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| Status |
Public on Jun 08, 2023 |
| Title |
MELAS, macula, RPE/choroid (scRNA-seq) |
| Sample type |
SRA |
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| Source name |
RPE/choroid
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| Organism |
Homo sapiens |
| Characteristics |
individual: MELAS patient
tissue: RPE/choroid
region: macular
genotype: m.3243A/m.3243G (heteroplasmic)
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
Neural retina was dissected away from the underlying retinal pigment epithelium and choroid prior to tissue dissociation. Neural retina samples were dissociated with 20 units/mL of papain (Worthington Biochemical Corporation) with 0.0005% DNase I (Worthington Biochemical Corporation) on a shaker at 37˚C for 75 minutes. RPE/choroid samples were dissociated mechanically using razor blades into one-millimeter pieces then dissociated enzymatically using collagenase on a shaker at 37˚C for 60 minutes. Following dissociation, cells were cryopreserved in DMSO-based Recovery Cell Cryopreservation Media (Gibco). After overnight cooling at -80˚C, samples were transferred to liquid nitrogen (vapor phase) for long-term storage prior to encapsulation and library preparation.
Cryopreserved cells were rapidly thawed at 37 °C and resuspended in dPBS-/- (Gibco) with 0.04% non-acetylated bovine serum albumin (New England Biolabs). Cells were filtered through a 70µm filter and diluted to target 8,000 cells per run. Single cells were then partitioned and barcoded with the Chromium Controller instrument (10X Genomics) and Single Cell 3' Reagent (v3.1 chemistry) kit (10X Genomics) according to the manufacturer’s specifications with no modification (Rev C). Final libraries were quantified using the Qubit dsDNA HS Assay Kit (Life Technologies) and diluted to 3ng/µL in buffer EB (Qiagen). Library quality and concentration was confirmed using the Bioanalyzer High Sensitivity DNA Assay (Agilent) prior to sequencing.
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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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| Data processing |
FASTQ files were generated from base calls with the bcl2fastq software (Illumina).
Reads were mapped to the pre-built GRCh38 reference with Cell Ranger v3.0.1 (10X Genomics) using the ‘count’ function.
Cells were filtered with the Seurat (v3.1) subset function. Cells with nUMIs less than 500 (to remove cells with poor read quality) or greater than 7000 (to remove cells likely to be doublets) were removed. RPE/choroid cells with greater than 65% of reads originating from mitochondrial genes were also removed. Mitochondrial genes were identified with the following command: seurat_obj[["mito.genes"]] <- PercentageFeatureSet(seurat_obj, pattern = "^MT-"). RPE/choroid cells with greater than 40% of reads originating from ribosomal RNA genes were also removed. Retinal cells with greater than 40% of reads originating from mitochondrial genes or 20% of reads originating from ribosomal RNA genes were also removed.
For each of the eight libraries, reads were normalized with the Seurat (v3.1) NormalizeData function, and variable features were found with the following commands: my _object <- NormalizeData(my_object, normalization.method = "LogNormalize", scale.factor = 10000), my_object <- FindVariableFeatures(my_object, selection.method = “vst”, nfeatures = 2000)
Integration anchors from the first 25 dimensions of the canonical correlation analysis were used to integrate data with the following commands: retina.anchors <- FindIntegrationAnchors(object.list = my_object_list, dims=1:25), RETINA.combined <- IntegrateData(anchorset = retina.anchors, dims = 1:25)
Data scaling, principal component analysis, and clustering were performed with the following commands: RETINA.combined <- ScaleData(RETINA.combined, verbose = FALSE), RETINA.combined <- RunPCA(retina.combined, npcs = 30, verbose = FALSE), RETINA.combined <- RunUMAP(RETINA.combined, reduction = "pca", dims = 1:22), RETINA.combined <- FindNeighbors(RETINA.combined, reduction = "pca", dims = 1:23), RETINA.combined <- FindClusters(RETINA.combined, resolution = 0.5)
Assembly: GRCh38
Supplementary files format and content: Processed expression data matrix files are provided in comma-delimited format for count (*_count.csv) and processed/normalized (*_normalized.csv) expression values. For processed/normalized files, log-normalized expression values (from GetAssayData(object = seurat_object)) were appended to relevant metadata (barcode, cluster label, and donor number from the manuscript). Each row represents a unique cell, and columns correspond to metadata and log normalized gene expression values. For count files, raw counts were obtained (from GetAssayData(object = seurat_object, slot = “counts”)) for each cell type in each cluster and appended to relevant metadata (barcode, cluster label, and donor number from the manuscript).
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| Submission date |
May 11, 2022 |
| Last update date |
Jun 08, 2023 |
| Contact name |
Nathaniel Kevin Mullin |
| Organization name |
University of Iowa
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| Street address |
375 Newton Road
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| City |
Iowa City |
| State/province |
IA |
| ZIP/Postal code |
52246 |
| Country |
USA |
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| Platform ID |
GPL24676 |
| Series (2) |
| GSE202735 |
Gene expression from single cells of the retina and choroid in human MELAS (m.3243A>G) and control samples [scRNA-seq] |
| GSE202747 |
Non-random distribution of mitochondrial m.3243A>G heteroplasmy in human retina and its impact on cellular phenotype |
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| Relations |
| BioSample |
SAMN28191568 |
| SRA |
SRX15233591 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM6131953_21_016_macula_rpe_chor_counts.csv.gz |
3.8 Mb |
(ftp)(http) |
CSV |
| GSM6131953_21_016_macula_rpe_chor_normalized.csv.gz |
4.9 Mb |
(ftp)(http) |
CSV |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
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