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Title
Abstract
Figures
Introduction
Discussion and Conclusions
References

A Microelectrode-Cell Sensor Model for Real Time Monitoring

Profile image of Alberto YUferaAlberto YUfera

2011

Abstract

In this paper the application of a cell- microelectrode model to cell biometry experiments is proposed, using the cell-electrode area overlap as main parameter. The model can be applied to cell size identification, cell count, and their extension to cell growth and dosimetry protocols. Experimental results using AA8 cell line are presented, obtaining promising results.

Figures (12)
Figure 3. Proposed model for the an electrode-solution-cell model with area A, uncovered with cells (a) and covered and area A, (b).
Figure 3. Proposed model for the an electrode-solution-cell model with area A, uncovered with cells (a) and covered and area A, (b).
Figure 2. Two electrodes for ECIS: e; (sensing) and e2 (reference). AC current i, is injected between e)-e2, and voltage response V, is measured. and ground connected, being its resistance small enough to be rejected. Figure 4 represents the impedance magnitude, Z,, for the sensor system in Fig. 2, considering that e,; could be either empty, partially or totally covered by cells. The parameter ff, called fill factor, can be zero for A.=0 (e; electrode empty), and 1 for A.=A (e; electrode full). It is defined Z, (f=0)=Z,, as the impedance magnitude of the sensor without cells.
Figure 2. Two electrodes for ECIS: e; (sensing) and e2 (reference). AC current i, is injected between e)-e2, and voltage response V, is measured. and ground connected, being its resistance small enough to be rejected. Figure 4 represents the impedance magnitude, Z,, for the sensor system in Fig. 2, considering that e,; could be either empty, partially or totally covered by cells. The parameter ff, called fill factor, can be zero for A.=0 (e; electrode empty), and 1 for A.=A (e; electrode full). It is defined Z, (f=0)=Z,, as the impedance magnitude of the sensor without cells.
Figure 4. Impedance evolution when fill factor increases 32 x 32 jm’. mode simulator, with Analog Hardware Description Language (AHDL) for circuits in Fig. 3. An advantage of using AHDL models is the possibility of including non-linear performance of circuit elements, in our case, the frequency squared-root function at the Warburg impedance.
Figure 4. Impedance evolution when fill factor increases 32 x 32 jm’. mode simulator, with Analog Hardware Description Language (AHDL) for circuits in Fig. 3. An advantage of using AHDL models is the possibility of including non-linear performance of circuit elements, in our case, the frequency squared-root function at the Warburg impedance.
Figure 5. Normalized impedance r versus frequency derived from Fig. 4. Curves correspond to ff in the range of 0.1 (near empty) to 0.9 (near full).
Figure 5. Normalized impedance r versus frequency derived from Fig. 4. Curves correspond to ff in the range of 0.1 (near empty) to 0.9 (near full).
TABLE ME: CELL NUMBER (cel!) OBTAINED FROM IMPEDANCE Z- MEASURE IN Fig. 9, AND USING THE r CURVES PROPOSED FOR 8W10E SENSORS. C: Dosimetry
TABLE ME: CELL NUMBER (cel!) OBTAINED FROM IMPEDANCE Z- MEASURE IN Fig. 9, AND USING THE r CURVES PROPOSED FOR 8W10E SENSORS. C: Dosimetry
Figure 6. Obtained curves for r vs frequency, for ff € [0.1,0.9] and Reap = 22kQ, using 8W1O0E electrodes.
Figure 6. Obtained curves for r vs frequency, for ff € [0.1,0.9] and Reap = 22kQ, using 8W1O0E electrodes.
Figure 7. Obtained curves for r vs frequency, for Rgap € [10kQ,100kQ] in steps of 10kQ, for ff=0.9, using 8W10E electrodes.
Figure 7. Obtained curves for r vs frequency, for Rgap € [10kQ,100kQ] in steps of 10kQ, for ff=0.9, using 8W10E electrodes.
Figure 8. Obtained curves for the r vs frequency, for Rgap=22kQ. and ff=0.9, for differents electrode areas (In to 100n). 49n (49.10°m?) corresponds to a circular electrode with a 250j1m diameter.
Figure 8. Obtained curves for the r vs frequency, for Rgap=22kQ. and ff=0.9, for differents electrode areas (In to 100n). 49n (49.10°m?) corresponds to a circular electrode with a 250j1m diameter.
Figure 9. (a) Impedance evolution of the cell growth experiment. (b) The normalised impedance r evolution obtained.
Figure 9. (a) Impedance evolution of the cell growth experiment. (b) The normalised impedance r evolution obtained.
TABLE III: EXPERIMENTAL VALUES FOR RELATIVE IMPEDANCE (r) AND FILL FACTOR (ff) AT VARIOUS FREQUENCIES. Znc = 4800, 400Q, AND 315Q FOR 2, 4 AND 10 KHz WORKING FREQUENCY RESPECTIVELY.
TABLE III: EXPERIMENTAL VALUES FOR RELATIVE IMPEDANCE (r) AND FILL FACTOR (ff) AT VARIOUS FREQUENCIES. Znc = 4800, 400Q, AND 315Q FOR 2, 4 AND 10 KHz WORKING FREQUENCY RESPECTIVELY.
For medium resistance (W1) and high drug concentrations wells (W6-W8), the resistance measured is below to Z,,, so eq. (1) can no be applied for r calculation. Figure 10. Impedance measure in dosimetry at 8 wells for 4kHz frequency. W1: Medium. W2: Control. W3: 0.2M. W4: 0.5 uM. W5: 1 uM. W6: 5 pM. W7: 10 uM and W8: 50 uM.
For medium resistance (W1) and high drug concentrations wells (W6-W8), the resistance measured is below to Z,,, so eq. (1) can no be applied for r calculation. Figure 10. Impedance measure in dosimetry at 8 wells for 4kHz frequency. W1: Medium. W2: Control. W3: 0.2M. W4: 0.5 uM. W5: 1 uM. W6: 5 pM. W7: 10 uM and W8: 50 uM.

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References (15)

  1. S. Grimnes and O. Martinsen, "Bio-impedance and Bioelectricity Basics," Second edition. Academic Press, Elsevier. 2008.
  2. R. D. Beach et al., "Towards a Miniature In Vivo Telemetry Monitoring System Dynamically Configurable as a Potentiostat or Galvanostat for Two-and Three-Electrode Biosensors," IEEE transaction on Instrumentation and Measurement, vol 54, nº1, pp:61- 72. 2005.
  3. A. Yúfera et al., "A Tissue Impedance Measurement Chip for Myocardial Ischemia Detection," IEEE transaction on Circuits and Systems: Part I. vol.52, nº:12 pp:2620-2628. 2005.
  4. S. Radke et al., "Design and Fabrication of a Microimpedance Biosensor for Bacterial Detection," IEEE Sensor J. vol 4,nº 4,434-440. 2004.
  5. A. Yúfera et al., "A Method for Bioimpedance Measure With Four- and Two-Electrode Sensor Systems," 30th Annual International IEEE EMBS Conference, pp: 2318-2321. 2008.
  6. D. A. Borkholder. Cell-Based Biosensors Using Microelectrodes. PhD Thesis, Stanford University. 1998.
  7. I. Giaever, et al., "Use of Electric Fields to Monitor the Dynamical Aspect of Cell Behaviour in Tissue Cultures," IEEE Transaction on Biomedical Engineering, vol. BME-33, No. 2, pp. 242-247. 1986.
  8. I. Giaever and C. R. Keese, "Micromotion of mammalian cells measured electrically," Proc. Nail. Acad. Sci. USA. Cell Biology, vol. 88, pp: 7896-7900, Sep. 1991.
  9. X. Huang et al., "Simulation of Microelectrode Impedance Changes Due to Cell Growth," IEEE Sensors J., vol.4, nº5, pp: 576-583. 2004.
  10. A. Olmo et al., "Computer Simulation of Microelectrode Based Bio- Impedance Measurements With COMSOL," BIODEVICES 2010. pp: 178-182. Valencia (Spain), 20-23, Jan. 2010.
  11. P. Wang and Q. Liu, editors. Cell-Based Biosensors: Principles and Applications, Artech House Series. 2010.
  12. Applied Biophysics. Http://www.biophysics.com/.
  13. A. Manickam, A. Chevalier, M. McDermott, A. D. Ellington, and A. Hassibi, "A CMOS Electrochemical Impedance Spectroscopy (EIS) Biosensor Array," IEEE Transactions on Biomedical Circuits and Systems, vol 4, nº 6. pp: 379-390. 2010.
  14. A. Yúfera and A. Rueda, "A Real-Time Cell Culture Monitoring CMOS System Based on Bio-impedance Measurements," Kluwer Academic P.. Analog Integrated Circuits and Signal Processing. 2011. (accepted)
  15. A. Yúfera A. and E. Gallego, "Generation of HDL Models for Bioimpedance Sensor Simulation Based on Microelectrodes," Sensors and Transducers Journal, pp: 160-170. vol. 10. Feb. 2011.

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