{"database": "metadata", "table": "run_metadata", "rows": [[25115, "SRR25605436", "SRX21332626", "SRS18578258", "SRP454539", "PRJNA1004255", "Unique activities of two overlapping PAX6 retinal enhancers", "GSE240575", "Transcriptome Analysis", "Enhancers play a critical role in development by precisely modulating spatial  temporal  and cell type specific gene expression. Sequence variants in enhancers have been implicated in disease  however establishing the functional consequences of these variants is challenging due to a lack of understanding of precise cell types and developmental stages where the enhancers are normally active. PAX6 is the master regulator of eye development  with a regulatory landscape containing multiple enhancers driving expression in the eye. Whether these enhancers perform additive  redundant  or distinct functions is unknown. Here we describe the precise cell types and regulatory activity of two PAX6 retinal enhancers  HS5 and NRE. Using a unique combination of live imaging and single cell RNA sequencing in dual enhancer reporter zebrafish embryos  we uncover differences in the spatiotemporal activity of these enhancers. Our results show that although overlapping  these enhancers have distinct activities in different cell types and therefore likely non redundant functions. This work demonstrates that unique cell type specific activities can be uncovered for apparently similar enhancers when investigated at high resolution in vivo. Overall design: In order to define the precise cell types within the retina where the PAX6 enhancers HS5 and NRE are active  we carried out scRNA seq on eyes from NRE eGFP/HS5 mCherry zebrafish reporter embryos. With this technique  we aimed to uncover cell type or transcriptional differences between the two enhancer active populations. We dissected eyes from 48 hpf NRE eGFP/HS5 mCherry embryos and used FACS to enrich for either mCherry positive/HS5 active cells or eGFP positive/NRE active cells. Three samples for each population were processed for scRNA seq using the 10x Genomics Chromium single cell three prime gene expression technology.", null, "pubmed:37643867", null, "gfp enriched rep2", "GSM7702833", null, "source name:Eye|tissue:Eye|genotype:NRE eGFP/HS5 mCherry|developmental stage:48 hpf|geo loc name:missing|collection date:missing", "gfp enriched rep2", "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 \u2265 500  library size \u2265 800  and mitochondrial reads \u2264 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 \u2018vars.to.regress\u2019. 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 \u2018s.features\u2019 and \u2018g2m.features\u2019 genes provided by Seurat. Assembly: GRCz11 Supplementary files format and content: Tab separated values files  matrix files  Seurat object RDS file", "Eye", null, "NRE eGFP/HS5 mCherry embryos were collected and treated with PTU from 12 hpf. At 48 hpf  embryos were anaesthetised with Tricaine 20\u201330 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\u00b0C  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 \u00b5l FACSmax  the samples were passed through a 35 \u00b5m 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\u2019 gene expression technology v3.1.", null, "tissue:Eye|genotype:NRE eGFP/HS5 mCherry|developmental stage:48 hpf", "GSM7702833", "GSM7702833: gfp enriched rep2; Danio rerio; RNA Seq", "GSM7702833 r1", "GSM7702833", "1", "NRE eGFP/HS5 mCherry embryos were collected and treated with PTU from 12 hpf. At 48 hpf  embryos were anaesthetised with Tricaine 20\u201330 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\u00b0C  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 \u00b5l FACSmax  the samples were passed through a 35 \u00b5m 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 three prime gene expression technology v3.1.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "SINGLE", "ILLUMINA", "NextSeq 2000", null, "SRP454539", null, "loader:fastq load.py|options:  allowEarlyFileEnd", "1907_GFP_pos_S2_L001_I1_001.fastq.gz 1907_GFP_pos_S2_L001_I2_001.fastq.gz 1907_GFP_pos_S2_L001_R1_001.fastq.gz 1907_GFP_pos_S2_L001_R2_001.fastq.gz", "fastq fastq fastq fastq", 22174775364.0, 160686778.0, "GSM7702833 r1", "0:10 1:10 2:28 3:90", "A:4207510387;C:3050740898;G:3362227609;T:3839333241;N:1997885", 10, 10, 28, 90, 4207510387, 3050740898, 3362227609, 3839333241, 1997885, "SRX21332626", "SRS18578258", "SRA1702612", "Wendy Bickmore, MRC Human Genetics Unit, University of Edinburgh", "Wendy Bickmore, MRC Human Genetics Unit, University of Edinburgh", 1, 0.93287, null, 0.11844, null, 0.81087, null, 0.55264, null, 90, null, "B", null, "usable mapping rate", "illumina", "nextseq_v2", "3prime", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United Kingdom", "2023-08-10", "Hatching", "Embryo", "Eye", "Sensory System"]], "columns": ["rowid", "run.accession", "experiment.accession", "sample.accession", "study.accession", "bioproject", "study.title", "study.alias", "study.type", "study.abstract", "study.attributes", "study.PMIDs", "sample.description", "sample.title", "sample.alias", "sample.centername", "sample.attributes", "GEOsample.title", "GEOsample.dataprocessing", "GEOsample.source", "GEOsample.treatmentprotocol", "GEOsample.extractprotocol", "GEOsample.growthprotocol", "GEOsample.characteristics", "GEOsample.accession", "experiment.title", "experiment.alias", "experiment.library_name", "experiment.design_description", "experiment.library_construction_protocol", "experiment.attributes", "experiment.library_strategy", "experiment.library_source", "experiment.library_selection", "experiment.library_layout", "experiment.platform", "experiment.instrument_model", "experiment.spot_descriptor", "experiment.study_ref", "run.title", "run.attributes", "run.filename", "run.semantic_name", "run.total_bases", "run.total_spots", "run.alias", "run.read_lengths", "run.base_counts", "run.r1_length", "run.r2_length", "run.r3_length", "run.r4_length", "run.Acount", "run.Ccount", "run.Gcount", "run.Tcount", "run.Ncount", "run.experiment", "run.pool_member", "submission.accession", "submission.srasource", "submission.bioprojectsource", "seqdetective.n_mates", "seqdetective.mapping_rate.mate1", "seqdetective.mapping_rate.mate2", "seqdetective.nofeature_rate.mate1", "seqdetective.nofeature_rate.mate2", "seqdetective.sparsity.mate1", "seqdetective.sparsity.mate2", "seqdetective.pos_strand_rate.mate1", "seqdetective.pos_strand_rate.mate2", "seqdetective.readlen.mate1", "seqdetective.readlen.mate2", "seqdetective.judgement.mate1", "seqdetective.judgement.mate2", "seqdetective.judgement.reason", "platform_family", "instrument_generation", "read_bias", "selection_class", "prep_kit", "sc_or_bulk", "tech_class", "technology", "tech_variant", "submission.bioprojectsource.country", "earliest_date", "devstage_curation", "devstage_curation_coarse", "tissue_curation", "tissue_curation_coarse"], "primary_keys": ["rowid"], "primary_key_values": ["25115"], "units": {}, "query_ms": 10.593733997666277}