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| Status |
Public on May 30, 2024 |
| Title |
WT, ADT,L1 |
| Sample type |
SRA |
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| Source name |
spleen
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| Organism |
Mus musculus |
| Characteristics |
tissue: spleen
cell type: splenocytes
genotype: wild type
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| Extracted molecule |
protein |
| Extraction protocol |
Sorted splenic NK cells from Reg-1fl/fl and Reg-1ΔNK were washed twice with PBS and suspended in PBS supplemented with 400 μg/mL non-acetylated bovine serum albumin (Sigma Aldrich, B6917). Cells were labeled with selected oligo barcode conjugated antibodies.
Individual cells were then coupled to beads using the 10X Genomics Chromium controller (10X Genomics). Single-cell sequencing libraries were prepared following the 10X Genomics Protocol using 5’ Reagent Kits v2 chemistry and sequenced to a median depth of approximately 20,000 reads per cell using an Illumina NovaSeq 6000 on rapid run.
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| Library strategy |
OTHER |
| Library source |
other |
| Library selection |
other |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
10x Genomics
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| Data processing |
Cell ranger (version 6.0.0; 10x Genomics) was used to process single-cell data. In brief, cellranger mkfastq was used to output FASTQ files. Sequencing reads in FASTQ files were aligned to the mm10 Mouse genome using cellranger count and gene-by-cell matrices were generated for downstream analysis. Gene-by-cell matrices were analyzed using Seurat (R package, version 4.1.1) and other packages.
We normalized the count data with NormalizeData and selected the top 5,000 highly variable features. Features were scaled with ScaleData and principal component analysis (PCA) was calculated with RunPCA. The top 20 principal components were used to obtain Uniform Manifold Approximation and Projection (UMAP) scatterplots.
Clustering analysis was performed using the top 20 principal components to generate an SNN graph with FindNeighbors, and clusters were identified with the Leiden algorithm implemented in FindClusters with resolution of 0.6. Cluster-specific markers were identified using FindAllMarkers to select differentially expressed genes between each cluster and the average of all remaining clusters with the Wilcoxon Rank Sum test.
Differentially expressed genes were selected with an adjusted p-value cutoff of 0.01 and log2 fold change of 1.0 in absolute value (equivalent to a twofold change). For the integration of datasets, batch correction was performed with RunCanek from the Canek package (R package, version 0.2.1) using the top 5,000 highly variable features in each dataset.
Assembly: mm10
Supplementary files format and content: h5
Library strategy: CITE-seq
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| Submission date |
Jul 18, 2023 |
| Last update date |
May 30, 2024 |
| Contact name |
Xin Sun |
| E-mail(s) |
sunxin@ifrec.osaka-u.ac.jp
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| Organization name |
IFReC, Osaka University
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| Street address |
3-1 Yamadaoka, Suita
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| City |
Osaka |
| ZIP/Postal code |
5650871 |
| Country |
Japan |
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| Platform ID |
GPL24247 |
| Series (2) |
| GSE237639 |
Transcriptomic profile at single cell level of splenic NK cells from Reg-1fl/fl and Reg-1ΔNK mice [CITE-Seq] |
| GSE237643 |
Deletion of the mRNA endonuclease Regnase-1 promotes NK cell anti-tumor activity via OCT2- dependent transcription of Ifng |
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| Relations |
| BioSample |
SAMN36517508 |
| SRA |
SRX21063783 |
| Supplementary data files not provided |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data are available on Series record |
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