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| Status |
Public on Jul 01, 2024 |
| Title |
Severe_2 |
| Sample type |
SRA |
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| Source name |
Severe
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| Organism |
Rattus norvegicus |
| Characteristics |
tissue: spinal cord
age: 8-week
degree of injury: severe
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| Treatment protocol |
Samples were obtained from the spinal cord of rats with varying degrees of injury
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| Growth protocol |
Samples were obtained from fresh rat spinal cord tissue
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| Extracted molecule |
total RNA |
| Extraction protocol |
The spinal cord tissue was dissected into seveal segments and the single cell suspension was obtained by enzymatic digestion. The cell pellet was resuspended in 50 μl of 1× PBS (0.04% BSA). The overall cell viability was confirmed by trypan blue exclusion , which needed to be above 85%, single cell suspensions were counted using a haemocytometer/ Countess II Automated Cell Counter and concentration adjusted to 700-1200 cells/μl.
Single-cell suspensions were loaded to 10x Chromium to capture single cell according to the manufacturer’s instructions of 10X Genomics Chromium Single-Cell 3’ kit (V3) .The following cDNA amplification and library construction steps were performed according to the standard protocol. Libraries were sequenced on an Illumina NovaSeq 6000 sequencing system (paired-end multiplexing run,150bp)
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Data processing |
Cellranger standard processing
**Cell filter** Seurat allows to easily explore QC metrics and filter cells, we can visualize gene and molecule counts, plot their relationship, and exclude cells with a clear outlier number of genes detected as potential multiplets. Followed criteria were used to filter cells: 1.gene counts 500-Inf per cell. 2.UMI counts 3. the percentage of mitochondrial genes < 25% %(customized). 4.DoubletFinder for detecting doublets.
**Cell clustering** Cell clustering contains the following steps: 1)Normalizing the data After removing unwanted cells from the dataset, the next step is to normalize the data. By default, we employ a global-scaling normalization method “LogNormalize” that normalizes the gene expression measurements for each cell by the total expression.
PCA(Principal component analysis): To overcome the extensive technical noise in any single gene for scRNA-seq data, Seurat clusters cells based on their PCA scores, with each PC essentially representing a ‘metagene’ that combines information across a correlated gene set.
t-SNE (t-distributed Stochastic Neighbor Embedding) visualization: Seurat continues to use t-SNE as a powerful tool to visualize and explore these datasets. The tSNE aims to place cells with similar local neighborhoods in high-dimensional space together in low-dimensional space.
Assembly: Rattus_norvegicus.Rnor_6.0
Supplementary files format and content: matrix
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| Submission date |
Sep 13, 2022 |
| Last update date |
Jul 01, 2024 |
| Contact name |
Erliang Li |
| E-mail(s) |
liel17@lzu.edu.cn
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| Organization name |
tangdu hospital
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| Street address |
xin si street
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| City |
xi'an |
| ZIP/Postal code |
710000 |
| Country |
China |
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| Platform ID |
GPL25947 |
| Series (1) |
| GSE213240 |
Single cell sequencing of rat spinal cord |
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| Relations |
| BioSample |
SAMN30825881 |
| SRA |
SRX17547944 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6576332_Severe_2_barcodes.tsv.gz |
40.6 Kb |
(ftp)(http) |
TSV |
| GSM6576332_Severe_2_features.tsv.gz |
249.6 Kb |
(ftp)(http) |
TSV |
| GSM6576332_Severe_2_matrix.mtx.gz |
61.6 Mb |
(ftp)(http) |
MTX |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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