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Sample GSM6996259 Query DataSets for GSM6996259
Status Public on Aug 28, 2024
Title C57 Day 8 after CTX muscle injury (stRNA-seq)
Sample type SRA
 
Source name Tibialis Anterior (TA)
Organism Mus musculus
Characteristics tissue: Tibialis Anterior (TA)
strain: C57BL/6J
treatment: CTX muscle injury
post-injury day: Day 8
Treatment protocol Mice (8-12 weeks-old male) were anaesthetized with isoflurane (adjusted flow rate or concentration to 1,5%) and 50 µl of 10uM cardiotoxin (EMD Millipore, 217503-1MG) was injected in the tibialis anterior (TA) muscle. Mice were brought out of anesthesia and monitored until they were euthanized and processed at various time points. Muscles were recovered for single cell analysis at day 1 to day 8 post CTX-injury. Dystrophic gastrocnemius muscles were isolated from 2 month old D2.mdx animals that received weekly treatment of Vehicle or Prednisone (5 mg/kg) orally for a month starting at 4 weeks of age.
Extracted molecule polyA RNA
Extraction protocol After tissue digestion and bead selection, CD45+ single-cell sorted suspensions were washed and resuspended in 0.04% BSA in PBS at a concentration of at least 400 cells/μL. Cells were counted manually with a hemocytometer to determine their concentration. Single-cell RNA-sequencing libraries were then prepared using the Chromium Single Cell 3’ reagent kit v3.1 (10X Genomics, Pleasanton, CA) in accordance with the manufacturer’s protocol. Briefly, the cells were diluted into the Chromium Single Cell A Chip as to yield a recovery of ~10,000 single-cell transcriptomes with < 5% doublet rate. Following the library preparation, the libraries were sequenced on the NovaSeq 6000 sequencer (Illumina, San Diego, CA) to produce about 450 million reads per library and on average a minimum of 40,000 reads per single cell. For spatial transcriptomics: fresh frozen skeletal muscle samples were cryosectioned (Leica CM1950). 10-µm sections were placed on the pre-chilled Optimization slides (Visium, 10X Genomics, PN-1000191), and the optimal lysis time was determined. The tissues were treated as recommended by 10X Genomics and the optimization procedure showed an optimal permeabilization time of 30 min of digestion and release of RNA from the tissue slide. Spatial gene expression slides (Visium, 10X Genomics, PN-1000185) were used for spatial transcriptomics following the Visium User Guides, and whole slide images were taken using a 20X objective of a Leica Aperio Versa scanner. Next-generation sequencing libraries were prepared according to the Visium user guide. Libraries were loaded at 300 pM and sequenced on a NovaSeq 6000 System (Illumina) as recommended by 10X Genomics.
 
Library strategy OTHER
Library source transcriptomic
Library selection other
Instrument model Illumina NovaSeq 6000
 
Description spatial transcriptomics (10x Visium)
SlideID: V10M31-107 Area: C1
Data processing Single cell sequencing reads were processed and aligned to the mouse reference transcriptome (mm10) with the Cell Ranger version 3.1.0 (10x Genomics, Pleasanton, CA). We used CellBender to eliminate technical artifacts. For spatial transcriptomics: reads were processed and aligned to the mouse reference transcriptome (mm10) with the Space Ranger version 1.3.1 (10x Genomics, Pleasanton, CA).
From the gene expression matrix, the downstream analysis was carried out with R version 4.0.2 (2020-06-22). Quality control, filtering, data clustering and visualization, and the differential expression analysis was carried out using Seurat (v3.2.2) R package (Butler et al., 2018) with some custom modifications to the standard pipeline. Genes expressed in less than 3 cells as well as cells < 1000 UMIs and < 200 genes were removed from the gene expression matrix. In addition, we removed any single-cell with > 5% UMIs mapped to mitochondrial genes, as well as obvious outliers in number of UMIs (cell doublets). For spatial transcriptomics: processed h5 files were analysed in R using Giotto and BayesSpace.
After log-normalizing the data, the expression of each gene was scaled regressing out the number of UMI and the percent mitochondrial gene expressed in each cell. We performed PCA on the gene expression matrix and used the first 30 principal components for clustering and visualization. Unsupervised shared nearest neighbor (SNN) clustering was performed with a resolution of 0.35 and visualization was done using t-distributed stochastic neighbor embedding (t-SNE). We performed a silhouette analysis (R cluster package) to select an optimal SNN resolution parameter, that balanced the number of expected clusters (given known marker expression) with a maximal average silhouette width. Finally, differential expression analysis was achieved using Seurat’s ‘‘FindAllMarkers’’ function using a likelihood ratio test that assumes the data follows a negative binomial distribution and only considering genes with > log2(0.25) fold-change and expressed in at least 40% of cells in the cluster. Feature plots were generated using Nebulosa package.
Assembly: mm10
Supplementary files format and content: Filtered feature barcode matrix generated by cellranger (v3.1.0). Filtered feature barcode matrix generated by spaceranged (v1.3.1).
Library strategy: Spatial Transcriptomics
 
Submission date Jan 26, 2023
Last update date Aug 28, 2024
Contact name Laszlo Halasz
E-mail(s) [email protected]
Phone +17276416811
Organization name Mount Sinai Icahn School of Medicine
Department Department of Oncological Sciences
Lab Dr. Miriam Merad
Street address 1470 Madison Avenue
City New York
State/province NY
ZIP/Postal code 10029
Country USA
 
Platform ID GPL24247
Series (1)
GSE223813 Spatiotemporal transcriptomic mapping of regenerative inflammation in skeletal muscle reveals a dynamic multilayered tissue architecture
Relations
BioSample SAMN32950602
SRA SRX19206821

Supplementary file Size Download File type/resource
GSM6996259_C57_D8_CTX_TA.tar.gz 15.5 Mb (ftp)(http) TAR
SRA Run SelectorHelp
Raw data are available in SRA
Processed data provided as supplementary file

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