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Luminex xMAP Assay to Quantify Cytokines in Cancer Patient Serum

  • Helena Kupcova Skalnikova
  • Katerina Vodickova Kepkova
  • Petr VodickaEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2108)

Abstract

Cytokines, chemokines, and growth factors are key mediators of cell proliferation, migration, and immune response, and in tumor microenvironment, such factors contribute to regulation of tumor growth, immune cell recruitment, angiogenesis, and metastasis. In body fluids, levels of inflammatory mediators reflect the patient immune response to the disease and may predict the effects of targeted therapies. Significant improvements in cytokine detection techniques have been made during last 10 years leading to sensitive quantification of such potent molecules present in low pg/mL levels. Among the techniques, Luminex xMAP® multiplex assays allow for simultaneous quantification of up to 100 analytes with high sensitivity, broad dynamic range of quantification, high throughput, and minimal sample requirements. In this chapter we describe a detailed protocol for the application of xMAP assays using Luminex® 200™ analyzer with xPonent® acquisition software to quantify cytokines, chemokines, and growth factors secreted to blood serum and plasma of cancer patients. We also discuss how sample preparation, instrument settings, and standard curve fitting algorithms can influence validity of obtained results. Special attention is paid to data analysis using open source R statistical environment and we provide an example dataset of cytokine levels measured in serum and corresponding R script for standard curve fitting and concentration estimates.

Key words

Luminex 200 xPonent software Multiplex xMAP assays Serum Plasma Cytokine Cancer R Bioconductor drLumi nCal 

Notes

Acknowledgements

This study was supported by Ministry of Education, Youth and Sports of the Czech Republic under National Sustainability Program I (project LO1609) and under Operational Programme Research, Development and Education (project CZ.02.1.01/0.0/0.0/16_019/0000785).

Supplementary material

471103_1_En_6_MOESM1_ESM.zip (183 kb)
Data S1 Supplementary_files (Zip 439 kb)

References

  1. 1.
    Molinaro R, Corbo C, Livingston M et al (2018) Inflammation and cancer: in medio stat nano. Curr Med Chem 25:4208–4223CrossRefGoogle Scholar
  2. 2.
    Nakamura K, Smyth MJ (2017) Targeting cancer-related inflammation in the era of immunotherapy. Immunol Cell Biol 95:325–332CrossRefGoogle Scholar
  3. 3.
    Lacina L, Plzak J, Kodet O et al (2015) Cancer microenvironment: what can we learn from the stem cell niche. Int J Mol Sci 16:24094–24110CrossRefGoogle Scholar
  4. 4.
    Dvořánková B, Szabo P, Kodet O et al (2017) Intercellular crosstalk in human malignant melanoma. Protoplasma 254:1143–1150CrossRefGoogle Scholar
  5. 5.
    Kupcova Skalnikova H, Cizkova J, Cervenka J et al (2017) Advances in proteomic techniques for cytokine analysis: focus on melanoma research. Int J Mol Sci 18:2697CrossRefGoogle Scholar
  6. 6.
    Valekova I, Skalnikova HK, Jarkovska K et al (2015) Multiplex immunoassays for quantification of cytokines, growth factors, and other proteins in stem cell communication. Methods Mol Biol 1212:39–63CrossRefGoogle Scholar
  7. 7.
    Fu Q, Zhu J, Eyk JEV (2010) Comparison of multiplex immunoassay platforms. Clin Chem 56:314–318CrossRefGoogle Scholar
  8. 8.
    Rosenberg-Hasson Y, Hansmann L, Liedtke M et al (2014) Effects of serum and plasma matrices on multiplex immunoassays. Immunol Res 58:224–233CrossRefGoogle Scholar
  9. 9.
    R Core Team (2018) R: a language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  10. 10.
    Wickham H, RStudio (2017) tidyverse: easily install and load the “tidyverse”. https://CRAN.R-project.org/package=tidyverse
  11. 11.
  12. 12.
    Sanz H, Aponte JJ, Harezlak J et al (2017) drLumi: an open-source package to manage data, calibrate, and conduct quality control of multiplex bead-based immunoassays data analysis. PLoS One 12:e0187901CrossRefGoogle Scholar
  13. 13.
    Sanz, H, Aponte JJ, Harezlak J et al (2015) drLumi: Multiplex Immunoassays Data Analysis. https://CRAN.R-project.org/package=drLumi
  14. 14.
    Fong Y, Sebestyen K, Yu X et al (2013) nCal: an R package for non-linear calibration. Bioinformatics 29:2653–2654CrossRefGoogle Scholar
  15. 15.
    Fong Y, Sebestyen K, Yu X (2018) nCal: Nonlinear Calibration. https://CRAN.R-project.org/package=nCal
  16. 16.
    RStudio Team (2018) RStudio: integrated development environment for R. RStudio, Inc., Boston, MAGoogle Scholar
  17. 17.
    Findlay JWA, Dillard RF (2007) Appropriate calibration curve fitting in ligand binding assays. AAPS J 9:E260–E267CrossRefGoogle Scholar
  18. 18.
    Fong Y, Yu X (2016) Transformation model choice in nonlinear regression analysis of fluorescence-based serial dilution assays. Stat Biopharm Res 8:1–11CrossRefGoogle Scholar
  19. 19.
    Ritz C, Streibig JC (2005) Bioassay analysis using R. J Stat Softw 12:1–22CrossRefGoogle Scholar
  20. 20.
    Chaturvedi AK, Kemp TJ, Pfeiffer RM et al (2011) Evaluation of multiplexed cytokine and inflammation marker measurements: a methodologic study. Cancer Epidemiol Biomark Prev 20:1902–1911CrossRefGoogle Scholar
  21. 21.
    Breen EJ, Tan W, Khan A (2016) The statistical value of raw fluorescence signal in Luminex xMAP based multiplex immunoassays. Sci Rep 6:26996CrossRefGoogle Scholar
  22. 22.
    Won J-H, Goldberger O, Shen-Orr SS et al (2012) Significance analysis of xMap cytokine bead arrays. Proc Natl Acad Sci U S A 109:2848–2853CrossRefGoogle Scholar
  23. 23.
    Clarke DC, Morris MK, Lauffenburger DA (2013) Normalization and statistical analysis of multiplexed bead-based immunoassay data using mixed-effects modeling. Mol Cell Proteomics 12:245–262CrossRefGoogle Scholar
  24. 24.
    Breen EJ (2017) Protein multiplexed immunoassay analysis with R. In: Greening DW, Simpson RJ (eds) Serum/plasma proteomics: methods and protocols. Springer New York, New York, NY, pp 495–537Google Scholar
  25. 25.
    Bates D, Mächler M, Bolker B et al (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 1406:67Google Scholar
  26. 26.
    Bates D, Mächler M, Bolker B, et al (2018) lme4: Linear Mixed-Effects Models using “Eigen” and S4. https://CRAN.R-project.org/package=lme4
  27. 27.
    Ringnér M (2008) What is principal component analysis? Nat Biotechnol 26:303–304CrossRefGoogle Scholar
  28. 28.
    van der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605Google Scholar
  29. 29.
    Mani A, Ravindran R, Mannepalli S et al (2015) Data mining strategies to improve multiplex microbead immunoassay tolerance in a mouse model of infectious diseases. PLoS One 10:e0116262CrossRefGoogle Scholar
  30. 30.
    Dugué B, Leppänen E, Gräsbeck R (1996) Preanalytical factors and the measurement of cytokines in human subjects. Int J Clin Lab Res 26:99–105CrossRefGoogle Scholar
  31. 31.
    Lee J-E, Kim J-W, Han B-G et al (2016) Impact of whole-blood processing conditions on plasma and serum concentrations of cytokines. Biopreserv Biobank 14:51–55CrossRefGoogle Scholar
  32. 32.
    Brøndum L, Sørensen BS, Eriksen JG et al (2016) An evaluation of multiplex bead-based analysis of cytokines and soluble proteins in archived lithium heparin plasma, EDTA plasma and serum samples. Scand J Clin Lab Invest 76:601–611CrossRefGoogle Scholar
  33. 33.
    Scholman RC, Giovannone B, Hiddingh S et al (2018) Effect of anticoagulants on 162 circulating immune related proteins in healthy subjects. Cytokine 106:114–124CrossRefGoogle Scholar
  34. 34.
    Moncunill G, Campo JJ, Dobaño C (2014) Quantification of multiple cytokines and chemokines using cytometric bead arrays. Methods Mol Biol 1172:65–86CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  • Helena Kupcova Skalnikova
    • 1
  • Katerina Vodickova Kepkova
    • 1
  • Petr Vodicka
    • 1
    Email author
  1. 1.Laboratory of Applied Proteome AnalysesInstitute of Animal Physiology and Genetics of The Czech Academy of SciencesLibechovCzech Republic

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