A microfluidic device for simultaneous detection of enzyme secretion and elongation of a single hypha.

Filamentous fungi grow through elongation of their apical region by exocytosis and secrete enzymes that can be of commercial or industrial importance. Their hyphae exhibit extensive branching, making it difficult to control hyphal growth for observation and analysis. Therefore, although hyphal morphology and productivity are closely related, the relationship between the two has not yet been clarified. Conventional morphology and productivity studies have only compared the results of macro imaging of fungal pellets cultured in bulk with the averaged products in the culture medium. Filamentous fungi are multicellular and their expression differs between different hyphae. To truly understand the relationship between morphology and productivity, it is necessary to compare the morphology and productivity of individual hyphae. To achieve this, we developed a microfluidic system that confines hyphae to individual channels for observation and investigated the relationship between their growth, morphology, and enzyme productivity. Furthermore, using Trichoderma reesei, a potent cellulase-producing fungus, as a model, we developed a cellulase detection assay with 4-MUC substrate to detect hyphal growth and enzyme secretion in a microfluidic device in real time. Using a strain that expresses cellobiohydrolase I (CBH I) fused with AcGFP1, we compared fluorescence from the detection assay with GFP fluorescence intensity, which showed a strong correlation between the two. These results indicate that extracellular enzymes can be easily detected in the microfluidic device in real time because the production of cellulase is synchronized in T. reesei. This microfluidic system enables real-time visualization of the dynamics of hypha and enzymes during carbon source exchange and the quantitative dynamics of gene expression. This technology can be applied to many biosystems from bioenergy production to human health.

Local calcium signal transmission in mycelial network exhibits decentralized stress responses.

Many fungi live as mycelia, which are networks of hyphae. Mycelial networks are suited for the widespread distribution of nutrients and water. The logistical capabilities are critical for the extension of fungal survival areas, nutrient cycling in ecosystems, mycorrhizal symbioses, and virulence. In addition, signal transduction in mycelial networks is predicted to be vital for mycelial function and robustness. A lot of cell biological studies have elucidated protein and membrane trafficking and signal transduction in fungal hyphae; however, there are no reports visualizing signal transduction in mycelia. This paper, by using the fluorescent Ca2+ biosensor, visualized for the first time how calcium signaling is conducted inside the mycelial network in response to localized stimuli in the model fungus Aspergillus nidulans. The wavy propagation of the calcium signal inside the mycelium or the signal blinking in the hyphae varies depending on the type of stress and proximity to the stress. The signals, however, only extended around 1,500 µm, suggesting that the mycelium has a localized response. The mycelium showed growth delay only in the stressed areas. Local stress caused arrest and resumption of mycelial growth through reorganization of the actin cytoskeleton and membrane trafficking. To elucidate the downstream of calcium signaling, calmodulin, and calmodulin-dependent protein kinases, the principal intracellular Ca2+ receptors were immunoprecipitated and their downstream targets were identified by mass spectrometry analyses. Our data provide evidence that the mycelial network, which lacks a brain or nervous system, exhibits decentralized response through locally activated calcium signaling in response to local stress.

The monitoring of oil production process by deep learning based on morphology in oleaginous yeasts.

BACKGROUND: Monitoring jar fermenter-cultured microorganisms in real time is important for controlling productivity of bioproducts in large-scale cultivation settings. Morphological data is used to understand the growth and fermentation states of these microorganisms during monitoring. Oleaginous yeasts are used for their high productivity of single-cell oils but the relationship between lipid productivity and morphology has not been elucidated in these organisms. RESULTS: In this study, we investigated the relationship between the morphology of oleaginous yeasts (Lipomyces starkeyi and Rhodosporidium toruloides were used) and their cultivation state in a large-scale cultivation setting using a real-time monitoring system. We combined this with deep learning by feeding a large amount of high-definition cell images obtained from the monitoring system to a deep learning algorithm. Our results showed that the cell images could be grouped into 7 distinct groups and that a strong correlation existed between each group and its biochemical activity (growth and oil-productivity). CONCLUSIONS: This is the first report describing the morphological variations of oleaginous yeasts in a large-scale cultivation, and describes a promising new avenue for improving productivity of microorganisms in large-scale cultivation through the use of a real-time monitoring system combined with deep learning. KEY POINTS: • A real-time monitoring system followed the morphological change of oleaginous yeasts. • Deep learning grouped them into 7 distinct groups based on their morphology. • A correlation between the cultivation state and the shape of the yeast was observed.

A real-time monitoring system for automatic morphology analysis of yeast cultivation in a jar fermenter.

The monitoring of microbial cultivation in real time and controlling their cultivation aid in increasing the production yield of useful material in a jar fermenter. Common sensors such as dissolved oxygen (DO) and pH can easily provide general-purpose indexes but do not reveal the physiological states of microbes because of the complexity of measuring them in culture conditions. It is well known from microscopic observations that the microbial morphology changes in response to the intracellular state or extracellular environment. Recently, studies have focused on rapid and quantitative image analysis techniques using machine learning or deep learning for gleaning insights into the morphological, physiological or gene expression information in microbes. During image analysis, it is necessary to retrieve high-definition images to analyze the microbial morphology in detail. In this study, we have developed a microfluidic device with a high-speed camera for the microscopic observation of yeast, and have constructed a system capable of generating their morphological information in real-time and at high definition. This system was connected to a jar fermenter, which enabled the automatic sampling for monitoring the cultivation. We successfully acquired high-definition images of over 10,000 yeast cells in about 2.2 s during ethanol fermentation automatically for over 168 h. We recorded 33,600 captures containing over 1,680,000 cell images. By analyzing these images, the morphological changes of yeast cells through ethanol fermentation could be captured, suggesting the expansion of the application of this system in controlling microbial fermentation using the morphological information generated. KEY POINTS: • Enables real-time visualization of microbes in a jar fermenter using microscopy. • Microfluidic device for acquiring high-definition images. • Generates a large amount of image data by using a high-speed camera.