 |
 |
GEO help: Mouse over screen elements for information. |
|
Status |
Public on Mar 29, 2024 |
Title |
DM_IFN, cDNA |
Sample type |
SRA |
|
|
Source name |
Bone marrow
|
Organism |
Mus musculus |
Characteristics |
cell type: LT-HSC (LSK; CD48-; CD150+) tissue: Bone marrow genotype: DM -/- age: 8-10 weeks old treatment: IFNa
|
Extracted molecule |
polyA RNA |
Extraction protocol |
LT-HSCs (Lineage-, c-Kit+, Sca-1+, CD150+, CD48-) were FACS-sorted (100 nozzle, 4°C sample and collection, into PBS/3%FBS) from bone marrow of mice treated with pegylated IFN alpha (25µg/kg, s.c. in 100µl) for 24 hours or vehicle (saline) and used for downstream single-cell RNAseq. Library was performed according to the manufacter’s instructions (single cell 3’ v2 protocol, 10x Genomics). Briefly, GCs were resuspended in the master mix and loaded together with partitioning oil and gel beads into the chip to generate the gel bead-in-emulsion (GEM). The polyA RNA from the cell lysate contained in every single GEM was retrotranscripted to cDNA, which contains an Ilumina R1 primer sequence, Unique Molecular Identifier (UMI) and the 10x Barcode. The pooled barcoded cDNA was then cleaned up with Silane DynaBeads, amplified by PCR and the apropiated sized fragments were selected with SPRIselect reagent for subsequent library construction. During the library construction Ilumina R2 primer sequence, paired-end constructs with P5 and P7 sequences and a sample index were added.
|
|
|
Library strategy |
RNA-Seq |
Library source |
transcriptomic single cell |
Library selection |
cDNA |
Instrument model |
Illumina NovaSeq 6000 |
|
|
Description |
10x Single Cell 3' V3 counts_cDNA.txt.gz
|
Data processing |
cDNA reads were aligned to ‘mm10’ genome using Ensembl 102 gene models including the sequence of human JAK2 gene and CreERT2 using the STARsolo tool (v2.7.9a) with default parameter values except the following parameters: soloUMIlen=12, soloBarcodeReadLength=0, clipAdapterType=CellRanger4, outFilterType=BySJout, outFilterMultimapNmax=10, outSAMmultNmax=1, soloType=CB_UMI_Simple, outFilterScoreMin=30, soloCBmatchWLtype=1MM_multi_Nbase_pseudocounts, soloUMIfiltering=MultiGeneUMI_CR, soloUMIdedup=1MM_CR, soloCellFilter=None. HTO libraries were also processed using the STARsolo tool with default parameters except soloCBmatchWLtype=1MM_multi_Nbase_pseudocounts, soloUMIfiltering= MultiGeneUMI_CR, soloUMIdedup=1MM_CR, soloCellFilter=None, clipAdapterType=False, soloType=CB_UMI_Simple, soloBarcodeReadLength=0, soloUMIlen=12, clip5pNbases=10, clip3pNbases=65. Further analysis steps were performed using R (v4.2.0). Cells were considered as high-quality if they got a confident assignment of an HTO reported by the function ‘hashedDrops’ and had at least 2000 counts, which is the threshold derived from the distribution of UMI counts across cells. Based on the expression of the proliferation marker Mki67, around 380 cells were progressing in the cell cycle and removed from the further analysis forming the final data set of 41187 cells. Multiple Bioconductor (v3.15) packages including DropletUtils (v1.16.0), scDblFinder (v1.10.0), scran (v1.24.1), scater (v1.24.0), scuttle (1.6.2) and batchelor (v1.12.3) were applied for the further analysis of the data set mostly following the steps of the workflow presented at https://bioconductor.org/books/release/OSCA/. Raw counts were normalized by deconvolution (Lun, Bach, and Marioni 2016). The function ‘computeDoubletDensity’ did not point at any group of cells, which could be potential doublets, confirming the efficiency of removing doublets while demultiplexing samples. The normalized gene expression of remaining cells was used to construct a shared nearest-neighbour (SNN) graph (k=10) (Xu and Su 2015), which nodes, i.e. cells, were clustered by ‘cluster_louvain’ method from the R igraph package (Blondel et al. 2008). A t-SNE dimensionality reduction was used for visualizing single cells on two dimensions. T-SNE coordinates were calculated using the ‘runTSNE’ function from the scater package and default parameters. For the visualization of cells based on the gene expression, coordinates of principal components and most variable genes were used as the input. To get most variable genes the function ‘getTopHVGs’ was applied with parameters fdr.threshold=0.01 and var.threshold=0.1. Assembly: mm10 with added Jak2-V617F sequence Supplementary files format and content: Tab-separated values files: hto.txt.gz with HTO sequences, counts_cDNA.tsv.gz with cDNA read counts, counts_HTO.tsv.gz with HTO read counts, CreERT2_hsa_JAK2_201_sequence_V617F.fa.gz and CreERT2_hsa_JAK2_201_sequence_V617F.gtf.gz with the human sequence of JAK2 and CreERT2, tsne_hto_clusters.tsv.gz with t-sne coordinates, sample demultiplexing and cluster assignment.
|
|
|
Submission date |
Feb 23, 2023 |
Last update date |
Mar 29, 2024 |
Contact name |
DBM Bioinformatics Core Facility |
Phone |
+41612073541
|
Organization name |
University of Basel
|
Department |
Departement of Biomedicine
|
Street address |
Hebelstrasse 20
|
City |
Basel |
State/province |
BS |
ZIP/Postal code |
4053 |
Country |
Switzerland |
|
|
Platform ID |
GPL24247 |
Series (1) |
GSE225918 |
Gene expression profile at single cell level of phenotypic LT-HSCs from single-mutant JAK2 V617F and double mutant JAK2 V617F;Dnmt3a-/- mice |
|
Relations |
BioSample |
SAMN33423653 |
SRA |
SRX19482791 |
Supplementary data files not provided |
SRA Run Selector |
Raw data are available in SRA |
Processed data are available on Series record |
|
|
|
|
 |