In this study, the official Space Ranger software (10× Genomics) was used for data preprocessing, quantitative gene expression analysis, and point identification. Sequencing data preprocessing included filtering the sequenced product, evaluating the quality of sequencing data, and calculating the sequence length distributions. The web-based ST spot detector software, Space Ranger (10× Genomics), was used to identify the spatial barcode markers in Reads1 and UMI markers of different transcripts. Read2 was aligned to the reference genome, human GRCh38 v86, and mouse mm10, using the transcriptome-specific STAR alignment software ( The sequence with a unique alignment position was selected for subsequent analysis. Space Ranger (10× Genomics) produced the bright field slide image of a single capture area and the fastq sequence, distinguished by tissue, background, and the detected spot barcodes. The gene spot matrix was generated by using Viscum spatial barcodes, and then point clustering and gene expression analysis were performed.

Seurat software ( was used to analyze and cluster the four samples. Low quality data were then filtered. Principal Component Analysis (PCA), including the t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) algorithms, were used to reduce and visualize the dimensions of the data. All spots from the four samples were clustered according to differentially-expressed genes, which were expressed in over 25% of the spots with LOGFC values greater than 0.25. Heat maps were generated with Seurat software using default hierarchical clustering of read counts.

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