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.
Keywords: Deep learning; Jar fermenter; Microscopy; Monitoring; Morphology; Oleaginous yeast.
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.