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| Status |
Public on Aug 24, 2023 |
| Title |
mcherry_enriched_rep1 |
| Sample type |
SRA |
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| Source name |
Eye
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| Organism |
Danio rerio |
| Characteristics |
tissue: Eye
genotype: NRE-eGFP/HS5-mCherry
developmental stage: 48 hpf
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| Extracted molecule |
polyA RNA |
| Extraction protocol |
NRE-eGFP/HS5-mCherry embryos were collected and treated with PTU from 12 hpf. At 48 hpf, embryos were anaesthetised with Tricaine (20–30 mg/l) and placed into Danieau's solution. Eyes were dissected from ~100-150 embryos using fine forceps (Dumont #5SF), and immediately placed into Danieau's solution on ice. Samples were centrifuged at 300g for 1 minute at 4°C, then washed with Danieau's solution. Washing step was carried out three times with Danieau's solution, and once with FACSmax (Amsbio). In a final 500 µl FACSmax, the samples were passed through a 35 µm cell strainer to obtain single cell suspension (on ice). Samples were sorted for mCherry and eGFP fluorescence using a FACS Aria II (BD) or CytoFLEX SRT (Beckman Coulter) machine. Forward and side scatter sorting was used to select single cells from clumps and debris, and DAPI staining was used to exclude dead cells.
Libraries were prepared using the 10x Genomics Chromium single cell 3’ gene expression technology (v3.1).
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| Library strategy |
RNA-Seq |
| Library source |
transcriptomic single cell |
| Library selection |
cDNA |
| Instrument model |
NextSeq 2000 |
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| Data processing |
Cell Ranger (v6.1.2) was used to perform alignment, filtering, barcode counting, and UMI counting. A custom reference genome was created for alignment using cellranger mkref, combining the Danio rerio GRCz11 genome assembly with manually annotated eGFP and mCherry sequences. Cell calling and QC: The emptyDrops function from DropletUtils was used to filter out empty droplets/barcodes not corresponding to cells (Lun et al., 2019). Mitochondrial and ribosomal genes were excluded from the emptyDrops analysis to improve the filtering of droplets containing ambient RNA or cell fragments. The scater package was used to filter cells based on the QC metrics of library size, detected genes, and mitochondrial reads (McCarthy et al., 2017). Cells with detected genes ≥ 500, library size ≥ 800, and mitochondrial reads ≤ 10% were retained. Within the processed dataset, mean reads per cell = 11239, and median genes per cell = 1554. Reference mapping and filtering: The SingleR package was used to annotate cell types based on mapping to the zebrafish single-cell transcriptome atlas (Aran et al., 2019; Farnsworth et al., 2019). Expression matrix and cell annotation data were downloaded from the UCSC cell browser (http://zebrafish-dev.cells.ucsc.edu); only the 2 dpf data were used for mapping. Erroneously sorted cells of non-retinal identity (for example pigmented cell types such as melanocytes with high autofluorescence) were filtered out at this stage. This was carried out to improve the resolution of clustering for retinal cell types. Clustering and cell type annotation: Seurat (v4) was used for clustering and further analysis for a total of 6,288 cells (Butler et al., 2018). SCTransform was used to perform log-normalisation, scaling, and highly variable gene (HVG) detection on a dataset consisting of the 6 samples merged into one. Standard SCTransform options were used, with regression of mitochondrial expression and cell-cycle stage using ‘vars.to.regress’. We performed Principle Component Analysis (PCA) on the normalized counts matrix restricted to HVGs, using Seurat's RunPCA function with number of PCs = 50. To enable integration of the samples, we then used Harmony to generate PCs corrected for batch effects between libraries (Korsunsky et al., 2019). The Harmony PCs were then used to perform K-nearest neighbour analysis (k=20) and Louvain clustering using Seurat (15 dimensions and resolution 0.6). Clusters were annotated as retinal cell types based on the highest expressed marker genes, and other known genes for each cell type, using information from the literature and ZFIN (Sprague et al., 2008). Cell cycle scoring was performed using the Seurat CellCycleScoring function, using zebrafish genes homologous to the ‘s.features’ and ‘g2m.features’ genes provided by Seurat.
Assembly: GRCz11
Supplementary files format and content: Tab separated values files, matrix files, Seurat object RDS file
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| Submission date |
Aug 10, 2023 |
| Last update date |
Aug 30, 2023 |
| Contact name |
Shipra Bhatia |
| E-mail(s) |
shipra.bhatia@ed.ac.uk, kirsty.uttley@ed.ac.uk
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| Organization name |
University of Edinburgh
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| Department |
MRC Human Genetics Unit
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| Lab |
Wendy Bickmore
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| Street address |
Crewe Rd South
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| City |
Edinburgh |
| ZIP/Postal code |
EH4 2XU |
| Country |
United Kingdom |
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| Platform ID |
GPL30614 |
| Series (1) |
| GSE240575 |
Unique activities of two overlapping PAX6 retinal enhancers |
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| Relations |
| BioSample |
SAMN36939676 |
| SRA |
SRX21332623 |
| Supplementary file |
Size |
Download |
File type/resource |
| GSM7702830_double_pos_barcodes.tsv.gz |
3.8 Mb |
(ftp)(http) |
TSV |
| GSM7702830_double_pos_matrix.mtx.gz |
46.8 Mb |
(ftp)(http) |
MTX |
| GSM7702830_double_pos_new_features.tsv.gz |
288.4 Kb |
(ftp)(http) |
TSV |
SRA Run Selector |
| Raw data are available in SRA |
| Processed data provided as supplementary file |
| Processed data are available on Series record |
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