Determination of cancer progression in breast cells by fiber optic bioimpedance spectroscopy system

Year 2020, Volume: 4 Issue: 1, 84 - 88, 02.01.2020

Tuba Denkçeken Ayşegül Çört

https://doi.org/10.28982/josam.671514

Cited By: 2

Abstract

Aim: It is well established that cancer can be most effectively treated when diagnosed at an early stage. Therefore, development, evaluation, and validation of new biomedical approaches for early detection of cancer and precancerous lesions are important priorities. Our aim was to distinguish low metastatic human breast cells from normal human breast cells using the Fiber Optic Bioimpedance Spectroscopy (FOBIS) system.

Methods: In the FOBIS system we developed, the diameters of the fibers and platinum wires are 50 and 25µm, respectively. The sensitivity of the system to differentiate different cell types was assessed with high metastatic (MDA-MB-231), low metastatic (MCF-7) and normal breast epithelial cells (MCF-10A). Statistical evaluation of data was performed by using Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA). Spectroscopic data obtained from FOBIS system on suspended human breast cells were evaluated by multivariate statistical analysis to obtain information about the cell type. Fiber optic and bioimpedance methods allow discrimination of different cell types based on their signature. By combining these two techniques, the sensitivity of the system to the differentiation of human breast cells was evaluated.

Results: The discrimination provided the sensitivity of 100% and specificity of 60% in distinguishing MCF-7 from MCF-10A cells.

Conclusion: A highly accurate distinction of breast cancer cells was achieved in cell culture by FOBIS system.

Keywords

Bioimpedance, Breast, Cell Distinguish, Fiber Optic, Spectroscopy

Supporting Institution

This research was funded by The Scientific and Technological Research Council of Turkey (TUBITAK).

Project Number

Grant number: 115E662

Fiber optik biyoimpedans spektroskopi sistemi ile meme hücrelerinde kanser gelişiminin belirlenmesi

Abstract

Amaç: Kanserin erken evrede teşhis edildiğinde en etkili şekilde tedavi edilebileceği iyi bilinmektedir. Bu nedenle, kanserin ve prekanseröz lezyonların erken tespiti için yeni biyomedikal yaklaşımların geliştirilmesi, değerlendirilmesi ve validasyonu önemli bir önceliktir. Amacımız, Fiber Optik Biyoimpedans Spektroskopisi (FOBIS) sistemini kullanarak düşük metastatik insan meme hücrelerini normal insan meme hücrelerinden ayırt etmekti.

Yöntemler: FOBİS sisteminde 50µm çaplı fiberler ve 25µm çaplı platin teller kullanılmıştır. Sistemin farklı hücre tiplerini ayırt etme duyarlılığı yüksek metastatik (MDA-MB-231), düşük metastatik (MCF-7) ve normal meme epitel hücreleri (MCF-10A) için hesaplanmıştır. Verilerin istatistiksel değerlendirmesi Temel Bileşenler Analizi (TBA) ve Doğrusal Ayırım Analizi (DAA) ile yapılmıştır. İnsan meme hücre kültürlerinde FOBİS sistemi ile elde edilen spektroskopik veriler, çok değişkenli istatistiksel analiz ile değerlendirilerek hücre tipi hakkında bilgi elde edilmiştir. Fiber optik ve biyoimpedans yöntemlerinden elde edilen spektroskopik veriler ile farklı hücre tipleri ayırt edilebilmektedir. Bu iki tekniğin birleştirilmesi ile sistemin insan meme hücrelerinin farklılaşmasına duyarlılığı test edilmiştir.

Bulgular: Buna göre %100 duyarlılık ve %60 seçicilik ile MCF-7 hücreleri MCF-10A hücrelerinden ayırt edilmiştir. 

Sonuç: FOBİS sistemi ile hücre kültüründe meme kanseri hücreleri yüksek duyarlılıkla ayırt edilmiştir.

Keywords

Biyoimpedans, Meme, Hücre ayırımı, Fiber optik, Spektroskopi

Project Number

Grant number: 115E662

References

• 1. Hodgkin AL, Huxley AF. A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol. 1952;117:500-44.
• 2. Schwan HP. Electrical properties of tissue and cell suspensions. Adv Biol Med Phys. 1957;5:147-209.
• 3. Lukaski HC. Biological indexes considered in the derivation of the bioelectrical impedance analysis. Am J Clin Nutr. 1996;64:397S-404S.
• 4. Selberg O, Selberg D. Norms and correlates of bioimpedance phase angle in healthy human subjects, hospitalized patients, and patients with liver cirrhosis. Eur J Appl Physiol. 2002;86:509-16.
• 5. Farre R, Blondeau K, Clement D, Vicario M, Cardozo L, Vieth M, et al. Evaluation of oesophageal mucosa integrity by the intraluminal impedance technique. Gut. 2011;60:885-92.
• 6. Salomon G, Hess T, Erbersdobler A, Eichelberg C, Greschner S, Sobchuk AN, et al. The feasibility of prostate cancer detection by triple spectroscopy. Eur Urol. 2009;55:376-83.
• 7. Grimnes S, Martinsen ØG. Geometrical Analysis in Bioimpedance and Bioelectricity Basics. Academic Press: Oxford; 2015. pp. 141-178.
• 8. Eriksson L, Andersson PL, Johansson E, Tysklind M. Megavariate analysis of environmental QSAR data. Part II--investigating very complex problem formulations using hierarchical, non-linear and batch-wise extensions of PCA and PLS. Mol Divers. 2006;10:187-205.
• 9. Abdi H, Williams LJ. Principal component analysis. Wiley Interdiscip Rev Comput Stat. 2010;2:433-59.
• 10. Fukunaga K. Introduction to Statistical Pattern Recognition. Elsevier Science; 2013.
• 11. Martinez AM, Kak AC. PCA versus LDA. IEEE T Pattern Anal. 2001;23:228-33.
• 12. Team TRDC. R: A Language and Environment for Statistical Computing. 2010.
• 13. Fawcett T. An introduction to ROC analysis. Pattern Recogn Lett. 2006;27:861-74.
• 14. Bolin FP, Preuss LE, Taylor RC, Ference RJ. Refractive index of some mammalian tissues using a fiber optic cladding method. Appl Opt. 1989;28:2297-303.
• 15. Tearney GJ, Brezinski ME, Southern JF, Bouma BE, Hee MR, Fujimoto JG. Determination of the refractive index of highly scattering human tissue by optical coherence tomography. Opt Lett. 1995;20:2258.
• 16. Alberts B, Johnson A, Lewis J, Walter P, Raff M, Roberts K. Molecular Biology of the Cell 4th Edition: International Student Edition. Routledge; 2002.
• 17. Palade GE. An electron microscope study of the mitochondrial structure. J Histochem Cytochem. 1953;1:188-211.
• 18. Maier JS, Walker SA, Fantini S, Franceschini MA, Gratton E. Possible correlation between blood glucose concentration and the reduced scattering coefficient of tissues in the near infrared. Opt Lett. 1994;19:2062-4.
• 19. Brunsting A, Mullaney PF. Differential light scattering from spherical mammalian cells. Biophys J. 1974;14:439-53.
• 20. Liu H, Beauvoit B, Kimura M, Chance B. Dependence of tissue optical properties on solute-induced changes in refractive index and osmolarity. J Biomed Opt. 1996;1:200-11.
• 21. Drezek R, Dunn A, Richards-Kortum R. Light scattering from cells: finite-difference time-domain simulations and goniometric measurements. Appl Opt. 1999;38:3651-61.
• 22. Liang XJ, Liu AQ, Lim CS, Ayi TC, Yap PH. Determining refractive index of single living cell using an integrated microchip. Sensors and Actuators a-Physical. 2007;133:349-54.
• 23. Videla FA, Schinca DC, Scaffardi LB. Sizing particles by backscattering spectroscopy and Fourier analysis. SPIE; 2006.
• 24. Keshtkar A. Application of Electrical Impedance Spectroscopy in Bladder Cancer Screening. Iran J Med Phys. 2013;10:1-21.
• 25. Chauveau N, Hamzaoui L, Rochaix P, Rigaud B, Voigt JJ, Morucci JP. Ex vivo discrimination between normal and pathological tissues in human breast surgical biopsies using bioimpedance spectroscopy. Ann NY Acad Sci. 1999;873:42-50.
• 26. Surowiec AJ, Stuchly SS, Barr JB, Swarup A. Dielectric properties of breast carcinoma and the surrounding tissues. IEEE Trans Biomed Eng. 1988;35:257-63.
• 27. Fricke H, Morse S. The Electric Capacity of Tumors of the Breast. J Cancer Res. 1926;10:340-76.
• 28. Faisy C, Rabbat A, Kouchakji B, Laaban JP. Bioelectrical impedance analysis in estimating nutritional status and outcome of patients with chronic obstructive pulmonary disease and acute respiratory failure. Intensive Care Med. 2000;26:518-25.
• 29. Ott M, Fischer H, Polat H, Helm EB, Frenz M, Caspary WF, et al. Bioelectrical impedance analysis as a predictor of survival in patients with human immunodeficiency virus infection. J Acquir Immune Defic Syndr Hum Retrovirol. 1995;9:20-5.
• 30. Schwenk A, Ward LC, Elia M, Scott GM. Bioelectrical impedance analysis predicts outcome in patients with suspected bacteremia. Infection. 1998;26:277-82.
• 31. Norman K, Stobaus N, Zocher D, Bosy-Westphal A, Szramek A, Scheufele R, et al. Cutoff percentiles of bioelectrical phase angle predict functionality, quality of life, and mortality in patients with cancer. Am J Clin Nutr. 2010;92:612-9.
• 32. Abdul S, Brown BH, Milnes P, Tidy JA. The use of electrical impedance spectroscopy in the detection of cervical intraepithelial neoplasia. Int J Gynecol Cancer. 2006;16:1823-32.
• 33. Halter RJ, Schned AR, Heaney JA, Hartov A. Passive bioelectrical properties for assessing high- and low-grade prostate adenocarcinoma. Prostate. 2011;71:1759-67.