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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Aug 03, 2024 |
| Title |
CT26 xenografts, MCB-294, CD45+ scRNAseq |
| Sample type |
SRA |
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| Source name |
CT26
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| Organism |
Mus musculus |
| Characteristics |
Sex: male
cell line: CT26
age: 6-8 weeks
strain: C57BL/6
treatment: MCB-294, 2days, bid
cell population: CD45+
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| Treatment protocol |
CT26 xenograft mouse model were treatment with MCB-294 (30 mg/kg, BID, IP ) or MCB-36 ( 60 mg/kg, BID, IP) for 2 days, followed by harvest for sorting cd45+ cells
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| Growth protocol |
Mice were housed in an animal facility with a 12-hour day/night light cycle, temperature maintained at 25°C and humidity at 60%. Mice were maintained under pathogen-free conditions, with food and water were provided ad libitum.
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| Extracted molecule |
total RNA |
| Extraction protocol |
Tumor xenografts were mechanically and enzymatically (10 U/mL collagenase I, 400 U/mL collagenase IV, and 100 μg/mL DNase I in FBS-free RPMI-1640 medium) digested to obtain a single-cell suspension. Intratumoral CD45+T cells were isolated using BD FACSAriaIII and resuspended in PBS containing 0.04% BSA.
Cells were counted and cell density was adjusted to that recommended for 10x Genomics Chromium single-cell 3’ v3 processing and library preparation. Sequencing was performed on an Illumina platform (NovaSeq 6000), by GENERGY BIO (Shanghai, China), at a sequencing depth of about 90,000 reads per single cell. Sequencing data in a bcl file were converted to FASTQ format using Illumina bcl2fastq2 Conversion Software v2.20 and were subjected to quality control by FastQCv0.11.9.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
10XGenomics
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| Data processing |
Reads were processed using the Cell Ranger (v5.0.1) pipeline with default and recommended parameters. FASTQs generated from Illumina sequencing output were aligned to the mouse genome, versionGRCm38, using the STAR algorithm.
Next, Gene-Barcode matrices were generated for each individual sample by counting UMIs and filtering non-cell associated barcodes.
Finally, we generate a gene-barcode matrix containing the barcoded cells and gene expression counts. This output was then imported into the Seurat (v4.1.1) R toolkit for quality control and downstream analysis of our single cell RNA seq data. All functions were run with default parameters, unless specified otherwise.
Assembly: mm10
Supplementary files format and content: Matrix files
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| Submission date |
Jul 29, 2024 |
| Last update date |
Aug 03, 2024 |
| Contact name |
新婷 夏 |
| E-mail(s) |
xxt021624@outlook.com
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| Phone |
17356583746
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| Organization name |
华东师范大学
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| Department |
生命科学学院
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| Lab |
逄Lab
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| Street address |
东川路500号
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| City |
上海市 |
| State/province |
上海市 |
| ZIP/Postal code |
200062 |
| Country |
China |
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| Platform ID |
GPL24247 |
| Series (1) |
| GSE273301 |
A pan-KRAS inhibitor and its derived degrader elicit multifaceted anti-tumor efficacy in KRAS-driven cancers [CD45+ scRNA-seq] |
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| Relations |
| BioSample |
SAMN42888531 |
| SRA |
SRX25497773 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM8425879_MCB_294_barcodes.tsv.gz |
42.1 Kb |
(ftp)(http) |
TSV |
| GSM8425879_MCB_294_features.tsv.gz |
284.1 Kb |
(ftp)(http) |
TSV |
| GSM8425879_MCB_294_matrix.mtx.gz |
94.5 Mb |
(ftp)(http) |
MTX |
SRA Run Selector |
| Raw data are available in SRA |
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