Advertisement

Cardiovascular Engineering and Technology

, Volume 6, Issue 3, pp 376–382 | Cite as

Towards a Novel Spatially-Resolved Hemolysis Detection Method Using a Fluorescent Indicator and Loaded Ghost Cells: Proof-of-Principle

  • Sebastian V. JansenEmail author
  • Indra Müller
  • Nicole Kiesendahl
  • Thomas Schmitz-Rode
  • Ulrich Steinseifer
Article
  • 104 Downloads

Abstract

It is of the utmost importance to reduce flow-induced hemolysis in devices such as heart-valve prostheses and blood pumps. Thus, in vitro measurements of hemolysis are performed in order to optimize their design in this regard. However, with existing measurement methods, hemolysis can only be assessed as an integrated value over the complete test-circuit. Currently, there are no spatially-resolved in vitro hemolysis measurement techniques known to the authors that would allow for a determination of the critical regions within a device. In this study, a novel spatially-resolved measurement principle is proposed. Ghost cells (i.e. erythrocytes with a lower hemoglobin concentration) were loaded with a calcium–dicitrato complex, and a fluorescent calcium indicator was suspended in the extracellular medium. Calcium and indicator are separated until the cell membrane ruptures (i.e. hemolysis occurs). In the moment of hemolysis, the two compounds bind to each other and emit a fluorescent signal that can be recorded and spatially-resolved in a setup very similar to a standard Particle Image Velocimetry measurement. A proof-of-principle experiment was performed by intentionally inducing hemolysis in a flow-model with a surfactant. The surfactant-induced hemolysis demonstrated a clear increase of the fluorescent signal compared to that of a negative reference. Furthermore, the signal was spatially restricted to the area of hemolysis. Although further challenges need to be addressed, a successful proof-of-principle for novel spatially-resolved hemolysis detection is presented. This method can contribute to better design optimization of devices with respect to flow-induced hemolysis.

Keywords

Hemolysis In vitro techniques Regional blood flow Erythrocyte ghost Flow-induced blood damage 

Notes

Acknowledgments

We want to thank I. Mager and J. Maas for their excellent support and assist during the blood trials.

Conflict of interest statement

Author S.V.Jansen, author I.Müller, author N.Kiesendahl, author T.Schmitz-Rode, and author U.Steinseifer declare that they have no conflict of interest.

Statement of human studies

No human studies were carried out by the authors for this article.

Statement of animal Studies

No animal studies were carried out by the authors for this article.

References

  1. 1.
    Apel, J., R. Paul, S. Klaus, T. Siess, and H. Reul. Assessment of hemolysis related quantities in a microaxial blood pump by computational fluid dynamics. Artif. Organs 25(5):341–347, 2001.CrossRefGoogle Scholar
  2. 2.
    Arora, D., M. Behr, and M. Pasquali. A tensor-based measure for estimating blood damage. Artif. Organs 28(11):1002–1015, 2004.CrossRefGoogle Scholar
  3. 3.
    Arvand, A., M. Hormes, and H. Reul. A validated computational fluid dynamics model to estimate hemolysis in a rotary blood pump. Artif. Organs 29(7):531–540, 2005.CrossRefGoogle Scholar
  4. 4.
    ASTM-F-1841:1997. Standard Practice for Assessment of Hemolysis in Continuous Flow Blood Pumps. ASTM International 1997; doi:  10.1520/F1841-97R13.
  5. 5.
    Bludszuweit, C. Model for a general mechanical blood damage prediction. Artif. Organs 19(7):583–589, 1995.CrossRefGoogle Scholar
  6. 6.
    Bodemann, H., and H. Passow. Factors controlling the resealing of the membrane of human erythrocyte ghosts after hypotonic hemolysis. J. Membr. Biol. 8(1):1–26, 1972.CrossRefGoogle Scholar
  7. 7.
    Dean, K. M., and A. E. Palmer. Advances in fluorescence labelling strategies for dynamic cellular imaging. Nat. Chem. Biol. 10:512–523, 2014.CrossRefGoogle Scholar
  8. 8.
    Einav, S., J. Avidor, and B. Vidne. Haemodynamics of coronary artery-saphenous vein bypass. J. Biomed. Eng. 7(4):305–309, 1985.CrossRefGoogle Scholar
  9. 9.
    Falati, S., P. Gross, G. Merrill-Skoloff, B. C. Furie, and B. Furie. Real-time in vivo imaging of platelets, tissue factor and fibrin during arterial thrombus formation in the mouse. Nat. Med. 8:1175–1181, 2002.CrossRefGoogle Scholar
  10. 10.
    Fraser, K. H., T. Zhang, M. E. Taskin, B. P. Griffith, and Z. J. Wu. A quantitative comparison of mechanical blood damage parameters in rotary ventricular assist devices; shear stress, exposure time and hemolysis index. J. Biomech. Eng. 134:081002, 2012.CrossRefGoogle Scholar
  11. 11.
    Goldsmith, H. L., and J. C. Marlow. Flow behavior of erythrocytes. II. Particle motions in concentrated suspensions of ghost cells. J. Colloid Interface Sci. 71(2):383–407, 1979.CrossRefGoogle Scholar
  12. 12.
    Hoffman, J. F., D. C. Tosteson, and R. Whittam. Retention of potassium by human erythrocyte ghosts. Nature 185:186–187, 1960.CrossRefGoogle Scholar
  13. 13.
    Juette, M. F., D. S. Terry, M. R. Wasserman, Z. Zhou, R. B. Altman, Q. Zheng, and S. C. Blanchard. The bright future of single-molecule fluorescence imaging. Curr. Opin. Chem. Biol. 20:103–111, 2014.CrossRefGoogle Scholar
  14. 14.
    Kakhniashvili, D. G., L. A. Bulla, and S. R. Goodman. The human erythrocyte proteome: analysis by ion trap mass spectrometry. Mol. Cell. Proteomics 3(5):501–509, 2004.CrossRefGoogle Scholar
  15. 15.
    Kreid, D. K., and R. J. Goldstein. Measurement of velocity profiles in simulated blood by the laser Doppler technique. ISA Symp. Flow 1(3):1377–1387, 1971.Google Scholar
  16. 16.
    Leverett, L. B., J. D. Hellums, C. P. Alfrey, and E. C. Lynch. Red blood cell damage by shear stress. Biophys. J. 12(3):257–273, 1972.CrossRefGoogle Scholar
  17. 17.
    Liepsch, D., G. Thurston, and M. Lee. Studies of fluids simulating blood-like rheological properties and applications in models of arterial branches. Biorheology 28(1–2):39–52, 1991.Google Scholar
  18. 18.
    Maruyama, O., T. Yamane, N. Tsunemoto, M. Nishida, T. Tsutsui, and T. Jikuya. A preliminary study of microcapsule suspension for hemolysis evaluation of artificial organs. Artif. Organs 23:274–279, 1999.CrossRefGoogle Scholar
  19. 19.
    Milanick, M. A., S. Ritter, and K. Meissner. Engineering erythrocytes to be erythrosensors: first steps. Blood Cells Mol. Dis. 47(2):100–106, 2011.CrossRefGoogle Scholar
  20. 20.
    Naito, K., K. Mizuguchi, and Y. Nose. The need for standardizing the index of hemolysis. Artif. Organs 18:7–10, 1994.CrossRefGoogle Scholar
  21. 21.
    Nash, G. B., and H. J. Meiselman. Red cell and ghost viscoelasticity. Biophys. J. 43:63–73, 1983.CrossRefGoogle Scholar
  22. 22.
    Parpart, A. K., and P. B. Lorenz. The osmotic resistance (fragility) of human red cells. J. Clin. Invest. 26(4):636–640, 1947.CrossRefGoogle Scholar
  23. 23.
    Paul, R., J. Apel, S. Klaus, F. Schugner, P. Schwindke, and H. Reul. Shear stress related blood damage in laminar couette flow. Artif. Organs 27(6):517–529, 2003.CrossRefGoogle Scholar
  24. 24.
    Ponder, E. On properties of the red cell ghost. J. Exp. Biol. 18(3):257–265, 1942.Google Scholar
  25. 25.
    Scott, M. D., F. A. Kuypers, P. Butikofer, R. M. Bookchin, O. E. Oritz, and B. H. Lubin. Effect of osmotic lysis and resealing on red cell structure and function. J. Lab. Clin. Med. 115:470–480, 1990.Google Scholar
  26. 26.
    Song, X., A. L. Throckmorton, H. G. Wood, J. Antaki, and D. Olsen. Quantitative evaluation of blood damage in a centrifugal VAD by computational fluid dynamics. J. Fluids Eng. 126:410–418, 2004.CrossRefGoogle Scholar
  27. 27.
    Sutera, S. P., and M. H. Mehrjardi. Deformation and fragmentation of human red blood cells in turbulent shear flow. Biopys. J. 15:1–10, 1975.CrossRefGoogle Scholar
  28. 28.
    Taskin, M. E., K. H. Fraser, T. Zhang, C. Wu, B. P. Griffith, and Z. J. Wu. Evaluation of Eulerian and Lagrangian models for hemolysis estimation. ASAIO J. 58:363–372, 2012.CrossRefGoogle Scholar
  29. 29.
    Teorell, T. Permeability properties of erythrocyte ghosts. J. Gen. Physiol. 35(5):669–701, 1952.CrossRefGoogle Scholar
  30. 30.
    Weed, R. I., P. L. LaCelle, and E. W. Merrill. Metabolic dependence of red cell deformability. J. Clin. Invest. 48:795–809, 1969.CrossRefGoogle Scholar
  31. 31.
    Yano, T., K. Sekine, A. Mitoh, Y. Mitamura, E. Okamoto, D. Kim, I. Nishimura, S. Murabayashi, and R. Yozu. An estimation method of hemolysis within an axial flow blood pump by computational fluid dynamics analysis. Artif. Organs 27(10):920–925, 2003.CrossRefGoogle Scholar

Copyright information

© Biomedical Engineering Society 2015

Authors and Affiliations

  • Sebastian V. Jansen
    • 1
    Email author
  • Indra Müller
    • 1
  • Nicole Kiesendahl
    • 1
  • Thomas Schmitz-Rode
    • 1
  • Ulrich Steinseifer
    • 1
  1. 1.Department of Cardiovascular Engineering, Institute of Applied Medical EngineeringRWTH Aachen UniversityAachenGermany

Personalised recommendations