2022 Fiscal Year Final Research Report
Identification of genes and key molecular pathways determining cluster/hub cells and analysis of their association with human diseases
| Project Area | Morphological features and gene expression patterns underlying hub neurons |
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| Project/Area Number |
20H05777
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| Research Category |
Grant-in-Aid for Transformative Research Areas (B)
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| Allocation Type | Single-year Grants |
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| Review Section |
Transformative Research Areas, Section (III)
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
Takata Atsushi 国立研究開発法人理化学研究所, 脳神経科学研究センター, チームリーダー (90643693)
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| Project Period (FY) |
2020-10-02 – 2023-03-31
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| Keywords | マルチモーダル一細胞 / RNAシーケンス / 精神神経疾患 / 遺伝統計 / 機械学習 |
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| Outline of Final Research Achievements |
In this project, we developed a prototype of an experimental and analytical pipeline to rapidly identify clusters/hub cells from wide field-of-view neural activity recording data, artificially label these cells, and analyze their transcriptomic profiles using single-cell RNA-seq, by closely collaborating with the Murayama and Takeda groups. Our Planned Research group established an informatics pipeline for single-cell RNA-seq data to quantify expression levels of genes including artificially expressed genes for labeling, identify genes characterizing the cell population of interest, and statistically evaluate the overlap between these genes and disease-associated genes or genomic loci. We also applied this pipeline to single-cell RNA-seq data from the brain of an animal model of neuropsychiatric disorder and identified cell types with prominent disease-associated alterations in the transcriptomic profile.
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| Free Research Field |
ゲノム医科学
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| Academic Significance and Societal Importance of the Research Achievements |
本領域では、神経活動データ・一細胞RNA-seqデータ・神経形態データ等を、神経科学者・数理情報学者・バイオインフォマティストの密な連携を通じて統合的に解析し、その対応関係等を明らかにするためのパイプライン開発を進めた。この枠組みは、本領域で主要対象としたハブ/クラスタ細胞のみならず、あらゆるタイプの関心細胞集団を対象としたマルチモーダル研究に応用可能であるため、学術的意義が高いものと考える（ただし、さらなる性能向上の必要性があるとも認識している）。また、こういった解析手法を用いたヒト脳疾患の研究は、病態理解に新たな洞察をもたらし治療・予防法開発にも繋がりうるという点で社会的意義を有する。
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