run_metadata: 44002

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
44002	SRR6211490	SRX3320765	SRS2626338	SRP121343	PRJNA415636	Simultaneous lineage tracing and cell type identification using CRISPR/Cas9 induced genetic scars	GSE106121	Other	A key goal of developmental biology is to understand how a single cell transforms into a full grown organism consisting of many different cell types. Single cell RNA sequencing scRNA seq has become a widely used method due to its ability to identify all cell types in a tissue or organ in a systematic manner. However a major challenge is to organize the resulting taxonomy of cell types into lineage trees revealing the developmental origin of cells. Here we present a strategy for simultaneous lineage tracing and transcriptome profiling in thousands of single cells. By combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes we reconstruct developmental lineage trees in zebrafish larvae and adult fish. In future analyses LINNAEUS LINeage tracing by Nuclease Activated Editing of Ubiquitous Sequences can be used as a systematic approach for identifying the lineage origin of novel cell types or of known cell types under different conditions. Overall design: Combining scRNA seq with computational analysis of lineage barcodes generated by genome editing of transgenic reporter genes.		pubmed:29644996		Heart 1 mRNA	GSM2830060		source name:Heart and blood\|strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	Heart 1 mRNA	Alignment and transcript counting of libraries were done using Cell Ranger 2.0.2. Cell numbers to be extracted were set at a minimum of 6000 but were increased if there were substantially more cells with more than 500 unique transcripts. Exact numbers can be found in Supplementary Table 1 in publication. Every sequencing read consists of a cellular barcode a UMI and a transcript sequence originating from an mRNA molecule. These transcript sequences were aligned using bwa aln3 with setting ' q 50' to a reference transcriptome constructed from Ensembl release 74 www.ensembl.org with extended three prime UTR regions. We filtered out all unmapped reads and all reads that were not uniquely mapped. post alignment we determined which cellular barcodes corresponded to cells. We defined a cell to be a cellular barcode with at least five hundred uniquely mapped molecules. For each cellular barcode we counted the number of molecules mapped to each gene using the UMI correction method described by Grün et al. This method corrects for the possibility of the same UMI being used for two different transcripts in the same cell with the formula t = K ln1 – k o/K with t the final number of transcripts k o the observed UMIs and K the total number of UMIs possible. As protection against barcode sequencing errors we counted the occurrence of each nucleotide for each barcode and filtered out barcodes in which one nucleotide occurred ten or more times. Furthermore we filtered out barcodes that were one nucleotide substitution removed from a barcode with at least eight times as many transcripts. Genome build: GRCz10 release 90 Supplementary files format and content: * matrix.mtx: Single cell transcript count table; * barcodes.tsv: List of cell barcodes.	Heart and blood		Single cell dissociation. 10X Genomics Chromium		strain/background:Zebrabow M\|tissue:Heart and blood\|developmental stage:Adult	GSM2830060	GSM2830060: Heart 1 mRNA; Danio rerio; RNA Seq	GSM2830060		1	Single cell dissociation. 10X Genomics Chromium	GEO Accession:GSM2830060	RNA-Seq	TRANSCRIPTOMIC	cDNA	PAIRED	ILLUMINA	NextSeq 500		SRP121343			H5_wt_R1.fastq.gz H5_wt_R2.fastq.gz	fastq fastq	11175332616.0	88693116.0	GSM2830060 r1	0:26 1:100	A:3373517159;C:2470603501;G:2531314673;T:2798372007;N:1525276	26	100			3373517159	2470603501	2531314673	2798372007	1525276	SRX3320765	SRS2626338	SRA623333	GEO	Max Delbrück Center	2	0.00264	0.95153	0.00046	0.05429	0.99669	0.88958	0.57635	0.67876	26	100	T	B	sc-like readlen	illumina	nextseq	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	10x		Germany	2017-10-24	Adult	Adult	Multi-tissue	Multi-system