Kandinsky - Visium spatial transcriptomics data from human colorectal cancer sample (FFPE)

This dataset is accessible under request because it includes sensitive data.

Users wishing to access the dataset will need to sign a Data Sharing Agreement document that will be provided at the time of the request submission.

Please write your request at [e-mail].

Visium spatial transcriptomics was performed on one CRC FFPE sample according to the manufacturer’s instructions (protocol CG000408 Revision D and CG000409 Revision C). The region of interest was scored on the FFPE block and a 5µm section was cut and placed on the Visium slide inside the 6x6mm2 fiducial frame. The slide was then incubated at 42 °C for 3h, deparaffinized, H&E stained and imaged using an Olympus VS200 slide scanner. Once imaged, the coverslip was removed, and the slide was decrosslinked. Visium Human Transcriptome Probe kit (v1, PN-1000363) was used for transcript hybridisation. Hybridised RNA molecules were released after tissue permeabilization and captured within each spot by barcoded oligonucleotides. Captured RNA molecules were used for sequencing library preparation and sequenced using NextSeq2000. Sequencing depth was calculated to ensure at least 25000 reads for each tissue covered spot. Visium fastq files were processed using spaceranger v2.0 to produce raw gene expression count data.In addition to standard output files generated with spaceranger, the dataset includes:

A1_CR48_TissueType_Anno.csv: spot annotation created via Loupe Browser v6.2. Each spot is classified according to underlying tissue type. Annotation is left empty for spots matching with empty tissue regions.

V12D05-285_CR48_20x_A1.tif: full-resolution Visium H&E image (tif format)

CRC_LCM_Ext_sigs.rds: list object in rds format containing immune (extrinsic) gene signatures described in Acha-Sagredo et al. and used for downstream analysis

All zipped(.zip) folders contained in the dataset (spatial/raw_feature_bc_matrix/filtered_feature_bc_matrix) should be unzipped before trying to load the dataset in R/python with packages like Kandinsky/Seurat/scannpy/squidpy etc.