Development of cell diversity analysis method based on gene regulatory prediction by Bayesian network

• Project/Area Number: 18H04124
• Research Category: Grant-in-Aid for Scientific Research (A)
• Allocation Type: Single-year Grants
• Section: 一般
• Review Section: Medium-sized Section 62:Applied informatics and related fields
• Research Institution: Osaka University
• Principal Investigator: MATSUDA Hideo 大阪大学, 情報科学研究科, 教授 (50183950)
• Co-Investigator(Kenkyū-buntansha): 瀬尾 茂人 大阪大学, 情報科学研究科, 准教授 (30432462)
• Project Period (FY): 2018-04-01 – 2022-03-31
• Project Status: Completed (Fiscal Year 2021)
• Budget Amount: ¥35,360,000 (Direct Cost: ¥27,200,000、Indirect Cost: ¥8,160,000); Fiscal Year 2021: ¥6,500,000 (Direct Cost: ¥5,000,000、Indirect Cost: ¥1,500,000); Fiscal Year 2020: ¥8,710,000 (Direct Cost: ¥6,700,000、Indirect Cost: ¥2,010,000); Fiscal Year 2019: ¥9,880,000 (Direct Cost: ¥7,600,000、Indirect Cost: ¥2,280,000); Fiscal Year 2018: ¥10,270,000 (Direct Cost: ¥7,900,000、Indirect Cost: ¥2,370,000)
• Keywords: 遺伝子制御ネットワーク推定 / 動的ベイジアンネットワークモデル / 1細胞RNAシーケンシング / 細胞系譜推定 / バイオインフォマティクス / １細胞RNAシーケンシング / 細胞系譜解析 / ネットワーク解析 / 遺伝子発現解析 / ベイジアンネットワーク / トランスクリプトーム解析 / 時系列データ解析 / 1細胞トランスクリプトーム解析

Outline of Final Research Achievements

For biological phenomena in which intracellular gene regulation changes over time, such as cell differentiation and cellular stimulus response, we have developed a cell lineage inference method that obtains data from single-cell RNA sequencing and maps each cell on a pseudo-temporal time. The cells were sorted by this cell lineage inference, and the gene expression levels of each cell were considered as a time-series expression profile, and the regulatory relationship between individual genes in each cell was quantified by a newly developed score called "edge gain". Based on this score, gene regulatory networks can be inferred by a dynamic Bayesian network model from the time-series gene expression profiles obtained along the cell lineage, and it was shown that the inference accuracy was higher than that of existing methods.

Academic Significance and Societal Importance of the Research Achievements

本研究では、1細胞RNAシーケンシング技術により得られる細胞ごとの遺伝子発現プロファイルから、細胞分化や細胞の刺激応答などの進行過程を表す細胞系譜を推定する手法を開発した。さらに、細胞系譜上で各細胞の遺伝子発現量を抽出することで、非常に細かい時間間隔で時系列遺伝子発現プロファイルを構成して、動的ベイジアンネットワークモデルにより遺伝子制御ネットワークを推定する手法を開発した。実際に、造血幹細胞からの細胞分化や免疫細胞の刺激応答に本手法を適用することで、細胞系譜と遺伝子制御ネットワークを高い精度で推定することが示され、本手法が多様な生命現象に適用可能であることが示唆された。

Research Products

• [Int'l Joint Research] National Cheng Kung University(その他の国・地域（台湾）)
• [Journal Article] A Novel Method for Gene Regulatory Network Inference with Pseudotime Data Using Information Criterion (2022)
  • Author(s): Shuhei Yao, Kaito Uemura, Shigeto Seno, Hideo Matsuda
  • Journal Title: International Journal of Bioscience, Biochemistry and Bioinformatics Volume: 12
  • Peer Reviewed / Open Access
• [Journal Article] Detection of Biomarkers for Epithelial-Mesenchymal Transition with Single-Cell Trajectory Inference (2022)
  • Author(s): Murayama Kosho、Matsuda Hideo
  • Journal Title: Frontiers in Bioscience-Landmark Volume: 27 Pages: 127-127
  • DOI: 10.31083/j.fbl2704127
  • Peer Reviewed / Open Access
• [Journal Article] A Method for Detection of Markers for Epithelial-Mesenchymal Transition based on Single Cell Transcriptomic Data (2022)
  • Author(s): Kosho Murayama, Hideo Matsuda
  • Journal Title: Proceedings of 12th International Conference on Bioscience, Biochemistry and Bioinformatics Volume: － Pages: 57-62
  • DOI: 10.1145/3510427.3510436
  • Peer Reviewed / Open Access
• [Journal Article] Detecting Lineage-specific Marker Genes for Tumor Evolution based on Single Cell Transcriptome (2021)
  • Author(s): Kosho Murayama, Hideo Matsuda
  • Journal Title: International Journal of Bioscience, Biochemistry and Bioinformatics Volume: 11
  • Peer Reviewed / Open Access
• [Journal Article] Long non-coding RNA 2310069B03Rik functions as a suppressor of Ucp1 expression under prolonged cold exposure in murine beige adipocytes (2020)
  • Author(s): Mari Iwase, Shoko Sakai, Shigeto Seno, Yu-Sheng Yeh, Tony Kuo, Haruya Takahashi, Wataru Nomura, Huei-Fen Jheng, Paul Horton, Naoki Osato, Hideo Matsuda, Kazuo Inoue, Teruo Kawada, Tsuyoshi Goto
  • Journal Title: Biosci. Biotechnol. Biochem Volume: 84 Pages: 305-313
  • DOI: 10.1080/09168451.2019.1677451
  • Peer Reviewed / Open Access / Int'l Joint Research
• [Journal Article] Improvement of detection performance of fusion genes from RNA-Seq data by clustering short reads (2019)
  • Author(s): Yoshiaki Sota, Shigeto Seno, Hironori Shigeta, Naoki Osato, Masafumi Shimoda, Shinzaburo Noguchi, Hideo Matsuda
  • Journal Title: Journal of Bioinformatics and Computational Biology Volume: 印刷中
  • Peer Reviewed
• [Presentation] A method for inferring gene regulatory networks based on pseudo time-series gene expression profiles from single-cell RNA-seq data (2019)
  • Author(s): Kaito Uemura, Naoki Osato, Hironori Shigeta, Shigeto Seno, Hideo Matsuda
  • Organizer: 27th Conference on Intelligent Systems for Molecular Biology and 18th European Conference on Computational Biology (ISMB/ECCB)
  • Int'l Joint Research
• [Presentation] Single-cell transcriptome analysis for elucidating cell dynamics (2019)
  • Author(s): Naoki Osato, Hironori Shigeta, Shigeto Seno, Yutaka Uchida, Masaru Ishii, Hideo Matsuda
  • Organizer: Single cell biology meets diagnostics - 12th International workshop on approaches to single cell analysis
  • Int'l Joint Research
• [Presentation] Detection of fusion genes from human breast cancer cell-line RNA-Seq data using shifted short read clustering (2018)
  • Author(s): Yoshiaki Sota, Shigeto Seno, Hironori Shigeta, Naoki Osato, Masafumi Shimoda, Shinzaburo Noguchi, Hideo Matsuda
  • Organizer: IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE 2018)
  • Int'l Joint Research