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| Status |
Public on Feb 21, 2024 |
| Title |
VDJ_pool_3 |
| Sample type |
SRA |
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| Source name |
BRAFV600E melanoma
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| Organism |
Mus musculus |
| Characteristics |
tissue: BRAFV600E melanoma
surface marker: CD45, CD8, CD3
cell type: Tumour infiltrating CD8+ T cells
treatment: none
condition: BRAFV600E in Ptger2-/-Ptger4fl/fl, CD4CrePtger2-/-Ptger4fl/fl, GzmBCrePtger2-/-Ptger4fl/fl and Wildtype (C57BL/6J background)
Sex: female
replicate: 3
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| Treatment protocol |
Tumours were harvested from mice 11 days post tumour cell inoculation.
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
Tumous were digested with DNase I (100 µg/mL) and Collagenase IV (200 U/mL) for 40 minutes at 37°C and subsequently mashed through a 70 µm and a 30 µm cell strainer. Cells were labelled with TotalSeq-C Hashtag antibodies (Hashtags 1-4, Biolegend) and stained for surface markers and viability. Intratumoural CD8+ T cells were then sorted (live CD8a+ CD3+ CD45+ cells).
Library construction was carried out by using the following kit and protocol: Chromium Next GEM Single Cell 5' Reagent Kits v2 User Guide with Feature Barcode technology for Cell Surface Protein, Rev D. For sequencing, libraries were pooled fulfilling the minimum criteria: 2x10^4 reads/cell for gene expression libraries and 5x10^3 reads/cell for TCR libraries. Pooled libraries were sent to Novogene for sequencing.
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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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| Description |
scRNA-Seq + TCR + Hashing (10x Genomics): The whole dataset consists of the following treatment groups and were demultiplexed using the indicated antibodies: BRAFV600E melanoma in Ptger2-/-Ptger4fl/fl (TotalSeq™-C 0302 anti-mouse Hashtag 2, clone: M1/42;30-F11, Biolegend # 155863), CD4CrePtger2-/-Ptger4fl/fl (TotalSeq™-C 0303 anti-mouse Hashtag 3, clone: M1/42;30-F11, Biolegend # 155865), GzmBCrePtger2-/-Ptger4fl/fl (TotalSeq™-C 0304 anti-mouse Hashtag 4, clone: M1/42;30-F11, Biolegend # 155867) and Wildtype (TotalSeq™-C 0301 anti-mouse Hashtag 1, clone: M1/42;30-F11, Biolegend # 155861) mice.
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| Data processing |
Read alignment, read counting and clonotype call: Performed with 10x Genomics Cell Ranger v6.1.1, using the pre-built mouse reference v2020-A (10x Genomics, Inc) based on mm10 - GENCODE vM23/Ensembl 98. The pipeline was modified according to manufacture instructions to enable compatibility between 5' Chromium Next GEM Single Cell Immune Profiling and cell hashing. At first, only the gene expression libraries were demultiplexed through sample tags with cellranger multi, setting the expected cell number to 21,000. The BAM files were converted to FASTQ files using the tool bamtofastq with the argument --reads-per-fastq set to the total number of reads in the BAM file plus ten thousand. After that, gene expression and TCR analysis were combined by running cellranger multi separately for each demultiplexed sample, disabling library concordance reinforcement. The algorithm was forced to find the number of cells identified in the first step of demultiplexing and sample-specific FASTQ files were used as input for the gene expression analysis pipeline. The pre-built Ensembl GRCm38 Mouse V(D)J Reference v5.0.0 was used for TCR analysis.
[Individual analysis of each group]
Filtering: Only cells with more than 2000 genes detected, less than 10% of mitochondrial genes and with UMI counts less than 3 standard deviations above the mean were kept for downstream analysis. Only data for genes detected in at least 3 cells in each sample were kept. Contaminating cells were identified based on the cluster expression of the marker genes Cd14, Lyz2, Fcgr3, Ms4a7, Fcer1g, Cst3, H2-Aa, Ly6d, Ms4a1 and Ly6d. Cycling cells were identified based on expression of Cdk1, Mcm2, Pclaf, H2afz, Birc5 and Mki67.
Normalization: Filtered read counts from each sample were normalized independently with sctransform v0.3.2 with glmGamPoi method.
Integration: Anchors between cells from different replicates were identified on top 1000 highly variable genes using Canonical correlation analysis and 30 canonical vectors. Data integration was performed on first 20 PCA dimensions.
Dimensional reduction: PCA was calculated for the integrated data on the top 1000 highly variable genes and both KNN graph and UMAP were computed on the 30 nearest neighbors and first 20 PCA dimensions.
Clustering: Louvain clusters were identified using the Shared nearest neighbor (SNN) modularity optimization-based algorithm at resolutions 0.9, 0.65 and 0.9 for the groups Ptger2-/-Ptger4fl/fl, CD4CrePtger2-/-Ptger4fl/fl and GzmBCrePtger2-/-Ptger4fl/fl, respectively.
[Integrative analysis between groups]
Filtering: Data was filtered as mentioned above along with the removal of contaminating cells and cycling cells.
Normalization: Filtered read counts from each sample were normalized independently with sctransform v0.3.2 with glmGamPoi method.
Integration: Anchors between cells from all groups and all their replicates were identified using a more conservative approach that resulted in a weaker integration. For that purpose, reciprocal PCA was applied on top 1000 highly variable genes and anchors were picked using the first 20 dimensions and one neighbor only.
Dimensional reduction: PCA was performed on the integrated data on the top 1000 highly variable genes. KNN graph and UMAP (spread 0.4, min. distance 0.01) were computed on first 20 PCs and 30 nearest neighbors.
Clustering: A resolution of 0.6 was used for Louvain clusters identification using the Shared nearest neighbor (SNN) modularity optimization-based algorithm.
Differential expression: Differentially expressed genes between two groups were identified using the Wilcoxon Rank Sum test and Bonferroni correction.
Trajectory: Transcriptional trajectories were inferred using the R package slingshot v2.4.0 over the UMAP calculated on the integrated data, approximating the curves by 150 points. The pseudotime was calculated as a weighted average across lineages, weighted by the assignment weight.
RNA velocity: Read splicing annotation was performed with velocyto v0.17 using the gene annotation from the pre-built mouse reference v2020-A (10x Genomics, Inc). Expressed repetitive elements were masked based on the genome annotation from UCSC assembly GRCm38/mm10. RNA velocity was estimated using the R package velocyto.R v0.6 with gene-relative model. Gamma fit was performed on the 2% quantile from both extremes and 30 nearest neighbors were used on slope calculation smoothing. For the calculation of cell distances, filtered read counts from all samples were merged and normalized together with sctransform v0.3.2 with glmGamPoi method. PCA was calculated for the merged data on the top 1000 highly variable genes and both KNN graph and UMAP (spread 0.4, min. distance 0.01) were computed on the 30 nearest neighbors and first 20 PCA dimensions. RNA velocities were visualized using correlation-based transition probability matrix within the kNN graph with square root scaling on the UMAP calculated on the integrated data.
TCR repertoire: TCR analysis of clonotype was performed using the R package scRepertoire v1.6.0. Clonotypes were called based on a combination of VDJC genes comprising the TCR and the nucleotide sequence of the CDR3 region.
Assembly: GRCm38.p6 (release 98) - GENCODE Release M23
Supplementary files format and content: Compressed files contain the filtered UMI counts matrix (Market Exchange Format for sparse matrices), TSV files with genes and barcode sequences (corresponding to row and column indices respectively), the filtered VDJ contig annotations as output by Cell Ranger and the .loom file as output from velocyto.R.
Supplementary files format and content: The supplementary metadata file contains annotations for each cell in the whole experiment, with rows being cells and columns being annotated features. It also contains the normalized expression data for each gene (column) in each cell (row).
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| Submission date |
Apr 29, 2023 |
| Last update date |
Feb 21, 2024 |
| Contact name |
Gustavo P. de Almeida |
| E-mail(s) |
gustavo.almeida@tum.de
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| Organization name |
Technical University of Munich
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| Department |
Division of Animal Physiology and Immunology
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| Street address |
Liesel-Beckmann-Str. 1
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| City |
Freising |
| State/province |
Bavaria |
| ZIP/Postal code |
85354 |
| Country |
Germany |
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| Platform ID |
GPL24247 |
| Series (2) |
| GSE231302 |
Single-cell RNA-sequencing and single-cell TCR-sequencing of tumour-infiltrating CD8+ T cells derived from mouse BRAFV600E melanoma [scRNA-Seq + TCR 10x] |
| GSE231340 |
PGE2 curtails IL-2-dependent effector expansion from tumour-infiltrating stem-like CD8+ T cells to promote cancer immune escape |
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| Relations |
| BioSample |
SAMN34436324 |
| SRA |
SRX20143860 |
| 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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