run_metadata: 43911
This data as json
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 43911 | SRR6176680 | SRX3287380 | SRS2596854 | SRP120009 | PRJNA414416 | Simultaneous single cell profiling of lineages and cell types in the vertebrate brain | GSE105010 | Other | The lineage relationships among the hundreds of cell types generated during development are difficult to reconstruct. A recent method GESTALT used CRISPR–Cas9 barcode editing for large scale lineage tracing but was restricted to early development and did not identify cell types. Here we present scGESTALT which combines the lineage recording capabilities of GESTALT with cell type identification by single cell RNA sequencing. The method relies on an inducible system that enables barcodes to be edited at multiple time points capturing lineage information from later stages of development. Sequencing of 60 000 transcriptomes from the juvenile zebrafish brain identified >100 cell types and marker genes. Using these data we generate lineage trees with hundreds of branches that help uncover restrictions at the level of cell types brain regions and gene expression cascades during differentiation. scGESTALT can be applied to other multicellular organisms to simultaneously characterize molecular identities and lineage histories of thousands of cells during development and disease. Overall design: inDrops libraries of single cell transcriptomes and scGESTALT barcodes and genomic DNA GESTALT libraries | pubmed:29608178 | DEW124 f4 | GSM2813950 | source name:zebrafish brain|tissue:brain|developmental stage:23 25dpf | DEW124 f4 | Single cell RNA Sequencing data FASTQ files were processed using the inDrops.py bioinformatics pipeline available at https://github.com/indrops/indrops. Transcriptome libraries were mapped to a zebrafish reference built from a custom GTF file and the zebrafish GRCz10 release 86 genome assembly. Bowtie version1.1.1 was used with parameter –e 200; UMI quantification was used with parameter –u 2 counts were ignored from UMIs split between more than 2 genes. genomic DNA GESTALT and scGESTALT libraries were processed using a custom pipeline available at https://github.com/shendurelab/Cas9FateMapping Genome build: GRCz10 CSV files for transcriptome data were generated using the inDrops pipeline. Each column in the CSV files contains a cell identifier and each row contains expression values for genes. Txt files for genomic DNA GESTALT libraries *allReadCounts contain lineage barcode sequences HMID column for each cell and their proportion in the sequenced libraries. scGESTALT data *GestMaster.txt contains the inDrops cell identifiers CellBarcode and BarcodeKey that were used to match barcodes to transcriptomes. They also contain lineage barcode sequences HMID column for each cell with a corresponding inDrops single cell gene expression profile as well as the the t SNE cluster membership number ClusterIdent column Txt files ending in *stats.txt contain information about the each individually captured UMI or cell per sample. The barcode sequence aligned to a reference unedited sequence mergedRead column mutations at each target site target[X] columns and the edited sequences at each target site sequence[X] columns were used for downstream analysis. inDropsExpMatrix noQ txt file is the gene expression matrix for the full dataset. Columns are individual cells from different batches of whole brains f1 f2 f3 f4 f5 f6 or brain regions fore mid hind . fall.inDrops.Robj is the processed Seurat R object which can be loaded into R and explored. | zebrafish brain | Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al. 2017 Nature Protocols PMID = 27929523 | tissue:brain|developmental stage:23 25dpf | GSM2813950 | GSM2813950: DEW124 f4; Danio rerio; RNA Seq | GSM2813950 | 1 | Single cell suspensions were processed through inDrops to generate single cell cDNA libraries. cDNAs were in vitro transcribed and fragmented. The three prime fragments were reverse transcribed and prepared for sequencing Libraries were prepared as described in Zilionis et al. 2017 Nature Protocols PMID = 27929523 | GEO Accession:GSM2813950 | RNA-Seq | TRANSCRIPTOMIC | cDNA | SINGLE | ILLUMINA | NextSeq 500 | SRP120009 | loader:fastq load.py|options: appendBCtoName | DEW124_Lane1.sorted.fastq.gz | fastq | 1779081927.0 | 30568871.0 | GSM2813950 r1 | 0:58.20 | A:505313337;C:347024008;G:355651877;T:571089434;N:3271 | 58 | 505313337 | 347024008 | 355651877 | 571089434 | 3271 | SRX3287380 | SRS2596854 | SRA619743 | GEO | Harvard University | 1 | 0.89472 | 0.23434 | 0.78904 | 0.55858 | 61 | B | usable mapping rate | illumina | nextseq | unknown | cdna_unspecified | unknown | sc | single_cell_droplet | indrops | United States | 2017-10-16 | Larval | Larval | Brain | Nervous System |