2022 Fiscal Year Final Research Report
Single cell manipulation and gene expression analysis for "Singularity Biology"
| Project Area | Singularity biology |
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| Project/Area Number |
18H05411
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| Research Category |
Grant-in-Aid for Scientific Research on Innovative Areas (Research in a proposed research area)
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| Allocation Type | Single-year Grants |
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| Review Section |
Complex systems
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| Research Institution | Institute of Physical and Chemical Research |
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Principal Investigator |
Shiroguchi Katsuyuki 国立研究開発法人理化学研究所, 生命機能科学研究センター, チームリーダー (00454059)
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| Project Period (FY) |
2018-06-29 – 2023-03-31
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| Keywords | １細胞解析 / イメージング / 網羅的遺伝子発現解析 / AI / 機械学習 |
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| Outline of Final Research Achievements |
To understand “singularity cell” or “singularity phenomena”, we developed a new technology. We first developed a multifunctional robot, the Automated Live imaging and cell Picking System (ALPS), and used it to perform single-cell RNA sequencing for microscopically observed cells. Using robotically obtained data that linked cell images and the whole transcriptome, we successfully predicted transcriptome-defined cell types and states using cell image-based deep learning. This noninvasive approach opens a new window to determine the live-cell whole transcriptome in real time. Moreover, this work, which is based on a data-driven approach, is a proof-of-concept for determining the transcriptome-defined (omics-based) phenotypes (i.e., not relying on specific genes) of any cell from cell images using a model trained on linked datasets.
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| Free Research Field |
生物物理学
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| Academic Significance and Societal Importance of the Research Achievements |
本研究で開発した、細胞の顕微鏡画像から細胞の遺伝子発現状態を推定するアプローチは、より多くの細胞種や状態の推定・同定ができる可能性を示した。細胞を壊さずに細胞の状態を推定できるため、例えば、細胞治療に用いる細胞を移植前に評価することなどに役立つ可能性がある。基礎研究においても、細胞種の同定に用いられてきた分子マーカーが不要になる可能性があり、手順の簡易化やコストの削減になる。さらに、これまでに適切なマーカーを得ることができていない細胞種や状態の同定にも貢献すると考えられる。
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