1932

Abstract

Understanding tumor immune microenvironments is critical for identifying immune modifiers of cancer progression and developing cancer immunotherapies. Recent applications of single-cell RNA sequencing (scRNA-seq) in dissecting tumor microenvironments have brought important insights into the biology of tumor-infiltrating immune cells, including their heterogeneity, dynamics, and potential roles in both disease progression and response to immune checkpoint inhibitors and other immunotherapies. This review focuses on the advances in knowledge of tumor immune microenvironments acquired from scRNA-seq studies across multiple types of human tumors, with a particular emphasis on the study of phenotypic plasticity and lineage dynamics of immune cells in the tumor environment. We also discuss several imminent questions emerging from scRNA-seq observations and their potential solutions on the horizon.

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2021-04-26
2024-03-28
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Literature Cited

  1. 1. 
    Mittal D, Gubin MM, Schreiber RD, Smyth MJ. 2014. New insights into cancer immunoediting and its three component phases—elimination, equilibrium and escape. Curr. Opin. Immunol. 27:16–25
    [Google Scholar]
  2. 2. 
    Schreiber RD, Old LJ, Smyth MJ. 2011. Cancer immunoediting: integrating immunity's roles in cancer suppression and promotion. Science 331:1565–70
    [Google Scholar]
  3. 3. 
    Littman DR. 2015. Releasing the brakes on cancer immunotherapy. Cell 162:1186–90
    [Google Scholar]
  4. 4. 
    Havel JJ, Chowell D, Chan TA. 2019. The evolving landscape of biomarkers for checkpoint inhibitor immunotherapy. Nat. Rev. Cancer 19:133–50
    [Google Scholar]
  5. 5. 
    Pardoll DM. 2012. The blockade of immune checkpoints in cancer immunotherapy. Nat. Rev. Cancer 12:252–64
    [Google Scholar]
  6. 6. 
    Doroshow DB, Sanmamed MF, Hastings K, Politi K, Rimm DL et al. 2019. Immunotherapy in non-small cell lung cancer: facts and hopes. Clin. Cancer Res. 25:4592–602
    [Google Scholar]
  7. 7. 
    Rolfo C, Caglevic C, Santarpia M, Araujo A, Giovannetti E et al. 2017. Immunotherapy in NSCLC: a promising and revolutionary weapon. Immunotherapy A Naing, J Hajjar 97–125 Cham, Switz: Springer
    [Google Scholar]
  8. 8. 
    Rosenberg SA, Restifo NP. 2015. Adoptive cell transfer as personalized immunotherapy for human cancer. Science 348:62–68
    [Google Scholar]
  9. 9. 
    Hodi FS, O'Day SJ, McDermott DF, Weber RW, Sosman JA et al. 2010. Improved survival with ipilimumab in patients with metastatic melanoma. N. Engl. J. Med. 363:711–23
    [Google Scholar]
  10. 10. 
    Nizard M, Roussel H, Diniz MO, Karaki S, Tran T et al. 2017. Induction of resident memory T cells enhances the efficacy of cancer vaccine. Nat. Commun. 8:15221
    [Google Scholar]
  11. 11. 
    Pardoll D. 2015. Cancer and the immune system: basic concepts and targets for intervention. Semin. Oncol. 42:523–38
    [Google Scholar]
  12. 12. 
    Mandal R, Samstein RM, Lee K-W, Havel JJ, Wang H et al. 2019. Genetic diversity of tumors with mismatch repair deficiency influences anti–PD-1 immunotherapy response. Science 364:6439485–91
    [Google Scholar]
  13. 13. 
    Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H et al. 2015. PD-1 blockade in tumors with mismatch-repair deficiency. N. Engl. J. Med. 372:2509–20
    [Google Scholar]
  14. 14. 
    Zhang Y, Zhang Z. 2020. The history and advances in cancer immunotherapy: understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol. Immunol. 17:8807–21
    [Google Scholar]
  15. 15. 
    Tsirigotis P, Savani BN, Nagler A. 2016. Programmed death-1 immune checkpoint blockade in the treatment of hematological malignancies. Ann. Med. 48:428–39
    [Google Scholar]
  16. 16. 
    Palucka AK, Coussens LM. 2016. The basis of oncoimmunology. Cell 164:1233–47
    [Google Scholar]
  17. 17. 
    DeNardo DG, Ruffell B. 2019. Macrophages as regulators of tumour immunity and immunotherapy. Nat. Rev. Immunol. 19:369–82
    [Google Scholar]
  18. 18. 
    Newman AM, Liu CL, Green MR, Gentles AJ, Feng W et al. 2015. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12:453–57
    [Google Scholar]
  19. 19. 
    Li B, Severson E, Pignon J-C, Zhao H, Li T et al. 2016. Comprehensive analyses of tumor immunity: implications for cancer immunotherapy. Genome Biol 17:174
    [Google Scholar]
  20. 20. 
    Tang F, Barbacioru C, Wang Y, Nordman E, Lee C et al. 2009. mRNA-Seq whole-transcriptome analysis of a single cell. Nat. Methods 6:377–82
    [Google Scholar]
  21. 21. 
    Hashimshony T, Senderovich N, Avital G, Klochendler A, de Leeuw Y et al. 2016. CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq. Genome Biol 17:77
    [Google Scholar]
  22. 22. 
    Picelli S, Faridani OR, Bjorklund AK, Winberg G, Sagasser S, Sandberg R. 2014. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9:171–81
    [Google Scholar]
  23. 23. 
    Ramskold D, Luo S, Wang Y-C, Li R, Deng Q et al. 2012. Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells. Nat. Biotechnol. 30:777–82
    [Google Scholar]
  24. 24. 
    Zheng GXY, Terry JM, Belgrader P, Ryvkin P, Bent ZW et al. 2017. Massively parallel digital transcriptional profiling of single cells. Nat. Commun. 8:14049
    [Google Scholar]
  25. 25. 
    Durante MA, Rodriguez DA, Kurtenbach S, Kuznetsov JN, Sanchez MI et al. 2020. Single-cell analysis reveals new evolutionary complexity in uveal melanoma. Nat. Commun. 11:496
    [Google Scholar]
  26. 26. 
    Gerber T, Willscher E, Loeffler-Wirth H, Hopp L, Schadendorf D et al. 2017. Mapping heterogeneity in patient-derived melanoma cultures by single-cell RNA-seq. Oncotarget 8:846–62
    [Google Scholar]
  27. 27. 
    Gide TN, Quek C, Menzies AM, Tasker AT, Shang P et al. 2019. Distinct immune cell populations define response to anti-PD-1 monotherapy and anti-PD-1/anti-CTLA-4 combined therapy. Cancer Cell 35:238–55.e6
    [Google Scholar]
  28. 28. 
    Ho Y-J, Anaparthy N, Molik D, Mathew G, Aicher T et al. 2018. Single-cell RNA-seq analysis identifies markers of resistance to targeted BRAF inhibitors in melanoma cell populations. Genome Res 28:1353–63
    [Google Scholar]
  29. 29. 
    Sade-Feldman M, Yizhak K, Bjorgaard SL, Ray JP, de Boer CG et al. 2018. Defining T cell states associated with response to checkpoint immunotherapy in melanoma. Cell 175:998–1013 e20. Erratum. 2019. Cell 176:404
    [Google Scholar]
  30. 30. 
    Tirosh I, Izar B, Prakadan SM, Wadsworth MH II, Treacy D et al. 2016. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science 352:189–96
    [Google Scholar]
  31. 31. 
    Li H, van der Leun AM, Yofe I, Lubling Y, Gelbard-Solodkin D et al. 2019. Dysfunctional CD8 T cells form a proliferative, dynamically regulated compartment within human melanoma. Cell 176:775–89 e18. Erratum. 2020. Cell 181:747
    [Google Scholar]
  32. 32. 
    Roider T, Seufert J, Uvarovskii A, Frauhammer F, Bordas M et al. 2020. Dissecting intratumour heterogeneity of nodal B-cell lymphomas at the transcriptional, genetic and drug-response levels. Nat. Cell Biol. 22:7896–906
    [Google Scholar]
  33. 33. 
    Goswami S, Walle T, Cornish AE, Basu S, Anandhan S et al. 2020. Immune profiling of human tumors identifies CD73 as a combinatorial target in glioblastoma. Nat. Med. 26:39–46
    [Google Scholar]
  34. 34. 
    Tirosh I, Venteicher AS, Hebert C, Escalante LE, Patel AP et al. 2016. Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma. Nature 539:309–13
    [Google Scholar]
  35. 35. 
    Filbin MG, Tirosh I, Hovestadt V, Shaw ML, Escalante LE et al. 2018. Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq. Science 360:331–35
    [Google Scholar]
  36. 36. 
    Yuan J, Levitin HM, Frattini V, Bush EC, Boyett DM et al. 2018. Single-cell transcriptome analysis of lineage diversity in high-grade glioma. Genome Med 10:57
    [Google Scholar]
  37. 37. 
    Pine AR, Cirigliano SM, Nicholson JG, Hu Y, Linkous A et al. 2020. Tumor microenvironment is critical for the maintenance of cellular states found in primary glioblastomas. Cancer Discov 10:964–79
    [Google Scholar]
  38. 38. 
    Zhang M, Yang H, Wan L, Wang Z, Wang H et al. 2020. Single-cell transcriptomic architecture and intercellular crosstalk of human intrahepatic cholangiocarcinoma. J. Hepatol. 73:51118–30
    [Google Scholar]
  39. 39. 
    Zhao J, Guo C, Xiong F, Yu J, Ge J et al. 2020. Single cell RNA-seq reveals the landscape of tumor and infiltrating immune cells in nasopharyngeal carcinoma. Cancer Lett 477:131–43
    [Google Scholar]
  40. 40. 
    Azizi E, Carr AJ, Plitas G, Cornish AE, Konopacki C et al. 2018. Single-cell map of diverse immune phenotypes in the breast tumor microenvironment. Cell 174:1293–1308.e36
    [Google Scholar]
  41. 41. 
    Chung W, Eum HH, Lee H-O, Lee K-M, Lee H-B et al. 2017. Single-cell RNA-seq enables comprehensive tumour and immune cell profiling in primary breast cancer. Nat. Commun. 8:15081
    [Google Scholar]
  42. 42. 
    Savas P, Virassamy B, Ye C, Salim A, Mintoff CP et al. 2018. Single-cell profiling of breast cancer T cells reveals a tissue-resident memory subset associated with improved prognosis. Nat. Med. 24:986–93
    [Google Scholar]
  43. 43. 
    Wagner J, Rapsomaniki MA, Chevrier S, Anzeneder T, Langwieder C et al. 2019. A single-cell atlas of the tumor and immune ecosystem of human breast cancer. Cell 177:1330–45.e18
    [Google Scholar]
  44. 44. 
    Davis RT, Blake K, Ma D, Gabra MBI, Hernandez GA et al. 2020. Transcriptional diversity and bioenergetic shift in human breast cancer metastasis revealed by single-cell RNA sequencing. Nat. Cell Biol. 22:310–20
    [Google Scholar]
  45. 45. 
    Puram SV, Tirosh I, Parikh AS, Patel AP, Yizhak K et al. 2017. Single-cell transcriptomic analysis of primary and metastatic tumor ecosystems in head and neck cancer. Cell 171:1611–24.e24
    [Google Scholar]
  46. 46. 
    Zhang L, Yu X, Zheng L, Zhang Y, Li Y et al. 2018. Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature 564:268–72
    [Google Scholar]
  47. 47. 
    Li H, Courtois ET, Sengupta D, Tan Y, Chen KH et al. 2017. Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors. Nat. Genet. 49:708–18
    [Google Scholar]
  48. 48. 
    Zhang L, Li Z, Skrzypczynska KM, Fang Q, Zhang W et al. 2020. Single-cell analyses inform mechanisms of myeloid-targeted therapies in colon cancer. Cell 181:442–59.e29
    [Google Scholar]
  49. 49. 
    Zhang Y, Song J, Zhao Z, Yang M, Chen M et al. 2020. Single-cell transcriptome analysis reveals tumor immune microenvironment heterogenicity and granulocytes enrichment in colorectal cancer liver metastases. Cancer Lett 470:84–94
    [Google Scholar]
  50. 50. 
    Sathe A, Grimes SM, Lau BT, Chen J, Suarez C et al. 2020. Single cell genomic characterization reveals the cellular reprogramming of the gastric tumor microenvironment. Clin. Cancer Res. 26:112640–53
    [Google Scholar]
  51. 51. 
    Zhang P, Yang M, Zhang Y, Xiao S, Lai X et al. 2019. Dissecting the single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer. Cell Rep 27:1934–47.e5
    [Google Scholar]
  52. 52. 
    Zhang M, Hu S, Min M, Ni Y, Lu Z et al. 2021. Dissecting transcriptional heterogeneity in primary gastric adenocarcinoma by single cell RNA sequencing. Gut 70:464–75
    [Google Scholar]
  53. 53. 
    Zhang Q, He Y, Luo N, Patel SJ, Han Y et al. 2019. Landscape and dynamics of single immune cells in hepatocellular carcinoma. Cell 179:829–45.e20
    [Google Scholar]
  54. 54. 
    Zheng C, Zheng L, Yoo J-K, Guo H, Zhang Y et al. 2017. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169:1342–56.e16
    [Google Scholar]
  55. 55. 
    Ho DW-H, Tsui Y-M, Sze KM-F, Chan L-K, Cheung T-T et al. 2019. Single-cell transcriptomics reveals the landscape of intra-tumoral heterogeneity and stemness-related subpopulations in liver cancer. Cancer Lett 459:176–85
    [Google Scholar]
  56. 56. 
    Zheng B, Wang D, Qiu X, Luo G, Wu T et al. 2020. Trajectory and functional analysis of PD-1high CD4+CD8+ T cells in hepatocellular carcinoma by single-cell cytometry and transcriptome sequencing. Adv. Sci. 7:132000224
    [Google Scholar]
  57. 57. 
    Young MD, Mitchell TJ, Braga FAV, Tran MGB, Stewart BJ et al. 2018. Single-cell transcriptomes from human kidneys reveal the cellular identity of renal tumors. Science 361:594–99
    [Google Scholar]
  58. 58. 
    Chevrier S, Levine JH, Zanotelli VRT, Silina K, Schulz D et al. 2017. An immune atlas of clear cell renal cell carcinoma. Cell 169:736–49.e18
    [Google Scholar]
  59. 59. 
    Enge M, Arda E, Mignardi M, Beausang J, Bottino R et al. 2017. Single-cell analysis of human pancreas reveals transcriptional signatures of aging and somatic mutation patterns. Cell 171:321–30.e14
    [Google Scholar]
  60. 60. 
    Peng J, Sun B-F, Chen C-Y, Zhou J-Y, Chen Y-S et al. 2019. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res 29:725–38
    [Google Scholar]
  61. 61. 
    Qadir MMF, Álvarez-Cubela S, Klein D, van Dijk J, Muñiz-Anquela R et al. 2020. Single-cell resolution analysis of the human pancreatic ductal progenitor cell niche. PNAS 117:10876–87
    [Google Scholar]
  62. 62. 
    Dimitrov-Markov S, Perales-Patón J, Bockorny B, Dopazo A, Muñoz M et al. 2020. Discovery of new targets to control metastasis in pancreatic cancer by single-cell transcriptomics analysis of circulating tumor cells. Mol. Cancer Ther. 19:81751–60
    [Google Scholar]
  63. 63. 
    Bernard V, Semaan A, Huang J, San Lucas FA, Mulu FC et al. 2019. Single-cell transcriptomics of pancreatic cancer precursors demonstrates epithelial and microenvironmental heterogeneity as an early event in neoplastic progression. Clin. Cancer Res. 25:2194–205
    [Google Scholar]
  64. 64. 
    Kuboki Y, Fischer CG, Guthrie VB, Huang W, Yu J et al. 2019. Single-cell sequencing defines genetic heterogeneity in pancreatic cancer precursor lesions. J. Pathol. 247:347–56
    [Google Scholar]
  65. 65. 
    Oh DY, Kwek SS, Raju SS, Li T, McCarthy E et al. 2020. Intratumoral CD4+ T cells mediate anti-tumor cytotoxicity in human bladder cancer. Cell 181:71612–25.e13
    [Google Scholar]
  66. 66. 
    Lee HW, Chung W, Lee HO, Jeong DE, Jo A et al. 2020. Single-cell RNA sequencing reveals the tumor microenvironment and facilitates strategic choices to circumvent treatment failure in a chemorefractory bladder cancer patient. Genome Med 12:47
    [Google Scholar]
  67. 67. 
    Izar B, Tirosh I, Stover EH, Wakiro I, Cuoco MS et al. 2020. A single-cell landscape of high-grade serous ovarian cancer. Nat. Med. 26:81271–79
    [Google Scholar]
  68. 68. 
    Clarke J, Panwar B, Madrigal A, Singh D, Gujar R et al. 2019. Single-cell transcriptomic analysis of tissue-resident memory T cells in human lung cancer. J. Exp. Med. 216:2128–49
    [Google Scholar]
  69. 69. 
    Guo X, Zhang Y, Zheng L, Zheng C, Song J et al. 2018. Global characterization of T cells in non-small-cell lung cancer by single-cell sequencing. Nat. Med. 24:978–85
    [Google Scholar]
  70. 70. 
    Lambrechts D, Wauters E, Boeckx B, Aibar S, Nittner D et al. 2018. Phenotype molding of stromal cells in the lung tumor microenvironment. Nat. Med. 24:1277–89
    [Google Scholar]
  71. 71. 
    Lavin Y, Kobayashi S, Leader A, Amir E-AD, Elefant N et al. 2017. Innate immune landscape in early lung adenocarcinoma by paired single-cell analyses. Cell 169:750–65
    [Google Scholar]
  72. 72. 
    Kim N, Kim HK, Lee K, Hong Y, Cho JH et al. 2020. Single-cell RNA sequencing demonstrates the molecular and cellular reprogramming of metastatic lung adenocarcinoma. Nat. Commun. 11:2285
    [Google Scholar]
  73. 73. 
    Stubbington MJT, Lonnberg T, Proserpio V, Clare S, Speak A et al. 2016. T cell fate and clonality inference from single-cell transcriptomes. Nat. Methods 13:329–32
    [Google Scholar]
  74. 74. 
    Ludwig LS, Lareau CA, Ulirsch JC, Christian E, Muus C et al. 2019. Lineage tracing in humans enabled by mitochondrial mutations and single-cell genomics. Cell 176:1325–39.e22
    [Google Scholar]
  75. 75. 
    La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H et al. 2018. RNA velocity of single cells. Nature 560:494–98
    [Google Scholar]
  76. 76. 
    Birnbaum KD. 2018. Power in numbers: single-cell RNA-seq strategies to dissect complex tissues. Annu. Rev. Genet. 52:203–21
    [Google Scholar]
  77. 77. 
    Svensson V, Natarajan KN, Ly L-H, Miragaia RJ, Labalette C et al. 2017. Power analysis of single-cell RNA-sequencing experiments. Nat. Methods 14:381–87
    [Google Scholar]
  78. 78. 
    Navin N, Kendall J, Troge J, Andrews P, Rodgers L et al. 2011. Tumour evolution inferred by single-cell sequencing. Nature 472:90–94
    [Google Scholar]
  79. 79. 
    Wang J, Fan HC, Behr B, Quake SR. 2012. Genome-wide single-cell analysis of recombination activity and de novo mutation rates in human sperm. Cell 150:402–12
    [Google Scholar]
  80. 80. 
    Fan HC, Fu GK, Fodor SPA. 2015. Combinatorial labeling of single cells for gene expression cytometry. Science 347:1258367
    [Google Scholar]
  81. 81. 
    Macosko EZ, Basu A, Satija R, Nemesh J, Shekhar K et al. 2015. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161:1202–14
    [Google Scholar]
  82. 82. 
    Stoeckius M, Hafemeister C, Stephenson W, Houck-Loomis B, Chattopadhyay PK et al. 2017. Simultaneous epitope and transcriptome measurement in single cells. Nat. Methods 14:865–68
    [Google Scholar]
  83. 83. 
    Chen C, Xing D, Tan L, Li H, Zhou G et al. 2017. Single-cell whole-genome analyses by linear amplification via transposon insertion (LIANTI). Science 356:189–94
    [Google Scholar]
  84. 84. 
    Zong C, Lu S, Chapman AR, Xie XS. 2012. Genome-wide detection of single-nucleotide and copy-number variations of a single human cell. Science 338:1622–26
    [Google Scholar]
  85. 85. 
    Huang L, Ma F, Chapman A, Lu S, Xie XS. 2015. Single-cell whole-genome amplification and sequencing: methodology and applications. Annu. Rev. Genom. Hum. Genet. 16:79–102
    [Google Scholar]
  86. 86. 
    Svensson V, Vento-Tormo R, Teichmann SA. 2018. Exponential scaling of single-cell RNA-seq in the past decade. Nat. Protoc. 13:599–604
    [Google Scholar]
  87. 87. 
    Islam S, Kjallquist U, Moliner A, Zajac P, Fan J-B et al. 2011. Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq. Genome Res 21:1160–67
    [Google Scholar]
  88. 88. 
    Islam S, Zeisel A, Joost S, La Manno G, Zajac P et al. 2014. Quantitative single-cell RNA-seq with unique molecular identifiers. Nat. Methods 11:163–66
    [Google Scholar]
  89. 89. 
    Cao J, Packer JS, Ramani V, Cusanovich DA, Huynh C et al. 2017. Comprehensive single-cell transcriptional profiling of a multicellular organism. Science 357:661–67
    [Google Scholar]
  90. 90. 
    Buenrostro JD, Wu B, Chang HY, Greenleaf WJ. 2015. ATAC-seq: a method for assaying chromatin accessibility genome-wide. Curr. Protoc. Mol. Biol. 109: 21.9 1–9
    [Google Scholar]
  91. 91. 
    Satpathy AT, Granja JM, Yost KE, Qi Y, Meschi F et al. 2019. Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol. 37:925–36
    [Google Scholar]
  92. 92. 
    Hou Y, Guo H, Cao C, Li X, Hu B et al. 2016. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res 26:304–19
    [Google Scholar]
  93. 93. 
    Ren X, Kang B, Zhang Z. 2018. Understanding tumor ecosystems by single-cell sequencing: promises and limitations. Genome Biol 19:211
    [Google Scholar]
  94. 94. 
    Efremova M, Vento-Tormo R, Park J-E, Teichmann SA, James KR. 2020. Immunology in the era of single-cell technologies. Annu. Rev. Immunol. 38:727–57
    [Google Scholar]
  95. 95. 
    Liu J, Liu X, Ren X, Li G. 2019. scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions. Bioinformatics 35:4264–71
    [Google Scholar]
  96. 96. 
    Rosenberg AB, Roco CM, Muscat RA, Kuchina A, Sample P et al. 2018. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 360:176–82
    [Google Scholar]
  97. 97. 
    Blank CU, Haining WN, Held W, Hogan PG, Kallies A et al. 2019. Defining ‘T cell exhaustion. ’. Nat. Rev. Immunol. 19:665–74
    [Google Scholar]
  98. 98. 
    Cuylen S, Blaukopf C, Politi AZ, Müller-Reichert T, Neumann B et al. 2016. Ki-67 acts as a biological surfactant to disperse mitotic chromosomes. Nature 535:308–12
    [Google Scholar]
  99. 99. 
    Thommen DS, Schumacher TN. 2018. T cell dysfunction in cancer. Cancer Cell 33:547–62
    [Google Scholar]
  100. 100. 
    Van der Leun AM, Thommen DS, Schumacher TN. 2020. CD8+ T cell states in human cancer: insights from single-cell analysis. Nat. Rev. Cancer 20:218–32
    [Google Scholar]
  101. 101. 
    Nikolich-Zugich J, Slifka MK, Messaoudi I. 2004. The many important facets of T-cell repertoire diversity. Nat. Rev. Immunol. 4:123–32
    [Google Scholar]
  102. 102. 
    He R, Hou S, Liu C, Zhang A, Bai Q et al. 2016. Follicular CXCR5-expressing CD8+ T cells curtail chronic viral infection. Nature 537:412–28 Erratum. 2016. Nature 540:470
    [Google Scholar]
  103. 103. 
    Utzschneider DT, Charmoy M, Chennupati V, Pousse L, Ferreira DP et al. 2016. T cell factor 1-expressing memory-like CD8+ T cells sustain the immune response to chronic viral infections. Immunity 45:415–27
    [Google Scholar]
  104. 104. 
    Kim JH, Park HE, Cho NY, Lee HS, Kang GH. 2016. Characterisation of PD-L1-positive subsets of microsatellite-unstable colorectal cancers. Br. J. Cancer 115:490–96
    [Google Scholar]
  105. 105. 
    Wu T, Ji Y, Moseman EA, Xu HC, Manglani M et al. 2016. The TCF1-Bcl6 axis counteracts type I interferon to repress exhaustion and maintain T cell stemness. Sci. Immunol. 1:6eaai8593
    [Google Scholar]
  106. 106. 
    Beltra J-C, Manne S, Abdel-Hakeem MS, Kurachi M, Giles JR et al. 2020. Developmental relationships of four exhausted CD8+ T cell subsets reveals underlying transcriptional and epigenetic landscape control mechanisms. Immunity 52:825–41.e8
    [Google Scholar]
  107. 107. 
    Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D et al. 2017. Systemic immunity is required for effective cancer immunotherapy. Cell 168:487–502.e15
    [Google Scholar]
  108. 108. 
    Byrne A, Savas P, Sant S, Li R, Virassamy B et al. 2020. Tissue-resident memory T cells in breast cancer control and immunotherapy responses. Nat. Rev. Clin. Oncol. 17:341–48
    [Google Scholar]
  109. 109. 
    Ganesan A-P, Clarke J, Wood O, Garrido-Martin EM, Chee SJ et al. 2017. Tissue-resident memory features are linked to the magnitude of cytotoxic T cell responses in human lung cancer. Nat. Immunol. 18:940–50
    [Google Scholar]
  110. 110. 
    Suri-Payer E, Amar AZ, Thornton AM, Shevach EM. 1998. CD4+CD25+ T cells inhibit both the induction and effector function of autoreactive T cells and represent a unique lineage of immunoregulatory cells. J. Immunol. 160:1212–18
    [Google Scholar]
  111. 111. 
    Dowling MR, Kan A, Heinzel S, Marchingo JM, Hodgkin PD, Hawkins ED. 2018. Regulatory T cells suppress effector T cell proliferation by limiting division destiny. Front. Immunol. 9:2461
    [Google Scholar]
  112. 112. 
    Chen M-L, Pittet MJ, Gorelik L, Flavell RA, Weissleder R et al. 2005. Regulatory T cells suppress tumor-specific CD8 T cell cytotoxicity through TGF-β signals in vivo. PNAS 102:419–24
    [Google Scholar]
  113. 113. 
    Josefowicz SZ, Lu L-F, Rudensky AY. 2012. Regulatory T cells: mechanisms of differentiation and function. Annu. Rev. Immunol. 30:531–64
    [Google Scholar]
  114. 114. 
    Vigano S, Alatzoglou D, Irving M, Ménétrier-Caux C, Caux C et al. 2019. Targeting adenosine in cancer immunotherapy to enhance T-cell function. Front. Immunol. 10:925
    [Google Scholar]
  115. 115. 
    Hermans D, Gautam S, García-Cañaveras JC, Gromer D, Mitra S et al. 2020. Lactate dehydrogenase inhibition synergizes with IL-21 to promote CD8+ T cell stemness and antitumor immunity. PNAS 117:6047–55
    [Google Scholar]
  116. 116. 
    Zhang L, Zhang Z. 2019. Recharacterizing tumor-infiltrating lymphocytes by single-cell RNA sequencing. Cancer Immunol. Res. 7:1040–46
    [Google Scholar]
  117. 117. 
    Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen Y-JJ et al. 2020. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature 579:274–78
    [Google Scholar]
  118. 118. 
    Liao M, Liu Y, Yuan J, Wen Y, Xu G et al. 2020. Single-cell landscape of bronchoalveolar immune cells in patients with COVID-19. Nat. Med. 26:6842–44
    [Google Scholar]
  119. 119. 
    Godfrey DI, Koay H-F, McCluskey J, Gherardin NA. 2019. The biology and functional importance of MAIT cells. Nat. Immunol. 20:1110–28
    [Google Scholar]
  120. 120. 
    Provine NM, Klenerman P. 2020. MAIT cells in health and disease. Annu. Rev. Immunol. 38:203–28
    [Google Scholar]
  121. 121. 
    Jiao S, Subudhi SK, Aparicio A, Ge Z, Guan B et al. 2019. Differences in tumor microenvironment dictate T helper lineage polarization and response to immune checkpoint therapy. Cell 179:1177–90.e13
    [Google Scholar]
  122. 122. 
    Borst J, Ahrends T, Babala N, Melief CJM, Kastenmueller W. 2018. CD4+ T cell help in cancer immunology and immunotherapy. Nat. Rev. Immunol. 18:635–47
    [Google Scholar]
  123. 123. 
    Wculek SK, Cueto FJ, Mujal AM, Melero I, Krummel MF, Sancho D 2020. Dendritic cells in cancer immunology and immunotherapy. Nat. Rev. Immunol. 20:7–24
    [Google Scholar]
  124. 124. 
    Haniffa M, Collin M, Ginhoux F 2013. Ontogeny and functional specialization of dendritic cells in human and mouse. Adv. Immunol. 120:1–49
    [Google Scholar]
  125. 125. 
    Guilliams M, Ginhoux F, Jakubzick C, Naik SH, Onai N et al. 2014. Dendritic cells, monocytes and macrophages: a unified nomenclature based on ontogeny. Nat. Rev. Immunol. 14:571–78
    [Google Scholar]
  126. 126. 
    Binnewies M, Mujal AM, Pollack JL, Combes AJ, Hardison EA et al. 2019. Unleashing type-2 dendritic cells to drive protective antitumor CD4+ T cell immunity. Cell 177:556–71.e16
    [Google Scholar]
  127. 127. 
    Ardouin L, Luche H, Chelbi R, Carpentier S, Shawket A et al. 2016. Broad and largely concordant molecular changes characterize tolerogenic and immunogenic dendritic cell maturation in thymus and periphery. Immunity 45:305–18
    [Google Scholar]
  128. 128. 
    Maier B, Leader AM, Chen ST, Tung N, Chang C et al. 2020. A conserved dendritic-cell regulatory program limits antitumour immunity. Nature 580:257–62
    [Google Scholar]
  129. 129. 
    Zilionis R, Engblom C, Pfirschke C, Savova V, Zemmour D et al. 2019. Single-cell transcriptomics of human and mouse lung cancers reveals conserved myeloid populations across individuals and species. Immunity 50:1317–34.e10
    [Google Scholar]
  130. 130. 
    Jin P, Han TH, Ren J, Saunders S, Wang E et al. 2010. Molecular signatures of maturing dendritic cells: implications for testing the quality of dendritic cell therapies. J. Transl. Med. 8:4
    [Google Scholar]
  131. 131. 
    Michea P, Noel F, Zakine E, Czerwinska U, Sirven P et al. 2018. Adjustment of dendritic cells to the breast-cancer microenvironment is subset specific. Nat. Immunol. 19:885–97
    [Google Scholar]
  132. 132. 
    Mills CD, Kincaid K, Alt JM, Heilman MJ, Hill AM. 2000. M-1/M-2 macrophages and the Th1/Th2 paradigm. J. Immunol. 164:6166–73
    [Google Scholar]
  133. 133. 
    Muller S, Kohanbash G, Liu SJ, Alvarado B, Carrera D et al. 2017. Single-cell profiling of human gliomas reveals macrophage ontogeny as a basis for regional differences in macrophage activation in the tumor microenvironment. Genome Biol 18:234
    [Google Scholar]
  134. 134. 
    Korb LC, Ahearn JM. 1997. C1q binds directly and specifically to surface blebs of apoptotic human keratinocytes: complement deficiency and systemic lupus erythematosus revisited. J. Immunol. 158:4525–28
    [Google Scholar]
  135. 135. 
    Benoit ME, Clarke EV, Morgado P, Fraser DA, Tenner AJ. 2012. Complement protein C1q directs macrophage polarization and limits inflammasome activity during the uptake of apoptotic cells. J. Immunol. 188:5682–93
    [Google Scholar]
  136. 136. 
    Jaitin DA, Adlung L, Thaiss CA, Weiner A, Li B et al. 2019. Lipid-associated macrophages control metabolic homeostasis in a Trem2-dependent manner. Cell 178:686–98.e14
    [Google Scholar]
  137. 137. 
    Wu K, Byers DE, Jin X, Agapov E, Alexander-Brett J et al. 2015. TREM-2 promotes macrophage survival and lung disease after respiratory viral infection. J. Exp. Med. 212:681–97
    [Google Scholar]
  138. 138. 
    Gu Z, Wang H, Xia J, Yang Y, Jin Z et al. 2015. Decreased ferroportin promotes myeloma cell growth and osteoclast differentiation. Cancer Res 75:2211–21
    [Google Scholar]
  139. 139. 
    Pinnix ZK, Miller LD, Wang W, D'Agostino R Jr, Kute T et al. 2010. Ferroportin and iron regulation in breast cancer progression and prognosis. Sci. Transl. Med. 2:43ra56
    [Google Scholar]
  140. 140. 
    Yuen KC, Liu LF, Gupta V, Madireddi S, Keerthivasan S et al. 2020. High systemic and tumor-associated IL-8 correlates with reduced clinical benefit of PD-L1 blockade. Nat. Med. 26:693–98
    [Google Scholar]
  141. 141. 
    Yost KE, Satpathy AT, Wells DK, Qi Y, Wang C et al. 2019. Clonal replacement of tumor-specific T cells following PD-1 blockade. Nat. Med. 25:1251–59
    [Google Scholar]
  142. 142. 
    Brummelman J, Mazza EMC, Alvisi G, Colombo FS, Grilli A et al. 2018. High-dimensional single cell analysis identifies stem-like cytotoxic CD8+ T cells infiltrating human tumors. J. Exp. Med. 215:2520–35
    [Google Scholar]
  143. 143. 
    Cabrita R, Lauss M, Sanna A, Donia M, Larsen MS et al. 2020. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 577:561–65 Erratum. 2020. Nature 580:E1
    [Google Scholar]
  144. 144. 
    Kurtulus S, Madi A, Escobar G, Klapholz M, Nyman J et al. 2019. Checkpoint blockade immunotherapy induces dynamic changes in PD-1CD8+ tumor-infiltrating T cells. Immunity 50:181–94.e6
    [Google Scholar]
  145. 145. 
    Siddiqui I, Schaeuble K, Chennupati V, Marraco SAF, Calderon-Copete S et al. 2019. Intratumoral Tcf1+PD-1+CD8+ T cells with stem-like properties promote tumor control in response to vaccination and checkpoint blockade immunotherapy. Immunity 50:195–211.e10
    [Google Scholar]
  146. 146. 
    Chen Z, Ji Z, Ngiow SF, Manne S, Cai Z et al. 2019. TCF-1-centered transcriptional network drives an effector versus exhausted CD8 T cell-fate decision. Immunity 51:840–55.e5
    [Google Scholar]
  147. 147. 
    Zhang F, Bai H, Gao R, Fei K, Duan J et al. 2020. Dynamics of peripheral T cell clones during PD-1 blockade in non-small cell lung cancer. Cancer Immunol. Immunother. 69:122599–611
    [Google Scholar]
  148. 148. 
    Griffiths JI, Wallet P, Pflieger LT, Stenehjem D, Liu X et al. 2020. Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy. PNAS 117:2716072–82
    [Google Scholar]
  149. 149. 
    Fairfax BP, Taylor CA, Watson RA, Nassiri I, Danielli S et al. 2020. Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma. Nat. Med. 26:193–99
    [Google Scholar]
  150. 150. 
    Sledzinska A, de Mucha MV, Bergerhoff K, Hotblack A, Demane DF et al. 2020. Regulatory T cells restrain interleukin-2-and Blimp-1-dependent acquisition of cytotoxic function by CD4+ T cells. Immunity 52:151–66.e6
    [Google Scholar]
  151. 151. 
    Fazal FM, Chang HY. 2019. Subcellular spatial transcriptomes: emerging frontier for understanding gene regulation. Cold Spring Harb. Symp. Quant. Biol. 84:31–45
    [Google Scholar]
  152. 152. 
    Lein E, Borm LE, Linnarsson S. 2017. The promise of spatial transcriptomics for neuroscience in the era of molecular cell typing. Science 358:64–69
    [Google Scholar]
  153. 153. 
    Gerner MY, Kastenmuller W, Ifrim I, Kabat J, Germain RN. 2012. Histo-cytometry: a method for highly multiplex quantitative tissue imaging analysis applied to dendritic cell subset microanatomy in lymph nodes. Immunity 37:364–76
    [Google Scholar]
  154. 154. 
    Boisset J-C, Vivié J, Grün D, Muraro MJ, Lyubimova A, van Oudenaarden A. 2018. Mapping the physical network of cellular interactions. Nat. Methods 15:547–53
    [Google Scholar]
  155. 155. 
    Halpern KB, Shenhav R, Massalha H, Toth B, Egozi A et al. 2018. Paired-cell sequencing enables spatial gene expression mapping of liver endothelial cells. Nat. Biotechnol. 36:962–70
    [Google Scholar]
  156. 156. 
    Giladi A, Cohen M, Medaglia C, Baran Y, Li B et al. 2020. Dissecting cellular crosstalk by sequencing physically interacting cells. Nat. Biotechnol. 38:629–37
    [Google Scholar]
  157. 157. 
    Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R. 2020. CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes. Nat. Protoc. 15:1484–506
    [Google Scholar]
  158. 158. 
    Browaeys R, Saelens W, Saeys Y. 2020. NicheNet: modeling intercellular communication by linking ligands to target genes. Nat. Methods 17:159–62
    [Google Scholar]
  159. 159. 
    Ren X, Zhong G, Zhang Q, Zhang L, Sun Y, Zhang Z. 2020. Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly. Cell Res 30:9763–78
    [Google Scholar]
  160. 160. 
    Saviano A, Henderson NC, Baumert TF. 2020. Single-cell genomics and spatial transcriptomics: discovery of novel cell states and cellular interactions in liver physiology and disease biology. J. Hepatol. 73:51219–30
    [Google Scholar]
  161. 161. 
    Moncada R, Barkley D, Wagner F, Chiodin M, Devlin JC et al. 2020. Integrating microarray-based spatial transcriptomics and single-cell RNA-seq reveals tissue architecture in pancreatic ductal adenocarcinomas. Nat. Biotechnol. 38:333–42
    [Google Scholar]
  162. 162. 
    Ji AL, Rubin AJ, Thrane K, Jiang S, Reynolds DL et al. 2020. Multimodal analysis of composition and spatial architecture in human squamous cell carcinoma. Cell 182:2497–514.e22
    [Google Scholar]
  163. 163. 
    Slagter M, Rozeman EA, Ding H, Versluis JM, Valenti M et al. 2020. Spatial proximity of CD8 T cells to tumor cells as an independent biomarker for response to anti-PD-1 therapy. J. Clin. Oncol. 38:15 Suppl10038
    [Google Scholar]
  164. 164. 
    Helmink BA, Reddy SM, Gao J, Zhang S, Basar R et al. 2020. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 577:549–55
    [Google Scholar]
  165. 165. 
    Sautes-Fridman C, Petitprez F, Calderaro J, Fridman WH. 2019. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat. Rev. Cancer 19:307–25
    [Google Scholar]
  166. 166. 
    Dieu-Nosjean M-C, Giraldo NA, Kaplon H, Germain C, Fridman WH, Sautes-Fridman C. 2016. Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol. Rev. 271:260–75
    [Google Scholar]
  167. 167. 
    Feichtenbeiner A, Haas M, Buettner M, Grabenbauer GG, Fietkau R, Distel LV. 2014. Critical role of spatial interaction between CD8+ and Foxp3+ cells in human gastric cancer: the distance matters. Cancer Immunol. Immunother. 63:111–19
    [Google Scholar]
  168. 168. 
    Cader FZ, Schackmann RCJ, Hu X, Wienand K, Redd R et al. 2018. Mass cytometry of Hodgkin lymphoma reveals a CD4+ regulatory T-cell-rich and exhausted T-effector microenvironment. Blood 132:825–36
    [Google Scholar]
  169. 169. 
    Oliveira SH, Lira S, Martinez AC, Wiekowski M, Sullivan L, Lukacs NW. 2002. Increased responsiveness of murine eosinophils to MIP-1β (CCL4) and TCA-3 (CCL1) is mediated by their specific receptors, CCR5 and CCR8. J. Leukoc. Biol. 71:1019–25
    [Google Scholar]
  170. 170. 
    Maurice NJ, McElrath MJ, Andersen-Nissen E, Frahm N, Prlic M. 2019. CXCR3 enables recruitment and site-specific bystander activation of memory CD8+ T cells. Nat. Commun. 10:4987
    [Google Scholar]
  171. 171. 
    Venteicher AS, Tirosh I, Hebert C, Yizhak K, Neftel C et al. 2017. Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq. Science 355:eaai8478
    [Google Scholar]
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