Evidence map›Paper›PMID 40892087›Full record

ArticleApplied microbiology and biotechnology2025

Extracting regions of lipid droplets from confocal microscopy images utilizing optical properties of oleaginous yeast.

Ryosuke Harakawa, Yuki Imai, Yuka Takahashi, Seiya Ueda, Hiromi Shoji, Ayaka Itani, Akihiro Nakamura, Yosuke Shida, Wataru Ogasawara, Masahiro Iwahashi

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In one paragraph

Article in Applied microbiology and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

10 authors.

Ryosuke HarakawaDepartment of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan. harakawa@vos.nagaokaut.ac.jp.
Yuki ImaiDepartment of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Yuka TakahashiDepartment of Civil Engineering and Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Seiya UedaDepartment of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Hiromi ShojiDepartment of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Ayaka ItaniDepartment of Materials Science and Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Akihiro NakamuraDepartment of Science of Technology Innovation, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Yosuke ShidaDepartment of Materials Science and Bioengineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Wataru OgasawaraDepartment of Science of Technology Innovation, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan.
Masahiro IwahashiDepartment of Electrical, Electronics and Information Engineering, Nagaoka University of Technology, 1603-1, Kamitomioka, Nagaoka, 940-2188, Niigata, Japan. iwahashi@vos.nagaokaut.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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:

Indexed as

Lipid DropletsLipomycesBiofuelsMicroscopy, ConfocalBiofuelsConfocal microscopyDeep learningFermentation scienceImage processingNon-invasive detectionOleaginous yeast

Identifiers

PMID40892087
PMCPMC12405027

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.