Neuronal maturation-dependent nano-neuro interaction and modulation

Nanotechnology-enabled neuromodulation is a promising minimally-invasive tool in neuroscience and engineering for both fundamental studies and clinical applications. However, the nano-neuro interaction at different stages of maturation of a neural network and its implications for the nano-neuromodulation remain unclear. Here, we report heterogeneous to homogeneous transformation of neuromodulation in a progressively maturing neural network. Utilizing plasmonic-fluors as ultrabright fluorescent nanolabels, we reveal that negative surface charge of nanoparticles renders selective nano-neuro interaction with a strong correlation between the maturation stage of the individual neurons in the neural network and the density of the nanoparticles bound on the neurons. In stark contrast to homogeneous neuromodulation in a mature neural network reported so far, the maturation-dependent density of the nanoparticles bound to neurons in a developing neural network resulted in a heterogeneous optical neuromodulation (i.e., simultaneous excitation and inhibition of neural network activity). This study advances our understanding of nano-neuro interactions and nano-neuromodulation with potential applications in minimally-invasive technologies for treating neuronal disorders in parts of the mammalian brain where neurogenesis persists throughout aging. We unveil that negative surface charge of nanoparticles renders selective nano-neuro interaction with a strong correlation between the maturation stage of individual neurons in the neural network and the density of nanoparticles bound on the neurons.

Automated summary

Nanotechnology-enabled neuromodulation is a promising minimally-invasive tool that can be used in neuroscience and engineering for both fundamental studies and clinical applications. This study reveals the transformation of neuromodulation from heterogeneous to homogeneous in a progressively maturing neural network, and the selective nano-neuro interaction between nanoparticles and neurons based on their surface charge. The density of nanoparticles bound to neurons in a developing neural network leads to heterogeneous optical neuromodulation, which simultaneously excites and inhibits neural network activity.