Université Côte d'azur, LJAD, CNRS UMR7351, Nice, France.
CNRS - IRL3457, CRM, Université de Montréal, Montréal, Canada.
Neuroinformatics. 2023 Jan;21(1):207-220. doi: 10.1007/s12021-022-09609-z. Epub 2022 Nov 8.
Recent technological advances have enabled the recording of neurons in intact circuits with a high spatial and temporal resolution, creating the need for modeling with the same precision. In particular, the development of ultra-fast two-photon microscopy combined with fluorescence-based genetically-encoded Ca-indicators allows capture of full-dendritic arbor and somatic responses associated with synaptic input and action potential output. The complexity of dendritic arbor structures and distributed patterns of activity over time results in the generation of incredibly rich 4D datasets that are challenging to analyze (Sakaki et al. in Frontiers in Neural Circuits 14:33, 2020). Interpreting neural activity from fluorescence-based Ca biosensors is challenging due to non-linear interactions between several factors influencing intracellular calcium ion concentration and its binding to sensors, including the ionic dynamics driven by diffusion, electrical gradients and voltage-gated conductances. To investigate those dynamics, we designed a model based on a Cable-like equation coupled to the Nernst-Planck equations for ionic fluxes in electrolytes. We employ this model to simulate signal propagation and ionic electrodiffusion across a dendritic arbor. Using these simulation results, we then designed an algorithm to detect synapses from Ca imaging datasets. We finally apply this algorithm to experimental Ca-indicator datasets from neurons expressing jGCaMP7s (Dana et al. in Nature Methods 16:649-657, 2019), using full-dendritic arbor sampling in vivo in the Xenopus laevis optic tectum using fast random-access two-photon microscopy. Our model reproduces the dynamics of visual stimulus-evoked jGCaMP7s-mediated calcium signals observed experimentally, and the resulting algorithm allows prediction of the location of synapses across the dendritic arbor. Our study provides a way to predict synaptic activity and location on dendritic arbors, from fluorescence data in the full dendritic arbor of a neuron recorded in the intact and awake developing vertebrate brain.
最近的技术进步使得以高时空分辨率记录完整回路中的神经元成为可能,这就需要进行同样精确的建模。特别是,超快速双光子显微镜与基于荧光的遗传编码 Ca 指示剂的结合,使得人们可以捕捉与突触输入和动作电位输出相关的完整树突分支和体细胞反应。树突分支结构的复杂性以及随时间分布的活动模式导致生成了令人难以置信的丰富的 4D 数据集,这些数据集很难进行分析(Sakaki 等人,《神经回路前沿》14:33, 2020)。由于影响细胞内钙离子浓度及其与传感器结合的几个因素之间存在非线性相互作用,包括扩散、电梯度和电压门控电导驱动的离子动力学,因此从基于荧光的 Ca 生物传感器解释神经活动具有挑战性。为了研究这些动力学,我们设计了一个基于电缆样方程的模型,该模型与电解质中离子通量的 Nernst-Planck 方程耦合。我们利用这个模型来模拟信号在树突分支上的传播和离子电扩散。利用这些模拟结果,我们设计了一种算法来从 Ca 成像数据集中检测突触。最后,我们将该算法应用于在活体非洲爪蟾视顶盖中用快速随机存取双光子显微镜进行全树突分支采样时表达 jGCaMP7s 的神经元的实验 Ca 指示剂数据集。我们的模型再现了实验中观察到的视觉刺激诱发 jGCaMP7s 介导的钙信号的动力学,并且得到的算法允许预测树突分支上突触的位置。我们的研究为从完整的神经元树突中记录的、在完整清醒的发育中的脊椎动物大脑中的荧光数据预测树突分支上的突触活动和位置提供了一种方法。