Spatial transcriptome of developmental mouse brain reveals temporal dynamics of gene expressions and heterogeneity of the claustrum
Yuichiro HaraiD, Takuma KumamotoiD, Naoko Yoshizawa-SugataiD, Kumiko Hirai, Song Xianghe, Hideya KawajiiD, Chiaki Ohtaka‐MaruyamaiD
Abstract
During the development of the mammalian cerebral cortex, numerous neurons are arranged in a six-layer structure with an inside-out fashion to form the neocortex and wire neural circuits. This process includes cell proliferation, differentiation, migration, and maturation, supported by precise genetic regulation. To understand this sequence of processes at the cellular and molecular levels, it is necessary to characterize the fundamental anatomical structures by gene expression. However, markers established in the adult brain sometimes behave differently in the fetal brain, actively changing during development. Spatial transcriptomes yield genome-wide gene expression profiles from each spot patterned on tissue sections, capturing RNA molecules from fresh-frozen sections and enabling sequencing analysis while preserving spatial information. However, a deeper understanding of this data requires computational estimation, including integration with single-cell transcriptome data and aggregation of spots on the single-cell cluster level. The application of such analysis to biomarker discovery has only begun recently, and its application to the developing fetal brain is largely unexplored. In this study, we performed a spatial transcriptome analysis of the developing mouse brain to investigate the spatiotemporal regulation of gene expression during development. Using these data, we conducted an integrated study with publicly available mouse data sets, the adult brains spatial transcriptome, and the fetal brains single-cell transcriptome. Our data-driven analysis identified novel molecular markers of the choroid plexus, piriform cortex, thalamus, and claustrum. In addition, we revealed that the internal structure of the embryonic claustrum is composed of heterogeneous cell populations.
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