| Project/Area Number |
15K13361
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Thin film/Surface and interfacial physical properties
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| Research Institution | Yokohama National University |
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Principal Investigator |
Ogino Toshio 横浜国立大学, 大学院工学研究院, 教授 (70361871)
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| Co-Investigator(Renkei-kenkyūsha) |
Shiba Kiyotaka 公益財団法人がん研究会, がん研究所, 部長 (40196415)
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| Project Period (FY) |
2015-04-01 – 2017-03-31
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| Project Status |
Completed (Fiscal Year 2016)
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| Budget Amount *help |
¥4,030,000 (Direct Cost: ¥3,100,000、Indirect Cost: ¥930,000)
Fiscal Year 2016: ¥1,950,000 (Direct Cost: ¥1,500,000、Indirect Cost: ¥450,000)
Fiscal Year 2015: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
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| Keywords | エクソソーム / ガン診断 / 固体表面 / 脂質二分子膜 / 吸着 / 機械学習 / 原子間力顕微鏡 / 固体基板 / 脂質二重膜 / タンパク質 |
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| Outline of Final Research Achievements |
Exosomes are nanovesicles with 30-150 nm in diameter and released from whole cells including cancer cells. In this study, we have developed a novel method to extract exosome properties using atomic force microscopy (AFM) for early diagnosis of cancer. When exosomes are immobilized on solid surfaces, they are deformed by interactions between biomolecules and sold surfaces. From this deformation, much information about exosome membranes and inclusions can be obtained. To precisely discriminate the deformation fashion of individual exosomes, we used machine learning, where 14 parameters are extracted from the individual AFM images and plotted in a 14-dimensional space. By plotting AFM data from unknown exosomes in the discriminator, we can assign those exosomes to the specific host cell. We have found that the correct assignment with probability from 65 to 85% can be achieved.
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