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Bioresour. Technol., Nov 16 2023, S0960-8524(23)01443-8 | https://doi.org/10.1016/j.biortech.2023.130015 Automated quantification of lipid contents of Lipomyces starkeyi using deep-learning based image segmentation

Jeong-Joo Oh ^a,b,1, Young Jin Ko ^c,1, Young Jun Kim ^d, Hyeokhyeon Kwon ^e, Changmin Lee ^f

^aDivision of Environmental Science & Ecological Engineering, College of Life Sciences & Biotechnology, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea

^bInstitute of Life Science and Natural Resources, Korea University, Seoul 02841, Republic of Korea

^cDepartment of Biotechnology, College of Applied Life Science (SARI), Jeju National University, Jeju 63243, Republic of Korea

^dInterdisciplinary Biological Sciences Program, Northwestern University, Evanston, IL, USA

^ePlayIdeaLab, 61, Yonsei-ro 2na-gil, Seodaemun-gu, Seoul, Republic of Korea

^fResearch Institute of Future City and Society, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Republic of Korea

¹Jeong-Joo Oh and Young Jin Ko contributed equally to this work.

Corresponding author : Changmin Lee

Abstract

Intracellular lipid droplets (LDs), subcellular organelles playing a role in long-term carbon storage, have immense potential in biofuel and dietary lipid production. Monitoring the state of LDs in living cells is of utmost importance for quick biomass harvest and screening promising isolates. Here, a deep-learning-based segmentation model was developed for automatic detection and segmentation of LDs using the model yeast species Lipomyces starkeyi, leading to fast and accurate quantification of lipid contents in liquid cultures. The trained model detected the yeast's cell and LDs in light microscopic images with an accuracy of 98% and 92%, respectively. Lipid content prediction using pixel numbers counted in segmented LDs showed high similarity to lipid quantification results obtained with gas chromatography-mass spectrometry. This automated quantification can highly reduce cost and time in real-time monitoring of lipid production, thereby providing an efficient tool in bio-fermentation.

논문정보

형식Research article
게재일2023년 11월 (BRIC 등록일 2023-11-27)
연구진국내 연구진
분야 생명과학 > 생물공학

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