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. 2016 Jan 19;49(1):17-26.
doi: 10.1021/acs.accounts.5b00345. Epub 2015 Dec 14.

Nanotechnology-Based Approaches for Guiding Neural Regeneration

Affiliations

Nanotechnology-Based Approaches for Guiding Neural Regeneration

Shreyas Shah et al. Acc Chem Res. .

Abstract

The mammalian brain is a phenomenal piece of "organic machinery" that has fascinated scientists and clinicians for centuries. The intricate network of tens of billions of neurons dispersed in a mixture of chemical and biochemical constituents gives rise to thoughts, feelings, memories, and life as we know it. In turn, subtle imbalances or damage to this system can cause severe complications in physical, motor, psychological, and cognitive function. Moreover, the inevitable loss of nerve tissue caused by degenerative diseases and traumatic injuries is particularly devastating because of the limited regenerative capabilities of the central nervous system (i.e., the brain and spinal cord). Among current approaches, stem-cell-based regenerative medicine has shown the greatest promise toward repairing and regenerating destroyed neural tissue. However, establishing controlled and reliable methodologies to guide stem cell differentiation into specialized neural cells of interest (e.g., neurons and oligodendrocytes) has been a prevailing challenge in the field. In this Account, we summarize the nanotechnology-based approaches our group has recently developed to guide stem-cell-based neural regeneration. We focus on three overarching strategies that were adopted to selectively control this process. First, soluble microenvironmental factors play a critical role in directing the fate of stem cells. Multiple factors have been developed in the form of small-molecule drugs, biochemical analogues, and DNA/RNA-based vectors to direct neural differentiation. However, the delivery of these factors with high transfection efficiency and minimal cytotoxicity has been challenging, especially to sensitive cell lines such as stem cells. In our first approach, we designed nanoparticle-based systems for the efficient delivery of such soluble factors to control neural differentiation. Our nanoparticles, comprising either organic or inorganic elements, were biocompatible and offered multifunctional capabilities such as imaging and delivery. Moving from the soluble microenvironment in which cells are immersed to the underlying surface, cells can sense and consequently respond to the physical microenvironment in which they reside. For instance, changes in cell adhesion, shape, and spreading are key cellular responses to surface properties of the underlying substrate. In our second approach, we modulated the surface chemistry of two-dimensional substrates to control neural stem cell morphology and the resulting differentiation process. Patterned surfaces consisting of immobilized extracellular matrix (ECM) proteins and/or nanomaterials were generated and utilized to guide neuronal differentiation and polarization. In our third approach, building on the above-mentioned approaches, we further tuned the cell-ECM interactions by introducing nanotopographical features in the form of nanoparticle films or nanofiber scaffolds. Besides providing a three-dimensional surface topography, our unique nanoscaffolds were observed to enhance gene delivery, facilitate axonal alignment, and selectively control differentiation into neural cell lines of interest. Overall, nanotechnology-based approaches offer the precise physicochemical control required to generate tools suitable for applications in neuroscience.

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Conflict of interest statement

Notes

The authors declare no competing financial interest.

Figures

Figure 1
Figure 1
Magnetic core−shell nanoparticles (MCNPs) efficiently deliver siRNA into rNSCs. (a) MCNPs were functionalized to complex negatively charged siRNA. (b) Transmission electron microscopy image of MCNPs. Scale bar: 10 nm. (c) MCNPs dispersed in water are attracted to a magnet. (d) Magnetically facilitated delivery of siRNA to induce neural differentiation of rNSCs using MCNPs. Fluorescence images depict (top) neuronal and (bottom) oligodendrocyte differentiation after siSOX9 and siCAV delivery, respectively. Adapted with permission from ref . Copyright 2013 Wiley.
Figure 2
Figure 2
DexAM facilitates codelivery of siRNA and hydrophobic small molecules into rNSCs. (a) DexAM comprises a positively charged polyamine backbone to complex negatively charged siRNA and a β-cyclodextrin moiety to encapsulate small-molecule drugs. (b) Complexed DexAM constructs were delivered to rNSCs to enhance neuronal differentiation. (c) Fluorescence images of cells stained for the neuronal marker TuJ1 and the astrocytic marker GFAP. Scale bars: 20 μm. (d) Plot showing quantification of the stained cells. Statistical significance is compared to RA−siSOX9 treatment (*, p < 0.001). Adapted from ref . Copyright 2013 American Chemical Society.
Figure 3
Figure 3
ECM protein patterns can guide neuronal differentiation of rNSCs. (a) Fabrication of ECM protein patterns of varying pattern geometries and dimensions to control rNSC differentiation. (b) Images showing rNSC growth at day 2 (A1-C1) and immunostaining for the neuronal marker TuJ1 (A2-C2) and the astrocytic marker GFAP (A3-C3) at day 6. Scale bars: 50 μm. (c) Plot showing quantification of the stained cells. Statistical significance is compared to grid patterns (*, p < 0.01; **, p < 0.001). Adapted with permission from ref . Copyright 2010 Wiley.
Figure 4
Figure 4
Patterned nanosized graphene oxide (NGO) induces neuronal differentiation of human adipose-derived mesenchymal stem cells (hADMSCs). (a) hADMSCs cultured on grid patterns of NGO facilitated differentiation into neuronal cells. (b) Images of hADMSCs grown on poly(L-lysine) (PLL)-coated Au (Au), NGO-coated Au (Au-NGO), and NGO grid-patterned substrates [Au-NGO (Grid)]. Scale bars: 20 μm. (c) Plot showing quantification of the percentage of cells expressing the neuronal marker TuJ1 (*, p < 0.01). (d) Images of cells stained for F-actin (green) and nucleus (blue) showing extensive cellular extension on NGO grid patterns. Scale bar: 50 μm. (e) Plot showing quantification of the length of cellular extension on various substrates (*, p < 0.01). Adapted from ref . Copyright 2015 American Chemical Society.
Figure 5
Figure 5
Nanotopographical features can mediate significant uptake of siRNA by rNSCs. (a) NanoRU facilitates cellular uptake of siRNA into rNSCs. (b) SEM image of rNSCs (orange) on NanoRU (blue). Scale bars: 10 μm and (inset) 500 nm. (c) Images stained for the neuronal marker TuJ1 (red) and the astrocytic marker GFAP (green) showing the extent of differentiation of rNSCs grown on bare glass with no NanoRU or siSOX9 coating (top), NanoRU without the siSOX9 coating (middle), and NanoRU with the siSOX9 coating (bottom). Scale bars: 50 μm. (d) Plot showing quantification of the percentage of cells expressing specific neural markers on various substrates (**, p < 0.001). Adapted with permission from ref . Copyright 2013 Nature Publishing Group.
Figure 6
Figure 6
Graphene-coated polymeric nanofibers dramatically enhance oligodendroglial differentiation of rNSCs. (a) Polymeric nanofibers [composed of polycaprolactone (PCL)] coated with graphene oxide (GO) and seeded with rNSCs show enhanced differentiation into oligodendrocytes. The SEM image at the right shows the morphology of cells grown on graphene−nanofiber hybrid scaffolds. Scale bar: 10 μm. (b) SEM images of PCL nanofibers coated with GO solutions of varying concentrations: 0.0 mg/mL (PCL), 0.1 mg/mL [PCL-GO (0.1)], 0.5 mg/mL [PCL-GO (0.5)], and 1.0 mg/mL [PCL-GO (1.0)]. Scale bars: 1 μm. (c) Quantitative PCR of rNSCs grown on various substrates. The plot shows the fold change in gene expression of markers indicative of neurons (TuJ1), astrocytes (GFAP), and oligodendrocytes (MBP). The gene expression is relative to GAPDH and normalized to the conventional PLL-coated glass control (*, p < 0.05; **, p < 0.01; n.s., no significance). Adapted with permission from ref . Copyright 2014 Wiley.

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