|
| Status |
Public on Apr 15, 2020 |
| Title |
E4-GP2 |
| Sample type |
SRA |
| |
|
| Source name |
Tibial Longbone Growth Plate
|
| Organism |
Mus musculus |
| Characteristics |
strain: CD-1
tissue: Bone
age: post natal day 3
genotype: wild type
cell type: Skeletal Stem Cell
|
| Extracted molecule |
total RNA |
| Extraction protocol |
Total single cell RNA profiles of SSCs isolated post-natal day 3 posterior frontal (PF), sagittal (SAG), coronal (COR) sutures and the growth plate (GP) of the tibial long bone from CD-1, wild-type mice were purified using Qiagen MiRNeasy Kit (Cat #217084) following the manufacturer’s instructions.
RNA libraries were prepared for sequencing using standard Illumina single cell RNA seq protocols.
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina HiSeq 4000 |
| |
|
| Description |
GP SSCs from Plate 2
|
| Data processing |
RNA-Seq alignment to the reference was performed using STAR v2.5.3a using default conditions as described in Dobin et al., Bioinformatics, 2013.
Gene counts were tabulated from STAR v2.5.3a by mapping against mouse mm9 reference and used as input to Seurat. The counts coincide with those produced by htseq-count with default parameters.
The R package Seurat v2.3.0 was used for QC, analysis, and exploration of single-cell RNA-Seq data using R v3.4.4.
Genes expressed in at least 3 cells and cells with at least 200 expressed genes were retained for further analysis. Gene expression measurements for each cell were normalized by their total expression, scaled by 10,000, and log-transformed. Following normalization, genes that varied between single cells were identified.
PCA was performed on genes to output a set of genes that most strongly defined a set of principal components. Seurat’s graph-based clustering approach was used to cluster the cells. Seurat was further used to perform tSNE clustering which placed cells with similar local neighborhoods in high-dimensional space together in low-dimensional space. Positive and negative markers in each cluster were identified by comparing genes in cells of one cluster against genes in all other cells. Only genes that were detected in at least 25% of cells in either of the two populations were tested. Violin plots were used to visualize expression of top markers in each cluster. Finally, a heatmap of the top 20 marker genes across all cells in the 4 clusters was plotted.
Genome_build: mm9
Supplementary_files_format_and_content: tab-delimited text files include gene count values for each Sample.
|
| |
|
| Submission date |
Oct 15, 2019 |
| Last update date |
Apr 15, 2020 |
| Contact name |
Natalina Quarto |
| E-mail(s) |
[email protected]
|
| Organization name |
Stanford University School of Medicine
|
| Department |
Surgery - Plastic and Reconstructive Surgery
|
| Lab |
Hagey Laboratory for Pediatric Regenerative Medicine
|
| Street address |
257 Campus Drive
|
| City |
Stanford |
| State/province |
CA |
| ZIP/Postal code |
94305 |
| Country |
USA |
| |
|
| Platform ID |
GPL21103 |
| Series (2) |
| GSE138881 |
Skeletal Stem Cell Powering Cranial Suture Fate: An Answer to Craniosynostosis (scRNA-seq) |
| GSE138882 |
Skeletal Stem Cell Powering Cranial Suture Fate: An Answer to Craniosynostosis |
|
| Relations |
| BioSample |
SAMN13032210 |
| SRA |
SRX6993581 |
