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| Status |
Public on May 23, 2024 |
| Title |
BLCA-B1 |
| Sample type |
SRA |
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| Source name |
bladder cancer
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| Organism |
Homo sapiens |
| Characteristics |
tissue: bladder cancer
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| Extracted molecule |
total RNA |
| Extraction protocol |
ST on FFPE slides were performed with the Visium spatial technology from 10X Genomics. Two to three consecutive tissue sections of 5-μm thickness were collected for RNA extraction with the Qiagen RNeasy FFPE Kit. To assess the RNA quality of the tissue, the purified RNA was immediately processed to calculate the percentage of total RNA fragments >200 nucleotides (DV200) using the Agilent RNA 6000 Pico Kit. Based on DV200 evaluation, blocks with DV200 >30% were selected for proceeding with sectioning. The area of interest (11 x 11 mm) on section was carefully placed within the allowable area to ensure compatibility with the Visium CytAssist instrument. The tissues were then deparaffinized, stained, and decross-linked, followed by probe hybridization, ligation, CytAssist enabled RNA digestion and oligo capture, release, and extension.
The Visium spatial gene expression FFPE libraries were constructed using the Visium CytAssist Spatial Gene Expression for FFPE Human Transcriptome Probe Kit (PN-1000444) following the manufacturer's guidance. Constructed libraries were sequenced on the Illumina NovaSeq 6000 platforms to achieve a depth of at least 75,000 mean read pairs per spot.
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| Library strategy |
OTHER |
| Library source |
transcriptomic |
| Library selection |
other |
| Instrument model |
Illumina NovaSeq 6000 |
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| Data processing |
METI takes spatial gene expression and histology image data as input. The ST gene expression data contains an N × M matrix of unique molecular identifier (UMI) counts, where N denotes the number of spots and M represents the number of genes. Each spot is associated with 2-dimensional spatial coordinates denoted as (x, y). The gene expression values for each spot are normalized by dividing the UMI count of each gene within that spot by the overall UMI count of all genes in the same spot. The result is then scaled up by a factor of 10,000 and converted to a natural logarithm scale.
Supplementary files format and content: Matrix
Library strategy: Spatial Transcriptomics
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| Submission date |
Oct 23, 2023 |
| Last update date |
Aug 16, 2024 |
| Contact name |
Linghua Wang |
| E-mail(s) |
lwang22@mdanderson.org
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| Organization name |
MD Anderson Cancer Center
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| Street address |
1881 East Road,
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| City |
Houston |
| ZIP/Postal code |
77054 |
| Country |
USA |
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| Platform ID |
GPL24676 |
| Series (1) |
| GSE246011 |
METI: Deep profiling of tumor ecosystems by integrating cell morphology and spatial transcriptomics |
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| Relations |
| BioSample |
SAMN43172989 |
| SRA |
SRX25691883 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM7853987_BLCA-B1-Metadata.csv.gz |
152.7 Kb |
(ftp)(http) |
CSV |
| GSM7853987_BLCA-B1-count.csv.gz |
13.4 Mb |
(ftp)(http) |
CSV |
| GSM7853987_BLCA-B1.tif.gz |
19.9 Mb |
(ftp)(http) |
TIFF |
| GSM7853987_BLCA-B1_spot_coordinates.csv.gz |
206.5 Kb |
(ftp)(http) |
CSV |
SRA Run Selector |
| Raw data are available in SRA |
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