|
| Status |
Public on May 14, 2019 |
| Title |
Tumor Endothelial Cells (TEC) |
| Sample type |
SRA |
| |
|
| Source name |
Endothelial Cells
|
| Organism |
Mus musculus |
| Characteristics |
cell type: Tumor Endothelial Cells
|
| Treatment protocol |
There were no treatments used in this study.
|
| Growth protocol |
Female 8 week old C57BL6/J mice (Cdh5Cre-ERT2:ZSGreenl/s/l, administered tamoxifen to induce ZSGreen expression) were injected in the mammary fat pad with 1X10^6 syngeneic EO771 mammary tumor cells. Tumors were allowed to grow for ~ three weeks at which point mice were euthanized and tumors or normal counterpart mammary glands were collected.
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Normal tissues or tumors were harvested and dispersed into single cell suspensions in a collagenase/dispase/DNAse cocktail according to our published methods (Xiao et al. Cancer Research 2014). After filtering and washing the suspensions, samples were subjected to FACS (fluorescence activated cell sorting) to collect live ZSGreen+ cells from each fraction (a live/dead stain was added to exclude dead cells). The collected fractions were prepared for analysis using the 10X genomics platform.
Libraries were prepared for sequencing on the 10X chromium according to the manufacturer's instructions for the single cell 3' reagent kit.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Description |
10X genomics single cell data
|
| Data processing |
FASTQ files created from basecall files using 10X genomics Cellranger v2.0.2
Reads mapped using STAR 2.5.1b and read counts created with Cellranger 2.0.2
Filtered gene-barcode matrices were imported into R using the Seurat package, keeping all genes expressed in at least five cells and all cells with at least 200 detectable expressed genes. We employed a global-scaling normalization that normalizes gene expression measurements for each cell by the total expression and log-transforms the result, followed by identification of highly variable genes to use for downstream analyses. We used the unsupervised graph-based clustering followed by principal component analysis (PCA) and t-stochastic neighbor embedding (tSNE) to identify 10 populations overall. Furthermore, we used the canonical correlation analysis (CCA) based approach to “align” the datasets such that gene expression and differential expression could be analyzed without confounding batch effects.
Genome_build: mm10
Supplementary_files_format_and_content: csv file of raw read counts by gene for each sample (tumor or control and cell barcode per column)
|
| |
|
| Submission date |
Aug 22, 2018 |
| Last update date |
May 14, 2019 |
| Contact name |
Stephen Turner |
| Organization name |
Signature Science, LLC
|
| Street address |
1670 Discovery Drive
|
| City |
Charlottesville |
| State/province |
VA |
| ZIP/Postal code |
22911 |
| Country |
USA |
| |
|
| Platform ID |
GPL19057 |
| Series (1) |
| GSE118904 |
Endothelial cell subtypes co-opt a TGFb/miR-30c-driven fibrinolytic pathway that supports tumor growth |
|
| Relations |
| BioSample |
SAMN09880159 |
| SRA |
SRX4596002 |
