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

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.

Keywords: AMReader; AMScorer; arbuscular mycorrhizal fungi; microscopy; root length colonization.

Figure 1

Arbuscular mycorrhizal colonization can be quantified by counting several microscopically visible structures: Spores (S), extraradical hyphae (EH), hyphopodia (H), intraradical hyphae (IH), arbuscules (A), and vesicles (V). Extraradical hyphae scavenge nutrients from the growth medium, which are transferred to the host plant at arbuscules. A graphic representation of fungal structures (A) compared to an image of colonised root tissue from Lotus japonicus stained with ink and vinegar (B).

Figure 2

The preparation of root samples for microscopic quantification of AM colonization. Harvested roots are cut into sections approximately 1.0-1.5 cm in length (A). Root pieces are mixed thoroughly to collect representatives of the whole root system for staining (B, C). Root pieces are spread out in mounting medium (D). Randomly selected root pieces are mounted onto a microscopy slide (E). The abundance of AM fungi is counted either per field-of-view (Torabi et al., 2021), or using the McGonigle magnified intersection method (McGonigle et al., 1990) (F).

Figure 3

An example workflow using AMScorer and AMReader. One ‘Counting Table’ per plant allows the quantification of colonization in 390 plants per experiment (A). Proofreading methods ensure accurate data input (B). The ‘Conditions’ sheet can account for multiple experimental parameters with up to 78 different combinations (C). AMScorer enables easy blind counting and data to be grouped in multiple ways (D). An example R code to read AMScorer sheet into AMReader (E). AMReader provides an easy way to (1) read data from AMScorer, (2) select which data to present, (3) conduct various statistical analyses, (4) display data alongside statistical information, and (5) adjust color schemes and other aesthetics of graphical outputs.

Figure 4

The main features of each counting table in AMScorer. The Excel “Named Range” feature enables selection of cells for data entry. Movement to the next cell in the counting table is achieved with the keyboard TAB button. Pressing SHIFT and TAB will allow movement to the previous cell. Cells where the presence of a fungal structure have been recorded change color to blue, whilst cells where there is no data entered will turn red and activate a corresponding warning. If AMScorer believes a data entry may have been accidental, it will highlight the “Too few counts?” warning. To record that no fungal structures were present in a sample, activate the “No fungus override” button by inputting a “1”. Counts of fungal structures are summed up below each table, and results transferred automatically to the “AM Results” sheet.

Figure 5

The two options for importing data from AMScorer into AMReader.

Figure 6

Example usage of AMReader. A simple workflow would involve loading data by identification of the AMScorer file (A), filtering of data to choose only those conditions and fungal structures of interest (B), statistical analysis (C), and later aesthetic changes to the graphical representation (D, E).