The supplementary information provides detailed methods and results for spatial reconstruction of single-cell gene expression in zebrafish embryos. The key points include: 1. **Spatially Diverse Landmark Genes**: The study assesses the impact of the number and type of landmark genes on the accuracy of spatial mapping using Seurat. It finds that a smaller number of landmark genes with diverse expression patterns outperforms a larger number of genes with overlapping or redundant patterns. For example, even with only 16 landmark genes, Seurat's cell mappings were highly accurate, with shifts of less than one bin on average. 2. **Redundant Landmark Genes**: The study also investigates the effect of redundant landmark genes. It shows that including two genes with overlapping expression patterns improves mapping accuracy, but additional redundancy provides diminishing returns. 3. **Identification of Rare Subpopulations**: The study identifies rare subpopulations such as prechordal plate progenitors, endodermal progenitors, and primordial germ cells (PGC) using unsupervised and supervised methods. These subpopulations are well-separated based on principal components and confirmed by classical markers. 4. **Supplementary Figures and Tables**: The supplementary material includes figures and tables that provide visual and quantitative evidence for the spatial reconstruction, such as variability in published in situ images, removal of EVL cells, mapping confidence, spatial prediction clustering, and additional in situ patterns. 5. **Seurat Analysis**: Detailed R Markdown files are provided for loading and analyzing the data, including normalization, PCA analysis, building gene expression models, inferring spatial origins, and analyzing populations. These files demonstrate the computational steps and results for each stage of the analysis. Overall, the supplementary information supports the robustness and effectiveness of the spatial reconstruction method, highlighting the importance of spatially diverse landmark genes and the ability to identify rare cell populations.The supplementary information provides detailed methods and results for spatial reconstruction of single-cell gene expression in zebrafish embryos. The key points include: 1. **Spatially Diverse Landmark Genes**: The study assesses the impact of the number and type of landmark genes on the accuracy of spatial mapping using Seurat. It finds that a smaller number of landmark genes with diverse expression patterns outperforms a larger number of genes with overlapping or redundant patterns. For example, even with only 16 landmark genes, Seurat's cell mappings were highly accurate, with shifts of less than one bin on average. 2. **Redundant Landmark Genes**: The study also investigates the effect of redundant landmark genes. It shows that including two genes with overlapping expression patterns improves mapping accuracy, but additional redundancy provides diminishing returns. 3. **Identification of Rare Subpopulations**: The study identifies rare subpopulations such as prechordal plate progenitors, endodermal progenitors, and primordial germ cells (PGC) using unsupervised and supervised methods. These subpopulations are well-separated based on principal components and confirmed by classical markers. 4. **Supplementary Figures and Tables**: The supplementary material includes figures and tables that provide visual and quantitative evidence for the spatial reconstruction, such as variability in published in situ images, removal of EVL cells, mapping confidence, spatial prediction clustering, and additional in situ patterns. 5. **Seurat Analysis**: Detailed R Markdown files are provided for loading and analyzing the data, including normalization, PCA analysis, building gene expression models, inferring spatial origins, and analyzing populations. These files demonstrate the computational steps and results for each stage of the analysis. Overall, the supplementary information supports the robustness and effectiveness of the spatial reconstruction method, highlighting the importance of spatially diverse landmark genes and the ability to identify rare cell populations.