|
| Status |
Public on Nov 08, 2021 |
| Title |
Pool 4 - COMBO 1-1-1 M19 & COMBO 80-15-5 M22 |
| Sample type |
SRA |
| |
|
| Source name |
Bone marrow and spleen cells
|
| Organism |
Mus musculus |
| Characteristics |
tissue: bone marrow and spleen cells
timepoint: Disease
|
| Treatment protocol |
Cells were transduced with SPLINTR barcode libraries in polybrene (8.5 mg/mL) via spinfection (90 minutes at 1250 g). After the expansion period, barcoded cells were transplanted into sub-lethally irradiated (5.5 Gy) Ptprca via lateral tail vein injection. Each mouse received 1.5 x106 barcoded cells.
|
| Growth protocol |
Secondary bone marrow cells from MLL-AF9 mice were cultured in RPMI-1640 medium supplemented with mouse IL-3 (10 ng/mL), human IL-6 (10 ng/mL), mouse SCF (50 ng/mL) 20% fetal bovine serum (FBS), streptomycin (100 ug/mL), penicillin (100 units/mL) and 2 mM glutamax in 5% CO2 at 37 °C.
|
| Extracted molecule |
polyA RNA |
| Extraction protocol |
Viable cells were sorted on the BD FACSAria III directly into Eppendorf tubes. The cells were then washed twice with PBS + 0.04% BSA. Cells were then spun down and resuspended in PBS + 0.04% BSA + 0.4 U/ uL RNase Inhibitor (Roche) to a final cell concentrationof >1500 cells per uL. The final cell suspension was put through a cell strainer then onto a 10X Chromium Chip
Single cells were captured in droplet emulsions using the 10X Chromium Single-Cell Instrument and reverse transcription, cDNA amplification and library preparation were performed based on the manufacturer’s protocol using the Chromium Single Cell 3′ Library & Gel Bead Kit v3 (10X Genomics)
|
| |
|
| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NextSeq 500 |
| |
|
| Data processing |
Count matrices were generated from demultiplexed scRNA-seq fastq files using the 10X Genomics Cell Ranger v3.1.0 count pipeline against the mm10/GRCm38 genome.
scRNA-seq quality control was performed using Seurat v3.2 in R. Low quality cells were removed by filtering out cells that had fewer than 500 genes or 1000 unique molecular identifiers (UMIs). Cells with greater than 15% mitochondrial content were also removed.
For detection of the SPLINTR barcodes from single cell data, unmapped reads (which contain reads corresponding to barcode transcripts) were extracted from BAM alignment files for each scRNAseq library using SAMtools v1.9.
BAM entries containing SPLINTR barcode reads were identified and extracted using a regular expression specific to the barcode structure.
Barcode reads were mapped to the corresponding reference library using Bowtie v1.2.2 end-to-end alignment allowing a maximum of two mismatches.
Genome_build: mm10
Supplementary_files_format_and_content: matrix of gene counts
Supplementary_files_format_and_content: matrix of gene names
Supplementary_files_format_and_content: matrix of 10x cell barcodes
Supplementary_files_format_and_content: Hashtag sequences
|
| |
|
| Submission date |
Nov 17, 2020 |
| Last update date |
Nov 08, 2021 |
| Contact name |
Mark Dawson |
| E-mail(s) |
mark.dawson@petermac.org
|
| Organization name |
Peter MacCallum Cancer Centre
|
| Street address |
305 Grattan Street
|
| City |
Melbourne |
| State/province |
VIC |
| ZIP/Postal code |
3000 |
| Country |
Australia |
| |
|
| Platform ID |
GPL19057 |
| Series (2) |
| GSE161673 |
Identifying non-genetic determinants of malignant clonal fitness at single cell resolution (clonal competition scRNAseq) |
| GSE161676 |
Identifying non-genetic determinants of malignant clonal fitness at single cell resolution |
|
| Relations |
| BioSample |
SAMN16822837 |
| SRA |
SRX9522952 |
