Periodic Reporting for period 1 - NanoVoltSens (Voltage-sensing nanorods for super-resolution voltage imaging in neurons)
Reporting period: 2017-05-01 to 2019-04-30
In this project we characterized an innovative voltage sensor for neurons that is fundamentally different from the existing voltage sensors. Our sensor is based on targetable voltage-sensing semi-conductor nanorods (vsNRs) that self-insert into the neuronal membrane. The goal of the project was to validate, calibrate and use vsNRs in neurons.
We developed vsNPs to be injectable, targetable, and self-insertable into the neuronal membrane such that they could optically and non-invasively report membrane potential at the single particle level at the nanoscale, at multiple sites and across a large field-of-view. We aim to provide a viable and user-friendly voltage-imaging technology that will be widely applicable for the study of signal integration in the brain.
We developed vsNPs to be injectable, targetable, and self-insertable into the neuronal membrane such that they could optically and non-invasively report membrane potential at the single particle level at the nanoscale, at multiple sites and across a large field-of-view. We aim to provide a viable and user-friendly voltage-imaging technology that will be widely applicable for the study of signal integration in the brain.
For vsNRs imaging in neurons we have built the dedicated imaging setup. The optical configuration of the setup allows simultaneous acquisition of vsNR signal at multiple wavelengths. The setup also has electrophysiological hardware and software for validation and calibration of the vsNR signal (Fig. 1).
We optimized conditions for loading vsNRs into primary dissociated cortical neurons. With our current loading protocol we typically get ~300 vsNRs per neuron without compromising the health of the cell. Efficient loading allows for collection of large datasets per experiment. We are able to load vsNRs to plasma membrane at various subcellular locations including cell bodies, as well as proximal and distal dendrites.
We developed an automated software for analysis of NR fluorescence in neurons, including algorithms that recognize single vsNRs in the optical field, effectively identify and excise blinking periods in vsNR fluorescent traces, and determine responsive particles based on the correlation of fluorescent signal with electrophysiological stimulation (Fig. 2). Since vsNR signal is collected from individual particles, the recordings suffer from poor signal to noise ratio (SNR) that could be overcome by averaging the signal of several particles located in close proximity of each other. At the current stage of vsNR development, we cannot yet archive membrane insertion efficiency that would allow us to find several properly inserted particle next to each other.
The project is a part of a large international interdisciplinary collaboration that includes four teams with different areas of expertise: Dr. Weiss (Chemistry; UCLA, USA), Dr. Oron (Nanophotonics; Weizmann Institute, Rehovot, Israel), Dr. Enderlein (Physical Chemistry; Georg August University, Goettingen, Germany), and the host laboratory of Dr. Triller (Neuroscience; ENS, Paris, France). The entire collaboration works on improving insertion efficiency. For each vsNR sample that we received from Dr. Weiss’s team, we perform initial series of experiments aimed to establish the relationship between the membrane potential and the fluorescent signal of vsNRs.
The first results of the project were recently published (Bar-Elli et al., 2018, ACS Photonics 5, 7, 2860-67). In this study we demonstrated that NRs are feasible for voltage sensing on a single-particle level at millisecond timescale. Now we are preparing the manuscript summarizing our data regarding utilization of vsNR for membrane potential recordings in neurons.
We optimized conditions for loading vsNRs into primary dissociated cortical neurons. With our current loading protocol we typically get ~300 vsNRs per neuron without compromising the health of the cell. Efficient loading allows for collection of large datasets per experiment. We are able to load vsNRs to plasma membrane at various subcellular locations including cell bodies, as well as proximal and distal dendrites.
We developed an automated software for analysis of NR fluorescence in neurons, including algorithms that recognize single vsNRs in the optical field, effectively identify and excise blinking periods in vsNR fluorescent traces, and determine responsive particles based on the correlation of fluorescent signal with electrophysiological stimulation (Fig. 2). Since vsNR signal is collected from individual particles, the recordings suffer from poor signal to noise ratio (SNR) that could be overcome by averaging the signal of several particles located in close proximity of each other. At the current stage of vsNR development, we cannot yet archive membrane insertion efficiency that would allow us to find several properly inserted particle next to each other.
The project is a part of a large international interdisciplinary collaboration that includes four teams with different areas of expertise: Dr. Weiss (Chemistry; UCLA, USA), Dr. Oron (Nanophotonics; Weizmann Institute, Rehovot, Israel), Dr. Enderlein (Physical Chemistry; Georg August University, Goettingen, Germany), and the host laboratory of Dr. Triller (Neuroscience; ENS, Paris, France). The entire collaboration works on improving insertion efficiency. For each vsNR sample that we received from Dr. Weiss’s team, we perform initial series of experiments aimed to establish the relationship between the membrane potential and the fluorescent signal of vsNRs.
The first results of the project were recently published (Bar-Elli et al., 2018, ACS Photonics 5, 7, 2860-67). In this study we demonstrated that NRs are feasible for voltage sensing on a single-particle level at millisecond timescale. Now we are preparing the manuscript summarizing our data regarding utilization of vsNR for membrane potential recordings in neurons.
Voltage nanosensors is an innovative tool that has a potential to revolutionize the field of optical voltage recordings. As compared to standard voltage-sensitive dyes and protein-based sensors, vsNRs: (1) display much larger voltage sensitivity, (2) display a large spectral shift as function of voltage, (3) have a very fast response in the range of ns, (4) are very bright and afford single-molecule detection, and (5) have excellent performance in the near-infrared spectral range that makes them comparable with the majority of calcium sensors and optogenetic proteins. In addition, vsNRs could be used at very low concentrations and could be targeted to specific neurons by conjugation with specific recognition molecules that makes them potentially applicable for in vivo recordings. These unique advantages of vsNRs open up a new avenue for the super-resolution voltage imaging.