Yajie Liu, Menghui Yang, Na Li, Yixin Huang, Chunxue Yang
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引用次数: 0
Abstract
Arbuscular mycorrhizal (AM) fungi, ubiquitously distributed across diverse terrestrial ecosystems, establish symbiotic associations with the majority of vascular plants, fulfilling essential physiological and ecological functions. Mycorrhizal development represents the initiation of host-fungus interactions and serves as a metric for assessing mutualistic efficacy. However, mycorrhizal detection underscores the urgent need to develop cost-effective, efficient, and environmentally benign dyestuff. Therefore, wild-collected and laboratory-grown roots of Medicago sativa were selected. Six reagents including black ink, red ink, acid fuchsin, trypan blue, Sudan IV, and aniline blue were evaluated in conjunction with computer vision techniques to identify optimal one. Concurrently, root characteristics were quantified, and interrelationships among root traits, image quality, and colonization indices were analyzed to unravel the mechanism of their interactions. The findings demonstrated that wild roots exhibited pronounced lignification, achieving a mycorrhizal colonization rate of 100 %, which was better than the two laboratory groups. And the fungal community displayed a markedly greater colonization intensity compared to the Claroideoglomus etunicatum. Evaluation of the six reagents revealed distinct staining efficacy, with significant variations in image clarity, gray-level co-occurrence matrix (GLCM) indices, and colonization parameters across treatments. Specifically, aniline blue proved ineffective, while Sudan IV showed selective binding. Notably, black ink in glacial acetic acid achieved optimal mycorrhizal detection efficacy. Moreover, correlation matrix identified microscopic image quality as critical determinant of quantification accuracy, influenced by both reagent types and root properties, and AvgDiam exerted the most substantial impact (|R| > 0.75).
期刊介绍:
The Journal of Microbiological Methods publishes scholarly and original articles, notes and review articles. These articles must include novel and/or state-of-the-art methods, or significant improvements to existing methods. Novel and innovative applications of current methods that are validated and useful will also be published. JMM strives for scholarship, innovation and excellence. This demands scientific rigour, the best available methods and technologies, correctly replicated experiments/tests, the inclusion of proper controls, calibrations, and the correct statistical analysis. The presentation of the data must support the interpretation of the method/approach.
All aspects of microbiology are covered, except virology. These include agricultural microbiology, applied and environmental microbiology, bioassays, bioinformatics, biotechnology, biochemical microbiology, clinical microbiology, diagnostics, food monitoring and quality control microbiology, microbial genetics and genomics, geomicrobiology, microbiome methods regardless of habitat, high through-put sequencing methods and analysis, microbial pathogenesis and host responses, metabolomics, metagenomics, metaproteomics, microbial ecology and diversity, microbial physiology, microbial ultra-structure, microscopic and imaging methods, molecular microbiology, mycology, novel mathematical microbiology and modelling, parasitology, plant-microbe interactions, protein markers/profiles, proteomics, pyrosequencing, public health microbiology, radioisotopes applied to microbiology, robotics applied to microbiological methods,rumen microbiology, microbiological methods for space missions and extreme environments, sampling methods and samplers, soil and sediment microbiology, transcriptomics, veterinary microbiology, sero-diagnostics and typing/identification.