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A droplet-based microfluidic chip as a platform for leukemia cell lysate identification using surface-enhanced Raman scattering

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Abstract

A new approach is presented for cell lysate identification which uses SERS-active silver nanoparticles and a droplet-based microfluidic chip. Eighty-nanoliter droplets are generated by injecting silver nanoparticles, KCl as aggregation agent, and cell lysate containing cell constituents, such as nucleic acids, carbohydrates, metabolites, and proteins into a continuous flow of mineral oil. This platform enables accurate mixing of small volumes inside the meandering channels of the quartz chip and allows acquisition of thousands of SERS spectra with 785 nm excitation at an integration time of 1 s. Preparation of three batches of three leukemia cell lines demonstrated the experimental reproducibility. The main advantage of a high number of reproducible spectra is to apply statistics for large sample populations with robust classification results. A support vector machine with leave-one-batch-out cross-validation classified SERS spectra with sensitivities, specificities, and accuracies better than 99% to differentiate Jurkat, THP-1, and MONO-MAC-6 leukemia cell lysates. This approach is compared with previous published reports about Raman spectroscopy for leukemia detection, and an outlook is given for transfer to single cells.

A quartz chip was designed for SERS at 785 nm excitation. Principal component analysis of SERS spectra clearly separates cell lysates using variations in band intensity ratios.

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References

  1. Kong K, Kendall C, Stone N, Notingher I. Raman spectroscopy for medical diagnostics—from in-vitro biofluid assays to in-vivo cancer detection. Adv Drug Deliver Rev. 2015;89:121–34.

    Article  CAS  Google Scholar 

  2. Krafft C, Schie IW, Meyer T, Schmitt M, Popp J. Developments in spontaneous and coherent Raman scattering microscopic imaging for biomedical applications. Chem Soc Rev. 2016;45(7):1819–49.

  3. Krafft C, Schmitt M, Schie IW, Cialla-May D, Matthaeus C, Bocklitz T, et al. Label-free molecular imaging of biological cells and tissues by linear and non-linear Raman spectroscopic approaches. Angew Chem Int Ed. 2017;56(16):4392–430. https://doi.org/10.1002/anie.201607604.

  4. Chan JW, Taylor DS, Lane SM, Zwerdling T, Tuscano J, Huser T. Nondestructive identification of individual leukemia cells by laser trapping Raman spectroscopy. Anal Chem. 2008;80(6):2180–7.

    Article  CAS  Google Scholar 

  5. Lau AY, Lee LP, Chan JW. An integrated optofluidic platform for Raman-activated cell sorting. Lab Chip. 2008;8(7):1116–20.

    Article  CAS  Google Scholar 

  6. Chan JW, Taylor DS, Thompson DL. The effect of cell fixation on the discrimination of normal and leukemia cells with laser tweezers Raman spectroscopy. Biopolymers. 2009;91(2):132–9.

    Article  CAS  Google Scholar 

  7. Vanna R, Ronchi P, Lenferink A, Tresoldi C, Morasso C, Mehn D, et al. Label-free imaging and identification of typical cells of acute myeloid leukaemia and myelodysplastic syndrome by Raman microspectroscopy. Analyst. 2015;140(4):1054–64.

    Article  CAS  Google Scholar 

  8. Happillon T, Untereiner V, Beljebbar A, Gobinet C, Daliphard S, Cornillet-Lefebvre P, et al. Diagnosis approach of chronic lymphocytic leukemia on unstained blood smears using Raman microspectroscopy and supervised classification. Analyst. 2015;140(13):4465–72.

    Article  CAS  Google Scholar 

  9. Managò S, Valente C, Mirabelli P, Circolo D, Basile F, Corda D, et al. A reliable Raman-spectroscopy-based approach for diagnosis, classification and follow-up of B-cell acute lymphoblastic leukemia. Sci Rep. 2016;6:24821.

    Article  Google Scholar 

  10. Jun BH, Noh MS, Kim J, Kim G, Kang H, Kim MS, et al. Multifunctional silver-embedded magnetic nanoparticles as SERS nanoprobes and their applications. Small. 2010;6(1):119–25.

    Article  CAS  Google Scholar 

  11. Nguyen CT, Nguyen JT, Rutledge S, Zhang J, Wang C, Walker GC. Detection of chronic lymphocytic leukemia cell surface markers using surface enhanced Raman scattering gold nanoparticles. Cancer Lett. 2010;292(1):91–7.

    Article  CAS  Google Scholar 

  12. MacLaughlin CM, Mullaithilaga N, Yang G, Ip SY, Wang C, Walker GC. Surface-enhanced Raman scattering dye-labeled Au nanoparticles for triplexed detection of leukemia and lymphoma cells and SERS flow cytometry. Langmuir. 2013;29(6):1908–19.

    Article  CAS  Google Scholar 

  13. Khetani A, Momenpour A, Alarcon EI, Anis H. Hollow core photonic crystal fiber for monitoring leukemia cells using surface enhanced Raman scattering (SERS). Biomed Opt Express. 2015;6(11):4599–609.

    Article  CAS  Google Scholar 

  14. Perozziello G, Candeloro P, De Grazia A, Esposito F, Allione M, Coluccio ML, et al. Microfluidic device for continuous single cells analysis via Raman spectroscopy enhanced by integrated plasmonic nanodimers. Opt Express. 2016;24(2):A180–90.

    Article  Google Scholar 

  15. Hassoun M, Schie IW, Tolstik T, Stanca S, Krafft C, Popp J. Surface enhanced Raman spectroscopy of cell lysates mixed with silver nanoparticles for tumor classification. Beilstein J Nanotechnol. 2017;8:1183–90.

  16. Walter A, März A, Schumacher W, Rösch P, Popp J. Towards a fast, high specific and reliable discrimination of bacteria on strain level by means of SERS in a microfluidic device. Lab Chip. 2011;11(6):1013–21.

    Article  CAS  Google Scholar 

  17. Dochow S, Beleites C, Henkel T, Mayer G, Albert J, Clement J, et al. Quartz microfluidic chip for tumour cell identification by Raman spectroscopy in combination with optical traps. Anal Bioanal Chem. 2013;405(8):2743–6. https://doi.org/10.1007/s00216-013-6726-3.

    Article  CAS  Google Scholar 

  18. Strehle KR, Cialla D, Rösch P, Henkel T, Köhler M, Popp J. A reproducible surface-enhanced Raman spectroscopy approach. Online SERS measurements in a segmented microfluidic system. Anal Chem. 2007;79(4):1542–7.

    Article  CAS  Google Scholar 

  19. Zhang X, Yin H, Cooper JM, Haswell SJ. Characterization of cellular chemical dynamics using combined microfluidic and Raman techniques. Anal Bioanal Chem. 2008;390(3):833–40.

    Article  CAS  Google Scholar 

  20. Zhang JY, Do J, Premasiri WR, Ziegler LD, Klapperich CM. Rapid point-of-care concentration of bacteria in a disposable microfluidic device using meniscus dragging effect. Lab Chip. 2010;10(23):3265–70.

    Article  CAS  Google Scholar 

  21. Leopold N, Lendl B. A new method for fast preparation of highly surface-enhanced Raman scattering (SERS) active silver colloids at room temperature by reduction of silver nitrate with hydroxylamine hydrochloride. J Phys Chem B. 2003;107(24):5723–7.

    Article  CAS  Google Scholar 

  22. März A, Ackermann KR, Malsch D, Bocklitz T, Henkel T, Popp J. Towards a quantitative SERS approach–online monitoring of analytes in a microfluidic system with isotope-edited internal standards. J Biophotonics. 2009;2(4):232–42.

    Article  Google Scholar 

  23. CoreTeam R. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2012.

    Google Scholar 

  24. Beleites C, Sergo V (2015) hyperSpec: a package to handle hyperspectral data sets in R. R package version 0.98-20150304. http://hyperSpec.r-forge.r-project.org

  25. Liland KH, Mevik B-H. Baseline: baseline correction of spectra. R Package Version. 2015;1:2–1. https://CRAN.R-project.org/package=baseline

    Google Scholar 

  26. Moskovits M. Surface-enhanced spectroscopy. Rev Mod Phys. 1985;57(3):783.

    Article  CAS  Google Scholar 

  27. Freitag I, Matthäus C, Csaki A, Clement JH, Cialla-May D, Weber K, et al. Differentiation of MCF-7 tumor cells from leukocytes and fibroblast cells using epithelial cell adhesion molecule targeted multicore surface-enhanced Raman spectroscopy labels. J Biomed Opt. 2015;20(5):055002-055002.

    Article  Google Scholar 

  28. Nima ZA, Mahmood M, Xu Y, Mustafa T, Watanabe F, Nedosekin DA, et al. Circulating tumor cell identification by functionalized silver-gold nanorods with multicolor, super-enhanced SERS and photothermal resonances. Sci Rep. 2014;4:4752.

    Article  CAS  Google Scholar 

  29. Chan JW, Taylor DS, Zwerdling T, Lane SM, Ihara K, Huser T. Micro-Raman spectroscopy detects individual neoplastic and normal hematopoietic cells. Biophys J. 2006;90(2):648–56.

    Article  CAS  Google Scholar 

  30. Freitag I, Beleites C, Dochow S, Clement J, Krafft C, Popp J. Recognition of tumor cells by immune SERS markers in a microfluidic chip at continuous flow. Analyst. 2016;141:5986–9.

    Article  CAS  Google Scholar 

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Acknowledgements

The PhD work of M. Hassoun is supported by a grant from the German Academic Exchange Service (DAAD). Funding of the research project "EXASENS" (13N13856) by the Federal Ministry of Education and Research, Germany, is acknowledged. The authors gratefully thank Dr. Izabella Jahn for introducing in microfluidic experiments.

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Authors and Affiliations

  1. Leibniz Institute of Photonic Technology, Albert-Einstein-Str. 9, 07745, Jena, Germany

    Mohamed Hassoun, Jan Rüger, Tatiana Kirchberger-Tolstik, Iwan W. Schie, Thomas Henkel, Karina Weber, Dana Cialla-May, Christoph Krafft & Jürgen Popp

  2. Institute of Physical Chemistry and Abbe Center of Photonics, Friedrich Schiller University Jena, Helmholtzweg 4, 07745, Jena, Germany

    Mohamed Hassoun, Jan Rüger, Karina Weber, Dana Cialla-May & Jürgen Popp

  3. Department of Internal Medicine IV, Division of Gastroenterology, Hepatology and Infectious Diseases, Jena University Hospital, Am Klinikum 1, 07747, Jena, Germany

    Tatiana Kirchberger-Tolstik

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  1. Mohamed Hassoun
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  2. Jan Rüger
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  3. Tatiana Kirchberger-Tolstik
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  4. Iwan W. Schie
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  5. Thomas Henkel
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  7. Dana Cialla-May
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  8. Christoph Krafft
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  9. Jürgen Popp
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Correspondence to Christoph Krafft.

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Published in the topical collection celebrating ABCs 16th Anniversary.

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Hassoun, M., Rüger, J., Kirchberger-Tolstik, T. et al. A droplet-based microfluidic chip as a platform for leukemia cell lysate identification using surface-enhanced Raman scattering. Anal Bioanal Chem 410, 999–1006 (2018). https://doi.org/10.1007/s00216-017-0609-y

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  • DOI: https://doi.org/10.1007/s00216-017-0609-y

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