run_metadata: 53252
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 |
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| 53252 | SRR9825125 | SRX6581795 | SRS5147493 | SRP216250 | PRJNA556200 | Cross species analysis across 450 million yrs of evolution reveals conservation and divergence of the microglia program scRNA seq | GSE134705 | Other | Here we characterize microglia transcriptional program across ten species spanning more than 450 million yrs of evolution. We find that microglia express a conserved core gene program of orthologous genes from rodents to human including ligands and receptors associated with interactions between glia and neurons. In most species microglia show a single dominant transcriptional state while humans express significant microglia heterogeneity. In addition we observed notable differences in complement phagocytic and several critical signaling pathways that are enriched with susceptibility genes to brain disorders including Alzheimer's and Parkinson's disease in microglia of common animal models as compared to human. Our study provides an essential resource of conserved and divergent microglia pathways across evolution with important implications for future development of microglia based therapies in humans. Overall design: Single cell and bulk RNA seq of microglia from 10 different species and comparison of transcriptional landcape over species. Biological replicates of different species were n=3 6. | parent bioproject:PRJNA556197 | pubmed:31835035 | AB5657 | GSM3963892 | source name:Total brain|strain:eGFP mPEG|tissue:Brain|cell type:Immune cells|selection marker:mPEG GFP|source:NA|age:4 5mo | AB5657 | bcl2fastq/2.15.0.4 Sequences with RMT of low quality defined as RMT with minimum Phred score of less than 27 were filtered out. Pool barcode and well barcode RMT were extracted from the first and second end of the read respectively and concatenated to the fastq header delimited by a underscore i.e. POOL BARCODE WELL BARCODE RMT while "NNNNNN" was used as a place holders if plate barcode was not used. Reads were separated by POOL BARCODE WELL BARCODE header data allowing 1 sequencing error. This process created a single fastq file for each source well. Genome build: hg38 Genome build: Mmul 8.0.1 Genome build: calJac3 Genome build: Oar v3.1 Genome build: Rnor 6.0 Genome build: mm10 Genome build: S.galili v1.0 Genome build: MesAur1.0 Genome build: galGal5 Genome build: danRer10 Supplementary files format and content: tab delimited text files include mRNA molecule count values for each Sample | Total brain | Single cell libraries were prepared as previously described Keren Shaul Nature Protocols 2019. In brief mRNA from cell sorted into cell capture plates are barcoded and converted into cDNA and pooled using an automated pipeline. The pooled sample is then linearly amplified by T7 in vitro transcription and the resulting RNA is fragmented and converted into a sequencing ready library by tagging the samples with pool barcodes and Illumina sequences during ligation RT and PCR. Each pool of cells was tested for library quality and concentration is assessed as described earlier Keren Shaul Nature Protocols 2019. Single cell RNA seq libraries were prepared as previously described {Jaitin 2014}. In brief mRNA from single cells sorted into capture plates were barcoded and converted into cDNA and then pooled using an automated pipeline. The pooled sample was linearly amplified by T7 in vitro transcription and the resulting RNA was fragmented and converted into a sequencing ready library by tagging the samples with pool barcodes and Illumina sequences during ligation RT and PCR. Each pool of cells was tested for library quality and concentration as described previously {Jaitin 2014} | strain:eGFP mPEG|tissue:Brain|cell type:Immune cells|selection marker:mPEG GFP|source:NA|catalog#:NA|age:4 5mo | GSM3963892 | GSM3963892: AB5657; Danio rerio; RNA Seq | GSM3963892 | 1 | Single cell libraries were prepared as previously described Keren Shaul Nature Protocols 2019. In brief mRNA from cell sorted into cell capture plates are barcoded and converted into cDNA and pooled using an automated pipeline. The pooled sample is then linearly amplified by T7 in vitro transcription and the resulting RNA is fragmented and converted into a sequencing ready library by tagging the samples with pool barcodes and Illumina sequences during ligation RT and PCR. Each pool of cells was tested for library quality and concentration is assessed as described earlier Keren Shaul Nature Protocols 2019. Single cell RNA seq libraries were prepared as previously described {Jaitin 2014}. In brief mRNA from single cells sorted into capture plates were barcoded and converted into cDNA and then pooled using an automated pipeline. The pooled sample was linearly amplified by T7 in vitro transcription and the resulting RNA was fragmented and converted into a sequencing ready library by tagging the samples with pool barcodes and Illumina sequences during ligation RT and PCR. Each pool of cells was tested for library quality and concentration as described previously {Jaitin 2014} | GEO Accession:GSM3963892 | RNA-Seq | TRANSCRIPTOMIC | cDNA | SINGLE | ILLUMINA | NextSeq 500 | SRP216250 | loader:fastq load.py|options: appendBCtoName | AB5657_SB283_S14_R1_001.fastq.gz | fastq | 331332549.0 | 4801921.0 | GSM3963892 r1 | 0:69 | A:89857088;C:69771659;G:81302142;T:90395650;N:6010 | 69 | 89857088 | 69771659 | 81302142 | 90395650 | 6010 | SRX6581795 | SRS5147493 | SRA926018 | GEO | Immunology, Weizmann Institute of Science | 1 | 0.88981 | 0.33973 | 0.93507 | 0.6341 | 69 | B | usable mapping rate | illumina | nextseq | unknown | poly_a | unknown | sc_generic | bulk | bulk | Israel | 2019-07-23 | Undetermined | Undetermined | Brain | Nervous System |