, Volume 1, Issue 4, pp 183–191 | Cite as

Biochips for Regenerative Medicine: Real-time Stem Cell Continuous Monitoring as Inferred by High-Throughput Gene Analysis

  • Lisha Zhu
  • Giovanna del Vecchio
  • Giovanni de Micheli
  • Yuanhua Liu
  • Sandro Carrara
  • Laura Calzà
  • Christine Nardini


Regenerative medicine is a novel clinical branch aiming at the cure of diseases by replacement of damaged tissues. The crucial use of stem cells makes this area rich of challenges, given the poorly understood mechanisms of differentiation. One highly needed and yet unavailable technology should allow us to monitor the exact (metabolic) state of stem cells differentiation to maximize the effectiveness of their implant in vivo. This is challenged by the fact that not all relevant metabolites in stem cells differentiation are known and not all metabolites can currently be continuously monitored. To bring advancements in this direction, we propose the enhancement and integration of two available technologies into a general pipeline. Namely, high-throughput biochip for gene expression screening to pre-select the variables that are most likely to be relevant in the identification of the stem cells’ state and low-throughput biochip for continuous monitoring of cell metabolism with highly sensitive carbon nanotubes-based sensors. Intriguingly, additionally to the involvement of multidisciplinary expertise (medicine, molecular biology, computer science, engineering, and physics), this whole query heavily relies on biochips: it starts in fact from the use of high-throughput ones, which output, in turn, becomes the base for the design of low-throughput, highly sensitive biochips. Future research is warranted in this direction to develop and validated the proposed device.


High-throughput biology Nano-structured biochip Stem cell differentiation Metabolic pathways/markers 



This work is funded by the Sino-Swiss Science and Technology Cooperation Project (grant no.: GJHZ0911 on the Chinese side and grant no.: IZLCZ2 123967 on the Swiss side).

Supplementary material

12668_2011_28_MOESM1_ESM.pdf (255 kb)
(PDF 254 KB)


  1. 1.
    Chien, K. R., Domian, I. J., Parker, K. K. (2008). Cardiogenesis and the complex biology of regenerative cardiovascular medicine. Science, 322(5907), 1494–1497.CrossRefGoogle Scholar
  2. 2.
    Brown, P. O., & Botstein, D. (1999). Exploring the new world of the genome with dna microarrays. Nature Genetics, 21(1), 33–37.CrossRefGoogle Scholar
  3. 3.
    Holt, R, A., & Jones, S. J. (2008). The new paradigm of flow cell sequencing. Genome Research, 18(6), 839–846.CrossRefGoogle Scholar
  4. 4.
    Wang, Z., Gerstein, M., Snyder, M. (2009). RNA-Seq: A revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57–63.CrossRefGoogle Scholar
  5. 5.
    Fu, X., Fu, N., Guo, S., Yan, Z., Xu, Y., Hu, H., et al. (2009). Estimating accuracy of RNA-Seq and microarrays with proteomics. BMC Genomics, 10, 161–161.CrossRefGoogle Scholar
  6. 6.
    Bloom, J. S., Khan, Z., Kruglyak, L., Singh, M., Caudy, A. A. (2009). Measuring differential gene expression by short read equencing: Quantitative comparison to 2-channel gene expression microarrays. BMC Genomics, 10, 22.CrossRefGoogle Scholar
  7. 7.
    Ji, H., & Davis, R. W. (2006). Data quality in genomics and microarrays. Nature Biotechnology, 24, 1112–1113.CrossRefGoogle Scholar
  8. 8.
    Poscia, A., Mascini, M., Moscone, D., Luzzana, M., Caramenti, G., Cremonesi, P., et al. (2003). A microdialysis technique for continuous subcutaneous glucose monitoring in diabetic patients (part 1). Biosensors & Bioelectronics, 18(7), 891–898.CrossRefGoogle Scholar
  9. 9.
    Varalli, M., Marelli, G., Maran, A., Bistoni, S., Luzzana, M., Cremonesi, P., et al. (2003). A microdialysis technique for continuous subcutaneous glucose monitoring in diabetic patients (part 2). Biosensors & Bioelectronics, 18(7), 899–905.CrossRefGoogle Scholar
  10. 10.
    Poscia, A., Messeri, D., Moscone, D., Ricci, F., Valgimigli, F. (2005). A novel continuous subcutaneous lactate monitoring system. Biosensors & Bioelectronics, 20(11), 2244–2250.CrossRefGoogle Scholar
  11. 11.
    Boero, C., Carrara, S., Del Vecchio, G., Calza, L., De Micheli, G. (2011). Targeting of multiple metabolites in neural cell monitored by using protein-based carbon nanotubes. Sensors and Actuators, 157(1), 216–224.CrossRefGoogle Scholar
  12. 12.
    Valgimigli, F., Lucarelli, F., Scuffi, C., Morandi, S., Sposato, I. (2010). Evaluating the clinical accuracy of glucomen day: A novel microdialysis-based continuous glucose monitor. Journal of Diabetes Science and Technology, 4(5), 1182–1192.Google Scholar
  13. 13.
    Carrara, S., Bolomey, L., Boero, C., Cavallini, A., Meurville, E., De Micheli, G., et al. (2011). Single-metabolite bio-nano-sensors and system for remote monitoring in animal model. In IEEE international conference sensors 2011, Limerick.Google Scholar
  14. 14.
    Bistolas, N., Wollenberger, U., Jung, C., Scheller, F. (2005). Cytochrome p450 biosensors—a review. Biosensors & Bioelectronics, 20(12), 2408–2423.CrossRefGoogle Scholar
  15. 15.
    Carrara, S., Cavallini, A., Erokhin, V., De Micheli, G. (2011). Multi-panel drugs detection in human serum for personalized therapy. Biosensors & Bioelectronics, 26, 3914–3919.CrossRefGoogle Scholar
  16. 16.
    Carrara, S., Shumyantseva, V. V., Archakov, A. I., Samorì, B. (2008). Screen-printed electrodes based on carbon nanotubes and cytochrome p450scc for highly sensitive cholesterol biosensors. Biosensors & Bioelectronics, 24(1), 148–150.CrossRefGoogle Scholar
  17. 17.
    Cavallini, A., De Micheli, G., Carrara, S. (2011). Comparison of three methods of biocompatible multi-walled carbon nanotubes confinement for the development of implantable amperometric ATP biosensors. Sensor Letters (in press).Google Scholar
  18. 18.
    Mering, C., Huynen, M., Jaeggi, D., Schmidt, S., Bork, P., Snel, B. (2011). STRING: A database of predicted functional associations between proteins. Nucleic Acids Research, 31(1), 258. ISSN 0305-1048.CrossRefGoogle Scholar
  19. 19.
    Jeffery, I. B., Higgins, D. G., Culhane, A. C. (2006). Comparison and evaluation of methods for generating differentially expressed gene lists from microarray data. BMC Bioinformatics, 7, 359–359.CrossRefGoogle Scholar
  20. 20.
    Pirondi, S., Fernández, M., Chen, B. L., Del Vecchio, G., Alessandri, M., Farnedi, A., et al. (2011). Isolation of rat embryonic stem-like cells: A tool for stem cell research and drug discovery. Developmental Dynamics, 240(11), 2482–2494. doi: 10.1002/dvdy.22761.CrossRefGoogle Scholar
  21. 21.
    Chan, Y. S., Yang, L., Ng, H. H. (2011). Transcriptional regulatory networks in embryonic stem cells. Progress in Drug Research, 67, 239–252.Google Scholar
  22. 22.
    Kanehisa, M., Goto, S., Kawashima, S., Okuno, Y., Hattori, M. (2004). The kegg resource for deciphering the genome. Nucleic Acids Research, 32(Database issue), 277–280.CrossRefGoogle Scholar
  23. 23.
    Roach, M. L. & McNeish, J. D. (2002). Methods for the isolation and maintenance of murine embryonic stem cells. In K. Turksen (Ed.), Embryonic stem cells: Methods and protocols. NJ: Humana Press Inc. doi: 10.1385/1-59259-241-4:1.Google Scholar
  24. 24.
    Guo, L., Lobenhofer, E. K, Wang, C., Shippy, R., Harris, S. C., Zhang, L., et al. (2006). Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nature Biotechnology, 24(9), 1162–1169. ISSN 1087-0156.CrossRefGoogle Scholar
  25. 25.
    Smyth, G. (2005). Limma: Linear models for microarray data. In Bioinformatics and computational biology solutions using R and bioconductor (pp. 397–420).Google Scholar
  26. 26.
    Qin, J., Díaz-Cueto, L., Schwarze, J. E, Takahashi, Y., Imai, M., Isuzugawa, K., et al. (2005). Effects of progranulin on blastocyst hatching and subsequent adhesion and outgrowth in the mouse. Biology of Reproduction, 73(3), 434–442.CrossRefGoogle Scholar
  27. 27.
    Wang, L., Schulz, T. C., Sherrer, E. S., Dauphin, D. S., Shin, S., Nelson, A. M., et al. (2007). Self-renewal of human embryonic stem cells requires insulin-like growth factor-1 receptor and ERBB2 receptor signaling. Blood, 110(12), 4111–4119.CrossRefGoogle Scholar
  28. 28.
    Li, Y., & Geng, Y. J. (2010). A potential role for insulin-like growth factor signaling in induction of pluripotent stem cell formation. Growth Hormone IGF Research, 20(6), 391–398.MathSciNetzbMATHCrossRefGoogle Scholar
  29. 29.
    Ratajczak, J., Wan, W., Liu, R., Shin, D.-M., Kucia, M., Bartke, A., et al. (2010). Unexpected evidence that chronic IGF-1 deficiency in laron dwarf mice maintains high levels of hematopoietic stem cells (HSCs) in BM—are HSCs gradually depleted from BM with age in an IGF-1 cdependent manner? Implications for the novel effect of caloric restriction on the hematopoietic stem cell compartment and longevity. Blood (ASH Annual Meeting Abstracts), 116(1551).Google Scholar
  30. 30.
    Sepúlveda, D. E., Andrews, B. A., Papoutsakis, E. T., Asenjo, J. A. (2010). Metabolic flux analysis of embryonic stem cells using three distinct differentiation protocols and comparison to gene expression patterns. Biotechnology Progress, 26(5), 1222–1229.CrossRefGoogle Scholar
  31. 31.
    Yanes, O., Clark, J., Wong, D. M., Patti, G. J., Sánchez-Ruiz, A., Benton, H. P., et al. (2010). Metabolic oxidation regulates embryonic stem cell differentiation. Nature Chemical Biology, 6(6), 411–417.CrossRefGoogle Scholar
  32. 32.
    Filvaroff, E. H., Guillet, S., Zlot, C., Bao, M., Ingle, G., Steinmetz, H. (2002). Stanniocalcin 1 alters muscle and bone structure and function in transgenic mice. Endocrinology, 143(9), 3681–3690.CrossRefGoogle Scholar
  33. 33.
    Dallmann, R., Touma, C., Palme, R., Albrecht, U., Steinlechner, S. (2006). Impaired daily glucocorticoid rhythm in per1 (brd) mice. Journal of Comparative Physiology A, Sensory, Neural, and Behavioral Physiology, 192(7), 769–775.CrossRefGoogle Scholar
  34. 34.
    Ando, H., Takamura, T., Matsuzawa-Nagata, N., Shima, K. R., Eto, T., Misu, H., et al. (2009). Clock gene expression in peripheral leucocytes of patients with type 2 diabetes. Diabetologia, 52(2), 329–335.CrossRefGoogle Scholar
  35. 35.
    Krapivner, S., Chernogubova, E., Ericsson, M., Ahlbeck-Glader, C., Hamsten, A., van ’t Hooft, F. M. (2007). Human evidence for the involvement of insulin-induced gene 1 in the regulation of plasma glucose concentration. Diabetologia, 50(1), 94–102.CrossRefGoogle Scholar
  36. 36.
    Nebert, W. D., Dalton, P. T. (2006). The role of cytochrome p450 enzymes in endogenous signalling pathways and environmental carcinogenesis. Nature Reviews Cancer, 6, 947–960.CrossRefGoogle Scholar
  37. 37.
    Sarath Babu, V. R., Patra, N. G., Karanth, S., Kumar, M. A., Thakur, M. S. (2007). Development of a biosensor for caffeine. Analytica Chimica Acta, 582(2), 329–334.CrossRefGoogle Scholar
  38. 38.
    Saitoh, H., Namatame, Y., Hirano, A., Sugawara, M. (2004). An excised patch membrane sensor for arachidonic acid released in mouse hippocampal slices under stimulation of l-glutamate. Analytical Biochemistry, 329(2), 163–172.CrossRefGoogle Scholar
  39. 39.
    Turner, S. K., Daff, K. L., Chapman, S. N., Holt, R. A., Govindaraj, S., Poulos, T. L., et al. (1997). Redox control of the catalytic cycle of flavocytochrome p-450 BM3. Biochemistry, 36(45), 13816–13823.CrossRefGoogle Scholar
  40. 40.
    Navet, W. R., Alberici, L. C., Douette, P., Sluse-Goffart, C. M., Sluse, F. E., Vercesi, A. E. (2004) Redox state of endogenous coenzyme q modulates the inhibition of linoleic acid-induced uncoupling by guanosine triphosphate in isolated skeletal muscle mitochondria. Journal of Bioenergetics and Biomembranes, 36(5), 493–502.CrossRefGoogle Scholar
  41. 41.
    Luo, Y.-C., Do, J.-S., Liu, C.-C. (2006). An amperometric uric acid biosensor based on modified Ir-C electrode. Biosensors and Bioelectronics, 22, 482–488.CrossRefGoogle Scholar
  42. 42.
    Jobst, I. G., Aschauer, E., Svasek, P., Varahram, M., Urban, G. (1995). Miniaturized thin film glutamate and glutamine biosensors. Biosensors and Bioelectronics, 10, 527–532.CrossRefGoogle Scholar
  43. 43.
    Guiducci, C., & Nardini, C. (2008). High parallelism, portability and broad accessibility: Technologies for genomics. ACM Journal on Emerging Technologies in Computing Systems, 4(1),1–39 (Article 3).CrossRefGoogle Scholar
  44. 44.
    Fronza, R., Tramonti, M., Atchley, W. R., Nardini, C. (2011). Joint analysis of transcriptional and post- transcriptional brain tumor data: Searching for emergent properties of cellular systems. BMC Bioinformatics, 12, 86–86.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Lisha Zhu
    • 1
  • Giovanna del Vecchio
    • 2
  • Giovanni de Micheli
    • 3
  • Yuanhua Liu
    • 1
  • Sandro Carrara
    • 3
  • Laura Calzà
    • 2
  • Christine Nardini
    • 1
  1. 1.Key Laboratory of Computational Biology, CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological SciencesChinese Academy of SciencesShanghaiPeople’s Republic of China
  2. 2.Health Sciences and Technologies-Interdepartmental Center for Industrial Research (HST-ICIR)University of BolognaBolognaItaly
  3. 3.Integrated Systems CentreEPFLLausanneSwitzerland

Personalised recommendations