@article {68690, title = {Sensitivity optimization of a rhodopsin-based fluorescent voltage indicator}, journal = {Neuron}, volume = {111}, year = {2023}, month = {2023 May 17}, pages = {1547-1563}, abstract = {

The ability to optically image cellular transmembrane voltages at millisecond-timescale resolutions can offer unprecedented insight into the function of living brains in behaving animals. Here, we present a\ point mutation\ that increases the sensitivity of Ace2 opsin-based voltage indicators. We use the mutation to develop Voltron2, an improved chemigeneic voltage indicator that has a 65\% higher sensitivity to single APs and 3-fold higher sensitivity to subthreshold potentials than Voltron. Voltron2 retained the sub-millisecond kinetics and photostability of its predecessor, although with lower baseline fluorescence. In multiple\ in\ vitro\ and\ in\ vivo\ comparisons with its predecessor across multiple species, we found Voltron2 to be more sensitive to APs and subthreshold fluctuations. Finally, we used Voltron2 to study and evaluate the possible mechanisms of\ interneuron\ synchronization in the mouse hippocampus. Overall, we have discovered a generalizable mutation that significantly increases the sensitivity of Ace2 rhodopsin-based sensors, improving their voltage reporting capability.

}, issn = {08966273}, doi = {10.1016/j.neuron.2023.03.009}, url = {https://linkinghub.elsevier.com/retrieve/pii/S0896627323002052}, author = {Abdelfattah, Ahmed S. and Zheng, Jihong and Singh, Amrita and Huang, Yi-Chieh and Reep, Daniel and Tsegaye, Getahun and Tsang, Arthur and Arthur, Benjamin J. and Rehorova, Monika and Olson, Carl V.L. and Shuai, Yichun and Zhang, Lixia and Fu, Tian-Ming and Milkie, Daniel E. and Moya, Maria V. and Weber, Timothy D. and Lemire, Andrew L. and Baker, Christopher A. and Falco, Natalie and Zheng, Qinsi and Grimm, Jonathan B. and Yip, Mighten C. and Walpita, Deepika and Chase, Martin and Campagnola, Luke and Murphy, Gabe J. and Wong, Allan M. and Forest, Craig R. and Mertz, Jerome and Economo, Michael N. and Turner, Glenn C. and Koyama, Minoru and Lin, Bei-Jung and Betzig, Eric and Novak, Ondrej and Lavis, Luke D. and Svoboda, Karel and Korff, Wyatt and Chen, Tsai-Wen and Schreiter, Eric R. and Hasseman, Jeremy P. and Kolb, Ilya} } @article {67211, title = {High-fidelity estimates of spikes and subthreshold waveforms from 1-photon voltage imaging in~vivo.}, journal = {Cell Reports}, volume = {35}, year = {2021}, month = {2021 Apr 06}, pages = {108954}, abstract = {

The ability to probe the membrane potential of multiple genetically defined neurons simultaneously would have a profound impact on neuroscience research. Genetically encoded voltage indicators are a promising tool for this purpose, and recent developments have achieved a high signal-to-noise ratio in\ vivo with 1-photon fluorescence imaging. However, these recordings exhibit several sources of noise and signal extraction remains a challenge. We present an improved signal extraction pipeline, spike-guided penalized matrix decomposition-nonnegative matrix factorization (SGPMD-NMF), which resolves supra- and subthreshold voltages in\ vivo. The method incorporates biophysical and optical constraints. We validate the pipeline with simultaneous patch-clamp and optical recordings from mouse layer 1 in\ vivo and with simulated and composite datasets with realistic noise. We demonstrate applications to mouse hippocampus expressing paQuasAr3-s or SomArchon1, mouse cortex expressing SomArchon1 or Voltron, and zebrafish spines expressing zArchon1.

}, issn = {2211-1247}, doi = {10.1016/j.celrep.2021.108954}, author = {Xie, Michael E and Adam, Yoav and Fan, Linlin Z and B{\"o}hm, Urs L and Kinsella, Ian and Zhou, Ding and Rozsa, Marton and Singh, Amrita and Svoboda, Karel and Paninski, Liam and Cohen, Adam E} } @article {67230, title = {VolPy: Automated and scalable analysis pipelines for voltage imaging datasets.}, journal = {PLoS Computational Biology}, volume = {17}, year = {2021}, month = {2021 Apr 14}, pages = {e1008806}, abstract = {

Voltage imaging enables monitoring neural activity at sub-millisecond and sub-cellular scale, unlocking the study of subthreshold activity, synchrony, and network dynamics with unprecedented spatio-temporal resolution. However, high data rates (\>800MB/s) and low signal-to-noise ratios create bottlenecks for analyzing such datasets. Here we present VolPy, an automated and scalable pipeline to pre-process voltage imaging datasets. VolPy features motion correction, memory mapping, automated segmentation, denoising and spike extraction, all built on a highly parallelizable, modular, and extensible framework optimized for memory and speed. To aid automated segmentation, we introduce a corpus of 24 manually annotated datasets from different preparations, brain areas and voltage indicators. We benchmark VolPy against ground truth segmentation, simulations and electrophysiology recordings, and we compare its performance with existing algorithms in detecting spikes. Our results indicate that VolPy{\textquoteright}s performance in spike extraction and scalability are state-of-the-art.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1008806}, author = {Cai, Changjia and Friedrich, Johannes and Singh, Amrita and Eybposh, M Hossein and Pnevmatikakis, Eftychios A and Podgorski, Kaspar and Giovannucci, Andrea} } @article {65316, title = {Multiple network properties overcome random connectivity to enable stereotypic sensory responses.}, journal = {Nature Communications}, volume = {11}, year = {2020}, month = {2020 Feb 24}, pages = {1023}, abstract = {

Connections between neuronal populations may be genetically hardwired or random. In the insect olfactory system, projection neurons of the antennal lobe connect randomly to Kenyon cells of the mushroom body. Consequently, while the odor responses of the projection neurons are stereotyped across individuals, the responses of the Kenyon cells are variable. Surprisingly, downstream of Kenyon cells, mushroom body output neurons show stereotypy in their responses. We found that the stereotypy is enabled by the convergence of inputs from many Kenyon cells onto an output neuron, and does not require learning. The stereotypy emerges in the total response of the Kenyon cell population using multiple odor-specific features of the projection neuron responses, benefits from the nonlinearity in the transfer function, depends on the convergence:randomness ratio, and is constrained by sparseness. Together, our results reveal the fundamental mechanisms and constraints with which convergence enables stereotypy in sensory responses despite random connectivity.

}, issn = {2041-1723}, doi = {10.1038/s41467-020-14836-6}, author = {Mittal, Aarush Mohit and Gupta, Diksha and Singh, Amrita and Lin, Andrew C and Gupta, Nitin} } @article {65081, title = {Bright and photostable chemigenetic indicators for extended in vivo voltage imaging.}, journal = {Science}, volume = {365}, year = {2019}, month = {2019 Aug 13}, pages = {699-704}, abstract = {

Imaging changes in membrane potential using genetically encoded fluorescent voltage indicators (GEVIs) has great potential for monitoring neuronal activity with high spatial and temporal resolution. Brightness and photostability of fluorescent proteins and rhodopsins have limited the utility of existing GEVIs. We engineered a novel GEVI, "Voltron", that utilizes bright and photostable synthetic dyes instead of protein-based fluorophores, extending the combined duration of imaging and number of neurons imaged simultaneously by more than tenfold relative to existing GEVIs. We used Voltron for in vivo voltage imaging in mice, zebrafish, and fruit flies. In mouse cortex, Voltron allowed single-trial recording of spikes and subthreshold voltage signals from dozens of neurons simultaneously, over 15 min of continuous imaging. In larval zebrafish, Voltron enabled the precise correlation of spike timing with behavior.

}, issn = {1095-9203}, doi = {10.1126/science.aav6416}, author = {Abdelfattah, Ahmed S and Kawashima, Takashi and Singh, Amrita and Novak, Ondrej and Liu, Hui and Shuai, Yichun and Huang, Yi-Chieh and Campagnola, Luke and Seeman, Stephanie C and Yu, Jianing and Zheng, Jihong and Grimm, Jonathan B and Patel, Ronak and Friedrich, Johannes and Mensh, Brett D and Paninski, Liam and Macklin, John J and Murphy, Gabe J and Podgorski, Kaspar and Lin, Bei-Jung and Chen, Tsai-Wen and Turner, Glenn C and Liu, Zhe and Koyama, Minoru and Svoboda, Karel and Ahrens, Misha B and Lavis, Luke D and Schreiter, Eric R} }