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| Status |
Public on Sep 22, 2020 |
| Title |
KO-5 |
| Sample type |
SRA |
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| Source name |
B16 melanoma tumor
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| Organism |
Mus musculus |
| Characteristics |
strain: C57BL/6
tissue: B16 melanoma tumor
Sex: male
genotype: ATPIF1 knock-out
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
Single cells were collected from tumor tissues as described previously (Zhang et al., 2020). Briefly, tumor tissues were cut into approximately 1-2 mm3 pieces in the RPMI-1640 medium (Gibco), and enzymatically digested with gentleMACS (Miltenyi) using program 37C_m_TDK_1 on a rotor at 37°C, according to manufacturer’s instruction. The dissociated cells were subsequently passed through a 100 µm SmartStrainer and centrifuged at 400 g for 5 min. After the supernatant was removed, the pelleted cells were suspended in red blood cell lysis buffer (Miltenyi) and incubated on ice for 1-2 min to lyse red blood cells. After washing twice with 1× PBS (Gibco), the cell pellets were re-suspended in sorting buffer (PBS supplemented with 2% fetal bovine serum (FBS, Gibco)).
Cells were loaded around 17,400 cells/chip position using the 10x Chromium Single cell 5’ Library, Gel Bead & Multiplex Kit and Chip Kit (10x Genomics, V1.0 barcoding chemistry) according to the manufacturer’s instructions.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
10x Genomics 5' library
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| Data processing |
Following sequencing, the raw data from each sample were demultiplexed, aligned to the mm10 reference genome, and UMI counts were quantified using the 10x Genomics Cell Ranger pipeline (v3.1.0, 10X Genomics). Then, we continued the data analysis with the raw barcode matrix files using the Seurat package (v3.2.0). Cells more than 100 detected genes and less than 10% mitochondrial reads were considered valid cells and were kept for downstream analysis. In order to prevent clusters from being biased by mitochondrial transcript content, gene expression values were scaled based on the cell mitochondrial transcript content. Next, "LogNormalize" and "vst" was applied to normalize and find variable features within the single cell gene expression data. Clustering and differential expression analysis was performed using R package Seurat with default parameters. Based on the PC ElbowPlot, we picked a certain number of principal components (PCs) for the clustering analysis when the number reached the baseline of the standard deviation of PC. We generally used the cluster resolution that higher one did not result in increasing clusters linearly. Cell clusters were visualized using UMAP. For differential gene expression, we used model-based analysis of single-cell Transcriptomics (MAST) (log fc ≥0.25, min.pct = 0.1, min.diff.pct > -Inf) and only selected the genes with adjusting p value <0.05. In order to predict cellular differentiation, cells were ordered in pseudotime using Monocle2 (v2.14.0) with default parameters. All analyses were carried out in R 3.6.1 environment.
Genome_build: mm10
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| Submission date |
Sep 21, 2020 |
| Last update date |
Sep 22, 2020 |
| Contact name |
Genshen Zhong |
| E-mail(s) |
zhonggs@xxmu.edu.cn
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| Phone |
+8618749102436
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| Organization name |
Xinxiang Medical University
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| Street address |
Jinsui Road, 601#
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| City |
Xinxiang |
| State/province |
Henan Province |
| ZIP/Postal code |
453003 |
| Country |
China |
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| Platform ID |
GPL24247 |
| Series (1) |
| GSE158278 |
Induction of antitumor activity of CD8+ T cells by inactivation of ATPIF1 through F1Fo-ATPase-mediated metabolic reprogramming |
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| Relations |
| BioSample |
SAMN16231078 |
| SRA |
SRX9162511 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM4796304_KO-raw_barcodes.tsv.gz |
2.4 Mb |
(ftp)(http) |
TSV |
| GSM4796304_KO-raw_features.tsv.gz |
272.8 Kb |
(ftp)(http) |
TSV |
| GSM4796304_KO-raw_matrix.mtx.gz |
38.3 Mb |
(ftp)(http) |
MTX |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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