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使用AMScorer和AMReader高效记录和处理来自丛枝菌根定殖测定的数据。

Efficiently recording and processing data from arbuscular mycorrhizal colonization assays using AMScorer and AMReader.

作者信息

Jarratt-Barnham Edwin, Oldroyd Giles E D, Choi Jeongmin

机构信息

Crop Science Centre, University of Cambridge, Lawrence Weaver Road, Cambridge, United Kingdom.

出版信息

Front Plant Sci. 2024 May 17;15:1405598. doi: 10.3389/fpls.2024.1405598. eCollection 2024.

Abstract

Arbuscular mycorrhizal (AM) fungi engage with land plants in a widespread, mutualistic endosymbiosis which provides their hosts with increased access to nutrients and enhanced biotic and abiotic stress resistance. The potential for reducing fertiliser use and improving crop resilience has resulted in rapidly increasing scientific interest. Microscopic quantification of the level of AM colonization is of fundamental importance to this research, however the methods for recording and processing these data are time-consuming and tedious. In order to streamline these processes, we have developed AMScorer, an easy-to-use Excel spreadsheet, which enables the user to record data rapidly during from microscopy-based assays, and instantly performs the subsequent data processing steps. In our hands, AMScorer has more than halved the time required for data collection compared to paper-based methods. Subsequently, we developed AMReader, a user-friendly R package, which enables easy visualization and statistical analyses of data from AMScorer. These tools require only limited skills in Excel and R, and can accelerate research into AM symbioses, help researchers with variable resources to conduct research, and facilitate the storage and sharing of data from AM colonization assays. They are available for download at https://github.com/EJarrattBarnham/AMReader, along with an extensive user manual.

摘要

丛枝菌根(AM)真菌与陆地植物形成广泛的互利共生内共生关系,为宿主提供更多获取养分的机会,并增强其对生物和非生物胁迫的抗性。减少化肥使用和提高作物抗逆性的潜力引发了科学界越来越浓厚的兴趣。对AM定殖水平进行微观量化对这项研究至关重要,然而记录和处理这些数据的方法既耗时又繁琐。为了简化这些流程,我们开发了AMScorer,这是一个易于使用的Excel电子表格,它能让用户在基于显微镜的检测过程中快速记录数据,并立即执行后续的数据处理步骤。在我们的操作中,与基于纸质的方法相比,AMScorer将数据收集所需时间减少了一半以上。随后,我们开发了AMReader,这是一个用户友好的R包,它能轻松实现对AMScorer数据的可视化和统计分析。这些工具仅需有限的Excel和R技能,可加速对AM共生关系的研究,帮助资源各异的研究人员开展研究,并促进AM定殖检测数据的存储和共享。它们可在https://github.com/EJarrattBarnham/AMReader上下载,同时还有一份详尽的用户手册。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ff94/11140075/6ad6894bea72/fpls-15-1405598-g001.jpg

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