SynQuant: an automatic tool to quantify synapses from microscopy images
SynQuant: an automatic tool to quantify synapses from microscopy images
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SynQuant：一种从显微镜图像中量化突触的自动工具
DOI：
10.1093/bioinformatics/btz760
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发表时间：
2019
期刊：
影响因子：
5.8
通讯作者：
Tian, Lin
中科院分区：
文献类型：
--
作者：
Wang, Yizhi;Wang, Congchao;Ranefall, Petter;Broussard, Gerard Joey;Wang, Yinxue;Shi, Guilai;Lyu, Boyu;Wu, Chiung-Ting;Wang, Yue;Tian, Lin
动机突触是神经信号传递的关键。因此，从图像中量化突触和相关神经突对于深入了解大脑功能和疾病的潜在途径至关重要。尽管突触点成像数据的广泛可用性，一些问题阻碍了令人满意的量化这些结构的当前工具。首先，用于标记突触的抗体并不完全特异于突触。这些抗体可能存在于神经突或其他细胞隔室中。第二，由于抗体浓度的变化和突触内在差异，不同神经突和突触点的亮度是不均匀的。第三，由于实验设备和敏感抗体的可用性的限制，图像通常具有低信噪比。这些问题使得突触的检测具有挑战性，需要开发一种新的工具来轻松，准确地量化synaps.ResultsWe提出了一个自动概率原则的突触检测算法，并将其集成到我们的突触量化工具SynQuant。该方法基于顺序统计量理论，控制了错误发现率，提高了突触检测能力。SynQuant是无监督的，适用于2D和3D数据，并且可以处理多个染色通道。通过在一个合成数据集和三个带有地面实况注释或手动标记的真实的数据集上进行广泛的实验，证明SynQuant的性能优于同行专业的无监督突触检测工具以及通用点检测方法。可用性和实施Java源代码、Fiji插件和测试数据可在https://github.com/yu-lab-vt/SynQuant.Supplementary信息上获得补充数据可在Bioinformatics online上获得。
MotivationSynapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.ResultsWe present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.Availability and implementationJava source code, Fiji plug-in, and test data are available at https://github.com/yu-lab-vt/SynQuant.Supplementary informationSupplementary data are available atBioinformaticsonline.