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GEO help: Mouse over screen elements for information. |
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| Status |
Public on Jul 05, 2022 |
| Title |
Mouse WT3 |
| Sample type |
SRA |
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| Source name |
Bone marrow from mouse
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| Organism |
Mus musculus |
| Characteristics |
cell type: CD34+
tissue: Bone marrow
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| Extracted molecule |
genomic DNA |
| Extraction protocol |
All animal procedures were completed in accordance with the Guidelines for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committees at MSKCC. The Dnmt3a R878H mouse model has been described previously (Guryanova et al., 2016), and was crossed to the Tal1-creERT2 transgenic model to allow for inducible control of the R878H mutation within the hematopoietic system. To induce recombination of the conditional alleles, age and gender-matched 10-16 week old Tal1-creERT2 control mice and Dnmt3a R878H Tal1-creERT2 mice were treated with tamoxifen (4 mg/kg/day; Cayman Chemical, Ann Arbor, Michigan) for 2 doses, separated 2 days apart. The mice were sacrificed 4-8 weeks after tamoxifen-induction. Primary mouse bone marrow (BM) cells were isolated into cold phosphate-buffered saline (PBS), without Ca2+ and Mg2+, and supplemented with 2% bovine serum albumin (BSA) to generate single cell suspensions. Red blood cells (RBCs) were removed using ammonium chloride-potassium bicarbonate (ACK) lysis buffer, resuspended in PBS/2% BSA, and filtered through a 40μm cell strainer. Total nucleated cells were quantified by Vi-Cell XR cell counter (Beckman Coulter, Brea, CA) and used for downstream data production. Cryopreserved patient samples approved by the Institutional Review Board of the University of Chicago were thawed and stained using standard procedures (10 min, 4°C) with the surface antibody CD34-PE-Vio770 (clone AC136, Miltenyi Biotec) and DAPI (Sigma-Aldrich). Cells were then sorted for DAPI-negative, CD34+ and CD34-negative cells using BD Influx.
Hematopoietic progenitors (Lin-1-, c-Kit+) were sorted from wildtype (n = 3 mice) or Dnmt3a R878H (n = 3 mice) via c-Kit enrichment as directed by the manufacturer (CD117 Microbeads, clone 3C1, Miltenyi, Auburn, CA; LS Columns (Cat. No. #130-042-401), Miltenyi) followed by FACS (Lin-1 BV421 (Cat. No. #133311), Biolegend, San Diego, CA; CD117 APC (clone 2B8, Invitrogen, Waltham, MA). Nuclei isolation was performed as suggested by the manufacturer (10x Genomics, Pleasanton, CA). Briefly, single cell suspensions were centrifuged at 300 rcf for 5 minutes and cell pellets were resuspended in 100 μl of lysis buffer (Tris-HCl pH 7.4, 10mM; NaCl 10mM; MgCl2 3mM; Tween-20 0.1%; Nonidet P40 substitute (Sigma-Aldrich, St. Louis, MO) 0.1%; Digitonin 0.01%; BSA 1%; DTT 1 mM; RNase inhibitor 1 U/µL (Sigma-Aldrich, St. Louis, MO)) and kept on ice for 3 minutes. Then, 1 ml of wash buffer (Tris-HCl pH 7.4, 10mM; NaCl 10mM; MgCl2 3mM; BSA 1%; Tween-20 0.1%; DTT 1 mM; Sigma Protector RNase inhibitor 1 U/µL) was added. The isolated nuclei were centrifuged for 5 min at 500 rcf, and pellets were resuspended in Diluted Nuclei Buffer (10x Genomics Nuclei Buffer 1X; DTT 1 mM; Sigma Protector RNase inhibitor 1 U/µL). Nuclei concentration was determined by hemocytometer and processed as indicated by the manufacturer (10x Genomics User Guide: Chromium Next GEM Single Cell Multiome ATAC + Gene Expression, CG000338). Single nucleus ATAC and Gene Expression (GEX) libraries were constructed in parallel and assessed for quality control metrics using Agilent Bioanalyzer 2100 and Qubit respectively. ATAC libraries were sequenced to a depth of 25,000 read pairs per nucleus (paired-end, dual indexing: Read 1N 50 cycles, i7 Index 8 cycles, i5 Index 24 cycles, Read 2N 49 cycles) and GEX libraries were sequenced to a depth of 20,000 read pairs per nucleus (paired-end, dual indexing: 28 cycles for Read 1, 10 cycles for i7 Index, 10 cycles for i5 Index, 90 cycles for Read 2).
Multiome Single-nucleus ATAC + RNA-seq (10X) with targeted amplification of a mutation of interest for the patient sample.
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| Library strategy |
ATAC-seq |
| Library source |
genomic single cell |
| Library selection |
other |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
ATAC Fragments
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| Data processing |
Pre-processing was performed using 10x Genomics Cell Ranger ARC (v1.0.1). Reads were de-multiplexed using the cellranger-arc mkfastq function. Single cell feature counts for each sample were then generated using the cellranger-arc count function. The gene expression information for these libraries exhibited exceedingly low UMI and genes per cell consistent with lower quality RNA in single-cell nuclei Multiome data; as such, we moved forward utilizing only the ATAC data for analysis. ATAC data was processed using the ArchR package (v1.0.1) using the atac_fragments.tsv.gz file generated by the cellranger-arc count function as input. Arrow files were created using a minimum TSS enrichment score of 5 and a minimum number of unique nuclear fragments of 1,000. Doublet scores were calculated using the addDoubletScores function with k = 10, knnMethod = “umap” and LSImethod = 1. Doublets were removed using the filterDoublets function with default parameters. Dimensionality reduction was performed through iterative semantic index (LSI) using the cell by genomic window (500 bp) matrix as input, using the addIterativeLSI function with the following parameters: iterations = 3, resolution = 0.2, sampleCells = 1,000, var.features = 25,000 and dimsToUse = 1:30. Cell clusters were identified using the addClusters function using the iterative latent semantic index (LSI) dimensions as input, with method = “Seurat”, resolution = 0.8. For visualization, UMAP dimensionality reduction was performed using the LSI dimensions as input, using the addUMAP function with: nNeighbors = 30, minDist = 0.5 and metric = “cosine”. Cell identities were assigned based on gene accessibility scores of known marker genes. Custom motif accessibility deviations were calculated as follows: position weight matrices in HOMER format (P < 0.001) were downloaded from the HOCOMOCO v11 mouse database. Motif occurrences were identified using the scanGenomeWide function of the HOMER package. To include only high confidence motif sites, we applied a minimum odds ratio score threshold of 6. We next created custom peakAnnotations using ArchR and performed ChromaVar analysis using the addDeviationsMatrix function with default parameters.
snATAC-seq data for CH05 was generated as described above using the Multiome platform (10x Genomics) and GoT performed as described above using the cDNA generated from the Multiome workflow. The gene expression information for these libraries exhibited very low UMI and genes per cell consistent with lower quality RNA in single-cell nuclei Multiome data; as such, we moved forward utilizing only the ATAC data for analysis. For the analysis, fragment files were generated by processing the fastq files using cell-ranger-ARC (v.1.0.0). Downstream analysis was performed using the ArchR (v1.0.1) pipeline. Based on the distribution of total fragments and TSS enrichment per cell, empty droplets were filtered out by requiring a minimum of 3,000 fragments per cell and a TSS enrichment score of 7.5. Potential doublets were detected using the addDoubletScores function, using KNN on the UMAP dimensionality reduction with k = 10. Cell barcodes with high enrichment for doublet scores were removed using the filterDoublets function with default parameters. Next, we performed dimensionality reduction through iterative latent semantic indexing (LSI) using the top 25,000 variable features. Cell clustering was performed using the addClusters function, with the following parameters: reduceDims = “IterativeLSI”; method = “Seurat”; resolution =1. For visualization, further dimensionality reduction was performed by applying UMAP to the iterative LSI space using the addUMAP function with the following parameters: nNeighbors = 30; minDist = 0.5; metric = “cosine”. Cell type identification was performed by manually inspecting the genes showing up-regulated gene accessibility scores (FDR < 0.01 and log2(fold change) > 1.25) for each of the defined clusters (Extended Data Fig. 13c). Motif occurrences were defined using the position weight matrices (PWMs) obtained from the Hocomoco (v.11.0) motif database or our custom PWMs for hypo-methylated and shuffled motifs using HOMER (v4.9), requiring a minimum enrichment score above 6. Transcription factor, hypo-methylated and shuffled motif accessibility was calculated using ChromVAR within the ArchR (v1.0.1) pipeline. Supervised pseudotime trajectories for either erythroid or lymphoid fates were defined within the ArchR (v1.0.1) pipeline applying the addTrajectory function.
Assembly: mm10
Supplementary files format and content: Processed 10X data is a fragments file of ATAC-seq fragments for each cell barcode.
Supplementary files format and content: Output matrices processed from amplicon data have two columns for cell barcode and assigned genotype
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| Submission date |
Jun 17, 2022 |
| Last update date |
Nov 29, 2022 |
| Contact name |
Neville Dusaj |
| E-mail(s) |
ned2010@med.cornell.edu
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| Organization name |
Weill Cornell Medicine
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| Lab |
Landau Lab
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| Street address |
413 E 69th St.
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| City |
New York |
| State/province |
NY |
| ZIP/Postal code |
10021 |
| Country |
USA |
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| Platform ID |
GPL24247 |
| Series (1) |
| GSE158067 |
Single-cell multi-omics in human clonal hematopoiesis reveals that DNMT3A R882 mutations perturb early progenitor states through selective hypomethylation. |
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| Relations |
| BioSample |
SAMN29164622 |
| SRA |
SRX15779334 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6251265_WT3_atac_fragments.tsv.gz |
1.4 Gb |
(ftp)(http) |
TSV |
SRA Run Selector |
| Processed data provided as supplementary file |
| Raw data are available in SRA |
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