Automated image-based analysis of spatio-temporal fungal dynamics

Ulzurrun, Guillermo Vidal-Diez de; Baetens, Jan; Bulcke, Jan Van den; López-Molina, Carlos; Windt, Imke De

doi:10.1016/j.fgb.2015.09.004

Cited by 28 publications

(27 citation statements)

References 70 publications

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“…Many software tools can be adapted to achieve the same image processing results described above, and a number of studies have demonstrated successful image processing approaches for observing spatial and temporal patterns of plasmodia [11–14]. The GIS-based image analysis approach offers an expanding array of tools and intuitive interfaces, workflows history (provenance), and excellent documentation influenced by the current proliferation of multidisciplinary image-producing technologies.…”

Section: Resultsmentioning

confidence: 99%

Geographic information system (GIS)-based image analysis for assessing growth of Physarum polycephalum on a solid medium

Tran

Stephenson

Tullis

2015

Fungal Biol Biotechnol

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BackgroundThe conventional method used to assess growth of the plasmodium of the slime mold Physarum polycephalum in solid culture is to measure the extent of plasmodial expansion from the point of inoculation by using a ruler. However, plasmodial growth is usually rather irregular, so the values obtained are not especially accurate. Similar challenges exist in quantification of the growth of a fungal mycelium.ResultsIn this paper, we describe a method that uses geographic information system software to obtain highly accurate estimates of plasmodial growth over time. This approach calculates plasmodial area from images obtained at particular intervals following inoculation. In addition, the correlation between plasmodial area and its dry cell weight value was determined. The correlation could be used for biomass estimation without the need of having to terminate the cultures in question.ConclusionThe method described herein is simple but effective and could also be used for growth measurements of other microorganisms such as fungi on solid media.

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Section: Resultsmentioning

confidence: 99%

Geographic information system (GIS)-based image analysis for assessing growth of Physarum polycephalum on a solid medium

Tran

Stephenson

Tullis

2015

Fungal Biol Biotechnol

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“…Sigmoidal growth curves were apparent for each of the three fungal species and for both types of media (Fig. 5, I and L), which is typical of filamentous fungal growth (29,30). A lag phase, the duration of which varied depending on the species, is followed by an almost exponential growth phase, after which most species reached a plateau (once the studied growth area was full of hyphae).…”

Section: Resultsmentioning

confidence: 86%

“…The image analysis algorithm behind the mycelium characterization function is the most complex employed by FFT and it is adapted from previously published algorithms (29,39). The first step of this algorithm is to simplify the information contained in the raw images by reducing noise.…”

Section: Methodsmentioning

confidence: 99%

“…For instance, we obtained growth-related measures such as the total length of the mycelium (as the sum of all the edges in the graphs (42)), the number of hyphal tips (as the nodes connected to only one edge), and the area of the mycelium (as the convex hull of the nodes composing the graph). We refer the reader to (29) for more detail about these measures.…”

Section: Methodsmentioning

confidence: 99%

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

Ulzurrun

Huang

Chang

et al. 2019

Preprint

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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. Author Summary One of the main obstacles to study filamentous fungi is the lack of tools for characterizing fungal phenotypes in an efficient and quantitative manner. Assessment of cell growth and numbers rely on tedious manual techniques that often result in subjective and imprecise measurements. In response to those limitations, we developed Fungal Feature Tracker (FFT), a user-friendly software that allows researchers to characterize different phenotypic features of filamentous fungi such as sporulation, spore morphology and mycelial growth. In addition, FFT can recognize and quantify other fungal structures including the fungal traps developed by nematode-trapping fungi. In order to show the capabilities and potential of our software, we conducted a detailed characterization and comparison of different fungal species. Our comparison relies on a series of experimental set-ups using standard and easily accessible equipment to ensure reproducibility in other laboratories. In summary, FFT is an easy to use and powerful tool that can quantitatively characterize fungal morphology, cell number and quantitatively measures the filamentous growth, which will allow advance our understanding of the growth and biology of filamentous fungi.

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“…Prior measures of fungal network architecture have characterized population distributions of morphological features in fungal colonies. Such features include both local measures (such as number of tips, node degree at branch points, and branch length [10,11,12,13,14]) and global measures (such as fractal dimension [15], predicted global transport efficiency, and resilience [1,10,11,12]). However, most of the phenotypic plasticity and behaviour is likely to arise at an intermediate scale (i.e., a mesoscale) that reflects how smaller units (hyphae, branches, and cords) are organized locally to produce spatial domains with differing architecture and behaviour that collectively yield global behaviour and temporal changes in such behaviour.…”

Section: Introductionmentioning

confidence: 99%

Mesoscale analyses of fungal networks as an approach for quantifying phenotypic traits

Lee

Fricker

Porter

2014

Preprint

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We investigate the application of mesoscopic response functions (MRFs) to characterize a large set of networks of fungi and slime moulds grown under a wide variety of different experimental treatments, including inter-species competition and attack by fungivores. We construct 'structural networks' by estimating cord conductances (which yield edge weights) from the experimental data, and we construct 'functional networks' by calculating edge weights based on how much nutrient traffic is predicted to occur along each edge. Both types of networks have the same topology, and we compute MRFs for both families of networks to illustrate two different ways of constructing taxonomies to group the networks into clusters of related fungi and slime moulds. Although both network taxonomies generate intuitively sensible groupings of networks across species, treatments, and laboratories, we find that clustering using the functional-network measure appears to give groups with lower intra-group variation in species or treatments. We argue that MRFs provide a useful quantitative analysis of network behaviour that can (1) help summarize an expanding set of increasingly complex biological networks and (2) help extract information that captures subtle changes in intra-specific and inter-specific phenotypic traits that are integral to a mechanistic understanding of fungal behaviour and ecology. As an accompaniment to our paper, we also make a large data set of fungal networks available in the public domain.

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Automated image-based analysis of spatio-temporal fungal dynamics

Cited by 28 publications

References 70 publications

Geographic information system (GIS)-based image analysis for assessing growth of Physarum polycephalum on a solid medium

Geographic information system (GIS)-based image analysis for assessing growth of Physarum polycephalum on a solid medium

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

Mesoscale analyses of fungal networks as an approach for quantifying phenotypic traits

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