Hybrid Nano-Scaffolds for Neuroengineering

The mammalian brain is a phenomenal piece of organic machinery. While remarkable advances have been made to better understand how the brain works and how to overcome neural deficiencies, its inherent complexity makes it difficult to fully comprehend, repair and manipulate. This is partly due to the lack of adequate tools to effectively probe the central nervous system (CNS). Yet, given that the biomolecular interactions and chemical communication occur at the nanoscale, there is great potential in leveraging advanced nanomaterials for neuroscience research. Herein, we demonstrate rationally-designed approaches, based on the assembly of hybrids comprised of synthetic nanomaterials and biomaterial scaffolds, to address challenges in neural regeneration and neural modulation. In the first approach, a hybrid nano-scaffold was developed to guide the selective differentiation of stem cells for neural regeneration. Establishing a controlled methodology to guide differentiation into specific cells of interest is a prevailing challenge. In neural regeneration, the selective differentiation into oligodendrocytes is desirable, yet difficult due to the overwhelming tendency of neural stem cells to become astrocytes. Most studies have focused on employing specific biomolecules or introducing genetic modifications to guide this process. In contrast, we combined a graphene-based nanomaterial with an electrospun nanofiber mesh to achieve remarkable differentiation into mature oligodendrocytes. By combining the unique properties of graphene (e.g. enhanced protein adsorption, high electrical conductivity) with the morphological features of nanofibers (e.g. size, topography, transplantability), our hybrid nano-scaffold has great scope for treating CNS damage. In the second approach, a hybrid nano-scaffold was developed to mediate near-infrared light (NIR)-triggerable neural modulation. Optogenetics is a revolutionary technology that has transformed neuroscience, allowing for the control of neuronal activity by using light and genetically-encoded photosensitive proteins. However, the efficient modulation of neuronal activity is contingent on delivering a sufficient dose of visible light to the target cells. Since light in the visible range is highly scattered and lacks deep tissue penetration, current approaches require invasive procedures to implant fiber optics in animal models. We overcame this barrier by employing upconverting nanoparticles (UCNPs), which have the capability to emit high energy visible light upon excitation with deep penetrating NIR light. By further embedding UCNPs within injectable polymers, we provide an early demonstration employing a scaffold to achieve modulation of neuronal activity. This approach takes us one step closer to a potentially wireless optogenetic solution. Overall, our innovative hybrid nanomaterial-based scaffolding approaches hold immense potential for advancing neuroscience research.

Recent Publications

August 09, 2017

A Cloud Native Approach to 5G Network Slicing

  • Francini A.
  • Miller R.
  • Sharma S.

5G networks will have to support a set of very diverse and often extreme requirements. Network slicing offers an effective way to unlock the full potential of 5G networks and meet those requirements on a shared network infrastructure. This paper presents a cloud native approach to network slicing. The cloud ...

August 01, 2017

Modeling and simulation of RSOA with a dual-electrode configuration

  • De Valicourt G.
  • Liu Z.
  • Violas M.
  • Wang H.
  • Wu Q.

Based on the physical model of a bulk reflective semiconductor optical amplifier (RSOA) used as a modulator in radio over fiber (RoF) links, the distributions of carrier density, signal photon density, and amplified spontaneous emission photon density are demonstrated. One of limits in the use of RSOA is the lower ...

July 12, 2017

PrivApprox: Privacy-Preserving Stream Analytics

  • Chen R.
  • Christof Fetzer
  • Le D.
  • Martin Beck
  • Pramod Bhatotia
  • Thorsten Strufe

How to preserve users' privacy while supporting high-utility analytics for low-latency stream processing? To answer this question: we describe the design, implementation and evaluation of PRIVAPPROX, a data analytics system for privacy-preserving stream processing. PRIVAPPROX provides three properties: (i) Privacy: zero-knowledge privacy (ezk) guarantees for users, a privacy bound tighter ...