PDF(Pubmed)
Abstract：
Regular spatial patterns are ubiquitous forms of organization in nature. In animals, regular patterns can be found from the cellular scale to the tissue scale, and from early stages of development to adulthood. To understand the formation of these patterns, how they assemble and mature, and how they are affected by perturbations, a precise quantitative description of the patterns is essential. However, accessible tools that offer in-depth analysis without the need for computational skills are lacking for biologists. Here, we present PatternJ, a novel toolset to analyze regular one-dimensional patterns precisely and automatically. This toolset, to be used with the popular imaging processing program ImageJ/Fiji, facilitates the extraction of key geometric features within and between pattern repeats in static images and time-lapse series. We validate PatternJ with simulated data and test it on images of sarcomeres from insect muscles and contracting cardiomyocytes, actin rings in neurons, and somites from zebrafish embryos obtained using confocal fluorescence microscopy, STORM, electron microscopy, and brightfield imaging. We show that the toolset delivers subpixel feature extraction reliably even with images of low signal-to-noise ratio. PatternJ\'s straightforward use and functionalities make it valuable for various scientific fields requiring quantitative one-dimensional pattern analysis, including the sarcomere biology of muscles or the patterning of mammalian axons, speeding up discoveries with the bonus of high reproducibility.
摘要：
规则的空间模式是自然界中普遍存在的组织形式。在动物中,从细胞尺度到组织尺度都可以找到规则的模式，从发育的早期阶段到成年。为了理解这些模式的形成，它们是如何组装和成熟的，以及它们如何受到扰动的影响，模式的精确定量描述是必不可少的。然而,生物学家缺乏提供深入分析而不需要计算技能的可访问工具。这里,我们介绍PatternJ,一种新颖的工具集，可以精确自动地分析规则的一维模式。这个工具集,与流行的图像处理程序ImageJ/斐济一起使用，有助于在静态图像和延时系列中的图案重复内和之间提取关键几何特征。我们用模拟数据验证PatternJ，并在昆虫肌肉和收缩心肌细胞的肉瘤图像上进行测试，神经元中的肌动蛋白环，和使用共聚焦荧光显微镜从斑马鱼胚胎中获得的体节，暴风雨,电子显微镜，和明场成像。我们表明，即使使用低信噪比的图像，该工具集也能可靠地实现亚像素特征提取。PatternJ的直接使用和功能使其对于需要定量一维模式分析的各种科学领域具有价值，包括肌肉的肌节生物学或哺乳动物轴突的模式，加快发现与高重现性的奖金。
