santé et de la Recherche Médicale (INSERM) UMRS 1138, Cordeliers Research CenterParisFrance
  2. 2.Sorbonne University, UMRS 1138, Cordeliers Research CenterParisFrance
  3. 3.Paris Descartes University, Sorbonne Paris Cité, UMRS 1138, Cordeliers Research CenterParisFrance
  4. 4.Laboratory “Cancer, immune control and Escape”INSERM, UMRS 1138, Cordeliers Research CenterParisFrance
  5. 5.“Cytokine Signaling” Unit, Department of ImmunologyInstitut Pasteur, INSERM U1221ParisFrance
  6. 6.Laboratory “Immune Intervention and Biotherapies”UPMC UMRS CR7—Inserm U1135—CNRS ERL 8255, Centre d’Immunologie et des Maladies Infectieuses (CIMI), Groupe hospitalier Pitié-SalpêtrièreParis Cedex 13France

Personalised recommendations