※本セミナーは中止となりました

最先端研究セミナー

 

講演者：小野 昌弘（ヒトレトロウイルス学共同研究センター　特任教授 /インペリアル・カレッジ・ロンドン 准教授）

演題：Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer

 

日時：2025年10月22日（水）　※※16：00-17：00　中止

※Zoomオンラインでの開催です

※ZOOMミーティングのURLはMoodleの「S-HIGO最先端研究セミナーA、B」にてご確認ください。

https://md.kumamoto-u.ac.jp/course/view.php?id=120331

 

Abstract:
Investigating the temporal dynamics of gene expression is crucial for understanding gene regulation across various biological processes. Using the Fluorescent Timer protein, the Timer-of-cell-kinetics-and-activity (Tocky) system enables analysis of transcriptional dynamics at the single-cell level. However, the complexity of Timer fluorescence data has limited its broader application.

In this talk, I will present an integrative approach that combines molecular biology and machine learning to analyse Foxp3 transcriptional dynamics using flow cytometric Timer data. We developed a convolutional neural network (CNN)-based method incorporating image transformation and class-specific feature visualization to identify regulatory patterns at the single-cell level. Using a CRISPR-engineered mutant Foxp3-Tocky mouse model lacking the enhancer Conserved Non-coding Sequence 2 (CNS2), we reveal enhancer-specific control of transcription frequency under immune stimulation.

In addition, our analysis of wild-type Foxp3-Tocky mice across developmental stages demonstrates age-dependent shifts in Foxp3 expression dynamics, particularly highlighting thymus-like transcriptional profiles in neonatal peripheral T cells.

In conclusion, this work establishes a framework integrating CRISPR mutagenesis, single-cell time-resolved analysis, and machine learning to decode transcriptional regulation in vivo.

 

担当分野：ゲノミクス・トランスクリプトミクス学分野　佐藤（6830)