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| Status |
Public on Jan 05, 2023 |
| Title |
Day 0 |
| Sample type |
SRA |
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| Source name |
muscle stem cell
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| Organism |
Mus musculus |
| Characteristics |
strain: C57Bl6
genotype: WT
cell type: muscle stem cells
treatment: None
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| Treatment protocol |
Treated with 0.033% DMSO or 1 mM MS023 in 0.033% DMSO. The medium was changed on day 2 and day 4 of culturing.
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| Growth protocol |
Purified MuSCs were cultured in Ham's F10 (Gibco) with 20% FBS (HyClone), 2.5 ng/mL human recombinant bFGF (Gibco), 1% Penicillin/Streptomycin (Wisent Inc.).
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| Extracted molecule |
total RNA |
| Extraction protocol |
scRNA libraries were generated using the GemCode Single-Cell Instrument (10x Genomics, Pleasanton, CA, USA) and Single Cell 3’ Library & Gel Bead Kit v2 and Chip Kit (P/N 120236 P/N 120237 10x Genomics). The sequencing ready libraries were purified with SPRIselect, quality controlled for sized distribution and yield (LabChip GX Perkin Elmer), and quantified using qPCR (KAPA Biosystems Library Quantification Kit for Illumina platforms P/N KK4824) as previously described (Couturier et al., 2020). Libraries were subsequently shipped and sequenced using Illumina NovaSeq6000 at IGM Genomics Center, UCSD, San Diego, CA.
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
musc1
0h
FACS-sorted quiescent muscle stem cells.
scRNA-seq
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| Data processing |
Cell barcodes and UMI (unique molecular identifiers) barcodes were demultiplexed and paired-end reads were first aligned to the mouse genome (mm10) using the Cell Ranger software v3.1.0.
Pre-processing was then carried out with the Seurat v3.2.0 R package (Butler et al., 2018). Genes detected in less than 3 cells as well as cells containing less than 200 genes detected were removed.
Cells were further filtered out for each sample based on the distribution of genes detected as well as the percentage of mitochondrial counts to balance the number of cells per sample to ~4000, and the raw count matrices of all samples were merged. Read counts for each cell were then normalized by the cell total, multiplied by 10000 and natural-log transformed.
The expression of the 2000 genes with highest cell-to-cell variation was standardized and the heterogeneity associated with mitochondrial contamination was regressed out. Principal component analysis was performed on the scaled data and the top 10 principal components were used to construct a K-nearest neighbor cell graph. Clustering of cells was carried out through the Louvain algorithm with the granularity parameter set to 0.4 and visualized with the Uniform Manifold Approximation and Projection (UMAP) (Becht et al., 2019) dimensional reduction technique using the Scanpy v1.5.2 python module (Wolf et al., 2018).
Cluster biomarkers were identified using the Wilcoxon Rank Sum test. Cell trajectories across pseudotime were analyzed by the Monocle v2.16.0 R package (Trapnell et al., 2014). Cell progress was defined by differentially expressed genes based on the clusters identified by Seurat. The dimensionality of the data was reduced through the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm to two dimensions and the cells were ordered along the trajectory according to pseudotime. Genes with branch-dependent expression were identified through the branched expression analysis modeling (BEAM) test. RNA velocity analysis was performed by the scVelo v0.2.2 python module (Bergen et al., 2020). Spliced and unspliced mRNAs were first distinguished through the velocyto v0.17.17 python module (La Manno et al., 2018). Velocities representing the direction and speed of cell motion were then computed and projected onto the UMAP embedding.
Assembly: mm10 (GRCm38)
Supplementary files format and content: barcodes.tsv, features.tsv, matrix.mtx files.
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| Submission date |
Mar 25, 2022 |
| Last update date |
Jan 05, 2023 |
| Contact name |
Stephane Richard |
| E-mail(s) |
stephane.richard@mcgill.ca
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| Organization name |
McGill University
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| Street address |
3999 chemin de la cote sainte catherine
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| City |
montreal |
| ZIP/Postal code |
H3T1E2 |
| Country |
Canada |
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| Platform ID |
GPL24247 |
| Series (1) |
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| Relations |
| BioSample |
SAMN26948112 |
| SRA |
SRX14617467 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM5972490_d0_barcodes.tsv.gz |
20.0 Mb |
(ftp)(http) |
TSV |
| GSM5972490_d0_features.tsv.gz |
272.8 Kb |
(ftp)(http) |
TSV |
| GSM5972490_d0_matrix.mtx.gz |
151.3 Mb |
(ftp)(http) |
MTX |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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