Published: 5 April 2022| Version 1 | DOI: 10.17632/ys6j8bndby.1
Youqiong Ye


Spatial transcriptomics of colorectal cancer


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Spatial Transcriptomics (ST) slides were printed with two identical capture areas from four CRC patients. The capture of gene expression information for ST slides was performed by the Visium Spatial platform of 10x Genomics through the use of spatially barcoded mRNA-binding oligonucleotides in the default protocol. Raw sequencing reads of spatial transcriptomics were quality checked and mapped by Space Ranger v1.1. The gene-spot matrices generated after ST data processing from ST and Visium samples were analyzed with the Seurat package (versions 3.2.1) in R. Spots were filtered for a minimum detected gene count of 200 genes while genes with fewer than 10 read counts or expressed in fewer than 3 spots were removed. Normalization across spots was performed with the LogVMR function. Dimensionality reduction and clustering were performed with independent component analysis (PCA) at resolution 1.1 with the first 30 PCs. Signature scoring derived from scRNA-seq or ST signatures was performed with the AddModuleScore function with default parameters in Seurat. Spatial feature expression plots were generated with the Spa- tialFeaturePlot function in Seurat (versions 3.2.1).


RNA Sequencing, Spatial Analysis