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| Status |
Public on Jul 22, 2023 |
| Title |
Nephrotoxic Nephritis sample of BTLA-KO mouse (replicate 1) |
| Sample type |
SRA |
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| Source name |
Knock out
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| Organism |
Mus musculus |
| Characteristics |
cell type: renal leukocytes
treatment: Knock-out
tissue: Kidney
disease state: Nephrotoxic Nephritis
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| Treatment protocol |
Induction of nephrotoxic nephritis 10 days prior to organ harvest
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| Growth protocol |
Not applicable
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| Extracted molecule |
total RNA |
| Extraction protocol |
kidneys where digested, CD45+ leukocytes isolated using MACS beads, then sorted using FACS gating on Living (DAPIneg),CD45+
10xGenomics 3`scRNAseq
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Data processing |
Libraries were sequenced on an Illumina NovaSeq 6000 sequencing instrument with 29+89bp read length.
Fastqs were generated using the command mkfastq of the 10x Genomics software "cellranger" (v6.1.2). The filtered gene count matrices of the four samples were generated using cellranger's count command and the mm10 reference genome (version preprocessed by cellranger). For the two WT and the two BtlaKO samples, 3.5K, 5.4K, 6.4K, and 8.4K cells were recovered, respectively. Sequencing depths of the samples were about 64.9K, 60.3K, 52.2K, and 37.2K reads per cell in the same order. That resulted in a median number of genes per cell between 2K and 2.3K for each sample. All given numbers were rounded to the first decimal place.
Downstream analysis was performed using the Seurat R package (version 4.1.1) 29. Cells with a very small (<500 genes) or large (>5000 genes) transcriptome recovery were discarded to reduce noise and the number of possible doublets, respectively. Only cells with mitochondrial expression of less than 5% and ribosomal expression of less than 50% were kept to avoid working with low-quality cells in downstream steps. For normalization, we have used the standard Log-normalization approach with the standard scaling factor (10e4). Samples were then integrated using the standard Seurat integration procedure.
We followed the standard Seurat-workflow to perform principal component analysis, extracting the first 100 principal components. Based on the data generated by Seurat's Elbow plot function, we automatically determined the number of dimensions used for dimensionality reduction and clustering of all immune cells. In particular, we selected the dimension where the standard deviation starts to drop below 2; this automatic selection procedure was confirmed by visual inspection of the elbow plot. Subsequently, we have executed the UMAP algorithm and the FindNeighbors function with the aforementioned dimensions. Clustering was performed using the FindClusters function with a resolution of 0.1. Otherwise, we used standard parameters. The dimensionality reduction and clustering procedure for the T cells was performed similarly, but we used only the first 12 dimensions by visual inspection of the elbow plot, as the complexity and variability within T cell population drops as expected significantly compared to first level of resolution (all immune cells). In this step we used a clustering resolution of 0.6. Cluster 6 was identified as contamination with macrophages, probably due to the formation of doublets and was discarded. The other cells were reclustered using the same workflow, this time with a clustering resolution of 0.5. For all downstream steps we worked with the remaining 5412, 7361, 2939, and 4521 cells for the knockout and wild-type samples, respectively. At T cell level, we worked accordingly with 826, 1433, 366, and 609 cells in the same mentioned order.
Markers of the clusters at both clustering levels were computed with the Wilcoxon rank sum test implemented in Seurat using the FindAllMarkers function. The top 20 positive markers of each cluster were used to manually curate the cell types of the immune cells (the initial clustering level of all cells). The reference database used to determine the cell types is the single-cell RNASeq database PanglaoDB30. We determined the identity of the resulting cluster subpopulations by visualizing the expression patterns of most basic markers (e.g., Cd4+, Cd8+, and some alpha, beta, gamma, and delta chain genes) across clusters to understand the approximate structure of T cell subpopulations in our dataset. Subsequently, we have computed the module scores of many known standard subpopulations of T cells for each cell in each cluster using the AddModuleScore function. By box plotting the distribution of the scores of each module for each cluster, we identify the subpopulations of the T cells by visually inspecting and comparing the module score distributions. To assign a cell type to a subpopulation we require that the median score is at least higher than zero or that no other type is fitting better while the median being close to zero. In the case that two cell type modules are fitting equally well into the same subcluster, we annotate the cluster with both unless we could biologically exclude one of them. For instance, in the case of clusters where almost all cells are Cd4+, we exclude the possibility of the cluster being a subpopulation of cytotoxic T cells. Cycling T cells were recognized using cell cycle genes integrated within Seurat and corresponding G2M and S scores. The markers of the various T cell subpopulations were taken from the Biocompare website
Assembly: mm10 (preprocessed release downloaded from the website of 10x Genomics)
Supplementary files format and content: four csv files for the raw counts generated by cellranger for each sample and three additional csv files; the first for the integrated and filtered object from the four raw samples and the other two contain the cell type annotation of all immune cells and the fine-granuled annotation of the T-cells, respectively
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| Submission date |
Jan 23, 2023 |
| Last update date |
Jul 22, 2023 |
| Contact name |
Ali T Abdallah |
| Organization name |
University of Cologne
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| Department |
Medical Faculty
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| Lab |
CECAD
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| Street address |
Joseph Stelzmann Str. 26
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| City |
Cologne |
| ZIP/Postal code |
50931 |
| Country |
Germany |
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| Platform ID |
GPL24247 |
| Series (1) |
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| Relations |
| BioSample |
SAMN32878342 |
| SRA |
SRX19144161 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6958403_KO_R1.csv.gz |
16.7 Mb |
(ftp)(http) |
CSV |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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