A mathematical approach to decoding biological environment inside the brain using imaging mass spectrometry

• Project/Area Number: 16KT0134
• Research Category: Grant-in-Aid for Scientific Research (C)
• Allocation Type: Multi-year Fund
• Section: 特設分野
• Research Field: Mathematical Sciences in Search of New Cooperation
• Research Institution: Oita University
• Principal Investigator: SUETANI Hiromichi 大分大学, 理工学部, 教授 (40507167)
• Co-Investigator(Kenkyū-buntansha): 矢尾 育子 関西学院大学, 理工学部, 教授 (60399681)
• Project Period (FY): 2016-07-19 – 2020-03-31
• Project Status: Completed (Fiscal Year 2019)
• Budget Amount: ¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000); Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000); Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000); Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
• Keywords: 質量分析イメージング / 多様体学習 / 計算トポロジー / 機械学習 / 神経変性疾患 / バイオマーカー / 多変量データ解析

Outline of Final Research Achievements

Recently, the methodologies of imaging mass spectrometry (IMS) are receiving attention in the various fields of science and technology. The data in association with IMS is composed of a huge number of mass spectra according to spatial observation points, which brings difficulty in applications of conventional statistical analysis.
In this study, we proposed an approach based on manifold learning for analyzing the IMS data of mice brains. To this end, we introduced a distance measure based on Fisher metric between two persistent diagrams (PDs) associated with mass spectra. Then, we applied the proposed approach to slices of the mice brains and showed that our prosed approach is useful for visualizing a set of the mass spectra as well-separated islands of points in the low-dimensional space, where each island reflects a specific character of chemical species in the brain. We also considered how our results can be interpreted in terms of neurophysiology.

Academic Significance and Societal Importance of the Research Achievements

本研究成果を発展させることで、遺伝子改変に伴う脳内環境の変化を検出するためのデータ解析法を提案し、多数のサンプルを用いた大規模な解析を通じて、パーキンソン病やレビー小体型認知症などの神経変性疾患に関与するバイオ・マーカーの候補の発見に繋がる。

Research Products

• [Journal Article] Sulfatide species with various fatty acid chains in oligodendrocytes at different developmental stages determined by imaging mass spectrometry (2017)
  • Author(s): Yukie Hirahara, Taketoshi Wakabayashi, Tetsuji Mori, Taro Koike, Ikuko Yao, Masayuki Tsuda , Koichi Honke, Hitoshi Gotoh, Katsuhiko Ono, Hisao Yamada.
  • Journal Title: Journal of Neurochemistry Volume: 140 Issue: 3 Pages: 435-450
  • DOI: 10.1111/jnc.13897
  • NAID: 120006778373
  • Peer Reviewed
• [Journal Article] Microscopic visualization of testosterone in mouse testis by use of imaging mass spectrometry. (2016)
  • Author(s): Shimma, S., Kumada, H-O., Taniguchi, H., Konno, A., Yao, I., Furuta, K., Matsuda, T. and Ito, S.
  • Journal Title: Analytical and bioanalytical chemistry Volume: 408 Issue: 27 Pages: 7607-7615
  • DOI: 10.1007/s00216-016-9594-9
  • Peer Reviewed
• [Presentation] 多様体学習によるマウス脳質量顕微鏡イメージングデータの解析 (2019)
  • Author(s): 末谷 大道、衞藤 史博、矢尾 育子
  • Organizer: 第４２回日本神経科学大会
• [Presentation] A manifold learning approach to imaging mass spectrometry analysis in the mice brains (2018)
  • Author(s): Hiromichi Suetani, Fumihiro Eto, Ikuko Yao
  • Organizer: 48th Annual Meeting of the Society for Neuroscience 2018
  • Int'l Joint Research
• [Presentation] 高次元空間に散らばる対象の隠れた構造を求めて (2018)
  • Author(s): 末谷大道
  • Organizer: 研究会「第５回非線形現象の捉え方」
• [Presentation] 超解像イメージングによるシナプス伝達制御機構の解析 (2017)
  • Author(s): 矢尾育子
  • Organizer: 第94回日本生理学会大会
  • Place of Presentation: アクトシティ浜松（静岡県浜松市）
  • Year and Date: 2017-03-29
• [Presentation] Mass spectrometry imaging for transitional condition of a drug to the mouse brain (2016)
  • Author(s): Ikuko Yao
  • Organizer: 第1回国際マスイメージングセンターミーティング
  • Place of Presentation: 浜松医科大学（静岡県浜松市）
  • Year and Date: 2016-10-06
  • Int'l Joint Research