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| Status |
Public on Aug 24, 2023 |
| Title |
scRNAseq_post_transplant_BM_WT_2 |
| Sample type |
SRA |
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| Source name |
Bone Marrow
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| Organism |
Mus musculus |
| Characteristics |
strain: C57BL/6J
tissue: Bone Marrow
cell type: LSK
genotype: Etv6+/+
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| Extracted molecule |
total RNA |
| Extraction protocol |
For each mouse, sort-purified CD45.1+ and CD45.2+ LSK and BM cells were washed three times with 1X Phosphate-Buffered Saline (PBS-, calcium-, and magnesium-free) containing 0.04% weight/volume BSA (Thermo Fisher Scientific, Waltham, Massachusetts) and manually counted. LSK and BM cells were mixed at a ratio 1:1 to have a total desired recovery target of 8,000-10,000 total cells and loaded onto a Chromium Next GEM Chip K (10X Genomics, Pleasanton, California) with Master Mix from Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index; 10X Genomics, Pleasanton, California), Single Cell VDJ 5' Gel Beads (10X Genomics, Pleasanton, California) and Partitioning Oil (10X Genomics, Pleasanton, California) following the manufacturer's protocol. Briefly, gel beads-in-emulsion (GEMs) were generated, lysed and 10x barcoded, and full-length cDNA from polyadenylated mRNA was produced. Next, GEMs were broken, pooled fractions were recovered, and cDNA was purified by Dynabeads MyOne SILANE (10X Genomics, Pleasanton, California). Barcoded, full-length cDNA was amplified via PCR, purified by SPRIselect Reagent (Beckman Coulter, Brea, California), and quantified using High Sensitivity D5000 ScreenTape with High Sensitivity D5000 Reagents at the Agilent 2200 TapeStation system and used for library preparation (10X Genomics, Pleasanton, California; Library Construction Kit, Dual Index Kit TT Set A). The High Sensitivity D1000 ScreenTape and the High Sensitivity D1000 Reagents were used to assess library’s quality at the Agilent 2200 TapeStation system. Libraries were sequenced on the NovaSeq 6000 (Illumina, San Diego, California) according to the manufacturer's recommendations.
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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 |
10x Genomics
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| Data processing |
Sequencing reads from 10X experiment were mapped to mouse reference genome (mm10) using cellranger-7.1.0 (transcriptome version: mm10-2020-A). Single cell counts matrices were then processed using Scanpy as described in the best practices workflow previously (https://www.sc-best-practices.org). For quality control, doublet cells were filtered out, and low-quality cells with excess outliers calculated as 3 MADs (Median absolute deviations) for no. of counts per barcode, no. of genes per barcode, and fraction of counts from mitochondrial genes. We performed SCRAN normalization followed by dimensionality reduction with top 40 PCs selected using elbow plot using UMAP and tSNE algorithms. We used the Leiden algorithm for finding clusters at resolution of 0.25,0.5, and 1 and selected 0.25 as final resolution with optimal number of clusters preserving distinct cell populations.
For cell type identification across 22 clusters, we used a combination of known gene markers for cell populations or top gene markers that distinguished each cluster from other clusters identified using rank_genes_groups function in Scanpy. We used two determine hematopoietic sub populations of cells, bloodspot (https://servers.binf.ku.dk/bloodspot) and mouse cell atlas (https://bis.zju.edu.cn/MCA/atlas2.html; accessed April 2023) along with literature mining for known cell markers. We also used hscScore and scCATCH tools to aid cell identification. Finally, two authors (MB, NO) independently identified these clusters and finalized annotations.
We next performed integration of all 6 replicates, 3 each for wild-type and mutant genotypes, using Harmony algorithm, re-clustered the populations and annotated using the iterative process described above. Integrated anndata file was used to preform cell composition analysis within each cluster using scCODA Bayesian model for labeled clusters and Milo algorithm for unlabeled clusters. We used edgeR algorithm to identify significant differentially expressed genes within each cluster. We performed gene set enrichment analysis using MSigDb v.7.5.1 Hallmark genesets using python package GSEApy.
Assembly: mm10
Supplementary files format and content: Tab-separated values files and matrix files
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| Submission date |
Jun 02, 2023 |
| Last update date |
Feb 28, 2024 |
| Contact name |
Kim Nichols |
| E-mail(s) |
kim.nichols@stjude.org
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| Organization name |
St. Jude Children's Research Hospital
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| Department |
Oncology
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| Lab |
Nichols Lab
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| Street address |
262 Danny Thomas Pl
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| City |
Memphis |
| State/province |
TN |
| ZIP/Postal code |
38105 |
| Country |
USA |
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| Platform ID |
GPL24247 |
| Series (2) |
| GSE213597 |
ETV6 Represses Inflammatory Response Genes and Regulates HSPC Function During Stress Hematopoiesis in Mice |
| GSE233968 |
ETV6 Represses Inflammatory Response Genes and Regulates HSPC Function During Stress Hematopoiesis in Mice [scRNA-seq] |
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| Relations |
| BioSample |
SAMN35570582 |
| SRA |
SRX20578772 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM7439962_2547018_Trplt_WT_2.barcodes.tsv.gz |
1.9 Mb |
(ftp)(http) |
TSV |
| GSM7439962_2547018_Trplt_WT_2.features.tsv.gz |
254.1 Kb |
(ftp)(http) |
TSV |
| GSM7439962_2547018_Trplt_WT_2.matrix.mtx.gz |
94.9 Mb |
(ftp)(http) |
MTX |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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