Back

A microfluidic platform for the observation and quantification of fungal highways

Moratto, E.; Masters-Clark, E.; Clark, A. J.; Pachmann, A.; Bindschedler, S.; Junier, P.; Stanley, C. E.

2025-09-16 bioengineering
10.1101/2025.09.16.676496 bioRxiv
Show abstract

Soil is a complex system characterised by intra- and inter-kingdom interactions among microbial communities. While many different types of fungal-bacterial interactions have been described, hyphal-mediated transport of bacteria via the so-called "fungal highway" (FH) has not been mechanistically described. Bacteria require a liquid film for active movement; therefore, liquid saturation is a significant limiting factor for their dispersal in soil. Hyphal networks contribute to the connectivity between discrete soil microbial populations by providing a physical network through the unsaturated soil matrix. This network serves as a scaffold for liquid transport, thus allowing bacteria to migrate further or access previously isolated spaces. Studying these interactions is challenging due to the complex and stochastic nature of the soil environment; this "black box" aspect makes it difficult to visualise interactions at the microbial scale. Microfluidic technology can provide a solution by offering precise imaging at a high resolution in a physically and chemically controlled environment. We designed a microfluidic device, the Fungal Highways Device (FHD), that allows us to culture filamentous organisms in unsaturated environments and visualise and quantify bacterial dispersal along hyphal networks at the single-cell level. We showed that Pythium ultimum is essential for Pseudomonas putida movement across an unsaturated environment, and we identified mycelial biomass and hyphal front length as key factors influencing the bacterias movement towards the outlet. We propose that the liquid transport facilitated by P. ultimum mycelium influences the FH behaviour during its interaction with P. putida.

Matching journals

The top 3 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.