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Sample GSM6500965

Query DataSets for GSM6500965

Status

Public on Jan 17, 2024

Title

Control6_scRNAseq

Sample type

SRA

Source name

whole kidney tissue

Organism

Homo sapiens

Characteristics

disease: Control
tissue: adult kidney
age: 72
gender: Female

Extracted molecule

total RNA

Extraction protocol

RNA-Seq: 10X Genomics Chromium Prep (10X Genomics, PN-1000120). Barcoding and cDNA synthesis were performed according to the manufacturer's instructions.
RNA-Seq: The cDNA libraries were constructed using the Chromium Next GEM Single Cell 3′ GEM Kit v3.1 (10X Genomics, PN-1000121) according to the manufacturer’s original protocol.
ATAC-Seq: Chromium Single Cell ATAC Library & Gel Bead Kit (10X Genomics, PN-1000110). Transposing and barcoding were performed according to the manufacturer's instructions.
ATAC-Seq: The libraries were constructed using the Chromium Single Cell ATAC Library & Gel Bead Kit and Chromium i7 Multiplex Kit N (10X Genomics, PN-1000084) according to the manufacturer’s original protocol.
Spatial Transcriptomics: 5 µm tissue samples was cut and onto the Visium Spatial gene Expression Slide. After deparaffinization, H & E staining was performed. Keyence 1266 BZ-X810 microscope was used for whole slide imaging.
Spatial Transcriptomics: After scanning, de-crosslinking, probe hybridization, probe release and extension, library preparation was performed by single Index Kit TS Set A (10X Genomics, PN-3000511) according to the manufacturer’s protocol.

Library strategy

RNA-Seq

Library source

transcriptomic single cell

Library selection

cDNA

Instrument model

Illumina NovaSeq 6000

Description

processed data files include count matrix, metadata and umap files

Data processing

RNA-Seq: Cell Ranger v. 6.0.1 (http://10xgenomics.com) was used to process Chromium single cell 3’ RNA-seq output.
RNA-Seq: “cellranger count” aligned the Read2 to the human reference genome (GRCh38).
RNA-Seq: Aligned reads were used to generate data matrix only when they have valid barcodes and UMI, map to exons (GRCh38)
RNA-Seq: The data matrix of each sample was corrected in terms of ambient RNA using SoupX.
RNA-Seq: The output files for 29 samples were aggregated into one gene-cell matrix using Seurat v. 4.0.1.
RNA-Seq: Once the gene-cell data matrix was generated, poor quality cells were excluded, such as cells with <200 genes detected or with >3,000 unique expressed genes (as they are potentially cell doublets). Cells were also discarded if their mitochondrial gene percentages were over 50%.
Assembly: RNA-Seq: GRCh38
Supplementary files format and content: scRNA-Seq: EXPORT_scRNAseq_counts.rds: combined count matrix of all 11 samples; EXPORT_snRNAseq_metadata.txt: combined metadata; EXPORT_snRNAseq_umap.txt: UMAP embedding values for identified cell types.
scRNA-Seq: Cell Ranger v. 6.0.1 (http://10xgenomics.com) was used to process Chromium single cell 3’ RNA-seq output.
scRNA-Seq: “cellranger count” aligned the Read2 to the human reference genome (GRCh38).
scRNA-Seq: Aligned reads were used to generate data matrix only when they have valid barcodes and UMI, map to exons (GRCh38)
scRNA-Seq: The data matrix of each sample was corrected in terms of ambient RNA using SoupX.
scRNA-Seq: The output files for 11 samples were aggregated into one gene-cell matrix using Seurat v. 4.0.1.
Assembly: scRNA-Seq: Once the gene-cell data matrix was generated, poor quality cells were excluded, such as cells with <200 genes detected or with >3,000 unique expressed genes (as they are potentially cell doublets). Cells were also discarded if their mitochondrial gene percentages were over 15%.
Supplementary files format and content: scRNA-Seq: GRCh38
ATAC-Seq: Cell Ranger ATAC v. 2.0.0 (http://10xgenomics.com) was used to process Chromium single cell ATAC-seq output.
ATAC-Seq: The poor quality cells were removed from the matrix using the following criteria: peak_region_fragments < 3000 & peak_region_fragments > 20000 & pct_reads_in_peaks < 15 & nucleosome_signal > 4 & TSS.enrichment > 2
ATAC-Seq: The output files for 20 samples were aggregated into one matrix using Signac v. 1.3.0.
Assembly: ATAC-Seq: GRCh38
Spatial Transcriptomics: Space Ranger v1.0.0 (http://10xgenomics.com) was used to process Human FFPE Visium output
Spatial Transcriptomics: “spaceranger count” aligned thedata to the human reference genome (GRCh38) by using the human probe dataset (Visium_Human_Transcriptome_Probe_Set_v1.0_GRCh38) and data mapped to the H & E stained image.
Spatial Transcriptomics: Aligned data was loaded to Seurat object (Seurat . 4.0.1) and normalized by using SCT.
Spatial Transcriptomics: The objects of 7 samples were aggregated into one gene-cell matrix using Seurat v. 4.0.1.
Spatial Transcriptomics: The samples were merged together, using “merge” function of Seurat. Next, the data was subjected to principle component analysis (PCA) for linear dimension reduction and Harmony was used to integrate the datasets.
Assembly: Spatial Transcriptomics: GRCh38
Supplementary files format and content: EXPORT_ST_counts.rds: combined count matrix of all 7 samples; EXPORT_ST_metadata.txt: combined metadata; EXPORT_ST_umap.txt: UMAP embedding values for identified cell types.

Submission date

Aug 22, 2022

Last update date

Jan 17, 2024

Contact name

Jonathan Levinsohn

E-mail(s)

levinsohnj@email.chop.edu, jlevinsohn13@gmail.com

Phone

2155191527

Organization name

University of Pennsylvania