Abstract Elucidating the spatiotemporal dynamics of gene expression is essential for understanding complex physiological and pathological processes. Traditional technologies like in situ hybridization (ISH) and immunostaining have been restricted to analyzing expression patterns of a limited number of genes. Spatial transcriptomics (ST) has emerged as a robust alternative, enabling the investigation of spatial patterns of thousands of genes simultaneously. However, current ST methods are hindered by low read depths and limited gene detection capabilities. Here, we introduce Palette, a pipeline that infers detailed spatial gene expression patterns from bulk RNA-seq data, utilizing existing ST data as only reference. This method identifies more precise expression patterns by smoothing, imputing and adjusting gene expressions. We applied Palette to construct the D anio re rio S patio T emporal E xpression P rofiles ( Dre STEP) by integrating 53-slice serial bulk RNA-seq data from three developmental stages with existing ST references and 3D zebrafish embryo images. Dre STEP provides a comprehensive cartographic resource for examining gene expression and spatial cell-cell interactions within zebrafish embryos. Utilizing machine learning-based screening, we identified key morphogens and transcription factors (TFs) essential for anteroposterior (AP) axis development and characterized their dynamic distribution throughout embryogenesis. In addition, among these TFs, Hox family genes were found to be pivotal in AP axis refinement. Their expression was closely correlated with cellular AP identities, and hoxb genes may act as central regulators in this process.

Unravelling the progression of the zebrafish primary body axis with reconstructed spatiotemporal transcriptomics