The spatiotemporal dynamics of spatially variable genes in developing mouse brain revealed by a novel computational scheme.

To understand how brain regions form and work, it is important to explore the spatially variable genes (SVGs) enriched in specific brain regions during development. Spatial transcriptomics techniques provide opportunity to select SVGs in the high-throughput way. However, previous methods neglected the ranking order and combinatorial effect of SVGs, making them difficult to automatically select the high-priority SVGs from spatial transcriptomics data. Here, we proposed a novel computational pipeline, called SVGbit, to rank the individual and combinatorial SVGs for marker selection in various brain regions, which was tested in different kinds of public datasets for both human and mouse brains. We then generated the spatial transcriptomics and immunohistochemistry data from mouse brain at critical embryonic and neonatal stages. The results show that our ranking and clustering scheme captures the key SVGs which coincide with known anatomic regions in the developing mouse brain. More importantly, SVGbit can facilitate the identification of multiple gene combination sets in different brain regions. We identified three dynamical sub-regions which can be segregated by the staining of Sox2 and Calb2 in thalamus, and we also found that Nr4a2 expression gradually segregates the neocortex and hippocampus during the development. In summary, our work not only reveals the spatiotemporal dynamics of individual and combinatorial SVGs in developing mouse brain, but also provides a novel computational pipeline to facilitate the selection of marker genes from spatial transcriptomics data.