Fungal feature tracker (FFT): A tool for quantitatively characterizing the morphology and growth of filamentous fungi

Abstract

Filamentous fungi are ubiquitous in nature and serve as important biological models in various scientific fields including genetics, cell biology, ecology, evolution, and chemistry. A significant obstacle in studying filamentous fungi is the lack of tools for characterizing their growth and morphology in an efficient and quantitative manner. Consequently, assessments of the growth of filamentous fungi are often subjective and imprecise. In order to remedy this problem, we developed Fungal Feature Tracker (FFT), a user-friendly software comprised of different image analysis tools to automatically quantify different fungal characteristics, such as spore number, spore morphology, and measurements of total length, number of hyphal tips and the area covered by the mycelium. In addition, FFT can recognize and quantify specialized structures such as the traps generated by nematode-trapping fungi, which could be tuned to quantify other distinctive fungal structures in different fungi. We present a detailed characterization and comparison of a few fungal species as a case study to demonstrate the capabilities and potential of our software. Using FFT, we were able to quantify various features at strain and species level, such as mycelial growth over time and the length and width of spores, which would be difficult to track using classical approaches. In summary, FFT is a powerful tool that enables quantitative measurements of fungal features and growth, allowing objective and precise characterization of fungal phenotypes.

A fully automated pipeline for the dynamic at‐line morphology analysis of microscale Aspergillus cultivation

“…Until now many different approaches have been developed to accelerate morphological characterization: As one aspect, image analysis is a major bottleneck and therefore the development of automated algorithms detecting fungal structures has been in focus of research for many years [ 14 ]. In the last decade, more complex methods determining and combining multiple parameters as characterization factors, such as the morphology number, have been established [ 11 ] and latest studies even address fungal morphology with focus on three dimensional shape [ 15 ] or at the single-hypha scale [ 16 , 17 ]. Recently, a novel quantitative image analysis pipeline offering a simple image acquisition and automated analysis has been developed [ 13 ].…”

Morphological growth pattern of Phanerochaete chrysosporium cultivated on different Miscanthus x giganteus biomass fractions

“…Although the early growth rate was faster with the soluble fraction alone (culture C), growth on the non-fractionated substrate accelerated and by 52 h had surpassed the extent of growth on the soluble fraction (culture C). Neurospora crassa, Arthrobotrys oligospora and Trichoderma reesei showed different growth behaviours on PDA (Potato Dextrose Agar) and LMN (Low Nutrient Medium) [ 56 ]. Neurospora crassa colonized the entire PDA medium zone in less time (< 24 h) than on LMN (40 h).…”

“…It generated more hyphal tips, and its mycelial network was denser on PDA. All strains showed the same performance on LMN, but not on PDA [ 56 ].…”

“…Spore germination and mycelium formation of Penicillium expansum and Aspergillus niger have been observed on agar for extended periods using time-lapse photography [ 61 ]. Researchers have often monitored the growth of one mycelial colony originating from the germination of single spore without accounting for the realistic event in SSF in which a large population of spores is inoculated into the substrate [ 56 , 62 , 63 ]. Experimental data reported [ 61 ] heterogeneous germination times for individual spores even though they were all treated under the same conditions.…”

Hyphal network whole field imaging allows for accurate estimation of anastomosis rates and branching dynamics of the filamentous fungus Podospora anserina

“…A system combining image analysis and graph theory 13 was also developed to monitor the space-time mycelial growth of a variety of fungal species at different image resolutions. More recently, an automated and continuous video microscopy tracking of hyphal growth 14,15 allowed for a quantitative analysis of the growth rate and morphology of a thallus.…”