谷氨酸盐指标具有更高的敏感性和定制的失活率。

Abhi Aggarwal, Adrian Negrean, Yang Chen, Rishyashring Iyer, Daniel Reep, Anyi Liu, Anirudh Palutla, Michael E Xie, Bryan J MacLennan, Kenta M Hagihara, Lucas W Kinsey, Julianna L Sun, Pantong Yao, Jihong Zheng, Arthur Tsang, Getahun Tsegaye, Yonghai Zhang, Ronak H Patel, Benjamin J Arthur, Julien Hiblot, Philipp Leippe, Miroslaw Tarnawski, Jonathan S Marvin, Jason D Vevea, Srinivas C Turaga, Alison G Tebo, Matteo Carandini, L Federico Rossi, David Kleinfeld, Arthur Konnerth, Karel Svoboda, Glenn C Turner, Jeremy Hasseman, Kaspar Podgorski
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引用次数: 0

摘要

要确定神经元的输入-输出操作,需要同时测量完整大脑中数千个神经元输入中的许多神经元的突触传递。为了实现这一目标,我们设计并筛选了神经元培养中荧光蛋白谷氨酸指示物iGluSnFR3的3365个变体,并选择了小鼠视觉皮层中的变体。两种变体具有高灵敏度,快速激活(< 2 ms)和适合记录大量突触(iGluSnFR4s, 153 ms)或快速动态(iGluSnFR4f, 26 ms)的失活时间。通过成像轴突上的动作电位诱发信号和树突棘上的视觉诱发信号,我们发现iGluSnFR4s/4f主要以单囊敏感性检测局部突触谷氨酸。这些指标检测到广泛的自然突触传递,包括在振动皮层第4层和海马CA1树突中。iGluSnFR4提高了神经网络在体内跟踪信息流的灵敏度和规模(4s)或速度(4f)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Glutamate indicators with increased sensitivity and tailored deactivation rates.

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neuron'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 .

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