Reproducibility and standardization of methods are key approaches to microbiological research. However, laboratories differ widely in equipment, protocols, and operator expertise. These disparities are especially pronounced in fungal biology, since accurate spore counting and precise measurements of colony growth remain vulnerable to subjective bias and inconsistent technique. To standardize these foundational tasks, we developed two free, open-source image-analysis tools - SporeQuant and Orbisv2 - that automate hemocytometer spore counting and colony-area measurement, respectively. SporeQuant applies image thresholding and blob detection to identify individual spores and compute their concentration, while Orbisv2 segments colonies and calculates colony areas through thresholding and contour detection. Compared with manual analysis, SporeQuant and Orbis v2 markedly reduce analysis time by 63% and 76%, respectively, while achieving high measurement reliability, with MAE=0 for SporeQuant and R2>0.975 for Orbis v2. These tools reduce user-to-user variability, and achieve comparable or improved accuracy versus previously established methods. Furthermore, both tools can be accessed through a web page on both a computer or smartphone, requiring no specialized hardware, software, or prior knowledge. They provide accessible solutions for low-budget and high-throughput laboratories alike, enabling more consistent, scalable analysis of fungal growth and morphology in research and industrial settings.

We conducted six metabarcoding studies from different aquatic habitats. It has been confirmed that fungal metabarcoding has the advantages of overcoming the cultivation bias, increasing the number of possible samples by high-throughput sequencing, and enabling the detection of yet uncultivated fungal taxa. However, the already well-known limitations, in particular the lack of taxonomic accuracy when using automated classification also occurred. During the manual curation of pipeline-retrieved datasets, the quality of the automatic classification, specifically phylogenetic rank and current nomenclature, was verified. The manual curation step revealed that only 10-20% of the Operational Taxonomic Units (OTUs) were identified correctly by the automatic classification. Re-blasting of the remaining OTUs resulted in taxonomic and nomenclatural correction of a further 30-40% of OTUs. Approximately 50% of the OTUs resulted in insufficient identifications, which can be attributed to shortcomings in the database, including yet unidentified species, missing reference sequences, and the disadvantages of the genetic marker used. Pipeline parameters that can be adjusted to improve the classification of taxa in metabarcoding studies include adjusting the cut-off threshold for sequence similarity, updating and refining databases, and aligning and collapsing of OTUs to species hypotheses. Here, we describe a method of manual data curation and its impact on improving the taxonomic resolution of a metabarcoding dataset. Shortcomings as well as advantages are discussed to illustrate the need for bioinformatic tools that support the process of data curation.

No widely available ovine monocyte/macrophage cell system exists. We present an affinity magnetic separation method combining gradient separation and magnetic bead selection to isolate pure primary ovine monocytes from blood, yielding large quantities of viable primary macrophages suitable for microbiological infection studies using reservoir-specific host cells targeted by intracellular pathogens.