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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Jan 31, 2023 |
| Title |
P5Tx_Liver |
| Sample type |
SRA |
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| Source name |
Liver tissue from model established in postnatal day 5
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| Organisms |
Homo sapiens; Mus musculus |
| Characteristics |
mouse strain: NOD scid gamma mouse
mouse age at model establishment: P5
tissue: Liver tissue
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| Treatment protocol |
Around 37.5 thousand HepG2 cells suspended in 1.5 ul of Matrigel were injected to each liver of five-day-old (P5) and sixty-day-old NSG mice.
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| Extracted molecule |
total RNA |
| Extraction protocol |
The liver tissue and bulk tumors were minced and then enzymatically digested in advanced DMEM/F-12 (Thermo Fisher Scientific, Waltham, MA), containing 10 U/mL Papain (Worthington, Lakewood, NJ), 1 mmol/L N-acetyl cysteine (Sigma-Aldrich, St. Louis, MO), 12mg/mL DNase I (Sigma-Aldrich). The mixture was incubated at 37°C for about 30 min for the enzymatic digestion. The additional mechanical digestion was performed by gently pipetting up and down. Thereafter, the cell suspensions were passed through a 100-μm strainer and washed twice with 5ml of cold Advance DMEM/F12 and centrifugated at 300g for 5min at 4°C.
The single cells dissociated from liver and tumor tissues were processed by the Chromium Single Cell Platform using the Chromium Single Cell 3’ Library and Gel Bead Kit v2 (10X Genomics) as per the manufacturer’s protocol. Briefly, the single cells were washed once with PBS plus 0.04% BSA and counted with Luna-FL Fluorescence Cell Counter (VitaScientific). Then the cells were added to each lane of the chip and partitioned into Gel Beads in emulsion in the Chromium Controller. The cells were lysed, barcoded and reversely transcribed, followed by amplification, fragmentation, adaptor attachment and indexing. The libraries were sequenced by Novaseq 6000 (Illumina).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
Single-cell RNA-Seq
mRNA
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| Data processing |
To distinguish the human cancer cells from the mouse TME cells, we aligned the sequencing data of each sample to GRCh37 (hg19) and GRCm38 (mm10) reference genomes separately using the Cell Ranger Single-Cell Software Suite (v-2.0.1, 10X Genomics), and estimated the unique molecular identifiers (UMIs) with the default parameters.
We then merged the UMI files of the 4 samples (P5Tx Liver, P5Tx Tumor, P60Tx Liver and P60Tx Tumor) for both human cancer cells and mouse TME cells with an in-house script. Thereafter, we introduced the Seurat (v-3.2.2, PMID: 31178118) R package to remove the low-quality cells following these criteria: human cancer cells expressed < 1000 or > 8000 genes or mitochondrial gene content > 15% of total UMIs; mouse TME cells expressed <200 or >3000 genes or mitochondrial gene content > 10% of total UMIs.
To normalize the data, we divided the UMI count of each gene by the total UMI counts of the cell and scaled the library size to 1,000,000, followed by a natural log plus 1 transformation (“NormalizeData”). Then the normalized data was further scaled so that the mean expression across cells is 0 and the variance is 1 (“ScaleData”). The principle component analysis (PCA) was employed for dimensionality reduction (“RunPCA”). To determine the number of components to choose, we implemented a resampling test inspired by the JackStraw procedure (“JackStraw” and “ScoreJackStraw”) and picked the first 15 principal components which explained sufficient observed variance. Thereafter, to identify the clusters from the reduced dimensional space, we constructed a k-nearest neighborhood (KNN) graph based on the Euclidean distance in PCA space and refined the edge weights among cells based on the shared overlap in local neighborhoods (“FindNeighbors”). We then applied the modularity optimization technique named Louvain algorithm to iteratively group cell together with a resolution parameter of 0.5 (“FindClusters”). Finally, the Uniform Manifold Approximation and Projection (UMAP) was used to visualize the clustering results (“DimPlot”).
Genome_build: mm10/hg19
Supplementary_files_format_and_content: *.tar.gz: Tar archives include Cell Ranger count matrix for single cells in each sample; barcodes.tsv, genes.tsv, matrix.mtx.
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| Submission date |
Oct 16, 2021 |
| Last update date |
Jan 31, 2023 |
| Contact name |
Qingfei Pan |
| E-mail(s) |
Qingfei.Pan@stjude.org
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| Phone |
1-901-356-6214
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| Organization name |
St. Jude Children's Research Hospital
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| Department |
Department of Computational Biology
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| Lab |
Yu Lab
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| Street address |
262 Danny Thomas Place
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| City |
Memphis |
| State/province |
TN |
| ZIP/Postal code |
38105 |
| Country |
USA |
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| Platform ID |
GPL25526 |
| Series (1) |
| GSE186027 |
A Developmentally Prometastatic Niche in Neonatal Liver to Hepatoblastoma mediated by Cxcl1/Cxcr2 Axis |
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| Relations |
| BioSample |
SAMN22374246 |
| SRA |
SRX12677732 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM5629085_P5Tx_Liver.tar.gz |
36.4 Mb |
(ftp)(http) |
TAR |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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