| Project/Area Number |
19K15398
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| Research Category |
Grant-in-Aid for Early-Career Scientists
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| Allocation Type | Multi-year Fund |
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| Review Section |
Basic Section 28030:Nanomaterials-related
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| Research Institution | National Institute for Materials Science |
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Principal Investigator |
王 洪欣 国立研究開発法人物質・材料研究機構, 先端材料解析研究拠点, NIMSポスドク研究員 (60813756)
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| Project Period (FY) |
2019-04-01 – 2020-03-31
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| Project Status |
Discontinued (Fiscal Year 2019)
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| Budget Amount *help |
¥3,380,000 (Direct Cost: ¥2,600,000、Indirect Cost: ¥780,000)
Fiscal Year 2022: ¥130,000 (Direct Cost: ¥100,000、Indirect Cost: ¥30,000)
Fiscal Year 2021: ¥260,000 (Direct Cost: ¥200,000、Indirect Cost: ¥60,000)
Fiscal Year 2020: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2019: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
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| Keywords | stress / machine learning / spectroscopy / ナノスケール応力 / 機械学習 / 原子間力顕微鏡スペクトロスコピー |
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| Outline of Research at the Start |
AFM探針が試料表面に押し込まれると、カンチレバーによって感知される力（フォース）は、材料の弾性変形と横方向の既存応力の両方によって誘発される力の合計である。これら二つの力は、荷重印加中におけるAFM探針の正確な半径の詳細な知識なしには分離できない。本申請では、探針の半径関数の隠れた情報を明らかにするために、スパースモデリングベースの機械学習法を使用することを提案する。機械学習支援AFMフォーススペクトロスコピーは、形態学と応力測定の両方において前例のない空間分解能を組み合わせることにより、材料科学と生物学におけるボトルネックの問題を解決することが期待される。
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| Outline of Annual Research Achievements |
Cells rely on cytoskeletal prestress to sense and transmit external force signals to induce chemical and mechanical responses. The intracellular spatial distribution of prestress thus determines the basic cytoskeletal functionalities of maintaining cellular homeostasis, which is lacking in diseased cell status including cancer. In this work, we measured spatially resolved prestress produced by local actomyosin machinery in a living cell with multivariable AFM force spectroscopy. The mechanical stimuli applied by the AFM probe were found to induce cellular prestress responses, in both normal cells and cancer cells, but in distinctively different manner at time scales from sub-second to hours. We demonstrated that a simple machine learning algorithm can be applied on un-segmented prestress distribution data to differentiate cancer cells and normal cells with accuracy above 95%. It promises a new biomarker for cancer cytology diagnosis on difficult cell specimens with high morphological similarities.
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