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| Status |
Public on Jul 01, 2024 |
| Title |
SAM24402086_BRM_nodox |
| Sample type |
SRA |
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| Source name |
pancreatic
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| Organism |
Homo sapiens |
| Characteristics |
tissue: pancreatic
cell line: KP4
cell type: pancreatic cells
genotype: stable cell line expressing dox-inducible BRM
treatment: untreated
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| Extracted molecule |
nuclear RNA |
| Extraction protocol |
Cultured cells were trypsinized into single cell suspensions, counted using the Vi-CELL XR cell counter (Beckman Coulter) and nuclei were isolated based on the 10x demonstrated protocol (CG000365_DemonstratedProtocol_NucleiIsolation_ATAC_GEX_Sequencing_RevC.pdf).
The nuclei were processed and libraries were constructed using the Chromium Single Cell Multiome ATAC + Gene Expression Reagent Kit (10x Genomics, Cat. No. 1000285) as per the manufacturer’s recommendations. Accessibility and expression libraries were profiled using the Tapestation High Sensitivity DNA kit (Agilent Technologies) and quantified with the Kapa Library Quantification Kit (Kapa Biosystems).
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| Library strategy |
OTHER |
| Library source |
transcriptomic |
| Library selection |
other |
| Instrument model |
Illumina NovaSeq 6000 |
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| Data processing |
Cell Ranger ARC (10X Genomics) was used to demultiplex BCL files into FASTQ files (cellranger-arc mkfastq command), align sequencing reads to the human genome (GRCh38), filter reads, call peaks, and generate counts for cell barcodes and UMIs (cellranger-arc count command).
Gene expression data produced by Cell Ranger ARC v2.0.1 were imported into Scanpy version 1.9.1, and data were filtered based on the following criteria: cells with a minimum of 200 genes expressed, genes detected in at least 3 cells, cells with minimum 5% and maximum 20% total counts from mitochondrial genes, and cells with a range of 1,000 to 10,000 genes with non-zero counts. Data were concatenated into a single AnnData object. Data was filtered for single cells using Scrublet version 0.2.3, with an expected doublet rate of 0.06 and threshold of 0.4 (https://github.com/swolock/scrublet). Data was logarithmized and normalized (counts per 1x104). Principal component analysis was then run on the highly variable genes. Data across samples were harmonized using Harmonypy version 0.0.6 (https://github.com/slowkow/harmonypy) before using the top 40 principal components to compute a neighborhood graph. Scores for the S and G2M cell cycle phases were calculated with the score_genes_cell_cycle function from Scanpy using canonical markers. These scores were used to regress out cell cycle phase expression signatures using Scanpy’s regress_out function. Leiden and Louvain clustering were performed using Scanpy’s implementation of Leiden community detection and the Louvain algorithm, both with a resolution of 0.5. Cells were scored for gene signatures using Scanpy’s tl.score_genes function. Due to doxycycline treatment inducing BRM overexpression in only a subset of cells, doxycycline-treated cells were subdivided by BRM expression status (“BRM_low” vs. “BRM_high” expressors) based on Louvain clusters for subsequent analysis of this subset. Louvain clusters were used for subdivision of cells in order to focus on the BRM-overexpressing cells which cluster away from the naïve cells.
Assembly: GRCh38
Supplementary files format and content: The tsv and mtx files contains the cell barcode and gene counts.
Supplementary files format and content: The NGS3789_BRM_expression_status.csv file contains the BRM expression status annotation per cell. NGS3789_cell_density.csv contains the density values per cell. NGS3789_umap_coordinates.csv contains the UMAP coordinates per cell. NGS3789_louvain_clusters.csv contains the louvain cluster assignment per cell.
Library strategy: single-cell multiomics
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| Submission date |
Oct 12, 2023 |
| Last update date |
Jul 01, 2024 |
| Contact name |
Marc Hafner |
| E-mail(s) |
hafner.marc@gene.com
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| Organization name |
Genentech
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| Department |
Oncology Bioinformatics
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| Street address |
Building 45-1, 1 DNA Way
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| City |
South San Francisco |
| State/province |
CA |
| ZIP/Postal code |
94080 |
| Country |
USA |
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| Platform ID |
GPL24676 |
| Series (2) |
| GSE245242 |
CRISPR activation screens identify the SWI-SNF ATPases as suppressors of ferroptosis [NGS3789_snRNA] |
| GSE245249 |
CRISPR activation screens identify the SWI-SNF ATPases as suppressors of ferroptosis |
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| Relations |
| BioSample |
SAMN37798646 |
| SRA |
SRX22079052 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM7839370_SAM24402086_barcodes.tsv.gz |
26.5 Kb |
(ftp)(http) |
TSV |
| GSM7839370_SAM24402086_features.tsv.gz |
2.6 Mb |
(ftp)(http) |
TSV |
| GSM7839370_SAM24402086_matrix.mtx.gz |
203.3 Mb |
(ftp)(http) |
MTX |
SRA Run Selector |
| Raw data are available in SRA |
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