Optical approaches to monitor neural activity are transforming neuroscience, owing to a fast-evolving palette of genetically encoded molecular reporters. However, the field still requires robust and label-free technologies to monitor the multifaceted biomolecular changes accompanying brain development, aging or disease. Here, we have developed vibrational fiber photometry as a low-invasive method for label-free monitoring of the biomolecular content of arbitrarily deep regions of the mouse brain in vivo through spontaneous Raman spectroscopy. Using a tapered fiber probe as thin as 1 µm at its tip, we elucidate the cytoarchitecture of the mouse brain, monitor molecular alterations caused by traumatic brain injury, as well as detect markers of brain metastasis with high accuracy. We view our approach, which introduces a deep learning algorithm to suppress probe background, as a promising complement to the existing palette of tools for the optical interrogation of neural function, with application to fundamental and preclinical investigations of the brain and other organs.

Vibrational fiber photometry: label-free and reporter-free minimally invasive Raman spectroscopy deep in the mouse brain

Gentile, Francesco
Methodology
;
2024-01-01

Abstract

Optical approaches to monitor neural activity are transforming neuroscience, owing to a fast-evolving palette of genetically encoded molecular reporters. However, the field still requires robust and label-free technologies to monitor the multifaceted biomolecular changes accompanying brain development, aging or disease. Here, we have developed vibrational fiber photometry as a low-invasive method for label-free monitoring of the biomolecular content of arbitrarily deep regions of the mouse brain in vivo through spontaneous Raman spectroscopy. Using a tapered fiber probe as thin as 1 µm at its tip, we elucidate the cytoarchitecture of the mouse brain, monitor molecular alterations caused by traumatic brain injury, as well as detect markers of brain metastasis with high accuracy. We view our approach, which introduces a deep learning algorithm to suppress probe background, as a promising complement to the existing palette of tools for the optical interrogation of neural function, with application to fundamental and preclinical investigations of the brain and other organs.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12317/104148
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