Minty Gavin, Hoppen Alex, Boehm Ines, Alhindi Abrar, Gibb Larissa, Potter Ellie, Wagner Boris C, Miller Janice, Skipworth Richard J E, Gillingwater Thomas H, Jones Ross A
Edinburgh Medical School: Biomedical Sciences (Anatomy), University of Edinburgh, Edinburgh EH8 9AG, UK.
Euan MacDonald Centre for Motor Neurone Disease Research, University of Edinburgh, Edinburgh EH8 9AG, UK.
R Soc Open Sci. 2020 Apr 15;7(4):200128. doi: 10.1098/rsos.200128. eCollection 2020 Apr.
Large-scale data analysis of synaptic morphology is becoming increasingly important to the field of neurobiological research (e.g. 'connectomics'). In particular, a detailed knowledge of neuromuscular junction (NMJ) morphology has proven to be important for understanding the form and function of synapses in both health and disease. The recent introduction of a standardized approach to the morphometric analysis of the NMJ-'NMJ-morph'-has provided the first common software platform with which to analyse and integrate NMJ data from different research laboratories. Here, we describe the design and development of a novel macro-'automated NMJ-morph' or 'aNMJ-morph'-to update and streamline the original NMJ-morph methodology. ImageJ macro language was used to encode the complete NMJ-morph workflow into seven navigation windows that generate robust data for 19 individual pre-/post-synaptic variables. The aNMJ-morph scripting was first validated against reference data generated by the parent workflow to confirm data reproducibility. aNMJ-morph was then compared with the parent workflow in large-scale data analysis of original NMJ images (240 NMJs) by multiple independent investigators. aNMJ-morph conferred a fourfold increase in data acquisition rate compared with the parent workflow, with average analysis times reduced to approximately 1 min per NMJ. Strong concordance was demonstrated between the two approaches for all 19 morphological variables, confirming the robust nature of aNMJ-morph. aNMJ-morph is a freely available and easy-to-use macro for the rapid and robust analysis of NMJ morphology and offers significant improvements in data acquisition and learning curve compared to the original NMJ-morph workflow.
突触形态的大规模数据分析在神经生物学研究领域(如“连接组学”)正变得越来越重要。特别是,事实证明,详细了解神经肌肉接头(NMJ)的形态对于理解健康和疾病状态下突触的形式和功能都很重要。最近引入的一种标准化的NMJ形态计量分析方法——“NMJ-morph”——提供了第一个通用软件平台,用于分析和整合来自不同研究实验室的NMJ数据。在这里,我们描述了一种新型宏程序——“自动化NMJ-morph”或“aNMJ-morph”——的设计与开发,以更新和简化原始的NMJ-morph方法。使用ImageJ宏语言将完整的NMJ-morph工作流程编码到七个导航窗口中,这些窗口可为19个单独的突触前/后变量生成可靠的数据。aNMJ-morph脚本首先针对母工作流程生成的参考数据进行验证,以确认数据的可重复性。然后,在多个独立研究人员对原始NMJ图像(240个NMJ)的大规模数据分析中,将aNMJ-morph与母工作流程进行比较。与母工作流程相比,aNMJ-morph的数据采集率提高了四倍,平均分析时间减少到每个NMJ约1分钟。两种方法对所有19个形态变量都表现出高度一致性,证实了aNMJ-morph的稳健性。aNMJ-morph是一个免费且易于使用的宏程序,用于快速、稳健地分析NMJ形态,与原始的NMJ-morph工作流程相比,在数据采集和学习曲线方面有显著改进。