Microorganisms have played an important role as producers in various bio-industries such as food, cosmetics, pharmaceuticals, biomaterials, and fuels1,2,3,4. In particular, in the bio-industry, microbial fermentation produces not only food, but also a variety of supplements such as antioxidants, flavors, colorants, preservatives, and sweeteners5,6. According to the BCC Market Research Report on Fermentation Industry, the global market for bioproducts (petroleum, natural gas, plastics/polymers, composites, pharmaceuticals, chemicals, and power) was estimated at $9.7 trillion in 2020. It will increase at a compounded annual growth rate (CAGR) of 4.8% to reach nearly $12.3 trillion by 2025. In particular, the global market for fermented products (excluding biofuels and biopolymers) is expected to grow at a CAGR of 17.7% over the next five years to reach $69 billion by 2025. The bio-industry growth is due to the rapid development of fundamental life sciences and advanced biotechnology, such as genetic engineering, process engineering, mass production, and purification7.
Fermentation is a metabolic process that causes chemical changes in organic substrates through enzymatic actions of microorganisms8. During microbial fermentation in a bioreactor, environmental factors such as temperature, dissolved oxygen, pH, agitation rate, and monitoring of cell and nutrient concentrations are very important in mass production9,10. In particular, it is well known that the shape and concentration of cells during fermentation can affect the productivity of targeted metabolites11, 12, 13, 14, 15. Therefore, in fermentation, an understanding of the correlation between the growth of microorganisms and the production of metabolites is required, and various cell quantification techniques have been developed.
In general, cell quantification is divided into direct and indirect measurements. Fig. 1 shows a schematic diagram of cell quantification, including representative examples of direct and indirect techniques. The most well-known direct methods are microscopic cell count, plate medium, and dry cell weight (DCW) measurements. Indirect methods include ATP bioluminescence measurements, turbidity measurements, and spectrophotometric measurements16, 17.
Among direct methods, the DCW method is useful by measuring the weight of filamentous fungi that do not grow in a certain form18. However, before weighing the sample, it must be centrifuged and dried. Therefore, the analysis takes a long time. In addition, real-time monitoring is difficult. On the other hand, the indirect method has a relatively short analysis time, and real-time monitoring is relatively easy. Though, most of the applicable samples are limited to microbes with uniform shapes such as Escherichia coli, Bacillus, and yeast. It is difficult to apply an indirect method to filamentous fungi or mycelium that grow in the shape of a branch. In addition, if the sample contains non-cellular or colored substances, it may interfere with the measurement and decreases the accuracy of the result16, 19.
Recently, technologies that overcome deficiencies of direct and indirect methods have been reported. However, most reports have applied fractals to analyze mycelial growth and develop models through correlation with metabolites produced12, 13. Those models could be used to understand the characteristics of cells growing in complex shapes. However, they are not suitable for quantitative analysis. Therefore, fast and accurate cell quantification techniques applied to the bio-industry for fermenting fungal mycelium are needed.
In this study, a concise image analysis model was designed for quantifying fungal mycelium more quickly and accurately. A microscopic image intensity (MII) model was designed to analyze the correlation between the intensity value of hyphae morphological image and the weight of dry cells. It was based on the linear regression model targeting Cordyceps militaris, a filamentous fungus with a non-uniform cell shape. This strain is an improved strain for the production of cordycepin as a functional biomaterial in our previous study20. Its optimal production conditions have been determined. Finally, the developed MII model was evaluated by comparing predicted and experimental values of mycelial growth of C. militaris.