Imaging Core Facility, Biocenter, University of Würzburg, Würzburg, Germany.
Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany.
PLoS One. 2018 Oct 8;13(10):e0205348. doi: 10.1371/journal.pone.0205348. eCollection 2018.
Synaptic vesicles (SVs) are a key component of neuronal signaling and fulfil different roles depending on their composition. In electron micrograms of neurites, two types of vesicles can be distinguished by morphological criteria, the classical "clear core" vesicles (CCV) and the typically larger "dense core" vesicles (DCV), with differences in electron density due to their diverse cargos. Compared to CCVs, the precise function of DCVs is less defined. DCVs are known to store neuropeptides, which function as neuronal messengers and modulators [1]. In C. elegans, they play a role in locomotion, dauer formation, egg-laying, and mechano- and chemosensation [2]. Another type of DCVs, also referred to as granulated vesicles, are known to transport Bassoon, Piccolo and further constituents of the presynaptic density in the center of the active zone (AZ), and therefore are important for synaptogenesis [3]. To better understand the role of different types of SVs, we present here a new automated approach to classify vesicles. We combine machine learning with an extension of our previously developed vesicle segmentation workflow, the ImageJ macro 3D ART VeSElecT. With that we reliably distinguish CCVs and DCVs in electron tomograms of C. elegans NMJs using image-based features. Analysis of the underlying ground truth data shows an increased fraction of DCVs as well as a higher mean distance between DCVs and AZs in dauer larvae compared to young adult hermaphrodites. Our machine learning based tools are adaptable and can be applied to study properties of different synaptic vesicle pools in electron tomograms of diverse model organisms.
突触小泡（SVs）是神经元信号传递的关键组成部分，根据其组成具有不同的功能。在神经突的电子显微镜中，可以根据形态学标准区分出两种类型的小泡，即经典的“透明核心”小泡（CCV）和通常较大的“致密核心”小泡（DCV），由于其不同的货物，它们在电子密度上存在差异。与 CCVs 相比，DCVs 的精确功能定义较少。已知 DCVs 储存神经肽，神经肽作为神经元信使和调节剂发挥作用[1]。在秀丽隐杆线虫中，它们在运动、 dauer 形成、产卵以及机械和化学感觉中发挥作用[2]。另一种类型的 DCVs，也称为颗粒状小泡，已知可将 Bassoon、Piccolo 和突触前致密区的其他成分运输到活性区（AZ）的中心，因此对突触发生很重要[3]。为了更好地理解不同类型 SVs 的作用，我们在这里提出了一种新的自动分类小泡的方法。我们将机器学习与我们之前开发的小泡分割工作流程的扩展相结合，即 ImageJ 宏 3D ART VeSElecT。通过这种方法，我们可以使用基于图像的特征可靠地区分秀丽隐杆线虫 NMJs 电子断层扫描中的 CCVs 和 DCVs。对基础真实数据的分析表明，与年轻成年雌雄同体相比， dauer 幼虫中 DCVs 的比例增加，并且 DCVs 与 AZs 之间的平均距离也增加。我们基于机器学习的工具具有适应性，可以应用于研究不同模式生物的电子断层扫描中不同突触小泡库的特性。
