A unified spatial transcriptome profiling of ten mouse organs

Spatial transcriptomics has enabled numerous deep learning models in this area, and training them requires large amounts of high-quality data, especially expression matrices paired with histological images. Here, we present a unified spatial transcriptomic dataset generated using the Stereo-seq platform, covering 10 mouse organsincluding brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterusencompassing 23 tissue sections generated from 21 chips, each with matched ssDNA or H&E staining images. The dataset comprises single-cell-resolution (cell-bin) or square bin-50 (25 m 25 m) expression matrices for each sample, accompanied by corresponding cell type annotations. Annotation robustness was further supported by concordance across different sections of the same tissue and corroboration with canonical marker gene expression patterns. Finally, we compared the characteristics of the cell-bin and bin-50 expression matrices and demonstrated the advantages of cell-bin resolution for cell annotation. This dataset provides a standardized resource for spatial transcriptomics method development, benchmarking, and multimodal analysis. Stereo-seq spatial transcriptomics was performed on fresh-frozen tissue sections collected from 13 normal 6-week-old C57BL/6J mice. The dataset includes 23 tissue sections derived from 21 Stereo-seq chips, covering 10 mouse organs: brain, kidney, lung, thymus, large intestine, skin, spleen, ovary, testis, and uterus. Each section was paired with ssDNA or H&E staining images. Depending on image quality, samples were processed to generate either single-cell-resolution cell-bin expression matrices or bin-50 expression matrices, followed by cell type annotation and comparison of the two spatial expression representations. *************************************************************** Submitter states that missing raw files are due to file loss. ***************************************************************