CDISE, Skoltech, Moscow, Russian Federation.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States of America.
PLoS Comput Biol. 2019 May 13;15(5):e1007012. doi: 10.1371/journal.pcbi.1007012. eCollection 2019 May.
Neuronal synapses transmit electrochemical signals between cells through the coordinated action of presynaptic vesicles, ion channels, scaffolding and adapter proteins, and membrane receptors. In situ structural characterization of numerous synaptic proteins simultaneously through multiplexed imaging facilitates a bottom-up approach to synapse classification and phenotypic description. Objective automation of efficient and reliable synapse detection within these datasets is essential for the high-throughput investigation of synaptic features. Convolutional neural networks can solve this generalized problem of synapse detection, however, these architectures require large numbers of training examples to optimize their thousands of parameters. We propose DoGNet, a neural network architecture that closes the gap between classical computer vision blob detectors, such as Difference of Gaussians (DoG) filters, and modern convolutional networks. DoGNet is optimized to analyze highly multiplexed microscopy data. Its small number of training parameters allows DoGNet to be trained with few examples, which facilitates its application to new datasets without overfitting. We evaluate the method on multiplexed fluorescence imaging data from both primary mouse neuronal cultures and mouse cortex tissue slices. We show that DoGNet outperforms convolutional networks with a low-to-moderate number of training examples, and DoGNet is efficiently transferred between datasets collected from separate research groups. DoGNet synapse localizations can then be used to guide the segmentation of individual synaptic protein locations and spatial extents, revealing their spatial organization and relative abundances within individual synapses. The source code is publicly available: https://github.com/kulikovv/dognet.
神经元突触通过突触小泡、离子通道、支架和衔接蛋白以及膜受体的协调作用在细胞间传递电化学信号。通过多重成像对大量突触蛋白进行原位结构特征分析，有助于采用自下而上的方法对突触进行分类和表型描述。在这些数据集内，高效、可靠的突触检测的客观自动化对于进行高通量的突触特征研究至关重要。卷积神经网络可以解决突触检测的这个广义问题，但是，这些架构需要大量的训练样本来优化其数千个参数。我们提出了 DogNet，这是一种神经网络架构，它弥合了经典计算机视觉斑点探测器（如高斯差分（DoG）滤波器）和现代卷积网络之间的差距。DogNet 经过优化，可用于分析高度多重化的显微镜数据。其少量的训练参数允许 DogNet 用少量的示例进行训练，这使其能够应用于新数据集而不会过度拟合。我们在来自原代小鼠神经元培养物和小鼠皮层组织切片的多重荧光成像数据上评估了该方法。我们表明，DogNet 在具有少量到中等数量的训练示例时表现优于卷积网络，并且可以在来自不同研究小组的数据集之间有效地进行转移。然后，可以使用 DogNet 的突触定位来指导单个突触蛋白位置和空间范围的分割，揭示它们在单个突触内的空间组织和相对丰度。该源代码可在以下网址获得：https://github.com/kulikovv/dognet。
