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BioNanoScience

, 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
Article

Abstract

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.

Keywords

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

Notes

Acknowledgement

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)

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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

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