Kaltdorf Kristin Verena, Schulze Katja, Helmprobst Frederik, Kollmannsberger Philip, Dandekar Thomas, Stigloher Christian
Division of Electron Microscopy, Biocenter, University of Wuerzburg, Wuerzburg, Germany.
Department of Bioinformatics, Biocenter, University of Wuerzburg, Wuerzburg, Germany.
PLoS Comput Biol. 2017 Jan 5;13(1):e1005317. doi: 10.1371/journal.pcbi.1005317. eCollection 2017 Jan.
Automatic image reconstruction is critical to cope with steadily increasing data from advanced microscopy. We describe here the Fiji macro 3D ART VeSElecT which we developed to study synaptic vesicles in electron tomograms. We apply this tool to quantify vesicle properties (i) in embryonic Danio rerio 4 and 8 days past fertilization (dpf) and (ii) to compare Caenorhabditis elegans N2 neuromuscular junctions (NMJ) wild-type and its septin mutant (unc-59(e261)). We demonstrate development-specific and mutant-specific changes in synaptic vesicle pools in both models. We confirm the functionality of our macro by applying our 3D ART VeSElecT on zebrafish NMJ showing smaller vesicles in 8 dpf embryos then 4 dpf, which was validated by manual reconstruction of the vesicle pool. Furthermore, we analyze the impact of C. elegans septin mutant unc-59(e261) on vesicle pool formation and vesicle size. Automated vesicle registration and characterization was implemented in Fiji as two macros (registration and measurement). This flexible arrangement allows in particular reducing false positives by an optional manual revision step. Preprocessing and contrast enhancement work on image-stacks of 1nm/pixel in x and y direction. Semi-automated cell selection was integrated. 3D ART VeSElecT removes interfering components, detects vesicles by 3D segmentation and calculates vesicle volume and diameter (spherical approximation, inner/outer diameter). Results are collected in color using the RoiManager plugin including the possibility of manual removal of non-matching confounder vesicles. Detailed evaluation considered performance (detected vesicles) and specificity (true vesicles) as well as precision and recall. We furthermore show gain in segmentation and morphological filtering compared to learning based methods and a large time gain compared to manual segmentation. 3D ART VeSElecT shows small error rates and its speed gain can be up to 68 times faster in comparison to manual annotation. Both automatic and semi-automatic modes are explained including a tutorial.
自动图像重建对于处理来自先进显微镜技术的不断增加的数据至关重要。我们在此描述了我们开发的用于研究电子断层扫描中突触小泡的斐济宏程序3D ART VeSElecT。我们应用此工具来量化（i）受精后4天和8天（dpf）的斑马鱼胚胎中的小泡特性，以及（ii）比较秀丽隐杆线虫N2神经肌肉接头（NMJ）野生型及其septin突变体（unc-59（e261））。我们在两个模型中都证明了突触小泡池的发育特异性和突变特异性变化。通过将我们的3D ART VeSElecT应用于斑马鱼NMJ，我们证实了该宏程序的功能，结果显示8 dpf胚胎中的小泡比4 dpf时更小，这通过手动重建小泡池得到了验证。此外，我们分析了秀丽隐杆线虫septin突变体unc-59（e261）对小泡池形成和小泡大小的影响。在斐济中，自动小泡配准和表征作为两个宏程序（配准和测量）来实现。这种灵活的安排尤其允许通过可选的手动修订步骤减少误报。预处理和对比度增强在x和y方向上1nm/像素的图像堆栈上进行。集成了半自动细胞选择。3D ART VeSElecT去除干扰成分，通过3D分割检测小泡，并计算小泡体积和直径（球形近似，内径/外径）。使用RoiManager插件以彩色收集结果，包括手动去除不匹配的混杂小泡的可能性。详细评估考虑了性能（检测到的小泡）、特异性（真实小泡）以及精度和召回率。与基于学习的方法相比，我们还展示了在分割和形态学滤波方面的优势，与手动分割相比有很大的时间优势。3D ART VeSElecT显示出较小的错误率，与手动注释相比，其速度提升可达68倍。文中解释了自动和半自动模式，包括一个教程。
