Department of Science of Technology Innovation, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
Department of Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, Niigata, 940-2188, Japan.
Appl Microbiol Biotechnol. 2023 Feb;107(2-3):915-929. doi: 10.1007/s00253-022-12338-7. Epub 2022 Dec 28.
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.
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).
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.
• 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.
在大规模培养环境中，实时监测罐式发酵罐培养的微生物对于控制生物制品的生产力非常重要。形态数据用于了解这些微生物在监测过程中的生长和发酵状态。产油酵母因其单细胞油的高生产力而被使用，但在这些生物中，油脂生产力与形态之间的关系尚未阐明。
在这项研究中，我们使用实时监测系统研究了大规模培养环境中产油酵母（使用拉斯塔酵母和粘红酵母）的形态与其培养状态之间的关系。我们通过将大量从监测系统获得的高清晰度细胞图像输入深度学习算法，将其与深度学习相结合。我们的结果表明，细胞图像可以分为 7 个不同的组，并且每个组与其生化活性（生长和油脂生产力）之间存在很强的相关性。
这是第一篇描述产油酵母在大规模培养中形态变化的报告，并描述了一种有前途的新途径，通过使用实时监测系统结合深度学习来提高微生物在大规模培养中的生产力。
实时监测系统跟踪了产油酵母形态的变化。
深度学习根据它们的形态将它们分为 7 个不同的组。
观察到培养状态和酵母形状之间的相关性。
