Jingmin Kang, Qingsong Li, Jie Liu, Lin Du, Peng Liu, Fuyan Liu, Yue Wang, Xunan Shen, Xujiao Luo, Ninghe Wang, Renhua Wu, Lei Song, Jizheng Wang, Xin Liu
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Exploring the cellular and molecular basis of murine cardiac development through spatiotemporal transcriptome sequencing.
Background: Spatial transcriptomics is a powerful tool that integrates molecular data with spatial information, thereby facilitating a deeper comprehension of tissue morphology and cellular interactions. In our study, we utilized cutting-edge spatial transcriptome sequencing technology to explore the development of the mouse heart and construct a comprehensive spatiotemporal cell atlas of early murine cardiac development.
Results: Through the analysis of this atlas, we elucidated the spatial organization of cardiac cellular lineages and their interactions during the developmental process. Notably, we observed dynamic changes in gene expression within fibroblasts and cardiomyocytes. Moreover, we identified critical genes, such as Igf2, H19, and Tcap, as well as transcription factors Tcf12 and Plagl1, which may be associated with the loss of myocardial regeneration ability during early heart development. In addition, we successfully identified marker genes, like Adamts8 and Bmp10, that can distinguish between the left and right atria.
Conclusion: Our study provides novel insights into murine cardiac development and offers a valuable resource for future investigations in the field of heart research, highlighting the significance of spatial transcriptomics in understanding the complex processes of organ development.
期刊介绍:
GigaScience seeks to transform data dissemination and utilization in the life and biomedical sciences. As an online open-access open-data journal, it specializes in publishing "big-data" studies encompassing various fields. Its scope includes not only "omic" type data and the fields of high-throughput biology currently serviced by large public repositories, but also the growing range of more difficult-to-access data, such as imaging, neuroscience, ecology, cohort data, systems biology and other new types of large-scale shareable data.