ABSTRACT
Initiation of timely and sufficient zygotic genome activation (ZGA) is crucial for the beginning of life, yet our knowledge of transcription factors (TFs) contributing to ZGA remains limited. Here, we screened the proteome of early mouse embryos after cycloheximide (CHX) treatment and identified maternally derived KLF17 as a potential TF for ZGA genes. Using a conditional knockout (cKO) mouse model, we further investigated the role of maternal KLF17 and found that it promotes embryonic development and full fertility. Mechanistically, KLF17 preferentially binds to promoters and recruits RNA polymerase II (RNA Pol II) in early 2-cell embryos, facilitating the expression of major ZGA genes. Maternal Klf17 knockout resulted in a downregulation of 9% of ZGA genes and aberrant RNA Pol II pre-configuration, which could be partially rescued by introducing exogenous KLF17. Overall, our study provides a strategy for screening essential ZGA factors and identifies KLF17 as a crucial TF in this process.
Subject(s)
RNA Polymerase II , Zygote , Animals , Mice , Embryonic Development/genetics , Gene Expression Regulation, Developmental , Genome , RNA Polymerase II/metabolism , Transcription Factors/metabolism , Zygote/metabolismABSTRACT
Advances in transcriptomic technologies have deepened our understanding of the cellular gene expression programs of multicellular organisms and provided a theoretical basis for disease diagnosis and therapy. However, both bulk and single-cell RNA sequencing approaches lose the spatial context of cells within the tissue microenvironment, and the development of spatial transcriptomics has made overall bias-free access to both transcriptional information and spatial information possible. Here, we elaborate development of spatial transcriptomic technologies to help researchers select the best-suited technology for their goals and integrate the vast amounts of data to facilitate data accessibility and availability. Then, we marshal various computational approaches to analyze spatial transcriptomic data for various purposes and describe the spatial multimodal omics and its potential for application in tumor tissue. Finally, we provide a detailed discussion and outlook of the spatial transcriptomic technologies, data resources and analysis approaches to guide current and future research on spatial transcriptomics.
