@article {69833, title = {Glutamate indicators with increased sensitivity and tailored deactivation rates}, journal = {bioRxiv}, year = {2025}, month = {2025 Mar 20}, abstract = {

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neuron{\textquoteright}s thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (\< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo.

}, doi = {10.1101/2025.03.20.643984}, url = {https://www.biorxiv.org/content/early/2025/03/24/2025.03.20.643984}, author = {Aggarwal, Abhi and Negrean, Adrian and Chen, Yang and Iyer, Rishyashring and Reep, Daniel and Liu, Anyi and Palutla, Anirudh and Xie, Michael E. and MacLennan, Bryan J. and Hagihara, Kenta M. and Kinsey, Lucas W. and Sun, Julianna L. and Yao, Pantong and Zheng, Jihong and Tsang, Arthur and Tsegaye, Getahun and Zhang, Yonghai and Patel, Ronak H. and Arthur, Benjamin J. and Hiblot, Julien and Leippe, Philipp and Tarnawski, Miroslaw and Marvin, Jonathan S. and Vevea, Jason D. and Turaga, Srinivas C. and Tebo, Alison G. and Carandini, Matteo and Rossi, L. F. and Kleinfeld, David and Konnerth, Arthur and Svoboda, Karel and Turner, Glenn C. and Hasseman, Jeremy and Podgorski, Kaspar} } @article {69831, title = {iGABASnFR2: Improved genetically encoded protein sensors of GABA}, journal = {bioRxiv}, year = {2025}, month = {2025 Mar 25}, abstract = {

Monitoring GABAergic inhibition in the nervous system has been enabled by development of an intensiometric molecular sensor that directly detects GABA. However the first generation iGABASnFR exhibits low signal-to-noise and suboptimal kinetics, making in vivo experiments challenging. To improve sensor performance, we targeted several sites in the protein for near-saturation mutagenesis, and evaluated the resulting sensor variants in a high throughput screening system using evoked synaptic release in primary cultured neurons. This identified a sensor variant, iGABASnFR2, with 4.2-fold improved sensitivity and 20\% faster kinetics, and binding affinity that remained in a range sensitive to changes in GABA concentration at synapses. We also identified sensors with an inverted response, decreasing fluorescence intensity upon GABA binding. We termed the best such negative-going sensor iGABASnFR2n, which can be used to corroborate observations with the positive-going sensor. These improvements yielded a qualitative enhancement of in vivo performance, enabling us to make the first measurements of direction selective GABA release in the retina and confirm a longstanding hypothesis for how sensitivity to motion arises in the visual system.

}, doi = {10.1101/2025.03.25.644953}, url = {https://www.biorxiv.org/content/early/2025/03/26/2025.03.25.644953}, author = {Kolb, Ilya and Hasseman, Jeremy P. and Matsumoto, Akihiro and Arthur, Benjamin J. and Zhang, Yan and Tsang, Arthur and Reep, Daniel and Tsegaye, Getahun and Zheng, Jihong and Patel, Ronak and Looger, Loren L. and Marvin, Jonathan S. and Korff, Wyatt L. and Yonehara, Keisuke and Turner, Glenn C.} } @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 {67229, title = {SongExplorer: A deep learning workflow for discovery and segmentation of animal acoustic communication signals}, journal = {bioRxiv}, year = {2021}, month = {03/2021}, abstract = {

Many animals produce distinct sounds or substrate-borne vibrations, but these signals have proved challenging to segment with automated algorithms. We have developed SongExplorer, a web-browser based interface wrapped around a deep-learning algorithm that supports an interactive workflow for (1) discovery of animal sounds, (2) manual annotation, (3) supervised training of a deep convolutional neural network, and (4) automated segmentation of recordings. Raw data can be explored by simultaneously examining song events, both individually and in the context of the entire recording, watching synced video, and listening to song. We provide a simple way to visualize many song events from large datasets within an interactive low-dimensional visualization, which facilitates detection and correction of incorrectly labelled song events. The machine learning model we implemented displays higher accuracy than existing heuristic algorithms and similar accuracy as two expert human annotators. We show that SongExplorer allows rapid detection of all song types from new species and of novel song types in previously well-studied species.Competing Interest StatementThe authors have declared no competing interest.

}, doi = {10.1101/2021.03.26.437280}, url = {https://www.biorxiv.org/content/early/2021/03/26/2021.03.26.437280}, author = {Arthur, Benjamin J. and Ding, Yun and Sosale, Medhini and Khalif, Faduma and Kim, Elizabeth and Waddell, Peter and Turaga, Srinivas C. and Stern, David L.} }