|The Brain Simulation Platform "Live Papers"|
Authors: Anastasia Ludwig1,2*, Pablo Serna1, Lion Morgenstein3, Gaoling Yang4, Omri Bar-Elli4, Gloria Ortiz5, Evan Miller5, Dan Oron4, Asaf Grupi3, Shimon Weiss6,3, and Antoine Triller1
Author information: 1 L'Ecole Normale Supérieure, Institute of Biology (IBENS), Paris Sciences et Lettres (PSL), CNRS UMR 8197, Inserm 1024, 46 Rue dUlm, Paris 75005, France. 2 Current address: Neuroscience Center, Helsinki Institute of Life Science, University of Helsinki, FI-00014, Helsinki, Finland. 3 Department of Physics, Institute for Nanotechnology and Advanced Materials, Bar-Ilan University, Ramat-Gan 52900, Israel 4 Department of Physics of Complex Systems, Weizmann Institute of Science, Rehovot 76100, Israel 5 Departments of Chemistry, Molecular & Cell Biology, and Helen Wills Neuroscience Institute, 227 Hildebrand Berkeley, CA 94720-1460, United States 6 Department of Chemistry and Biochemistry, Department of Physiology, and California NanoSystems Institute, University of California Los Angeles, Los Angeles, California 90095, United States
Download Url: BioRxiv.
Citation: Ludwig et al, BioRxiv 838342 (2019)
In the last decade, optical imaging methods have significantly improved our understanding of the information processing principles in the brain. Although many promising tools have been designed, sensors of membrane potential are lagging behind the rest. Semiconductor nanoparticles are an attractive alternative to classical voltage indicators, such as voltage-sensitive dyes and proteins. Such nanoparticles exhibit high sensitivity to external electric fields via the quantum-confined Stark effect. Here we report the development of lipid-coated semiconductor voltage-sensitive nanorods (vsNRs) that self-insert into the neuronal membrane. We describe a workflow to detect and process the photoluminescent signal of vsNRs after wide-field time-lapse recordings. We also present data indicating that vsNRs are feasible for sensing membrane potential in neurons at a single-particle level. This shows the potential of vsNRs for detection of neuronal activity with unprecedentedly high spatial and temporal resolution.
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The source code is available on github at the following url. Please refer to the README file for more details.