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Microenvironment Cytometry

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Single Cell Analysis

Part of the book series: Series in BioEngineering ((SERBIOENG))

Abstract

It is becoming self-evident that biological systems—from microbes to human tissues—create local microenvironments that can foster survival or impose Darwinian selection that creates unwanted phenotypes. Understanding the dynamic nature of cancer-cell microcommunities in such situations presents a challenge from the conceptual level of cell attractors to the practical level of tracking discrete molecular events and relevant changes by cytometry. There has been a significant increase in the attractiveness of therapeutic strategies that seek to disrupt the ability of neoplastic cells to exploit their microenvironments and present drug-resistant and pro-metastatic phenotypes. Aligned with this is the demand for 3D cellular systems that can mimic aspects of the microenvironment. The chapter discusses methods of cell encapsulation, in particular the application of hollow-fiber technology. Strategies also need to take into account patterns of cellular plasticity and the dynamic heterogeneity in target molecule presentation, especially in co-culture systems. The focus here is on the cytometry of cellular communities within their microenvironments with examples of targeted therapeutic agents. A critical feature of cellular microenvironments, arising from inadequate vascularity, is reduced oxygenation levels. The chapter highlights some of the cellular responses to hypoxia, while research experience suggests that the targeting of hypoxic cells in tumor regions has therapeutic potential.

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References

  1. Jessop R (2012) Coinage of the term environment: a word without authority and carlyle’s displacement of the mechanical metaphor. Lit Compass 9:708–720

    Google Scholar 

  2. Lidstrom ME, Konopka MC (2010) The role of physiological heterogeneity in microbial population behavior. Nat Chem Biol 6:705–712. doi:10.1038/nchembio.436

    Article  Google Scholar 

  3. Ferrari BC, Winsley TJ, Bergquist PL, Van Dorst J (2012) Flow cytometry in environmental microbiology: a rapid approach for the isolation of single cells for advanced molecular biology analysis. Methods Mol Biol 881:3–26. doi:10.1007/978-1-61779-827-6_1

    Article  Google Scholar 

  4. Sgier L, Freimann R, Zupanic A, Kroll A (2016) Flow cytometry combined with viSNE for the analysis of microbial biofilms and detection of microplastics. Nat Commun 7:11587. doi:10.1038/ncomms11587

    Article  Google Scholar 

  5. LaGory EL, Giaccia AJ (2016) The ever-expanding role of HIF in tumour and stromal biology. Nat Cell Biol 18:356–365. doi:10.1038/ncb3330

    Article  Google Scholar 

  6. McKeown SR (2014) Defining normoxia, physoxia and hypoxia in tumours-implications for treatment response. Br J Radiol 87:20130676. doi:10.1259/bjr.20130676

    Article  Google Scholar 

  7. Perez-Velazquez J, Gevertz JL, Karolak A, Rejniak KA (2016) Microenvironmental niches and sanctuaries: a route to acquired resistance. Adv Exp Med Biol 936:149–164. doi:10.1007/978-3-319-42023-3_8

    Article  Google Scholar 

  8. Broxterman HJ, Georgopapadakou NH (2007) Anticancer therapeutics: a surge of new developments increasingly target tumor and stroma. Drug Resist Updat 10:182–193. doi:10.1016/j.drup.2007.07.001

    Article  Google Scholar 

  9. McMillin DW, Negri JM, Mitsiades CS (2013) The role of tumour-stromal interactions in modifying drug response: challenges and opportunities. Nat Rev Drug Discov 12:217–228. doi:10.1038/nrd3870

    Article  Google Scholar 

  10. Feig C, Gopinathan A, Neesse A, Chan DS, Cook N, Tuveson DA (2012) The pancreas cancer microenvironment. Clin Cancer Res 18:4266–4276. doi:10.1158/1078-0432.CCR-11-3114, 18/16/4266 [pii]

  11. Crespo J, Sun H, Welling TH, Tian Z, Zou W (2013) T cell anergy, exhaustion, senescence, and stemness in the tumor microenvironment. Curr Opin Immunol 25:214–221. doi:10.1016/j.coi.2012.12.003

    Article  Google Scholar 

  12. Hamada S, Masamune A, Shimosegawa T (2013) Novel therapeutic strategies targeting tumor-stromal interactions in pancreatic cancer. Front Physiol 4:331. doi:10.3389/fphys.2013.00331

    Google Scholar 

  13. Pitt JM, Marabelle A, Eggermont A, Soria JC, Kroemer G, Zitvogel L (2016) Targeting the tumor microenvironment: removing obstruction to anticancer immune responses and immunotherapy. Ann Oncol 27:1482–1492. doi:10.1093/annonc/mdw168

    Article  Google Scholar 

  14. Smyth MJ, Ngiow SF, Ribas A, Teng MW (2016) Combination cancer immunotherapies tailored to the tumour microenvironment. Nat Rev Clin Oncol 13:143–158. doi:10.1038/nrclinonc.2015.209

    Article  Google Scholar 

  15. Beavis PA, Slaney CY, Kershaw MH, Gyorki D, Neeson PJ, Darcy PK (2016) Reprogramming the tumor microenvironment to enhance adoptive cellular therapy. Semin Immunol 28:64–72. doi:10.1016/j.smim.2015.11.003

    Article  Google Scholar 

  16. Csermely P, Korcsmaros T (2013) Cancer-related networks: a help to understand, predict and change malignant transformation. Semin Cancer Biol 23:209–212. doi:10.1016/j.semcancer.2013.06.011

    Article  Google Scholar 

  17. Barker HE, Paget JT, Khan AA, Harrington KJ (2015) The tumour microenvironment after radiotherapy: mechanisms of resistance and recurrence. Nat Rev Cancer 15:409–425. doi:10.1038/nrc3958

    Article  Google Scholar 

  18. Ratajczak MZ, Suszynska M, Kucia M (2016) Does it make sense to target one tumor cell chemotactic factor or its receptor when several chemotactic axes are involved in metastasis of the same cancer? Clin Transl Med 5:28. doi:10.1186/s40169-016-0113-6

    Article  Google Scholar 

  19. Smith PJ, Khan IA, and Errington RJ (2009) Cytomics and cellular informatics—coping with asymmetry and heterogeneity in biological systems. Drug Discov Today 14:271–277. doi: 10.1016/j.drudis.2008.11.012, S1359–6446(08)00404-2 [pii]

  20. Smith PJ, Falconer RA, Errington RJ (2013) Micro-community cytometry: sensing changes in cell health and glycoconjugate expression by imaging and flow cytometry. J Microsc 251:113–122. doi:10.1111/jmi.12060

    Article  Google Scholar 

  21. Marr C, Zhou JX, Huang S (2016) Single-cell gene expression profiling and cell state dynamics: collecting data, correlating data points and connecting the dots. Curr Opin Biotechnol 39:207–214. doi:10.1016/j.copbio.2016.04.015

    Article  Google Scholar 

  22. Atkuri KR, Stevens JC, Neubert H (2015) Mass cytometry: a highly multiplexed single-cell technology for advancing drug development. Drug Metab Dispos 43:227–233. doi:10.1124/dmd.114.060798

    Article  Google Scholar 

  23. Mair F, Hartmann FJ, Mrdjen D, Tosevski V, Krieg C, Becher B (2016) The end of gating? An introduction to automated analysis of high dimensional cytometry data. Eur J Immunol 46:34–43. doi:10.1002/eji.201545774

    Article  Google Scholar 

  24. Coghlin C, Murray GI (2014) The role of gene regulatory networks in promoting cancer progression and metastasis. Future Oncol 10:735–748. doi:10.2217/fon.13.264

    Article  Google Scholar 

  25. Huang S (2011) On the intrinsic inevitability of cancer: from foetal to fatal attraction. Semin Cancer Biol 21:183–199. doi:10.1016/j.semcancer.2011.05.003

    Article  Google Scholar 

  26. Haldane JB, Waddington CH (1931) Inbreeding and linkage. Genetics 16:357–374

    Google Scholar 

  27. Waddington CH (1939) Introduction to modern genetics. Macmillan, New York

    Google Scholar 

  28. Esteller M (2009) Epigenetics in biology and medicine. In: Esteller M (ed) C. Press, UK

    Google Scholar 

  29. Creixell P, Schoof EM, Erler JT, Linding R (2012) Navigating cancer network attractors for tumor-specific therapy. Nat Biotechnol 30:842–848. doi:10.1038/nbt.2345

    Article  Google Scholar 

  30. Koulakov AA, Lazebnik Y (2012) The problem of colliding networks and its relation to cell fusion and cancer. Biophys J 103:2011–2020. doi:10.1016/j.bpj.2012.08.062

    Article  Google Scholar 

  31. Huang S, Kauffman S (2013) How to escape the cancer attractor: rationale and limitations of multi-target drugs. Semin Cancer Biol 23:270–278. doi:10.1016/j.semcancer.2013.06.003

    Article  Google Scholar 

  32. Cheng WY, Ou Yang TH, Anastassiou D (2013) Biomolecular events in cancer revealed by attractor metagenes. PLoS Comput Biol 9:e1002920. doi:10.1371/journal.pcbi.1002920

    Article  Google Scholar 

  33. Smith PJ (2016) Cytometric routes to single cell transcriptomics. Cytometry A 89:424–426. doi:10.1002/cyto.a.22869

    Article  Google Scholar 

  34. Sun L, Fang J (2016) Epigenetic regulation of epithelial-mesenchymal transition. Cell Mol Life Sci 73:4493–4515. doi:10.1007/s00018-016-2303-1

    Article  Google Scholar 

  35. Li Q, Wennborg A, Aurell E, Dekel E, Zou JZ, Xu Y, Huang S, Ernberg I (2016) Dynamics inside the cancer cell attractor reveal cell heterogeneity, limits of stability, and escape. Proc Natl Acad Sci USA 113:2672–2677. doi:10.1073/pnas.1519210113

    Article  Google Scholar 

  36. Campbell K, Casanova J (2016) A common framework for EMT and collective cell migration. Development 143:4291–4300. doi:10.1242/dev.139071

    Article  Google Scholar 

  37. Bennun SV, Hizal DB, Heffner K, Can O, Zhang H, Betenbaugh MJ (2016) Systems glycobiology: integrating glycogenomics, glycoproteomics, glycomics, and other ‘omics data sets to characterize cellular glycosylation processes’. J Mol Biol 428:3337–3352. doi:10.1016/j.jmb.2016.07.005

    Article  Google Scholar 

  38. Hirabayashi J, Arata Y, Kasai K (2001) Glycome project: concept, strategy and preliminary application to Caenorhabditis elegans. Proteomics 1:295–303. doi:10.1002/1615-9861(200102)1:2<295:aid-prot295>3.0.co;2-c

    Article  Google Scholar 

  39. Nie H, Li Y, Sun XL (2012) Recent advances in sialic acid-focused glycomics. J Proteomics 75:3098–3112. doi:10.1016/j.jprot.2012.03.050, S1874-3919(12)00201-1 [pii]

  40. Falconer RA, Errington RJ, Shnyder SD, Smith PJ, Patterson LH (2012) Polysialyltransferase: a new target in metastatic cancer. Curr Cancer Drug Targets 12:925–939

    Google Scholar 

  41. Rutishauser U (1992) NCAM and its polysialic acid moiety: a mechanism for pull/push regulation of cell interactions during development? Dev Suppl 99–104

    Google Scholar 

  42. Rutishauser U (2008) Polysialic acid in the plasticity of the developing and adult vertebrate nervous system. Nat Rev Neurosci 9:26–35. doi:10.1038/nrn2285

    Article  Google Scholar 

  43. El Maarouf A, Rutishauser U (2010) Use of PSA-NCAM in repair of the central nervous system. Adv Exp Med Biol 663:137–147. doi:10.1007/978-1-4419-1170-4_9

    Article  Google Scholar 

  44. Al-Saraireh YM, Sutherland M, Springett BR, Freiberger F, Ribeiro Morais G, Loadman PM, Errington RJ, Smith PJ, Fukuda M, Gerardy-Schahn R, Patterson LH, Shnyder SD, Falconer RA (2013) Pharmacological inhibition of polysialyltransferase ST8SiaII modulates tumour cell migration. PLoS ONE 8:e73366. doi:10.1371/journal.pone.0073366

    Article  Google Scholar 

  45. Jokilammi A, Ollikka P, Korja M, Jakobsson E, Loimaranta V, Haataja S, Hirvonen H, Finne J (2004) Construction of antibody mimics from a noncatalytic enzyme-detection of polysialic acid. J Immunol Methods 295:149–160. doi:10.1016/j.jim.2004.10.006, S0022-1759(04)00367-9 [pii]

  46. Jakobsson E, Schwarzer D, Jokilammi A, Finne J (2012) Endosialidases: versatile tools for the study of polysialic acid. Top Curr Chem. doi:10.1007/128_2012_349

    Google Scholar 

  47. Smith PJ, Furon E, Wiltshire M, Chappell S, Patterson LH, Shnyder SD, Falconer RA, Errington RJ (2013) NCAM polysialylation during adherence transitions: live cell monitoring using an antibody-mimetic EGFP-endosialidase and the viability dye DRAQ7. Cytometry A 83:659–671. doi:10.1002/cyto.a.22306

    Article  Google Scholar 

  48. Giam M, Rancati G (2015) Aneuploidy and chromosomal instability in cancer: a jackpot to chaos. Cell Div 10:3. doi:10.1186/s13008-015-0009-7

    Article  Google Scholar 

  49. Frattini A, Fabbri M, Valli R, De Paoli E, Montalbano G, Gribaldo L, Pasquali F, Maserati E (2015) High variability of genomic instability and gene expression profiling in different HeLa clones. Sci Rep 5:15377. doi:10.1038/srep15377

    Article  Google Scholar 

  50. Sharma S, Bhonde R (2015) Mesenchymal stromal cells are genetically stable under a hostile in vivo-like scenario as revealed by in vitro micronucleus test. Cytotherapy 17:1384–1395. doi:10.1016/j.jcyt.2015.07.004

    Article  Google Scholar 

  51. Raslova H, Kauffmann A, Sekkai D, Ripoche H, Larbret F, Robert T, Le Roux DT, Kroemer G, Debili N, Dessen P, Lazar V, Vainchenker W (2007) Interrelation between polyploidization and megakaryocyte differentiation: a gene profiling approach. Blood 109:3225–3234. doi:10.1182/blood-2006-07-037838

    Article  Google Scholar 

  52. Pandit SK, Westendorp B, de Bruin A (2013) Physiological significance of polyploidization in mammalian cells. Trends Cell Biol 23:556–566. doi:10.1016/j.tcb.2013.06.002

    Article  Google Scholar 

  53. Fox DT, Duronio RJ (2013) Endoreplication and polyploidy: insights into development and disease. Development 140:3–12. doi:10.1242/dev.080531

    Article  Google Scholar 

  54. Shi Q, King RW (2005) Chromosome nondisjunction yields tetraploid rather than aneuploid cells in human cell lines. Nature 437:1038–1042. doi:10.1038/nature03958

    Article  Google Scholar 

  55. Musacchio A, Salmon ED (2007) The spindle-assembly checkpoint in space and time. Nat Rev Mol Cell Biol 8:379–393. doi:10.1038/nrm2163

    Article  Google Scholar 

  56. Rello-Varona S, Vitale I, Kepp O, Senovilla L, Jemaa M, Metivier D, Castedo M, Kroemer G (2009) Preferential killing of tetraploid tumor cells by targeting the mitotic kinesin Eg5. Cell Cycle 8:1030–1035. doi:10.4161/cc.8.7.7950

    Article  Google Scholar 

  57. Filby A, Day W, Purewal S, Martinez-Martin N (2016) The analysis of cell cycle, proliferation, and asymmetric cell division by imaging flow cytometry. Methods Mol Biol 1389:71–95. doi:10.1007/978-1-4939-3302-0_5

    Article  Google Scholar 

  58. Davoli T, de Lange T (2011) The causes and consequences of polyploidy in normal development and cancer. Annu Rev Cell Dev Biol 27:585–610. doi:10.1146/annurev-cellbio-092910-154234

    Article  Google Scholar 

  59. Storchova Z, Kuffer C (2008) The consequences of tetraploidy and aneuploidy. J Cell Sci 121:3859–3866. doi:10.1242/jcs.039537

    Article  Google Scholar 

  60. Smith PJ, Marquez N, Wiltshire M, Chappell S, Njoh K, Campbell L, Khan IA, Silvestre O, Errington RJ (2007) Mitotic bypass via an occult cell cycle phase following DNA topoisomerase II inhibition in p 53 functional human tumor cells. Cell Cycle 6:2071–2081. 4585 [pii]

    Google Scholar 

  61. Mc Gee MM (2015) Targeting the mitotic catastrophe signaling pathway in cancer. Mediators Inflamm 2015:146282. doi:10.1155/2015/146282

    Article  Google Scholar 

  62. Duelli D, Lazebnik Y (2007) Cell-to-cell fusion as a link between viruses and cancer. Nat Rev Cancer 7:968–976. doi:10.1038/nrc2272

    Article  Google Scholar 

  63. Gomez-Icazbalceta G, Ruiz-Rivera MB, Lamoyi E, Huerta L (2015) FRET in the analysis of in vitro cell-cell fusion by flow cytometry. Methods Mol Biol 1313:217–227. doi:10.1007/978-1-4939-2703-6_16

    Article  Google Scholar 

  64. Mohr M, Zaenker KS, Dittmar T (2015) Fusion in cancer: an explanatory model for aneuploidy, metastasis formation, and drug resistance. Methods Mol Biol 1313:21–40. doi:10.1007/978-1-4939-2703-6_2

    Article  Google Scholar 

  65. Gong J, Traganos F, Darzynkiewicz Z (1995) Discrimination of G2 and mitotic cells by flow cytometry based on different expression of cyclins A and B1. Exp Cell Res 220:226–231. doi:10.1006/excr.1995.1310

    Article  Google Scholar 

  66. Thomas N, Kenrick M, Giesler T, Kiser G, Tinkler H, Stubbs S (2005) Characterization and gene expression profiling of a stable cell line expressing a cell cycle GFP sensor. Cell Cycle 4:191–195

    Google Scholar 

  67. Nakazawa N, Mehrotra R, Arakawa O, Yanagida M (2016) ICRF-193, an anticancer topoisomerase II inhibitor, induces arched telophase spindles that snap, leading to a ploidy increase in fission yeast. Genes Cells 21:978–993. doi:10.1111/gtc.12397

    Article  Google Scholar 

  68. Smith PJ, Blunt N, Wiltshire M, Hoy T, Teesdale-Spittle P, Craven MR, Watson JV, Amos WB, Errington RJ, Patterson LH (2000) Characteristics of a novel deep red/infrared fluorescent cell-permeant DNA probe, DRAQ5, in intact human cells analyzed by flow cytometry, confocal and multiphoton microscopy. Cytometry 40:280–291

    Google Scholar 

  69. Choi M, Shi J, Jung SH, Chen X, Cho KH (2012) Attractor landscape analysis reveals feedback loops in the p 53 network that control the cellular response to DNA damage. Sci Signal 5:ra83. doi:10.1126/scisignal.2003363

  70. Shaltiel IA, Krenning L, Bruinsma W, Medema RH (2015) The same, only different—DNA damage checkpoints and their reversal throughout the cell cycle. J Cell Sci 128:607–620. doi:10.1242/jcs.163766

    Article  Google Scholar 

  71. Meek DW (2015) Regulation of the p53 response and its relationship to cancer. Biochem J 469:325–346. doi:10.1042/bj20150517

    Article  Google Scholar 

  72. Luo K, Yuan J, Chen J, Lou Z (2009) Topoisomerase IIalpha controls the decatenation checkpoint. Nat Cell Biol 11:204–210. doi:10.1038/ncb1828

    Article  Google Scholar 

  73. Iwai M, Hara A, Andoh T, Ishida R (1997) ICRF-193, a catalytic inhibitor of DNA topoisomerase II, delays the cell cycle progression from metaphase, but not from anaphase to the G1 phase in mammalian cells. FEBS Lett 406:267–70

    Google Scholar 

  74. Smith PJ, Chin SF, Njoh K, Khan IA, Chappell MJ, Errington RJ (2008) Cell cycle checkpoint-guarded routes to catenation-induced chromosomal instability. SEB Exp Biol Ser 59:219–242

    Google Scholar 

  75. Chang LJ, Eastman A (2012) Differential regulation of p21 (waf1) protein half-life by DNA damage and Nutlin-3 in p53 wild-type tumors and its therapeutic implications. Cancer Biol Ther 13:1047–1057. doi:10.4161/cbt.21047

    Article  Google Scholar 

  76. Chang LJ, Eastman A (2012) Decreased translation of p21waf1 mRNA causes attenuated p53 signaling in some p53 wild-type tumors. Cell Cycle 11:1818–1826. doi:10.4161/cc.20208

    Article  Google Scholar 

  77. Humpton TJ, Vousden KH (2016) Regulation of cellular metabolism and hypoxia by p 53. Cold Spring Harb Perspect Med 6. doi:10.1101/cshperspect.a026146

  78. Friedrich J, Seidel C, Ebner R, Kunz-Schughart LA (2009) Spheroid-based drug screen: considerations and practical approach. Nat Protoc 4:309–324. doi:10.1038/nprot.2008.226

    Article  Google Scholar 

  79. Akagi J, Kordon M, Zhao H, Matuszek A, Dobrucki J, Errington R, Smith PJ, Takeda K, Darzynkiewicz Z, Wlodkowic D (2013) Real-time cell viability assays using a new anthracycline derivative DRAQ7(R). Cytometry A 83:227–234. doi:10.1002/cyto.a.22228

    Article  Google Scholar 

  80. Wan X, Li Z, Ye H, Cui Z (2016) Three-dimensional perfused tumour spheroid model for anti-cancer drug screening. Biotechnol Lett 38:1389–1395. doi:10.1007/s10529-016-2035-1

    Article  Google Scholar 

  81. Fong EL, Harrington DA, Farach-Carson MC, Yu H (2016) Heralding a new paradigm in 3D tumor modeling. Biomaterials 108:197–213. doi:10.1016/j.biomaterials.2016.08.052

    Article  Google Scholar 

  82. Liu L, Sun B, Pedersen JN, Aw Yong KM, Getzenberg RH, Stone HA, Austin RH (2011) Probing the invasiveness of prostate cancer cells in a 3D microfabricated landscape. Proc Natl Acad Sci USA 108:6853–6856. doi:10.1073/pnas.1102808108

    Article  Google Scholar 

  83. Costa EC, Moreira AF, de Melo-Diogo D, Gaspar VM, Carvalho MP, Correia IJ (2016) 3D tumor spheroids: an overview on the tools and techniques used for their analysis. Biotechnol Adv 34:1427–1441. doi:10.1016/j.biotechadv.2016.11.002

    Article  Google Scholar 

  84. Smith PJ, Wiltshire M, Chappell SC, Cosentino L, Burns PA, Pors K, Errington RJ (2013) Kinetic analysis of intracellular Hoechst 33342–DNA interactions by flow cytometry: misinterpretation of side population status? Cytometry A 83:161–169. doi:10.1002/cyto.a.22224

    Article  Google Scholar 

  85. Curran S, Vantangoli MM, Boekelheide K, Morgan JR (2015) Architecture of chimeric spheroids controls drug transport. Cancer Microenviron 8:101–109. doi:10.1007/s12307-015-0171-0

    Article  Google Scholar 

  86. Errington RJ, Ameer-Beg SM, Vojnovic B, Patterson LH, Zloh M, Smith PJ (2005) Advanced microscopy solutions for monitoring the kinetics and dynamics of drug-DNA targeting in living cells. Adv Drug Deliv Rev 57:153–167. doi:10.1016/j.addr.2004.05.005

    Article  Google Scholar 

  87. Evans ND, Errington RJ, Shelley M, Feeney GP, Chapman MJ, Godfrey KR, Smith PJ, Chappell MJ (2004) A mathematical model for the in vitro kinetics of the anti-cancer agent topotecan. Math Biosci 189:185–217. doi:10.1016/j.mbs.2004.01.007, S002555640400063X [pii]

  88. Cheung SY, Evans ND, Chappell MJ, Godfrey KR, Smith PJ, Errington RJ (2008) Exploration of the intercellular heterogeneity of topotecan uptake into human breast cancer cells through compartmental modelling. Math Biosci 213:119–134. doi:10.1016/j.mbs.2008.03.008, S0025-5564(08)00059-X [pii]

  89. Chappell MJ, Evans ND, Errington RJ, Khan IA, Campbell L, Ali R, Godfrey KR, and Smith PJ (2008) A coupled drug kinetics-cell cycle model to analyse the response of human cells to intervention by topotecan. Comput Methods Programs Biomed 89:169–178. doi:10.1016/S0169-2607(07)00265-9, 10.1016/j.cmpb.2007.11.002 [pii]

  90. Thanos CG, Bintz B, Emerich DF (2010) Microencapsulated choroid plexus epithelial cell transplants for repair of the brain. Adv Exp Med Biol 670:80–91

    Google Scholar 

  91. Uludag H, De Vos P, Tresco PA (2000) Technology of mammalian cell encapsulation. Adv Drug Deliv Rev 42:29–64

    Google Scholar 

  92. Workman VL, Dunnett SB, Kille P, Palmer DD (2007) Microfluidic chip-based synthesis of alginate microspheres for encapsulation of immortalized human cells. Biomicrofluidics 1(1):014105. doi:10.1063/1.2431860

  93. Lanza RP, Butler DH, Borland KM, Staruk JE, Faustman DL, Solomon BA, Muller TE, Rupp RG, Maki T, Monaco AP, Chick WL (1991) Xenotransplantation of canine, bovine, and porcine islets in diabetic rats without immunosuppression. Proc Nat Acad Sci USA 88:11100–11104

    Google Scholar 

  94. Hollingshead MG, Alley MC, Camalier RF, Abbott BJ, Mayo JG, Malspeis L, Grever MR (1995) In vivo cultivation of tumor cells in hollow fibers. Life Sci 57:131–141

    Google Scholar 

  95. Casciari JJ, Hollingshead MG, Alley MC, Mayo JG, Malspeis L, Miyauchi S, Grever MR, Weinstein JN (1994) Growth and chemotherapeutic response of cells in a hollow-fiber in-vitro solid tumor-model. J Nat Cancer Inst 86:1846–1852

    Google Scholar 

  96. Roberts I, Baila S, Rice RB, Janssens ME, Nguyen K, Moens N, Ruban L, Hernandez D, Coffey P, Mason C (2012) Scale-up of human embryonic stem cell culture using a hollow fibre bioreactor. Biotechnol Lett 34:2307–2315. doi:10.1007/s10529-012-1033-1

    Article  Google Scholar 

  97. Wung N, Acott SM, Tosh D, Ellis MJ (2014) Hollow fibre membrane bioreactors for tissue engineering applications. Biotechnol Lett 36:2357–2366. doi:10.1007/s10529-014-1619-x

    Article  Google Scholar 

  98. Bettahalli NM, Groen N, Steg H, Unadkat H, de Boer J, van Blitterswijk CA, Wessling M, Stamatialis D (2014) Development of multilayer constructs for tissue engineering. J Tissue Eng Regen Med 8:106–119. doi:10.1002/term.1504

    Article  Google Scholar 

  99. Martin Y, Vermette P (2005) Bioreactors for tissue mass culture: Design, characterization, and recent advances. Biomaterials 26:7481–7503. doi:10.1016/j.biomaterials.2005.05.057

    Article  Google Scholar 

  100. Lamers CHJ, Gratama JW, Luider-Vrieling B, Bolhuis RLH, and Bast E (1999) Large-scale production of natural cytokines during activation and expansion of human T lymphocytes in hollow fiber bioreactor cultures. J Immunother 22:299–307

    Google Scholar 

  101. Jain E, Kumar A (2008) Upstream processes in antibody production: evaluation of critical parameters. Biotechnol Adv 26:46–72. doi:10.1016/j.biotechadv.2007.09.004

    Article  Google Scholar 

  102. Kalbfuss B, Genzel Y, Wolff M, Zimmermann A, Morenweiser R, Reichl U (2007) Harvesting and concentration of human influenza A virus produced in serum-free mammalian cell culture for the production of vaccines. Biotechnol Bioeng 97:73–85. doi:10.1002/bit.21139

    Article  Google Scholar 

  103. Schmelzer E, Triolo F, Turner ME, Thompson RL, Zeilinger K, Reid LM, Gridelli B, Gerlach JC (2010) Three-dimensional perfusion bioreactor culture supports differentiation of human fetal liver cells. Tissue Eng Part A 16:2007–2016. doi:10.1089/ten.tea.2009.0569

    Article  Google Scholar 

  104. Lu HF, Lim WS, Zhang PC, Chia SM, Yu H, Mao HQ, Leong KW (2005) Galactosylated poly(vinylidene difluoride) hollow fiber bioreactor for hepatocyte culture. Tissue Eng 11:1667–1677

    Google Scholar 

  105. De Bartolo L, Salerno S, Curcio E, Piscioneri A, Rende M, Morelli S, Tasselli F, Bader A, Drioli E (2009) Human hepatocyte functions in a crossed hollow fiber membrane bioreactor. Biomaterials 30:2531–2543. doi:10.1016/j.biomaterials.2009.01.011

    Article  Google Scholar 

  106. Chen G, Palmer AF (2009) Hemoglobin-based oxygen carrier and convection enhanced oxygen transport in a hollow fiber bioreactor. Biotechnol Bioeng 102:1603–1612. doi:10.1002/bit.22200

    Article  Google Scholar 

  107. Westmuckett AD, Lupu C, Roquefeuil S, Krausz T, Kakkar VV, and Lupu F (2000) Fluid flow induces upregulation of synthesis and release of tissue factor pathway inhibitor in vitro. Arterioscler Thromb Vasc Biol 20:2474–2482

    Google Scholar 

  108. Godara P, McFarland CD, Nordon RE (2008) Design of bioreactors for mesenchymal stem cell tissue engineering. J Chem Technol Biotechnol 83:408–420

    Google Scholar 

  109. Cucullo L, Couraud PO, Weksler B, Romero IA, Hossain M, Rapp E, Janigro D (2008) Immortalized human brain endothelial cells and flow-based vascular modeling: a marriage of convenience for rational neurovascular studies. J Cereb Blood Flow Metab 28:312–328. doi:10.1038/sj.jcbfm.9600525

    Article  Google Scholar 

  110. Pearson NC, Shipley RJ, Waters SL, Oliver JM (2014) Multiphase modelling of the influence of fluid flow and chemical concentration on tissue growth in a hollow fibre membrane bioreactor. Math Med Biol 31:393–430. doi:10.1093/imammb/dqt015

    Article  MathSciNet  MATH  Google Scholar 

  111. Chapman LA, Shipley RJ, Whiteley JP, Ellis MJ, Byrne HM, Waters SL (2014) Optimising cell aggregate expansion in a perfused hollow fibre bioreactor via mathematical modelling. PLoS ONE 9:e105813. doi:10.1371/journal.pone.0105813

    Article  Google Scholar 

  112. Li M, Tilles AW, Milwid JM, Hammad M, Lee J, Yarmush ML, Parekkadan B (2012) Phenotypic and functional characterization of human bone marrow stromal cells in hollow-fibre bioreactors. J Tissue Eng Regen Med 6:369–377. doi:10.1002/term.439

    Article  Google Scholar 

  113. Lubberstedt M, Muller-Vieira U, Biemel KM, Darnell M, Hoffmann SA, Knospel F, Wonne EC, Knobeloch D, Nussler AK, Gerlach JC, Andersson TB, Zeilinger K (2015) Serum-free culture of primary human hepatocytes in a miniaturized hollow-fibre membrane bioreactor for pharmacological in vitro studies. J Tissue Eng Regen Med 9:1017–1026. doi:10.1002/term.1652

    Article  Google Scholar 

  114. Billecke N, Raschzok N, Rohn S, Morgul MH, Schwartlander R, Mogl M, Wollersheim S, Schmitt KR, Sauer IM (2012) An operational concept for long-term cinemicrography of cells in mono- and co-culture under highly controlled conditions—the SlideObserver. J Biotechnol 159:83–89. doi:10.1016/j.jbiotec.2012.01.033

    Article  Google Scholar 

  115. Decker S, Hollingshead M, Bonomi CA, Carter JP, Sausville EA (2004) The hollow fibre model in cancer drug screening: the NCI experience. Eur J Cancer 40:821–826

    Google Scholar 

  116. Suggitt M, Bibby MC (2005) 50 years of preclinical anticancer drug screening: Empirical to target-driven approaches. Clin Cancer Res 11:971–981

    Google Scholar 

  117. Johnson JI, Decker S, Zaharevitz D, Rubinstein LV, Venditti J, Schepartz S, Kalyandrug S, Christian M, Arbuck S, Hollingshead M, Sausville EA (2001) Relationships between drug activity in NCI preclinical in vitro and in vivo models and early clinical trials. Br J Cancer 84:1424–1431

    Google Scholar 

  118. Sharma SV, Haber DA, Settleman J (2010) Cell line-based platforms to evaluate the therapeutic efficacy of candidate anticancer agents. Nat Rev Cancer 10:241–253. doi:10.1038/nrc2820

    Article  Google Scholar 

  119. Zhang GJ, Chen TB, Bednar B, Connolly BM, Hargreaves R, Sur C, Williams DL (2007) Optical Imaging of tumor cells in hollow fibers: evaluation of the antitumor activities of anticancer drugs and target validation. Neoplasia 9:652–661. doi:10.1593/neo.07421

    Article  Google Scholar 

  120. Zhang GJ, Safran M, Wei WY, Sorensen E, Lassota P, Zhelev N, Neuberg DS, Shapiro G, Kaelin WG (2004) Bioluminescent imaging of Cdk2 inhibition in vivo. Nat Med 10:643–648

    Google Scholar 

  121. Pearce CJ, Lantvit DD, Shen Q, Jarjoura D, Zhang X, Oberlies NH, Kroll DJ, Wani MC, Orjala J, Soejarto DD, Farnsworth NR, de Blanco EJ, Fuchs JR, Kinghorn AD, Swanson SM (2012) Use of the hollow fiber assay for the discovery of novel anticancer agents from fungi. Methods Mol Biol 944:267–277. doi:10.1007/978-1-62703-122-6_20

    Article  Google Scholar 

  122. Mi Q, Pezzuto JM, Farnsworth NR, Wani MC, Kinghorn AD, Swanson SM (2009) Use of the in vivo hollow fiber assay in natural products anticancer drug discovery. J Nat Prod 72:573–580. doi:10.1021/np800767a

    Article  Google Scholar 

  123. Shnyder SD (2009) Use of the hollow fibre assay for studies of tumor neovasculature. Methods Mol Biol 467:331–42

    Google Scholar 

  124. Shnyder SD, Hasan J, Cooper PA, Pilarinou E, Jubb E, Jayson GC, Bibby MC (2005) Development of a modified hollow fibre assay for studying agents targeting the tumour neovasculature. Anticancer Res 25:1889–1894

    Google Scholar 

  125. Temmink OH, Prins HJ, van Gelderop E, Peters GJ (2007) The Hollow Fibre Assay as a model for in vivo pharmacodynamics of fluoropyrimidines in colon cancer cells. Br J Cancer 96:61–66

    Google Scholar 

  126. GE Healthcare (2003) G2M cell cycle phase marker assay user manual [cited 2009 December]; GE Healthcare Life Sciences (formerly Amersham Biosciences). Available from: http://www4.gelifesciences.com/aptrix/upp00919.nsf/Content/0D387E3C4C6D4FFFC1257628001D05DD/$file/25-8010-50UM.pdf

  127. Thomas N (2003) Lighting the circle of life: fluorescent sensors for covert surveillance of the cell cycle. Cell cycle 2:545–549 (Georgetown, Tex)

    Google Scholar 

  128. Griesdoorn V, Brown MR, Wiltshire M, Smith PJ, Errington RJ (2016) Tracking the cyclin B1-GFP sensor to profile the pattern of mitosis versus mitotic bypass. Methods Mol Biol 1342:279–285. doi:10.1007/978-1-4939-2957-3_17

    Article  Google Scholar 

  129. Hassan SB, de la Torre M, Nygren P, Karlsson MO, Larsson R, Jonsson E (2001) A hollow fiber model for in vitro studies of cytotoxic compounds: activity of the cyanoguanidine CHS 828. AntiCancer Drugs 12:33–42

    Google Scholar 

  130. Sadar MD, Akopian VA, Beraldi E (2002) Characterization of a new in vivo hollow fiber model for the study of progression of prostate cancer to androgen independence. Mol Cancer Ther 1:629–637

    Google Scholar 

  131. Liu D, Queva C, Ready S, Webster K, Zabludoff S (2004) Testing cell cycle regulation effect of a compound using a hollow fibre cell implant. Patent number: WO2004106924

    Google Scholar 

  132. Suggitt M, Swaine DJ, Pettit GR, Bibby MC (2004) Characterization of the hollow fiber assay for the determination of microtubule disruption in vivo. Clin Cancer Res 10:6677–6685

    Google Scholar 

  133. Bishai WR, Karakousis PC (2006) Hollow fiber technique for in vivo study of cell populations. Patent Number US2006182685

    Google Scholar 

  134. Bridges EM, Bibby MC, Burchill SA (2006) The hollow fiber assay for drug responsiveness in the Ewing’s sarcoma family of tumors. J Pediatr 149:103–111

    Google Scholar 

  135. Wang G, Wang J, Sadar MD (2008) Crosstalk between the androgen receptor and beta-catenin in castrate-resistant prostate cancer. Cancer Res 68:9918–9927. doi:10.1158/0008-5472.can-08-1718

    Article  Google Scholar 

  136. Heindryckx F, Colle I, Van Vlierberghe H (2009) Experimental mouse models for hepatocellular carcinoma research. Int J Exp Pathol 90:367–386. doi:10.1111/j.1365-2613.2009.00656.x

    Article  Google Scholar 

  137. Hollingshead MG, Bonomi CA, Borgel SD, Carter JP, Shoemaker R, Melillo G, Sausville EA (2004) A potential role for imaging technology in anticancer efficacy evaluations. Eur J Cancer 40:890–898

    Google Scholar 

  138. Zhang GJ, Chen TB, Hargreaves R, Sur C, Williams DL (2008) Bioluminescence imaging of hollow fibers in living animals: its application in monitoring molecular pathways. Nat Protoc 3:891–899. doi:10.1038/nprot.2008.52

    Article  Google Scholar 

  139. Zhang GJ, Kaelin WG (2005) Bioluminescent imaging of ubiquitin ligase activity: measuring cdk2 activity in vivo through changes in p 27 turnover. In: Ubiquitin and Protein Degradation, Pt B. 2005, Elsevier Academic Press Inc, San Diego, p 530-+

    Google Scholar 

  140. Apple SK (2016) Sentinel lymph node in breast cancer: review article from a pathologist’s point of view. J Pathol Transl Med 50:83–95. doi:10.4132/jptm.2015.11.23

    Article  Google Scholar 

  141. Simonsen TG, Gaustad JV, Rofstad EK (2010) Development of hypoxia in a preclinical model of tumor micrometastases. Int J Radiat Oncol Biol Phys 76:879–888. doi:10.1016/j.ijrobp.2009.09.045

    Article  Google Scholar 

  142. Grimes DR, Kelly C, Bloch K, Partridge M (2014) A method for estimating the oxygen consumption rate in multicellular tumour spheroids. J R Soc Interface 11:20131124. doi:10.1098/rsif.2013.1124

    Article  Google Scholar 

  143. Chouaib S, Noman MZ, Kosmatopoulos K, Curran MA (2016) Hypoxic stress: obstacles and opportunities for innovative immunotherapy of cancer. Oncogene. doi:10.1038/onc.2016.225

    Google Scholar 

  144. Rey S, Schito L, Koritzinsky M, Wouters BG (2016) Molecular targeting of hypoxia in radiotherapy. Adv Drug Deliv Rev. doi:10.1016/j.addr.2016.10.002

    Google Scholar 

  145. Ming L, Byrne NM, Camac SN, Mitchell CA, Ward C, Waugh DJ, McKeown SR, Worthington J (2013) Androgen deprivation results in time-dependent hypoxia in LNCaP prostate tumours: informed scheduling of the bioreductive drug AQ4N improves treatment response. Int J Cancer 132:1323–1332. doi:10.1002/ijc.27796

    Article  Google Scholar 

  146. Dubois LJ, Niemans R, van Kuijk SJ, Panth KM, Parvathaneni NK, Peeters SG, Zegers CM, Rekers NH, van Gisbergen MW, Biemans R, Lieuwes NG, Spiegelberg L, Yaromina A, Winum JY, Vooijs M, Lambin P (2015) New ways to image and target tumour hypoxia and its molecular responses. Radiother Oncol 116:352–357. doi:10.1016/j.radonc.2015.08.022

    Article  Google Scholar 

  147. Ljungkvist AS, Bussink J, Kaanders JH, van der Kogel AJ (2007) Dynamics of tumor hypoxia measured with bioreductive hypoxic cell markers. Radiat Res 167:127–45

    Google Scholar 

  148. Tafreshi NK, Lloyd MC, Proemsey JB, Bui MM, Kim J, Gillies RJ, Morse DL (2016) Evaluation of CAIX and CAXII expression in breast cancer at varied O2 levels: CAIX is the superior surrogate imaging biomarker of tumor hypoxia. Mol Imaging Biol 18:219–231. doi:10.1007/s11307-015-0885-x

    Article  Google Scholar 

  149. Fleming IN, Manavaki R, Blower PJ, West C, Williams KJ, Harris AL, Domarkas J, Lord S, Baldry C, Gilbert FJ (2015) Imaging tumour hypoxia with positron emission tomography. Br J Cancer 112:238–250. doi:10.1038/bjc.2014.610

    Article  Google Scholar 

  150. Tran LB, Bol A, Labar D, Jordan B, Magat J, Mignion L, Gregoire V, Gallez B (2012) Hypoxia imaging with the nitroimidazole 18F-FAZA PET tracer: a comparison with OxyLite, EPR oximetry and 19F-MRI relaxometry. Radiother Oncol 105:29–35. doi:10.1016/j.radonc.2012.04.011

    Article  Google Scholar 

  151. Andersen AP, Moreira JMA, Pedersen SF (2014) Interactions of ion transporters and channels with cancer cell metabolism and the tumour microenvironment. Philos Trans Royal Soc B Biol Sci 369:20130098. doi:10.1098/rstb.2013.0098

    Article  Google Scholar 

  152. Davies LC, Taylor PR (2015) Tissue-resident macrophages: then and now. Immunology 144:541–548. doi:10.1111/imm.12451

    Article  Google Scholar 

  153. Davies LC, Rosas M, Smith PJ, Fraser DJ, Jones SA, Taylor PR (2011) A quantifiable proliferative burst of tissue macrophages restores homeostatic macrophage populations after acute inflammation. Eur J Immunol 41:2155–2164. doi:10.1002/eji.201141817

    Article  Google Scholar 

  154. Zhang G, Liu H, Huang J, Chen S, Pan X, Huang H, Wang L (2016) TREM-1low is a novel characteristic for tumor-associated macrophages in lung cancer. Oncotarget 7:40508–40517. doi:10.18632/oncotarget.9639

    Google Scholar 

  155. Kim JW, Tchernyshyov I, Semenza GL, Dang CV (2006) HIF-1-mediated expression of pyruvate dehydrogenase kinase: a metabolic switch required for cellular adaptation to hypoxia. Cell Metab 3:177–185. doi:10.1016/j.cmet.2006.02.002

    Article  Google Scholar 

  156. Colegio OR, Chu NQ, Szabo AL, Chu T, Rhebergen AM, Jairam V, Cyrus N, Brokowski CE, Eisenbarth SC, Phillips GM, Cline GW, Phillips AJ, Medzhitov R (2014) Functional polarization of tumour-associated macrophages by tumour-derived lactic acid. Nature 513:559–563. doi:10.1038/nature13490

    Article  Google Scholar 

  157. Kao SH, Wu KJ, Lee WH (2016) Hypoxia, epithelial-mesenchymal transition, and TET-mediated epigenetic changes. J Clin Med 5. doi:10.3390/jcm5020024

  158. Kusuma S, Zhao S, Gerecht S (2012) The extracellular matrix is a novel attribute of endothelial progenitors and of hypoxic mature endothelial cells. Faseb J 26:4925–4936. doi:10.1096/fj.12-209296

    Article  Google Scholar 

  159. Ioannou M, Papamichali R, Kouvaras E, Mylonis I, Vageli D, Kerenidou T, Barbanis S, Daponte A, Simos G, Gourgoulianis K, Koukoulis GK (2009) Hypoxia inducible factor-1 alpha and vascular endothelial growth factor in biopsies of small cell lung carcinoma. Lung 187:321–329. doi:10.1007/s00408-009-9169-z

    Article  Google Scholar 

  160. Ribatti D (2016) Tumor refractoriness to anti-VEGF therapy. Oncotarget. doi:10.18632/oncotarget.8694

    Google Scholar 

  161. Gray LH, Conger AD, Ebert M, Hornsey S, Scott OC (1953) The concentration of oxygen dissolved in tissues at the time of irradiation as a factor in radiotherapy. Br J Radiol 26:638–648. doi:10.1259/0007-1285-26-312-638

    Article  Google Scholar 

  162. Thomlinson RH, Gray LH (1955) The histological structure of some human lung cancers and the possible implications for radiotherapy. Br J Cancer 9:539–49

    Google Scholar 

  163. Margolin AA (2013) Oncogenic driver mutations: Neither tissue-specific nor independent. Sci Transl Med 5:214ec200

    Google Scholar 

  164. Liang D, Miller GH, Tranmer GK (2015) Hypoxia activated prodrugs: factors influencing design and development. Curr Med Chem 22:4313–25

    Google Scholar 

  165. Phillips RM (2016) Targeting the hypoxic fraction of tumours using hypoxia-activated prodrugs. Cancer Chemother Pharmacol 77:441–457. doi:10.1007/s00280-015-2920-7

    Article  Google Scholar 

  166. Wigerup C, Pahlman S, Bexell D (2016) Therapeutic targeting of hypoxia and hypoxia-inducible factors in cancer. Pharmacol Ther 164:152–169. doi:10.1016/j.pharmthera.2016.04.009

    Article  Google Scholar 

  167. Guise CP, Mowday AM, Ashoorzadeh A, Yuan R, Lin WH, Wu DH, Smaill JB, Patterson AV, Ding K (2014) Bioreductive prodrugs as cancer therapeutics: targeting tumor hypoxia. Chin J Cancer 33:80–86. doi:10.5732/cjc.012.10285

    Article  Google Scholar 

  168. Mehibel M, Singh S, Chinje EC, Cowen RL, Stratford IJ (2009) Effects of cytokine-induced macrophages on the response of tumor cells to banoxantrone (AQ4N). Mol Cancer Ther 8:1261–1269. doi:10.1158/1535-7163.mct-08-0927

    Article  Google Scholar 

  169. Foehrenbacher A, Secomb TW, Wilson WR, Hicks KO (2013) Design of optimized hypoxia-activated prodrugs using pharmacokinetic/pharmacodynamic modeling. Front Oncol 3:314. doi:10.3389/fonc.2013.00314

    Google Scholar 

  170. Meng F, Evans JW, Bhupathi D, Banica M, Lan L, Lorente G, Duan JX, Cai X, Mowday AM, Guise CP, Maroz A, Anderson RF, Patterson AV, Stachelek GC, Glazer PM, Matteucci MD, Hart CP (2012) Molecular and cellular pharmacology of the hypoxia-activated prodrug TH-302. Mol Cancer Ther 11:740–751. doi:10.1158/1535-7163.mct-11-0634

    Article  Google Scholar 

  171. Albertella MR, Loadman PM, Jones PH, Phillips RM, Rampling R, Burnet N, Alcock C, Anthoney A, Vjaters E, Dunk CR, Harris PA, Wong A, Lalani AS, Twelves CJ (2008) Hypoxia-selective targeting by the bioreductive prodrug AQ4N in patients with solid tumors: results of a phase I study. Clin Cancer Res 14:1096–1104. doi:10.1158/1078-0432.ccr-07-4020

    Article  Google Scholar 

  172. Nesbitt H, Byrne NM, Williams N, Ming L, Worthington J, Errington RJ, Patterson LH, Smith P, McKeown SR, McKenna DJ (2016) Targeting hypoxic prostate tumours using the novel hypoxia-activated prodrug OCT1002 inhibits expression of genes associated with malignant progression. Clin Cancer Res. doi:10.1158/1078-0432.ccr-16-1361

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Acknowledgements and Declarations

The authors acknowledge the contributions of Dr. Robert Falconer (Bradford University, UK; support by Yorkshire Cancer Research) and Dr. Emeline Furon (Cardiff University, UK, EPSRC Case award; Protec’Som SAS, Valognes, France) on NCAM-polysialylation studies. The authors also acknowledge the contributions of Mrs. Marie Wiltshire (Cardiff University), Mrs. Sally Chappell (Cardiff University; Malaghan Institute of Medical Research, New Zealand), and Dr. Peter Giles (Cardiff University) for flow cytometry, imaging, and bioinformatics support, respectively. Authors PJS and RJE declare that they are non-executive directors of Biostatus Ltd, the commercial supplier of DRAQ5 & DRAQ7. PJS is a director of Oncotherics Ltd, developer of uHAP technology. VG & ORS have no conflicts to declare. We thank Oncotherics Ltd for permission to reproduce artwork in Fig. 11.

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Authors and Affiliations

  1. School of Medicine, Institute of Cancer and Genetics, College of Biomedical and Life Sciences Cardiff University, Heath Park, Cardiff, CF14 4XN, UK

    Paul J. Smith, Victoria Griesdoorn, Oscar F. Silvestre & Rachel J. Errington

  2. OncoTherics Limited, 56 Charnwood Road, Shepshed, Leicestershire, LE129NP, UK

    Paul J. Smith

  3. Department of Nanophotonics, INL—International Iberian Nanotechnology Laboratory, Avenida Mestre José Veiga s/n, 4715-330, Braga, Portugal

    Oscar F. Silvestre

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  1. Purdue University Cytometry Laboratories, Department of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA

    J. Paul Robinson

  2. Chair of Pathology and Immunology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia School of Medicine, Modena, Italy

    Andrea Cossarizza

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Smith, P.J., Griesdoorn, V., Silvestre, O.F., Errington, R.J. (2017). Microenvironment Cytometry. In: Robinson, J., Cossarizza, A. (eds) Single Cell Analysis. Series in BioEngineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-4499-1_1

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