Neuronal Signal Transduction, Max Planck Florida Institute for Neuroscience, Jupiter, Florida, United States of America.
Neuroscience, Oregon Health and Science University School of Medicine, Portland, Oregon, United States of America.
PLoS One. 2018 Jul 5;13(7):e0199589. doi: 10.1371/journal.pone.0199589. eCollection 2018.
Synaptic plasticity, the cellular basis for learning and memory, is mediated by a complex biochemical network of signaling proteins. These proteins are compartmentalized in dendritic spines, the tiny, bulbous, post-synaptic structures found on neuronal dendrites. The ability to screen a high number of molecular targets for their effect on dendritic spine structural plasticity will require a high-throughput imaging system capable of stimulating and monitoring hundreds of dendritic spines in various conditions. For this purpose, we present a program capable of automatically identifying dendritic spines in live, fluorescent tissue. Our software relies on a machine learning approach to minimize any need for parameter tuning from the user. Custom thresholding and binarization functions serve to "clean" fluorescent images, and a neural network is trained using features based on the relative shape of the spine perimeter and its corresponding dendritic backbone. Our algorithm is rapid, flexible, has over 90% accuracy in spine detection, and bundled with our user-friendly, open-source, MATLAB-based software package for spine analysis.
突触可塑性是学习和记忆的细胞基础，由信号蛋白的复杂生化网络介导。这些蛋白质位于树突棘中进行分隔，树突棘是神经元树突上的微小、球状的突触后结构。为了筛选大量分子靶点对树突棘结构可塑性的影响，需要一种能够在各种条件下刺激和监测数百个树突棘的高通量成像系统。为此，我们提出了一种能够自动识别活细胞荧光组织中树突棘的程序。我们的软件依赖于机器学习方法，最大限度地减少用户对参数调整的需求。自定义阈值和二值化功能可用于“清理”荧光图像，神经网络使用基于脊突周长相对形状及其相应树突主干的特征进行训练。我们的算法快速、灵活，脊突检测准确率超过 90%，并与我们基于 MATLAB 的用户友好、开源的脊突分析软件包捆绑在一起。
