Data-driven and technical approaches to understand spatial gene regulation

 

OKI, Shinya

Department of Drug Discovery Medicine, Kyoto University Graduate School of Medicine

 

Multicellular organisms are composed of a variety of tissues and cell types, and their dynamic changes are organized by spatiotemporal gene expression. Although numbers of genes have been characterized to be expressed in specific tissues and time points, underlying mechanisms of the spatiotemporal gene regulation are poorly understood. To elucidate the mechanisms with a data-driven framework, we have developed ChIP-Atlas, a database fully integrating public ChIP-seq data (> 140,000 experiments). By taking advantage of the huge amount of data, we identified multiple transcription factors pivotally involved in cellular differentiation, genetic diseases, and drug actions. In addition, to understand spatial gene expression in a high-resolution manner, we established a transcriptome profiling method coupled with photo-isolation chemistry (PIC) that allows the determination of expression profiles specifically from photo-irradiated regions of interest. By using this method, we identified area-specific transcripts not only from microtissues in mouse embryos and brains but also from subcellular and subnuclear microstructures (stress granules and nuclear speckles, respectively).

In this seminar, I would like to talk about the regulatory mechanism of spatial gene expression revealed by the combination of data-driven and technical approaches, and further discuss the application for the research of developmental biology and pathological processes.

 

1) S. Oki, et al. ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep, 19(12), e46255, (2018).

2) M. Honda, S. Oki, et al. High-depth spatial transcriptome analysis by photo-isolation chemistry. Nat Commun, 12(1), 4416, (2021).