Abstract
We estimated the dynamic cell metabolic activity and the distribution of the pH value and oxygen concentration in tissue samples cultured in vitro by using real-time sensor records and a numerical simulation of the underlying reaction–diffusion processes. As an experimental tissue model, we used chicken spleen slices. A finite element method model representing the biochemical processes and including the relevant sensor data was set up. By fitting the calculated results to the measured data, we derived the spatiotemporal values of the pH value, the oxygen concentration and the absolute metabolic activity (extracellular acidification and oxygen uptake rate) of the samples. Notably, the location of the samples in relation to the sensors has a great influence on the detectable metabolic rates. The long-term vitality of the tissue samples strongly depends on their size. We further discuss the benefits and limitations of the model.
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References
Alemany-Ribes M, Semino CE (2014) Bioengineering 3D environments for cancer models. Adv Drug Deliv Rev 79–80(0):40–49. doi:10.1016/j.addr.2014.06.004
Arboleda G, Waters C, Gibson RM (2005) Metabolic activity: a novel indicator of neuronal survival in the murine dopaminergic cell line CAD Journal of molecular neuroscience. J Mol Neurosci 27(1):065–078. doi:10.1385/JMN:27:1:065
Bironaite D, Gera L, Stewart JM (2004) Characterization of the B2 receptor and activity of bradykinin analogs in SHP-77 cell line by Cytosensor microphysiometer. Chemico-biol interact 150(3):283–293. doi:10.1016/j.cbi.2004.09.021
Cairns RA, Harris IS, Mak TW (2011) Regulation of cancer cell metabolism. Nat Rev Cancer 11(2):85–95. doi:10.1038/nrc2981
Chitcholtan K, Asselin E, Parent S, Sykes PH, Evans JJ (2013) Differences in growth properties of endometrial cancer in three dimensional (3D) culture and 2D cell monolayer. Exp Cell Res 319(1):75–87. doi:10.1016/j.yexcr.2012.09.012
Cussler E (1997) Diffusion: mass transfer in fluid systems. Cambridge University Press, Cambridge
Diczfalusy E, Zsigmond P, Dizdar N, Kullman A, Loyd D, Wårdell K (2011) A model for simulation and patient-specific visualization of the tissue volume of influence during brain microdialysis. Med and Biol Eng and Comput 49(12):1459–1469. doi:10.1007/s11517-011-0841-0
Donini A (2005) Analysis of Na+, Cl-, K+, H+ and NH4+ concentration gradients adjacent to the surface of anal papillae of the mosquito Aedes aegypti: application of self-referencing ion-selective microelectrodes. J Exp Biol 208(4):603–610. doi:10.1242/jeb.01422
Evans NTS, Naylor PFD, Quinton TH (1981) The diffusion coefficient of oxygen in respiring kidney and tumour tissue. Respir physiol 43(3):179–188. doi:10.1016/0034-5687(81)90100-6
Gardiner BS, Thompson SL, Ngo JP, Smith DW, Abdelkader A, Broughton Brad R S, Bertram JF, Evans RG (2012) Diffusive oxygen shunting between vessels in the preglomerular renal vasculature: anatomic observations and computational modeling. Am J Physiol-Ren Physiol 303(5):F605–F618
Geitmann A, Cresti M, Heath I (2001) Cell biology of plant fungal tip growth. IOS Press, Amsterdam
Grundl D, Zhang X, Messaoud S, Pfister C, Demmel F, Mommer M, Wolf B, Brischwein M (2013) Reaction–diffusion modelling for microphysiometry on cellular specimens. Med and Biol Eng and Comput 51(4):387–395. doi:10.1007/s11517-012-1007-4
Hanahan D, Weinberg RA (2011) Hallmarks of cancer: the next generation. Cell 144(5):646–674. doi:10.1016/j.cell.2011.02.013
Kleinhans R, Brischwein M, Wang P, Becker B, Demmel F, Schwarzenberger T, Zottmann M, Wolf P, Niendorf A, Wolf B (2012) Sensor-based cell and tissue screening for personalized cancer chemotherapy. Med and Biol Eng and Comput 50(2):117–126. doi:10.1007/s11517-011-0855-7
MacdougallL JDB, MCCABE M (1967) Diffusion coefficient of oxygen through tissues. Nature 215(5106):1173–1174. doi:10.1038/2151173a0
Mestres P, Morguet A, Schmidt W, Kob A, Thedinga E (2006) A new method to assess drug sensitivity on breast tumor acute slices preparation. Ann NY Acad Sci 1091:460–469. doi:10.1196/annals.1378.088
Rubenstein V, Yin W, Frame MD (2011) Biofluid mechanics: an introduction to fluid mechanics, macrocirculation, and microcirculation. Academic Press
Takeshi Y, Hideaki K, Donald GM, Yoshiharu O, Yonson K, Yasushi S, Masahiko F, Kazuro S (2006) ADC measurement of abdominal organs and lesions using parallel imaging technique. Am J Roentgenol 187:1521–1530
Weigelt B, Ghajar CM, Bissell MJ (2014) The need for coplex 3D culture models to unravel novel pathways and identify accurate biomarkers in breast cancer. Adv Drug Deliv Rev 69–70:42–51. doi:10.1016/j.addr.2014.01.001
Wolf P, Brischwein M, Kleinhans R, Demmel F, Schwarzenberger T, Pfister C, Wolf B (2013) Automated platform for sensor-based monitoring and controlled assays of living cells and tissues. Biosens Bioelectron 50:111–117. doi:10.1016/j.bios.2013.06.031
Acknowledgments
We are greatly indebted to the German Federal Ministry of Education and Research (BMBF) for financial support and the cooperating partner HP Medizintechnik GmbH (Oberschleißheim, Germany) for their commitment in that project.
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Pfister, C., Forstmeier, C., Biedermann, J. et al. Estimation of dynamic metabolic activity in micro-tissue cultures from sensor recordings with an FEM model. Med Biol Eng Comput 54, 763–772 (2016). https://doi.org/10.1007/s11517-015-1367-7
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DOI: https://doi.org/10.1007/s11517-015-1367-7