Abstract
During the past decade, an abundance of scientific advancements have been made in the field of immunology. With these advancements, greater understanding of immune regulation and the role of the immune system in the tumor microenvironment has been revealed. In this chapter, classical techniques for studying the tumor microenvironment will be discussed. Recent developments in the field of immunology for the study of the tumor immune microenvironment will be outlined, and how these methodologies can be applied will be reviewed. First, cytometry-based approaches and how these techniques have greatly advanced in recent years will be touched upon, focusing on variations of flow cytometry and mass cytometry including conventional flow cytometry, spectral cytometry, and cytometry by time of flight (CyTOF). Then, imaging-based approaches to studying the tumor microenvironment, including fluorescent microscopy, multiplex immunohistochemistry, and co-detection by indexing (CODEX) will be discussed. Next, various genomic- and systems biology-based approaches for studying transcriptional and epigenetic changes within intra-tumoral immune cells including various RNA sequencing modalities and spatial genomic approaches will be reviewed. Multi-omic techniques focused in single-cell RNA sequencing and epigenetic manipulation will also be outlined, along with how machine learning can be applied to multi-omic analysis. Together, these tumor microenvironment experimental profiling techniques offer invaluable information about the immune environment. Further development in these areas will provide a more precise evaluation of the tumor microenvironment, and thus allow for the discovery of novel biomarkers and development of efficacious immunotherapies.
This is a preview of subscription content, log in via an institution to check access.
References
Anaya J-M et al (eds) (2013) Autoimmunity: From Bench to Bedside. El Rosario University Press. Bogota (Colombia)
Armingol E et al (2021) Deciphering cell-cell interactions and communication from gene expression. Nat Rev Genet 22(2):71–88. https://doi.org/10.1038/s41576-020-00292-x
Barteneva NS, Fasler-Kan E, Vorobjev IA (2012) Imaging flow cytometry: coping with heterogeneity in biological systems. J Histochem Cytochem. SAGE Publications 60(10):723–733. https://doi.org/10.1369/0022155412453052
Becht E et al (2020) Infinity flow: comprehensive single-cell protein profiling via massively parallel flow cytometry and machine learning. J Immunol 204(1 Suppl):159.2 LP-159.2. Available at: http://www.jimmunol.org/content/204/1_Supplement/159.2.abstract
Becht E et al (2021) High-throughput single-cell quantification of hundreds of proteins using conventional flow cytometry and machine learning. Sci Adv 7(39):eabg0505. https://doi.org/10.1126/sciadv.abg0505
Bentzen AK et al (2016) Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes. Nat Biotechnol 34(10):1037–1045. https://doi.org/10.1038/nbt.3662
Bodenmiller B (2016) Multiplexed epitope-based tissue imaging for discovery and healthcare applications. Cell Syst. United States 2(4):225–238. https://doi.org/10.1016/j.cels.2016.03.008
Bonilla DL, Reinin G, Chua E (2021) Full spectrum flow cytometry as a powerful technology for cancer immunotherapy research. Front Mol Biosci. Frontiers Media S.A. 7:612801. https://doi.org/10.3389/fmolb.2020.612801
Caushi JX et al (2021) Transcriptional programs of neoantigen-specific TIL in anti-PD-1-treated lung cancers. Nature 596(7870):126–132. https://doi.org/10.1038/s41586-021-03752-4
Çelik-Uzuner S et al (2017) Measurement of global DNA methylation levels by flow cytometry in mouse fibroblasts. In Vitro Cell Dev Biol Anim. Germany 53(1):1–6. https://doi.org/10.1007/s11626-016-0075-4
Chen Z et al (2017) Inference of immune cell composition on the expression profiles of mouse tissue. Sci Rep 7:40508. https://doi.org/10.1038/srep40508
Chen B et al (2018) Profiling tumor infiltrating immune cells with CIBERSORT. Methods Mol Biol (Clifton, NJ) 1711:243–24s. https://doi.org/10.1007/978-1-4939-7493-1_12
Cho C-S et al (2021) Microscopic examination of spatial transcriptome using Seq-Scope. Cell 184(13):3559–3572.e22. https://doi.org/10.1016/j.cell.2021.05.010
Clark IC et al (2021) Barcoded viral tracing of single-cell interactions in central nervous system inflammation. Science (New York, NY) 372(6540). https://doi.org/10.1126/science.abf1230
Coillard A, Segura E (2018) Visualization of RNA at the single cell level by fluorescent in situ hybridization coupled to flow cytometry. Bio-protocol 8(12):e2892–e2892. https://doi.org/10.21769/BioProtoc.2892
Crowell HL et al (2020) muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data. Nat Commun 11(1):6077. https://doi.org/10.1038/s41467-020-19894-4
Dawson CA et al (2021) Intravital microscopy of dynamic single-cell behavior in mouse mammary tissue. Nat Protoc 16(4):1907–1935. https://doi.org/10.1038/s41596-020-00473-2
Ding J et al (2020) Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38(6):737–746. https://doi.org/10.1038/s41587-020-0465-8
Dutertre C-A et al (2019) Single-cell analysis of human mononuclear phagocytes reveals subset-defining markers and identifies circulating inflammatory dendritic cells. Immunity. United States 51(3):573–589.e8. https://doi.org/10.1016/j.immuni.2019.08.008
Fan C, Kam S, Ramadori P (2021) Metabolism-associated epigenetic and immunoepigenetic reprogramming in liver cancer. Cancers 13(20). https://doi.org/10.3390/cancers13205250
Fang R et al (2021) Comprehensive analysis of single cell ATAC-seq data with SnapATAC. Nat Commun 12(1):1337. https://doi.org/10.1038/s41467-021-21583-9
Gabriel EM et al (2018) Intravital microscopy in the study of the tumor microenvironment: from bench to human application. Oncotarget. Impact Journals LLC 9(28):20165–20178. https://doi.org/10.18632/oncotarget.24957
Gadalla R et al (2019) Validation of CyTOF against flow cytometry for immunological studies and monitoring of human cancer clinical trials. Front Oncol 9:415. https://doi.org/10.3389/fonc.2019.00415
Goltsev Y et al (2018) Deep profiling of mouse splenic architecture with CODEX multiplexed imaging. Cell 174(4):968–981.e15. https://doi.org/10.1016/j.cell.2018.07.010
Goytain A, Ng T (2020) NanoString nCounter technology: high-throughput RNA validation. Methods Mol Biol (Clifton, NJ). United States 2079:125–139. https://doi.org/10.1007/978-1-4939-9904-0_10
Guerra L, Bonetti L, Brenner D (2020) Metabolic modulation of immunity: a new concept in cancer immunotherapy. Cell Rep. United States 32(1):107848. https://doi.org/10.1016/j.celrep.2020.107848
Hartmann FJ et al (2021) Single-cell metabolic profiling of human cytotoxic T cells. Nat Biotechnol 39(2):186–197. https://doi.org/10.1038/s41587-020-0651-8
Helmink BA et al (2020) B cells and tertiary lymphoid structures promote immunotherapy response. Nature. England 577(7791):549–555. https://doi.org/10.1038/s41586-019-1922-8
Henry M, Buck S, SavaÅŸan S (2018) Flow cytometry for assessment of the tumor microenvironment in pediatric Hodgkin lymphoma. Pediatr Blood Cancer 65(11):e27307. https://doi.org/10.1002/pbc.27307
Hodi FS et al (2010) Improved survival with ipilimumab in patients with metastatic melanoma. N Engl J Med. Massachusetts Medical Society 363(8):711–723. https://doi.org/10.1056/NEJMoa1003466
Jacquelot N et al (2021) Tertiary lymphoid structures and B lymphocytes in cancer prognosis and response to immunotherapies. Onco Targets Ther 10(1):1900508. https://doi.org/10.1080/2162402X.2021.1900508
Jew B et al (2020) Accurate estimation of cell composition in bulk expression through robust integration of single-cell information. Nat Commun 11(1):1971. https://doi.org/10.1038/s41467-020-15816-6
Katzenelenbogen Y et al (2020) Coupled scRNA-Seq and intracellular protein activity reveal an immunosuppressive role of TREM2 in cancer. Cell. s 182(4):872–885.e19. https://doi.org/10.1016/j.cell.2020.06.032
Keren L et al (2019) MIBI-TOF: a multiplexed imaging platform relates cellular phenotypes and tissue structure. Sci Adv 5(10):eaax5851. https://doi.org/10.1126/sciadv.aax5851
Klein S, Duda DG (2021) Machine learning for future subtyping of the tumor microenvironment of gastro-esophageal adenocarcinomas. Cancers. MDPI 13(19):4919. https://doi.org/10.3390/cancers13194919
Koh HWL et al (2019) iOmicsPASS: network-based integration of multiomics data for predictive subnetwork discovery. npj Sys Biol Appl 5(1):22. https://doi.org/10.1038/s41540-019-0099-y
Lichtman JW, Conchello J-A (2005) Fluorescence microscopy. Nat Methods 2(12):910–919. https://doi.org/10.1038/nmeth817
Liu Y et al (2020) High-spatial-resolution multi-omics sequencing via deterministic barcoding in tissue. Cell 183(6):1665–1681.e18. https://doi.org/10.1016/j.cell.2020.10.026
Lu S et al (2019) Comparison of biomarker modalities for predicting response to PD-1/PD-L1 checkpoint blockade: a systematic review and meta-analysis. JAMA Oncol 5(8):1195–1204. https://doi.org/10.1001/jamaoncol.2019.1549
Marx V (2021) Method of the year: spatially resolved transcriptomics. Nat Methods. United States 18(1):9–14. https://doi.org/10.1038/s41592-020-01033-y
Masedunskas A et al (2012) Intravital microscopy: a practical guide on imaging intracellular structures in live animals. BioArchitecture. Landes Bioscience 2(5):143–157. https://doi.org/10.4161/bioa.21758
Medaglia C et al (2017) Spatial reconstruction of immune niches by combining photoactivatable reporters and scRNA-seq. Science (New York, NY) 358(6370):1622–1626. https://doi.org/10.1126/science.aao4277
Merritt CR et al (2020) Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol. United States:586–599. https://doi.org/10.1038/s41587-020-0472-9
Moudgil A et al (2020) Self-reporting transposons enable simultaneous readout of gene expression and transcription factor binding in single cells. Cell 182(4):992–1008.e21. https://doi.org/10.1016/j.cell.2020.06.037
Newman AM et al (2015) Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12(5):453–457. https://doi.org/10.1038/nmeth.3337
Nolan JP, Condello D (2013) Spectral flow cytometry. Curr Protoc Cytom, Chapter 1, p. Unit1.27-Unit1.27. https://doi.org/10.1002/0471142956.cy0127s63
O’Connell P et al (2021) Adenoviral delivery of an immunomodulatory protein to the tumor microenvironment controls tumor growth. In: Molecular therapy – oncolytics. Elsevier. https://doi.org/10.1016/j.omto.2021.12.004
Pai JA, Satpathy AT (2021) High-throughput and single-cell T cell receptor sequencing technologies. Nat Methods. United States 18(8):881–892. https://doi.org/10.1038/s41592-021-01201-8
Park LM, Lannigan J, Jaimes MC (2020) OMIP-069: forty-color full spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cytometry A. John Wiley & Sons, Ltd 97(10):1044–1051. https://doi.org/10.1002/cyto.a.24213
Petitprez F et al (2018) Quantitative analyses of the tumor microenvironment composition and orientation in the era of precision medicine. Front Oncol 8:390. https://doi.org/10.3389/fonc.2018.00390
Pozarowski P, Darzynkiewicz Z (2004) Analysis of cell cycle by flow cytometry. Methods Mol Biol (Clifton, N.J.). United States 281:301–311. https://doi.org/10.1385/1-59259-811-0:301
Ptacek J et al (2020) Multiplexed ion beam imaging (MIBI) for characterization of the tumor microenvironment across tumor types. Lab Investig 100(8):1111–1123. https://doi.org/10.1038/s41374-020-0417-4
Quintelier K et al (2021) Analyzing high-dimensional cytometry data using FlowSOM. Nat Protoc. England 16(8):3775–3801. https://doi.org/10.1038/s41596-021-00550-0
Ranzoni AM et al (2021) Integrative single-cell RNA-Seq and ATAC-Seq analysis of human developmental hematopoiesis. Cell Stem Cell 28(3):472–487.e7. https://doi.org/10.1016/j.stem.2020.11.015
Rodosthenous T, Shahrezaei V, Evangelou M (2020) Integrating multi-OMICS data through sparse canonical correlation analysis for the prediction of complex traits: a comparison study. Bioinformatics 36(17):4616–4625. https://doi.org/10.1093/bioinformatics/btaa530
Rotem A et al (2015) High-throughput single-cell labeling (Hi-SCL) for RNA-Seq using drop-based microfluidics. PLoS One 10(5):e0116328. https://doi.org/10.1371/journal.pone.0116328
Sade-Feldman M et al (2018) Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175(4):998–1013.e20. https://doi.org/10.1016/j.cell.2018.10.038
Shakya R et al (2020) Immune contexture analysis in immuno-oncology: applications and challenges of multiplex fluorescent immunohistochemistry. Clin Transl Immunol. John Wiley and Sons Inc 9(10):e1183–e1183. https://doi.org/10.1002/cti2.1183
Shashni B et al (2018) Size-based differentiation of cancer and normal cells by a particle size analyzer assisted by a cell-recognition PC software. Biol Pharm Bull. Japan 41(4):487–503. https://doi.org/10.1248/bpb.b17-00776
Stoeckius M et al (2017) Simultaneous epitope and transcriptome measurement in single cells. Nat Methods 14(9):865–868. https://doi.org/10.1038/nmeth.4380
Sun X, Sun S, Yang S (2019) An efficient and flexible method for deconvoluting bulk RNA-Seq data with single-cell RNA-Seq data. Cell 8(10). https://doi.org/10.3390/cells8101161
Taguchi Y-H, Turki T (2021) Tensor-decomposition-based unsupervised feature extraction in single-cell multiomics data analysis. Genes 12(9). https://doi.org/10.3390/genes12091442
Tan WCC et al (2020) Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Commun (London, England). John Wiley and Sons Inc. 40(4):135–153. https://doi.org/10.1002/cac2.12023
Taube JM et al (2020) The Society for Immunotherapy of Cancer statement on best practices for multiplex immunohistochemistry (IHC) and immunofluorescence (IF) staining and validation. J Immunother Cancer. BMJ Publishing Group 8(1):e000155. https://doi.org/10.1136/jitc-2019-000155
Taylor MJ, Lukowski JK, Anderton CR (2021) Spatially resolved mass spectrometry at the single cell: recent innovations in proteomics and metabolomics. J Am Soc Mass Spectrom. American Society for Mass Spectrometry. Published by the American Chemical Society. All rights reserved 32(4):872–894. https://doi.org/10.1021/jasms.0c00439
Vaghela R et al (2021) Actually seeing what is going on – intravital microscopy in tissue engineering. Front Bioeng Biotechnol 9:93. https://doi.org/10.3389/fbioe.2021.627462
Viborg N et al (2019) T cell recognition of novel shared breast cancer antigens is frequently observed in peripheral blood of breast cancer patients. OncoImmunology. Taylor & Francis 8(12):e1663107. https://doi.org/10.1080/2162402X.2019.1663107
Wagner A et al (2021) Metabolic modeling of single Th17 cells reveals regulators of autoimmunity. Cell 184(16):4168–4185.e21. https://doi.org/10.1016/j.cell.2021.05.045
Wang X et al (2019) Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat Commun 10(1):380. https://doi.org/10.1038/s41467-018-08023-x
Xu H, Cong F, Hwang TH (2021) Machine learning and artificial intelligence driven spatial analysis of the tumor immune microenvironment in pathology slides. Eur Urol Focus. s 7(4):706–709. https://doi.org/10.1016/j.euf.2021.07.006
Yang Y, Wang Y (2021) Role of epigenetic regulation in plasticity of tumor immune microenvironment. Front Immunol 12:640369. https://doi.org/10.3389/fimmu.2021.640369
Yu Y-R et al (2020) Disturbed mitochondrial dynamics in CD8+ TILs reinforce T cell exhaustion. Nat Immunol 21(12):1540–1551. https://doi.org/10.1038/s41590-020-0793-3
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this entry
Cite this entry
Blake, M.K., O’Connell, P., Aldhamen, Y.A. (2022). Advances in Tumor Microenvironment Immune Profiling. In: Rezaei, N. (eds) Handbook of Cancer and Immunology. Springer, Cham. https://doi.org/10.1007/978-3-030-80962-1_85-1
Download citation
DOI: https://doi.org/10.1007/978-3-030-80962-1_85-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-80962-1
Online ISBN: 978-3-030-80962-1
eBook Packages: Springer Reference Biomedicine and Life SciencesReference Module Biomedical and Life Sciences