{"database": "metadata", "table": "run_metadata", "rows": [[76472, "SRR24974217", "SRX20731837", "SRS18022738", "SRP444940", "PRJNA985790", "Single cell analysis of innate spinal cord regeneration identifies intersecting modes of neuronal repair", "GSE235395", "Transcriptome Analysis", "Adult zebrafish have an innate ability to recover from severe spinal cord injury. Here  we report a comprehensive single nuclear RNA sequencing atlas that spans 6 weeks of regeneration. We identify cooperative roles for adult neurogenesis and neuronal plasticity during spinal cord repair. Neurogenesis of glutamatergic and GABAergic neurons restores the excitatory/inhibitory xxx post injury. In addition  a transient population of injury responsive neurons iNeurons show elevated plasticity between 1 and 3 xxx post injury. We found iNeurons are injury surviving neurons that acquire a neuroblast like gene expression xxx post injury. CRISPR/Cas9 mutagenesis showed iNeurons are required for functional recovery and employ vesicular trafficking as an essential mechanism that underlies neuronal plasticity. This study provides a comprehensive resource of the cells and mechanisms that direct spinal cord regeneration and establishes zebrafish as a model of plasticity driven neural repair. Overall design: we performed complete SC transections on adult zebrafish and dissected 3 mm sections of SC tissues surrounding the lesion site at 1  3  and 6 xxx post injury wpi for nuclear isolation. Corresponding tissue sections were collected from uninjured controls. SC tissues were pooled from 50 animals per time point  and 2 pools of independent biological replicates were analyzed for each time point. Our dataset spans key regenerative windows including early injury induced signals at 1 wpi  neuronal and glial regeneration at 3 wpi  and cellular remodeling at late stages of regeneration at 6 wpi Mokalled et al.  2016. Isolated nuclei were sequenced using 10x genomics platform three prime v3.1 chemistry and aligned to zebrafish genome GRCz11 with improved zebrafish transcriptome annotation Lawson et al.  2020; Matson et al.  2018. Nuclei were subsequently filtered using the Decontx and DoubletFinder packages to exclude droplets that include doublet nuclei or a high proportion of ambient mRNA  respectively McGinnis et al.  2019; Yang et al.  2020. A second round of filtering is performed by thresholding the covariates such as number of counts  genes and fraction of counts from mitochondrial genes per barcode. A total of 58 973 nuclei was obtained for downstream analysis using the Seurat package .", null, "pubmed:39147780", null, "EKAB Spinal Cord 6 wpi rep2", "GSM7501739", null, "source name:Spinal Cord|tissue:Spinal Cord|genotype:Wild type|treatment:Injured|geo loc name:missing|collection date:missing", "EKAB Spinal Cord 6 wpi rep2", "post sequencing  the Illumina output was processed using the CellRanger v6.0.0 recommended pipeline to generate gene barcode count matrices. A custom reference genome was made with the \u201ccellranger mkref\u201d command  using the fasta file of zebrafish reference genome GRCz11 constructed from the Ensemble genome build https://useast.ensembl.org/Danio rerio/Info/Index  and the sorted Gene Transfer Format file v4.3.2 from the improved zebrafish transcriptome annotation Lawson et al.  2020 Base call files for each sample from Illumina were demultiplexed into FASTQ reads. Then  the \u201ccellranger count\u201d pipeline was used to align sequencing reads in FASTQ files to the custom reference genome. Both exon and intron sequences were included during the alignment. The filtered gene barcode count matrices generated by \u201ccellranger count\u201d was used for downstream analysis. All the datasets were integrated and analyzed using Seurat v4.1.1 package with R v4.2.1 R Core Team  2018; Stuart et al.  2019.  Each sample count matrix was filtered for genes that were expressed in at least 3 cells and cells expressing at least 200 genes  followed by cell quality assessment using commonly used QC matrixes Ilicic et al.  2016. Cells having a unique number of genes between 200 to 4000 and a mitochondrial gene percentage <5 were used for downstream processing. Each dataset was independently normalized and scaled using the \u201cSCTransform\u201d function  which is an improved method for normalization  that performs a variance stabilizing transformation using negative binomial regression Hafemeister and Satija  2019. Standard integration workflow of Seurat was used to identify shared sources of variation across experiments as well as mutual nearest neighbors Butler et al.  2018; Haghverdi et al.  2018. Integration features were selected based on the top 4000 highly variable features using \u201cSelectIntegrationFeatures\u201d function nfeatures = 4000  which was used as input for the \u201canchor.features\u201d argument of the \u201cFindIntegrationAnchors\u201d function. PCA analysis was performed on the 4000 variable features and the top 50 principal components selected based on the elbow plot heuristic  which measures the contribution of variation in each component. These 50 principal components were used in \u201cFindNeighbors\u201d and \u201cFindClusters\u201d functions to perform graph based clustering on a shared nearest neighbor graph Levine et al.  2015; Xu and Su  2015. Louvain algorithm was used for modularity optimization in clustering the cells using \u201cFindClusters\u201d function. The resolution parameter res = 0.5 that determines the granularity of the clustering was selected by visually inspecting clusters with resolutions ranging 0.1   2.0 as well as clustree graphs Zappia and Oshlack  2018. Uniform Manifold Approximation and Reduction UMAP was used for non linear dimensional reduction of the first 50 principal components and visualize the data using \u201cRunUMAP\u201d function Becht et al.  2018. Data was graphed using different plot functions  such as \u201cDimPlot\u201d  \u201cVlnPlot\u201d  \u201cFeaturePlot\u201d  \u201cDotplot\u201d and \u201cDoHeatmap\u201d  to view the cell cluster identity and marker gene expression. Cell proportion data was extracted using \u201ctable\u201d and \u201cprop.table\u201d functions. Differential gene expression for individual cluster was identified using Wilcoxon rank sum test in the \u201cFindAllMarkers\u201d function. Marker genes detected in at least 25% of the clustered cells and with a logFC threshold of 0.25 were selected. The clusters of interest were subclustered using the \u201csubset\u201d function for subcluster analysis. The subset was again normalized and scaled using \u201cSCTransform\u201d function with glmGamPoi method Ahlmann Eltze and Huber  2021. Fifty principal components were used  and the resolution parameter was set to 0.5. Further downstream analysis was done as described above for the integrated analysis. Assembly: GRCz11 Supplementary files format and content: Compressed tab separated files and matrix files", "Spinal Cord", null, "For snRNA seq  30 \u00b5l of resuspension solution containing isolated nuclei at a concentration of 1000 nuclei/\u00b5l was submitted to sequencing.Two biological replicates of each timepoints at 0  1  3 and 6 wpi were used. cDNA was prepared post the GEM generation and barcoding  followed by the GEM RT reaction and bead cleanup steps.  cDNA was amplified for 11 13 cycles then purified using SPRIselect beads. Purified cDNA samples were then run on a Bioanalyzer to determine the cDNA concentration. GEX libraries were prepared as recommended by the 10x Genomics Chromium Single Cell 3\u2019 Reagent Kits User Guide v3.1 Chemistry Dual Index with appropriate modifications to the PCR cycles based on the calculated cDNA concentration. For sample preparation on the 10x Genomics platform  the Chromium Next GEM Single Cell 3\u2019 Kit v3.1  16 rxns PN 1000268  Chromium Next GEM Chip G Single Cell Kit  48 rxns PN 1000120  and Dual Index Kit TT Set A  96 rxns PN 1000215 were used. The concentration of each library was accurately determined through qPCR utilizing the KAPA library Quantification Kit according to the manufacturer\u2019s protocol KAPA Biosystems/Roche to produce cluster counts appropriate for the Illumina NovaSeq6000 instrument. Normalized libraries were sequenced on a NovaSeq6000 S4 Flow Cell using the XP workflow and a 50x10x16x150 sequencing recipe according to manufacturer protocol. A median sequencing depth of 50 000 reads/cell was targeted for each Gene Expression Library.", null, "tissue:Spinal Cord|genotype:Wild type|treatment:Injured", "GSM7501739", "GSM7501739: EKAB Spinal Cord 6 wpi rep2; Danio rerio; RNA Seq", "GSM7501739 r1", "GSM7501739", "1", "For snRNA seq  30 \u00b5l of resuspension solution containing isolated nuclei at a concentration of 1000 nuclei/\u00b5l was submitted to sequencing.Two biological replicates of each timepoints at 0  1  3 and 6 wpi were used. cDNA was prepared post the GEM generation and barcoding  followed by the GEM RT reaction and bead cleanup steps.  cDNA was amplified for 11 13 cycles then purified using SPRIselect beads. Purified cDNA samples were then run on a Bioanalyzer to determine the cDNA concentration. GEX libraries were prepared as recommended by the 10x Genomics Chromium Single Cell three prime Reagent Kits User Guide v3.1 Chemistry Dual Index with appropriate modifications to the PCR cycles based on the calculated cDNA concentration. For sample preparation on the 10x Genomics platform  the Chromium Next GEM Single Cell three prime Kit v3.1  16 rxns PN 1000268  Chromium Next GEM Chip G Single Cell Kit  48 rxns PN 1000120  and Dual Index Kit TT Set A  96 rxns PN 1000215 were used. The concentration of each library was accurately determined through qPCR utilizing the KAPA library Quantification Kit according to the manufacturer's protocol KAPA Biosystems/Roche to produce cluster counts appropriate for the Illumina NovaSeq6000 instrument. Normalized libraries were sequenced on a NovaSeq6000 S4 Flow Cell using the XP workflow and a 50x10x16x150 sequencing recipe according to manufacturer protocol. A median sequencing depth of 50 000 reads/cell was targeted for each Gene Expression Library.", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP444940", null, "loader:fastq load.py", "MLAG-EKAB_6wpi-EKAB_6wpi_060222-lib1_S1_L004_R2_001.fastq.gz MLAG-EKAB_6wpi-EKAB_6wpi_060222-lib1_S1_L004_R1_001.fastq.gz", "fastq fastq", 105391161150.0, 592085175.0, "GSM7501739 r1", "0:28 1:150", "A:35062246811;C:19059207172;G:22616069136;T:28650968975;N:2669056", 28, 150, null, null, 35062246811, 19059207172, 22616069136, 28650968975, 2669056, "SRX20731837", "SRS18022738", "SRA1659265", "Mokalled Lab, Developmental Biology, Washington University in St. Louis", "Developmental Biology, Washington University in St. Louis", 2, 0.00879, 0.72202, 0.00538, 0.51574, 0.99452, 0.83798, 0.28129, 0.53924, 28, 150, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "cdna_unspecified", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-06-20", "Undetermined", "Adult", "Spinal Cord", "Nervous 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": ["76472"], "units": {}, "query_ms": 10.286037999321707}