@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 {69661, title = {GESIAP3.0: Sensor-based Image Analysis Program for Transmission Visualization In Vivo}, journal = {bioRxiv}, year = {2024}, month = {2024 Oct 31}, abstract = {

Synaptic transmission mediated by various neurotransmitters influences a wide range of behaviors. However, understanding how neuromodulatory transmitters encode diverse behaviors and affect their functions remains challenging. Here, we introduce GESIAP3.0, an advanced, third-generation image analysis program based on genetically encoded sensors. This tool enables precise quantitative analysis of transmission in both awake, freely moving animals and immobilized subjects. GESIAP3.0 incorporates movement correction algorithms that effectively eliminate image displacement in behaving animals while optimizing synaptic information extraction and simplifying computations on commodity computers. Quantitative analysis of cholinergic, dopaminergic, and serotonergic transmission, corrected for tissue movement, revealed synaptic properties consistent with measurements from ex vivo wide-field and in vivo two-photon imaging under stable conditions. This validates the applicability of GESIAP3.0 for analyzing synaptic properties of neuromodulatory transmission in behaving animals.

}, doi = {10.1101/2024.10.28.620522}, url = {https://doi.org/10.1101/2024.10.28.620522}, author = {Zhu, Roger E. and Diao, Xintong and Liu, Xiaoyu and Ru, Qin and Wu, Zenan and Zhang, Ziyuan and Looger, Loren L. and Zhu, Jiazhu} } @inbook {69316, title = {Visualization of Glutamatergic Neurotransmission in Diverse Model Organisms with Genetically Encoded Indicators}, booktitle = {New Technologies for Glutamate Interaction: Neurons and Glia}, volume = {2780}, number = {Neuromethods}, year = {2024}, pages = {3{\textendash}34}, publisher = {Humana}, organization = {Humana}, address = {New York, NY}, abstract = {

Glutamate is the principal excitatory neurotransmitter, and occasionally subserves inhibitory roles, in the vertebrate nervous system. Glutamatergic synapses are dense in the vertebrate brain, at \textasciitilde1/μm3. Glutamate is released from and onto diverse components of the nervous system, including neurons, glia, and other cells. Methods for glutamate detection are critically important for understanding the function of synapses and neural circuits in normal physiology, development, and disease. Here we describe the development, optimization, and deployment of genetically encoded fluorescent glutamate indicators. We review the theoretical considerations governing glutamate sensor properties from first principles of synapse biology, microscopy, and protein structure-function relationships. We provide case studies of the state-of-the-art iGluSnFR glutamate sensor, encompassing design and optimization, mechanism of action, in vivo imaging, data analysis, and future directions. We include detailed protocols for iGluSnFR imaging in common preparations (bacteria, cell culture, and brain slices) and model organisms (worm, fly, fish, rodent).

}, isbn = {978-1-0716-3742-5}, doi = {10.1007/978-1-0716-3742-5_1}, url = {https://doi.org/10.1007/978-1-0716-3742-5_1}, author = {Aggarwal, Abhi and Chan, Joyce and Waring, Amelia K. and Negrean, Adrian and Marvin, Jonathan S. and Podgorski, Kaspar and Looger, Loren L.}, editor = {Kukley, Maria} } @article {65300, title = {A fast genetically encoded fluorescent sensor for faithful in vivo acetylcholine detection in mice, fish, worms and flies.}, journal = {bioRxiv}, year = {2020}, month = {2020 Feb 8}, abstract = {

Here we design and optimize a genetically encoded fluorescent indicator, iAChSnFR, for the ubiquitous neurotransmitter acetylcholine, based on a bacterial periplasmic binding protein. iAChSnFR shows large fluorescence changes, rapid rise and decay kinetics, and insensitivity to most cholinergic drugs. iAChSnFR revealed large transients in a variety of slice and\ in vivo\ preparations in mouse, fish, fly and worm. iAChSnFR will be useful for the study of acetylcholine in all animals.

}, doi = {https://doi.org/10.1101/2020.02.07.939504}, author = {Borden, Philip and Zhang, Peng and Shivange, Amol V and Marvin, Jonathan S and Cichon, Joseph and Dan, Chuntao and Podgorski, Kaspar and Figueiredo, Antonio and Novak, Ondrej and Tanimoto, Masashi and Shigetomi, Eiji and Lobas, Mark A and Kim, Hyuntae and Zhu, Paula and Zhang, Yajun and Zheng, W. Sharon and Fan, ChengCheng and Wang, Guangfu and Xiang, Bowen and Gan, Li and Zhang, Guang-Xian and Guo, Kaiming and Lin, Li and Cai, Yuan and Yee, Andrew G. and Aggarwal, Aman and Ford, Christopher P. and Rees, Douglas C. and Dietrich, Dirk and Khakh, Baljit S. and Dittman, Jeremy S. and Gan, Wen-Biao and Koyama, Minoru and Jayaraman, Vivek and Cheer, Joseph F. and Lester, Henry A. and Zhu, J. Julius and Looger, Loren L.} } @article {67669, title = {Development and Applications of Fluorescent Proteins for Correlative Light and Electron Microscopy}, journal = {Microscopy and Microanalysis}, volume = {24}, year = {2018}, month = {01/2018}, pages = {2318 - 2319}, abstract = {

Recent advances in super-resolution microscopy have pushed the resolution limit of light microscopy closer to that of electron microscopy. However, as they invariably rely on fluorescence, light microscopy techniques only visualize whatever gets labeled. On the other hand, while electron microscopy reveals cellular structures at the highest resolution, it offers no specificity. The information gap between the two imaging modalities can only be bridged by correlative light and electron microscopy (CLEM). Previously we have developed a probe (mEos4) whose fluorescence and photoconversion survive 0.5-1\% OsO4 fixation, allowing super-resolution visualization of organelles and fused proteins in the context of resinembedded ultrastructure in both transmission EM (TEM) and scanning EM (SEM) [1,2].

}, issn = {1431-9276}, doi = {10.1017/S1431927618012072}, url = {https://www.cambridge.org/core/product/identifier/S1431927618012072/type/journal_article}, author = {Paez-Segala, Maria G. and Wang, Yalin and Iyer, Nirmala and Li, Wei-Ping and Rivlin, Patricia K. and Looger, Loren L.} } @article {45065, title = {Mapping brain activity at scale with cluster computing.}, journal = {Nature Methods}, volume = {11}, year = {2014}, month = {2014 Jul 27}, pages = {941-950}, abstract = {

Understanding brain function requires monitoring and interpreting the activity of large networks of neurons during behavior. Advances in recording technology are greatly increasing the size and complexity of neural data. Analyzing such data will pose a fundamental bottleneck for neuroscience. We present a library of analytical tools called Thunder built on the open-source Apache Spark platform for large-scale distributed computing. The library implements a variety of univariate and multivariate analyses with a modular, extendable structure well-suited to interactive exploration and analysis development. We demonstrate how these analyses find structure in large-scale neural data, including whole-brain light-sheet imaging data from fictively behaving larval zebrafish, and two-photon imaging data from behaving mouse. The analyses relate neuronal responses to sensory input and behavior, run in minutes or less and can be used on a private cluster or in the cloud. Our open-source framework thus holds promise for turning brain activity mapping efforts into biological insights.

}, issn = {1548-7091}, doi = {10.1038/nmeth.3041}, author = {Freeman, Jeremy and Vladimirov, Nikita and Kawashima, Takashi and Mu, Yu and Sofroniew, Nicholas J. and Bennett, Davis V. and Rosen, Joshua and Yang, Chao-Tsung and Looger, Loren L. and Ahrens, Misha B.} }