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| Status |
Public on Mar 22, 2023 |
| Title |
CPT0025880013_snRNA |
| Sample type |
SRA |
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| Source name |
Kidney
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| Organism |
Homo sapiens |
| Characteristics |
cell type: tissue
tissue: Kidney
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| Extracted molecule |
nuclear RNA |
| Extraction protocol |
15-25 mg of pulverized tissue was placed in a 5mL Eppendorf tube on ice. Using a wide-bore pipette tip (Rainin), a lysis buffer prepared from the Nuclei Isolation protocol (10X Genomics) and SuperRNase inhibitor (Invitrogen) was added to the tube. The tissue solution was gently pipetted until the lysis liquid turned a slightly cloudy color. (The number of pipetting iterations depended on the specific tissue.) The tissue homogenate was then filtered through a 40-micron strainer (pluriSelect) and washed with a BSA wash buffer (2% BSA + 1x PBS + RNase inhibitor). The filtrate was collected, centrifuged at 500 x g for 6 minutes at 4°C, and resuspended with a BSA wash buffer. 100 uL of cell lysis solution was set aside for unstained reference, while the rest was stained with DRAQ5 or 7AAD for RNA or ATAC sequencing, respectively. Namely, snRNA-seq nuclei were stained with 1 uL of DRAQ5 per 300 uL of the sample, and snATAC-seq nuclei were stained with 1 uL of 7AAD per 500 uL of the sample. Sorting gates were based on size, granularity, and dye staining signal. Nuclei and barcoded beads were isolated in oil droplets via the 10x Genomics Chromium instrument. Single nuclei suspensions were counted and adjusted to a range of 500 to 1800 nuclei/µL using a hemocytometer. Reverse transcription was subsequently performed to incorporate cell and transcript-specific barcodes.
All snRNA-seq samples were run using the Chromium Next GEM Single Cell 3’ Library and Gel Bead Kit v3.1 (10x Genomics). For snATAC-seq, Chromium Next GEM Single Cell ATAC Library and Gel Bead Kit v1.1 prep (10x Genomics) were used for all samples. Barcoded libraries were then pooled and sequenced on the Illumina NovaSeq 6000 system with specific flow cell types (snRNA-seq: S4; snATAC-seq: S1).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic |
| Library selection |
cDNA |
| Instrument model |
Illumina NovaSeq 6000 |
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| Description |
10x Genomics
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| Data processing |
After single-nuclei prep and sequencing, Cell Ranger (v3.1.0) from 10X Genomics (with Count functionality) was used for aligning reads to the human genome reference (GRCh38) with the addition of pre-mRNA reference (v3.0.0). The reference file was downloaded from the 10X Genomics website (https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest). The pre-mRNA reference was added using the following code: awk 'BEGIN{FS="\t"; OFS="\t"} $3 == "transcript"{ $3="exon"; print}' refdata-cellranger-GRCh38-3.0.0/genes/genes.gtf > GRCh38-3.0.0.premrna.gtf; cellranger mkref --genome=GRCh38-3.0.0.premrna --fasta=refdata-cellranger-GRCh38-3.0.0/fasta/genome.fa --genes=GRCh38-3.0.0.premrna.gtf --nthreads 50. The parameters used with Count functionality include --chemistry=threeprime --expect-cells=6000 --jobmode=local --localcores=20 --localmem=350. The resulting gene-by-cell UMI count matrix was used by the R package Seurat (v.3.1.0)122 for all subsequent processing. Quality filters were applied to the data to remove barcodes that fell into any of the following categories: too few genes expressed (possible debris), too many associated UMIs (possibly more than one cell), and too high mitochondrial gene expression (possible dead cell). The cut-offs for these filters were based on recommendations by Seurat package documentation and manually adjusted to keep the number of cells after filtering under 6500 (detailed filtering parameters see Supplementary Data 2). Finally, doublets were filtered out using Scrublet (v.0.2.1). Scrublet was run on each sample separately with the following parameter settings: expected_doublet_rate=0.06, min_counts=2, min_cells=3, min_gene_variability_pctl=85, n_prin_comps=30. The doublet score threshold was adjusted manually, which can separate the two peaks of a bimodal simulated doublet score histogram.
The Cell Ranger ATAC tool (v.1.2.0, 10X Genomics) was used to process the raw snATAC-seq data (FASTQ). We utilized the cellranger-atac count pipeline to filter and map snATAC-reads and to identify transposase cut sites. The GRCh38 human reference was used for the reads mapping. Next, MACS2126 (v2.2.7.1) was used to perform peak calling. All peaks were resized to 501 bp centered at the peak summit defined by MACS2. After this, we combined all peaks and removed the ones overlapping with the peaks with greater signal, to get the set of non-overlapping peaks, as described in Schep et al127. The resulting set of sample peaks was used to calculate the peak-count matrix using FeatureMatrix function from the R package Signac (v.1.2.0; https://github.com/timoast/signac), which was also used for downstream analysis. QC-filtering of the snATAC-seq data was performed using functions from the Signac package. Filters that were applied for the cell calling include: 1,000 < number of fragments in peaks < 20,000, percentage of reads in peaks > 15, ENCODE blacklist regions percentage < 0.05 (https://www.encodeproject.org/annotations/ENCSR636HFF/), nucleosome banding pattern score < 10, and enrichment-score for Tn5-integration events at transcriptional start sites > 2.
Assembly: hg38
Supplementary files format and content: Tab-separated values files and matrix files.
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| Submission date |
Mar 21, 2023 |
| Last update date |
Mar 22, 2023 |
| Contact name |
Yige Wu |
| E-mail(s) |
yigewu.app@gmail.com
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| Phone |
3142950479
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| Organization name |
Washington University in St. Louis
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| Department |
Department of Medicine
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| Street address |
4520 McPherson Ave
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| City |
St. Louis |
| State/province |
MO |
| ZIP/Postal code |
63108 |
| Country |
USA |
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| Platform ID |
GPL24676 |
| Series (1) |
| GSE227898 |
Epigenetic and transcriptomic characterization reveals progression markers and essential pathways in clear cell renal cell carcinoma |
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| Supplementary file |
Size |
Download |
File type/resource |
| GSM7110428_snRNA.CPT0025880013.tar.gz |
160.8 Mb |
(ftp)(http) |
TAR |
| Raw data not provided for this record |
| Processed data provided as supplementary file |
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