Systems Biology of Minority and Majority of Cells
| Project/Area Number |
16K14703
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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 |
Biophysics
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| Research Institution | Hokkaido University |
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Principal Investigator |
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| Research Collaborator |
Fujita Katsumasa 大阪大学, 大学院工学研究科, 教授
Li Chun Biu ストックホルム大学, 数学科, 准教授
Taylor James Nicholas 北海道大学, 電子科学研究所, 特任助教
Teramoto Hiroshi 北海道大学, 電子科学研究所, 准教授
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
Fiscal Year 2016: ¥1,300,000 (Direct Cost: ¥1,000,000、Indirect Cost: ¥300,000)
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| Keywords | バイオイメージング / 細胞性粘菌 / 情報理論 / 因果推論 / アンサンブル学習 / トランススケーラブル / 個性 / 1分子計測 / ラマン分光イメージング / 細胞個性 / １分子解析 |
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| Outline of Final Research Achievements |
We developed an information theoretic analysis method to enable us to predict the cell states from a high dimensional feature space spanned by single cell Raman imaging in terms of rate-distortion theory in information theory taking into account noises from number fluctuation of finite measurements and ensemble machine learning. We showed its versatility by applying the methods to follicular thyroid carcinoma whose morphological features of cells and nucleus are almost identical to those of non-carcinoma, and non-alcoholic fatty liver disease, in which the transitions in disease states require morphological changes like fibrosis. We constructed a mathematical modelling to mimic leader-follower relationship, and we scrutinized the identifiability of leader and followers from their movements by using causal inference in information theory. Furthermore, we studied how organs, a set of huge number of cells, have rather uniform structures irrespective of randomness at cell level.
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| Academic Significance and Societal Importance of the Research Achievements |
遺伝子発現やマーカータンパク質に依拠する細胞判別法は大変強力な手法であるが、マーカーが不明な細胞や、遺伝型の相違がなくとも機能的な違いがある細胞の判別には原理的に利用できない。シグナル／ノイズ比が小さいデータに関して、誤差を考慮に入れたラマン分光イメージング解析技術は、細胞の表現型（すなわち、ミクロ環境の化学的多様性）に基づいて、細胞状態を生きたまま無標識で識別する新しい細胞判別法である。そのため、医学、創薬分野へ細胞ラマン分光技術が活用される可能性が高く、社会に与えるインパクトも高い。特に、標的となる病気のバイオマーカーが特定されていないような場合にも重要となる。
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Report
(4 results)
Research Products
(76 results)
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[Journal Article] Variable cell growth yields reproducible organ development through spatiotemporal averaging2016
Author(s)
L. Hong, M. Dumond, S. Tsugawa, A. Sapala, A.-L. Routier-Kierzkowska, Y. Zhou, C. Chen, A. Kiss, M. Zhu, O. Hamant, R. S. Smith, T. Komatsuzaki, C.-B. Li, A. Boudaoud & A. H. K. Roeder
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Journal Title
Developmental Cell
Volume: 38
Issue: 1
Pages: 15-32
DOI
Related Report
Peer Reviewed / Open Access / Int'l Joint Research
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