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Cyclic Multiplexed-Immunofluorescence (cmIF), a Highly Multiplexed Method for Single-Cell Analysis

  • Jennifer Eng
  • Guillaume Thibault
  • Shiuh-Wen Luoh
  • Joe W. Gray
  • Young Hwan Chang
  • Koei ChinEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2055)

Abstract

Immunotherapy harnesses the power of the adaptive immune system and has revolutionized the field of oncotherapy, as novel therapeutic strategies have been introduced into clinical use. The development of immune checkpoint inhibitors has led to durable control of disease in a subset of advanced cancer patients, such as those with melanoma and non-small cell lung cancer. However, predicting patient responses to therapy remains a major challenge, due to the remarkable genomic, epigenetic, and microenvironmental heterogeneity present in each tumor. Breast cancer (BC) is the most common cancer in women, where hormone receptor-positive (HR+; estrogen receptor and/or progesterone receptor) BC comprises the majority (>50%) and has better prognosis, while a minority (<20%) are triple negative BC (TNBC), which has an aggressive phenotype. There is a clinical need to identify predictors of late recurrence in HR+ BC and predictors of immunotherapy outcomes in advanced TNBC. Tumor-infiltrating lymphocytes (TILs) have recently been shown to predict late recurrence in HR+, counter to the findings that TILs confer good prognosis in TNBC and human epidermal growth factor receptor 2 positive (HER2+) subtypes. Furthermore, the spatial arrangement of TILs also appears to have prognostic value, with dense clusters of immune cells predicting poor prognosis in HR+ and good prognosis in TNBC. Whether TIL clusters in different breast cancer subtypes represent the same or different landscapes of TILs is unknown and may have treatment implications for a significant portion of breast cancer patients. Current histopathological staining technology is not sufficient for characterizing the ensembles of TILs and their spatial patterns, in addition to tumor and microenvironmental heterogeneity. However, recent advances in cyclic immunofluorescence enable differentiation of the subsets based on TILs, tumor heterogeneity, and microenvironment composition between good and poor responders. A computational framework for understanding the importance of the spatial relationships between TILs and tumor cells in cancer tissues, which will allow for quantitative interpretation of cyclic immunostaining, is also under development. This chapter will explore the workflow for a newly developed cyclic multiplexed-immunofluorescence (cmIF) assay, which has been optimized for formalin-fixed. paraffin-embedded tissues and developed to process digital images for quantitative single-cell based spatial analysis of tumor heterogeneity and microenvironment, including immune cell composition.

Key words

Immunotherapy Breast cancer Tumor infiltrating lymphocytes (TILs) Cyclic multiplexed-immunofluorescence (cmIF) Cyclic immunofluorescence Cyclic IF 

Notes

Acknowledgments

The authors thank Ting Zheng and Lydia Grace Campbell for technical assistance and Yuki Chin for proofreading. The development of cmIF was supported by the funding from Prospect Creek Foundation, Susan G. Komen Foundation, OHSU Foundation, OHSU Center for Spatial Systems Biomedicine (OCSSB), and J.W.G acknowledges the support from NIH/NCI U54 CA209988, NIH/NCI U2C CA233280, and NCI SBIR 1R44CA224994-01. Y.H.C acknowledges the support from Biomedical Innovation Program (Oregon Clinical & Translational Research Institute). The contents do not represent the view of the US Department of Veterans Affairs or the US government.

References

  1. 1.
    Perez EA et al (2016) Association of stromal tumor-infiltrating lymphocytes with recurrence-free survival in the N9831 adjuvant trial in patients with early-stage HER2-positive breast cancer. JAMA Oncol 2(1):56–64PubMedPubMedCentralGoogle Scholar
  2. 2.
    Luen SJ et al (2017) Tumour-infiltrating lymphocytes and the emerging role of immunotherapy in breast cancer. Pathology 49(2):141–155PubMedGoogle Scholar
  3. 3.
    Gerdes MJ et al (2013) Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci U S A 110(29):11982–11987PubMedPubMedCentralGoogle Scholar
  4. 4.
    Lin JR, Fallahi-Sichani M, Sorger PK (2015) Highly multiplexed imaging of single cells using a high-throughput cyclic immunofluorescence method. Nat Commun 6:8390PubMedPubMedCentralGoogle Scholar
  5. 5.
    McKinley ET et al (2017) Optimized multiplex immunofluorescence single-cell analysis reveals tuft cell heterogeneity. JCI Insight 2(11):93487PubMedGoogle Scholar
  6. 6.
    Lin JR et al (2018) Highly multiplexed immunofluorescence imaging of human tissues and tumors using t-CyCIF and conventional optical microscopes. elife 7:e31657PubMedPubMedCentralGoogle Scholar
  7. 7.
    Chae YK et al (2018) Current landscape and future of dual anti-CTLA4 and PD-1/PD-L1 blockade immunotherapy in cancer; lessons learned from clinical trials with melanoma and non-small cell lung cancer (NSCLC). J Immunother Cancer 6(1):39PubMedPubMedCentralGoogle Scholar
  8. 8.
    Denkert C et al (2018) Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol 19(1):40–50PubMedGoogle Scholar
  9. 9.
    Cortazar P et al (2014) Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384(9938):164–172PubMedGoogle Scholar
  10. 10.
    Savas P et al (2016) Clinical relevance of host immunity in breast cancer: from TILs to the clinic. Nat Rev Clin Oncol 13(4):228–241PubMedGoogle Scholar
  11. 11.
    Adams S et al (2019) Pembrolizumab monotherapy for previously treated metastatic triple-negative breast cancer: cohort A of the phase 2 KEYNOTE-086 study. Ann Oncol 30(3):397–404PubMedGoogle Scholar
  12. 12.
    Kelly K et al (2015) Avelumab (MSB0010718C), an anti-PD-L1 antibody, in patients with metastatic or locally advanced solid tumors: assessment of safety and tolerability in a phase I, open-label expansion study. J Clin Oncol 33(5):3044Google Scholar
  13. 13.
    Nanda R et al (2017) Pembrolizumab plus standard neoadjuvant therapy for high-risk breast cancer (BC): results from I-SPY 2. J Clin Oncol 35(15):506Google Scholar
  14. 14.
    Schmid P et al (2018) Atezolizumab and nab-paclitaxel in advanced triple-negative breast cancer. N Engl J Med 379(22):2108–2121PubMedGoogle Scholar
  15. 15.
    Solinas C et al (2017) Tumor-infiltrating lymphocytes in patients with HER2-positive breast cancer treated with neoadjuvant chemotherapy plus trastuzumab, lapatinib or their combination: A meta-analysis of randomized controlled trials. Cancer Treat Rev 57:8–15PubMedGoogle Scholar
  16. 16.
    Rugo HS, Delord J-P, Im S-A, Ott PA, Piha-Paul SA, Bedard PL, Sachdev J, Le Tourneau C, van Brummelen E, Varga A, Saraf S, Pietrangelo D, Karantza V, Tan A (2015) Preliminary efficacy and safety of pembrolizumab (MK-3475) in patients with PD-L1–positive, estrogen receptor-positive (ER+)/HER2-negative advanced breast cancer enrolled in KEYNOTE-028. In Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium. AACR, San Antonio, TXGoogle Scholar
  17. 17.
    Khoury T et al (2018) Prognostic significance of stromal versus Intratumoral infiltrating lymphocytes in different subtypes of breast cancer treated with cytotoxic neoadjuvant chemotherapy. Appl Immunohistochem Mol Morphol 26(8):523–532PubMedPubMedCentralGoogle Scholar
  18. 18.
    Chen X et al (2012) TNBCtype: a subtyping tool for triple-negative breast cancer. Cancer Informat 11:147–156Google Scholar
  19. 19.
    Lehmann BD et al (2016) Refinement of triple-negative breast cancer molecular subtypes: implications for neoadjuvant chemotherapy selection. PLoS One 11(6):e0157368PubMedPubMedCentralGoogle Scholar
  20. 20.
    Teschendorff AE et al (2007) An immune response gene expression module identifies a good prognosis subtype in estrogen receptor negative breast cancer. Genome Biol 8(8):R157PubMedPubMedCentralGoogle Scholar
  21. 21.
    Burstein MD et al (2015) Comprehensive genomic analysis identifies novel subtypes and targets of triple-negative breast cancer. Clin Cancer Res 21(7):1688–1698PubMedPubMedCentralGoogle Scholar
  22. 22.
    Brahmer JR et al (2010) Phase I study of single-agent anti-programmed death-1 (MDX-1106) in refractory solid tumors: safety, clinical activity, pharmacodynamics, and immunologic correlates. J Clin Oncol 28(19):3167–3175PubMedPubMedCentralGoogle Scholar
  23. 23.
    Kwon ED et al (2014) Ipilimumab versus placebo after radiotherapy in patients with metastatic castration-resistant prostate cancer that had progressed after docetaxel chemotherapy (CA184-043): a multicentre, randomised, double-blind, phase 3 trial. Lancet Oncol 15(7):700–712PubMedPubMedCentralGoogle Scholar
  24. 24.
    Madan RA, Heery CR, Gulley JL (2012) Combination of vaccine and immune checkpoint inhibitor is safe with encouraging clinical activity. Oncoimmunology 1(7):1167–1168PubMedPubMedCentralGoogle Scholar
  25. 25.
    Couzin-Frankel J (2013) Breakthrough of the year 2013. Cancer Immunother Sci 342(6165):1432–1433Google Scholar
  26. 26.
    Patnaik A et al (2015) Phase I study of pembrolizumab (MK-3475; anti-PD-1 monoclonal antibody) in patients with advanced solid Tumors. Clin Cancer Res 21(19):4286–4293Google Scholar
  27. 27.
    Royal RE et al (2010) Phase 2 trial of single agent Ipilimumab (anti-CTLA-4) for locally advanced or metastatic pancreatic adenocarcinoma. J Immunother 33(8):828–833PubMedGoogle Scholar
  28. 28.
    Shen W et al (2017) TGF-beta in pancreatic cancer initiation and progression: two sides of the same coin. Cell Biosci 7:39PubMedPubMedCentralGoogle Scholar
  29. 29.
    Haque S, Morris JC (2017) Transforming growth factor-beta: A therapeutic target for cancer. Hum Vaccin Immunother 13(8):1741–1750PubMedPubMedCentralGoogle Scholar
  30. 30.
    Hanahan D, Weinberg RA (2000) The hallmarks of cancer. Cell 100(1):57–70Google Scholar
  31. 31.
    Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674Google Scholar
  32. 32.
    Ellsworth RE et al (2017) Molecular heterogeneity in breast cancer: state of the science and implications for patient care. Semin Cell Dev Biol 64:65–72PubMedGoogle Scholar
  33. 33.
    Chin K et al (2004) In situ analyses of genome instability in breast cancer. Nat Genet 36(9):984–988PubMedGoogle Scholar
  34. 34.
    Ferguson LR et al (2015) Genomic instability in human cancer: molecular insights and opportunities for therapeutic attack and prevention through diet and nutrition. Semin Cancer Biol 35(Suppl):S5–S24PubMedPubMedCentralGoogle Scholar
  35. 35.
    Risom T et al (2018) Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat Commun 9(1):3815PubMedPubMedCentralGoogle Scholar
  36. 36.
    Creighton CJ et al (2009) Residual breast cancers after conventional therapy display mesenchymal as well as tumor-initiating features. Proc Natl Acad Sci U S A 106(33):13820–13825PubMedPubMedCentralGoogle Scholar
  37. 37.
    Heiser LM et al (2012) Subtype and pathway specific responses to anticancer compounds in breast cancer. Proc Natl Acad Sci U S A 109(8):2724–2729PubMedGoogle Scholar
  38. 38.
    Lesniak D et al (2013) Spontaneous epithelial-mesenchymal transition and resistance to HER-2-targeted therapies in HER-2-positive luminal breast cancer. PLoS One 8(8):e71987PubMedPubMedCentralGoogle Scholar
  39. 39.
    Gupta PB et al (2011) Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell 146(4):633–644PubMedGoogle Scholar
  40. 40.
    Chaffer CL et al (2011) Normal and neoplastic nonstem cells can spontaneously convert to a stem-like state. Proc Natl Acad Sci U S A 108(19):7950–7955PubMedPubMedCentralGoogle Scholar
  41. 41.
    Goldman A et al (2015) Temporally sequenced anticancer drugs overcome adaptive resistance by targeting a vulnerable chemotherapy-induced phenotypic transition. Nat Commun 6:6139PubMedPubMedCentralGoogle Scholar
  42. 42.
    Sharma SV et al (2010) A chromatin-mediated reversible drug-tolerant state in cancer cell subpopulations. Cell 141(1):69–80PubMedPubMedCentralGoogle Scholar
  43. 43.
    Fidler IJ, Kripke ML (1977) Metastasis results from preexisting variant cells within a malignant tumor. Science 197(4306):893–895PubMedGoogle Scholar
  44. 44.
    Fidler IJ (1978) Tumor heterogeneity and the biology of cancer invasion and metastasis. Cancer Res 38(9):2651–2660PubMedGoogle Scholar
  45. 45.
    Miller FR, Miller BE, Heppner GH (1983) Characterization of metastatic heterogeneity among subpopulations of a single mouse mammary tumor: heterogeneity in phenotypic stability. Invasion Metastasis 3(1):22–31PubMedGoogle Scholar
  46. 46.
    Weber CE, Kuo PC (2012) The tumor microenvironment. Surg Oncol 21(3):172–177PubMedGoogle Scholar
  47. 47.
    Bense RD et al (2017) Relevance of tumor-infiltrating immune cell composition and functionality for disease outcome in breast cancer. J Natl Cancer Inst 109(1)PubMedCentralGoogle Scholar
  48. 48.
    Denkert C et al (2010) Tumor-associated lymphocytes as an independent predictor of response to neoadjuvant chemotherapy in breast cancer. J Clin Oncol 28(1):105–113PubMedGoogle Scholar
  49. 49.
    Robert C et al (2015) Pembrolizumab versus Ipilimumab in Advanced Melanoma. N Engl J Med 372(26):2521–2532Google Scholar
  50. 50.
    Brahmer J et al (2015) Nivolumab versus docetaxel in advanced squamous-cell non-small-cell lung cancer. N Engl J Med 373(2):123–135PubMedPubMedCentralGoogle Scholar
  51. 51.
    Borghaei H et al (2015) Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N Engl J Med 373(17):1627–1639PubMedPubMedCentralGoogle Scholar
  52. 52.
    Ferris RL et al (2016) Nivolumab for recurrent squamous-cell carcinoma of the head and neck. N Engl J Med 375(19):1856–1867PubMedPubMedCentralGoogle Scholar
  53. 53.
    Motzer RJ et al (2015) Nivolumab versus everolimus in advanced renal-cell carcinoma. N Engl J Med 373(19):1803–1813PubMedPubMedCentralGoogle Scholar
  54. 54.
    Ansell SM et al (2015) PD-1 blockade with nivolumab in relapsed or refractory Hodgkin’s lymphoma. N Engl J Med 372(4):311–319PubMedPubMedCentralGoogle Scholar
  55. 55.
    Sarvaria A, Madrigal JA, Saudemont A (2017) B cell regulation in cancer and anti-tumor immunity. Cell Mol Immunol 14(8):662–674PubMedPubMedCentralGoogle Scholar
  56. 56.
    Kumar V et al (2016) The nature of myeloid-derived suppressor cells in the tumor microenvironment. Trends Immunol 37(3):208–220PubMedPubMedCentralGoogle Scholar
  57. 57.
    Marcus A et al (2014) Recognition of tumors by the innate immune system and natural killer cells. Adv Immunol 122:91–128PubMedPubMedCentralGoogle Scholar
  58. 58.
    Wimberly H et al (2015) PD-L1 expression correlates with tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy in breast cancer. Cancer Immunol Res 3(4):326–332PubMedGoogle Scholar
  59. 59.
    Hurwitz H et al (2004) Bevacizumab plus irinotecan, fluorouracil, and leucovorin for metastatic colorectal cancer. N Engl J Med 350(23):2335–2342PubMedGoogle Scholar
  60. 60.
    Sandler A et al (2006) Paclitaxel-carboplatin alone or with bevacizumab for non-small-cell lung cancer. N Engl J Med 355(24):2542–2550PubMedGoogle Scholar
  61. 61.
    Motzer RJ et al (2007) Sunitinib versus interferon alfa in metastatic renal-cell carcinoma. N Engl J Med 356(2):115–124PubMedGoogle Scholar
  62. 62.
    Brem SS, Gullino PM, Medina D (1977) Angiogenesis: a marker for neoplastic transformation of mammary papillary hyperplasia. Science 195(4281):880–882PubMedGoogle Scholar
  63. 63.
    Jensen HM et al (1982) Angiogenesis induced by “normal” human breast tissue: a probable marker for precancer. Science 218(4569):293–295PubMedGoogle Scholar
  64. 64.
    McLeskey SW et al (1998) Tumor growth of FGF or VEGF transfected MCF-7 breast carcinoma cells correlates with density of specific microvessels independent of the transfected angiogenic factor. Am J Pathol 153(6):1993–2006PubMedPubMedCentralGoogle Scholar
  65. 65.
    Weinstat-Saslow DL et al (1994) Transfection of thrombospondin 1 complementary DNA into a human breast carcinoma cell line reduces primary tumor growth, metastatic potential, and angiogenesis. Cancer Res 54(24):6504–6511PubMedGoogle Scholar
  66. 66.
    Mackey JR et al (2012) Controlling angiogenesis in breast cancer: a systematic review of anti-angiogenic trials. Cancer Treat Rev 38(6):673–688PubMedGoogle Scholar
  67. 67.
    Kather JN et al (2015) Continuous representation of tumor microvessel density and detection of angiogenic hotspots in histological whole-slide images. Oncotarget 6(22):19163–19176PubMedPubMedCentralGoogle Scholar
  68. 68.
    Weidner N (2008) Chapter 14. Measuring intratumoral microvessel density. Methods Enzymol 444:305–323PubMedGoogle Scholar
  69. 69.
    Tolaney SM et al (2015) Role of vascular density and normalization in response to neoadjuvant bevacizumab and chemotherapy in breast cancer patients. Proc Natl Acad Sci U S A 112(46):14325–14330PubMedPubMedCentralGoogle Scholar
  70. 70.
    Zhang S et al (2017) Intratumoral and peritumoral lymphatic vessel density both correlate with lymph node metastasis in breast cancer. Sci Rep 7:40364PubMedPubMedCentralGoogle Scholar
  71. 71.
    Kalluri R, Zeisberg M (2006) Fibroblasts in cancer. Nat Rev Cancer 6(5):392–401PubMedGoogle Scholar
  72. 72.
    Ostman A, Augsten M (2009) Cancer-associated fibroblasts and tumor growth--bystanders turning into key players. Curr Opin Genet Dev 19(1):67–73PubMedGoogle Scholar
  73. 73.
    Volpe NJ, Jampol LM (2018) Association of Retinal Macrovessels with Venous Malformations of the brain. JAMA Ophthalmol 136(4):380–381PubMedGoogle Scholar
  74. 74.
    Martinez-Outschoorn UE et al (2011) Anti-estrogen resistance in breast cancer is induced by the tumor microenvironment and can be overcome by inhibiting mitochondrial function in epithelial cancer cells. Cancer Biol Ther 12(10):924–938PubMedPubMedCentralGoogle Scholar
  75. 75.
    Loeffler M et al (2006) Targeting tumor-associated fibroblasts improves cancer chemotherapy by increasing intratumoral drug uptake. J Clin Invest 116(7):1955–1962PubMedPubMedCentralGoogle Scholar
  76. 76.
    Sun Y et al (2012) Treatment-induced damage to the tumor microenvironment promotes prostate cancer therapy resistance through WNT16B. Nat Med 18(9):1359–1368PubMedPubMedCentralGoogle Scholar
  77. 77.
    Mao Y et al (2013) Stromal cells in tumor microenvironment and breast cancer. Cancer Metastasis Rev 32(1–2):303–315PubMedPubMedCentralGoogle Scholar
  78. 78.
    Zeisberg EM et al (2007) Discovery of endothelial to mesenchymal transition as a source for carcinoma-associated fibroblasts. Cancer Res 67(21):10123–10128PubMedPubMedCentralGoogle Scholar
  79. 79.
    Weidner N et al (1992) Tumor angiogenesis: a new significant and independent prognostic indicator in early-stage breast carcinoma. J Natl Cancer Inst 84(24):1875–1887PubMedGoogle Scholar
  80. 80.
    Gasparini G et al (1994) Tumor microvessel density, p53 expression, tumor size, and peritumoral lymphatic vessel invasion are relevant prognostic markers in node-negative breast carcinoma. J Clin Oncol 12(3):454–466PubMedGoogle Scholar
  81. 81.
    Murri AM et al (2008) The relationship between the systemic inflammatory response, tumour proliferative activity, T-lymphocytic and macrophage infiltration, microvessel density and survival in patients with primary operable breast cancer. Br J Cancer 99(7):1013–1019PubMedPubMedCentralGoogle Scholar
  82. 82.
    Motz GT, Coukos G (2011) The parallel lives of angiogenesis and immunosuppression: cancer and other tales. Nat Rev Immunol 11(10):702–711PubMedGoogle Scholar
  83. 83.
    Lin F, Prichard J (2015) Handbook of practical immunohistochemistry: frequently asked questions. Springer, New YorkGoogle Scholar
  84. 84.
    Rekhtman N, Bishop JA (2011) Quick reference handbook for surgical pathologists. Springer, Berlin, HeidelbergGoogle Scholar
  85. 85.
    Setiadi AF et al (2010) Quantitative, architectural analysis of immune cell subsets in tumor-draining lymph nodes from breast cancer patients and healthy lymph nodes. PLoS One 5(8):e12420PubMedPubMedCentralGoogle Scholar
  86. 86.
    Levenson RM, Mansfield JR (2006) Multispectral imaging in biology and medicine: slices of life. Cytometry A 69(8):748–758Google Scholar
  87. 87.
    Tsujikawa T et al (2017) Quantitative multiplex immunohistochemistry reveals myeloid-inflamed tumor-immune complexity associated with poor prognosis. Cell Rep 19(1):203–217PubMedPubMedCentralGoogle Scholar
  88. 88.
    Gorris MAJ et al (2018) Eight-color multiplex immunohistochemistry for simultaneous detection of multiple immune checkpoint molecules within the tumor microenvironment. J Immunol 200(1):347–354PubMedGoogle Scholar
  89. 89.
    Bay H, Tuytelaars T, Van Gool L (2006) SURF: speeded up robust features. In: Leonardis A, Bischof H, Pinz A (eds) Computer vision—ECCV 2006. Springer, Berlin, Heidelberg, pp 404–417Google Scholar
  90. 90.
    Young Hwan C et al (2017) Deep learning based nucleus classification in pancreas histological images. Conf Proc IEEE Eng Med Biol Soc 2017:672–675Google Scholar
  91. 91.
    Carpenter AE et al (2006) CellProfiler: image analysis software for identifying and quantifying cell phenotypes. Genome Biol 7(10):R100PubMedPubMedCentralGoogle Scholar
  92. 92.
    Arena ET et al (2017) Quantitating the cell: turning images into numbers with ImageJ. Wiley Interdiscip Rev Dev Biol 6(2)Google Scholar
  93. 93.
    Wang A, Yan X, Wei Z (2018) ImagePy: an open-source, python-based and platform-independent software package for bioimage analysis. Bioinformatics 34(18):3238–3240PubMedGoogle Scholar
  94. 94.
    Tsujikawa T et al (2019) Robust cell detection and segmentation for image cytometry reveal Th17 cell heterogeneity. Cytometry AGoogle Scholar
  95. 95.
    Young Hwan C et al (2016) Quantitative analysis of histological tissue image based on cytological profiles and spatial statistics. Conf Proc IEEE Eng Med Biol Soc 2016:1175–1178Google Scholar
  96. 96.
    Arganda-Carreras I (2016) Introduction to Image Segmentation using ImageJ/Fiji. [Slides in PDF format] [cited 2016 November 17th]. https://imagej.net/_images/8/87/Arganda-Carreras-Segmentation-Bioimage-course-MDC-Berlin-2016.pdf
  97. 97.
    Lane RS et al (2018) IFNgamma-activated dermal lymphatic vessels inhibit cytotoxic T cells in melanoma and inflamed skin. J Exp Med 215(12):3057–3074PubMedPubMedCentralGoogle Scholar
  98. 98.
    Chang YH et al (2017) Sparse coding-based image cytometry for multiplexed Immunohistochemistry (Accepted), in the 32nd Congress of the International Society for Advancement of Cytometry (CYTO)Google Scholar
  99. 99.
    Gunderson AJ et al (2016) Bruton tyrosine kinase-dependent immune cell cross-talk drives pancreas cancer. Cancer Discov 6(3):270–285PubMedGoogle Scholar
  100. 100.
    Gopalakrishnan V et al (2018) Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359(6371):97–103PubMedGoogle Scholar
  101. 101.
    Pennock ND et al (2018) Ibuprofen supports macrophage differentiation, T cell recruitment, and tumor suppression in a model of postpartum breast cancer. J Immunother Cancer 6(1):98PubMedPubMedCentralGoogle Scholar
  102. 102.
    Goltsev Y et al (2018) Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174(4):968–981. e15PubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  • Jennifer Eng
    • 1
  • Guillaume Thibault
    • 1
  • Shiuh-Wen Luoh
    • 2
    • 3
  • Joe W. Gray
    • 1
    • 2
  • Young Hwan Chang
    • 1
    • 2
  • Koei Chin
    • 1
    • 2
    Email author
  1. 1.Department of Biomedical Engineering and OHSU Center for Spatial Systems BiomedicineOregon Health and Science UniversityPortlandUSA
  2. 2.Knight Cancer InstituteOregon Health and Science UniversityPortlandUSA
  3. 3.Veterans Administration Portland Health Care SystemPortlandUSA

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