Harakawa Ryosuke, Imai Yuki, Takahashi Yuka, Ueda Seiya, Shoji Hiromi, Itani Ayaka, Nakamura Akihiro, Shida Yosuke, Ogasawara Wataru, Iwahashi Masahiro
Department of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Department of Civil Engineering and Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Appl Microbiol Biotechnol. 2025 Sep 2;109(1):195. doi: 10.1007/s00253-025-13591-2.
Non-invasive methods for observing the morphology of living oleaginous yeast are ideal for optimizing the production of various oils, such as food oils, oleochemicals, and biodiesel, from oleaginous yeast. However, existing methods have been developed to target budding yeast without high oil production ability and extract regions of entire cells. This study is the first to target oleaginous yeast, namely, Lipomyces starkeyi, demonstrating a method for extracting regions of L. starkeyi directly influencing oil production through the unique optical properties of L. starkeyi. Specifically, we exploited changes in the brightness along the z-stack depth of multiple z-stack images obtained using confocal microscopy. Because this brightness change was unique to lipid droplets, pixels corresponding to lipid droplets were easily identified, allowing calculations of the parameters of visual features. The obtained parameters of visual features were then used as input for a semantic segmentation algorithm to accurately distinguish lipid droplets from other organelles, including organelles similar to lipid droplets in shape. Experimental results showed that our method successfully estimated the growth status of L. starkeyi, which, to date, is obtainable through invasive biochemical methods only. Moreover, our method non-invasively determined the shape of each yeast cell over the cultivation period to enable single-cell analysis, which has not been achieved with conventional biochemical methods. KEY POINTS: We propose a method to extract regions of L. starkeyi influencing oil production. Our method requires only confocal microscopy images and is completely non-invasive. Our method estimated the growth status of L. starkeyi to enable single-cell analysis.
观察活的产油酵母形态的非侵入性方法，对于优化从产油酵母中生产各种油类（如食用油、油脂化学品和生物柴油）而言是理想的。然而，现有的方法是针对没有高产油能力的出芽酵母开发的，并且提取的是整个细胞区域。本研究首次以产油酵母即斯达氏油脂酵母为目标，展示了一种通过斯达氏油脂酵母独特的光学特性来提取直接影响产油的斯达氏油脂酵母区域的方法。具体而言，我们利用了共聚焦显微镜获得的多个z-stack图像沿z-stack深度的亮度变化。由于这种亮度变化是脂滴所特有的，与脂滴对应的像素很容易被识别，从而能够计算视觉特征参数。然后，将获得的视觉特征参数用作语义分割算法的输入，以准确地将脂滴与其他细胞器区分开来，包括形状与脂滴相似的细胞器。实验结果表明，我们的方法成功地估计了斯达氏油脂酵母的生长状态，而迄今为止，这只能通过侵入性生化方法获得。此外，我们的方法在培养期间非侵入性地确定了每个酵母细胞的形状，以实现单细胞分析，这是传统生化方法所无法做到的。要点：我们提出了一种提取影响斯达氏油脂酵母产油区域的方法。我们的方法仅需要共聚焦显微镜图像，并且完全是非侵入性的。我们的方法估计了斯达氏油脂酵母的生长状态以实现单细胞分析。
