4.8 Article

Hybrid upconversion nanomaterials for optogenetic neuronal control

Journal

NANOSCALE
Volume 7, Issue 40, Pages 16571-16577

Publisher

ROYAL SOC CHEMISTRY
DOI: 10.1039/c5nr03411f

Keywords

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Funding

  1. National Institute of Health (NIH) [1DP20D006462-01]
  2. National Institute of Neurological Disorders and Stroke (NINDS) of the NIH [1R21 NS085569-02]
  3. N. J. Commission on Spinal Cord grant [CSR13ERG005]
  4. National Science Foundation (NSF) [NSF CHE-1429062]
  5. Rutgers IAMDN
  6. National Institute on Drug Abuse (NIDA) [R21 DA035594]
  7. Sinsheimer Scholar Award
  8. Robert Wood Johnson Foundation
  9. NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE [R21NS085569] Funding Source: NIH RePORTER
  10. NATIONAL INSTITUTE ON DRUG ABUSE [R21DA035594] Funding Source: NIH RePORTER
  11. OFFICE OF THE DIRECTOR, NATIONAL INSTITUTES OF HEALTH [DP2OD006462] Funding Source: NIH RePORTER
  12. Direct For Mathematical & Physical Scien [1429062] Funding Source: National Science Foundation

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Nanotechnology-based approaches offer the chemical control required to develop precision tools suitable for applications in neuroscience. We report a novel approach employing hybrid upconversion nanomaterials, combined with the photoresponsive ion channel channelrhodopsin-2 (ChR2), to achieve near-infrared light (NIR)-mediated optogenetic control of neuronal activity. Current optogenetic methodologies rely on using visible light (e.g. 470 nm blue light), which tends to exhibit high scattering and low tissue penetration, to activate ChR2. In contrast, our approach enables the use of 980 nm NIR light, which addresses the shortcomings of visible light as an excitation source. This was facilitated by embedding upconversion nanomaterials, which can convert NIR light to blue luminescence, into polymeric scaffolds. These hybrid nanomaterial scaffolds allowed for NIR-mediated neuronal stimulation, with comparable efficiency as that of 470 nm blue light. Our platform was optimized for NIR-mediated optogenetic control by balancing multiple physicochemical properties of the nanomaterial (e.g. size, morphology, structure, emission spectra, concentration), thus providing an early demonstration of rationally- designing nanomaterial-based strategies for advanced neural applications.

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