{"database": "metadata", "table": "run_metadata", "rows": [[29186, "SRR27292315", "SRX22969958", "SRS19936059", "SRP479046", "PRJNA1054617", "Modeling Neutrophil Heterogeneity Contribution to Burn Healing in Larval Zebrafish", "GSE250610", "Transcriptome Analysis", "Neutrophils accumulate early in burn wounds  and their activation is associated with more severe burns. Understanding their functionality would facilitate the development of a more targeted therapeutic strategy for healing. However  we still lack a view of the cellular and functional heterogeneity in neutrophils involved in thermal injuries. Here  we establish the use of larval zebrafish for understanding neutrophil responses in burn. Zebrafish model allows for linking neutrophil states and their functions in real time through genetic modifications and live imaging. We performed single cell transcriptional scRNA Seq mapping of myeloid cells during a 3 day time course in burn and unwounded conditions. We identified transcriptionally distinct states in myeloid cells that form a consistent population structure across time points and conditions. By comparing burn and unwounded conditions  we found subtype specific enrichment of biological processes and differential usage of gene regulatory networks. Pseudotime and RNA velocity analyses predict distinct branched trajectories for neutrophils  with one branch resembling the process of human neutrophil maturation. The other branch is not transcriptionally conserved with humans and is highly associated with leukocyte migration functionality  suggesting its engagement at the wound site. Transcriptional network analysis identified RAR/RXR family transcription factors as potential upstream factors driving this trajectory divergence in neutrophils. Furthermore  we characterized the transcriptional dynamics of cell cell interactions in both conditions by time. Among burn induced signaling pathways  we found il6 il6r signaling as a time point specific macrophage neutrophil interaction  suggesting the importance of timing in innate immune response in burn. Finally  to test the translational value of our fish model  we examined zebrafish neutrophil state signatures in human burn patient samples. We found homolog expression of immature neutrophils positively correlates with the degree of total body surface area TBSA in patients. Flow cytometry confirmed the presence of neutrophils carrying these signatures in patient's blood. Our findings demonstrate the potential of using zebrafish as a model to identify early innate immune response signatures that could inform a timely treatment of burn in humans. This work builds the molecular foundation and a comparative single cell genomic framework to guide future identification of actionable pathways to burn wound healing in patients. Overall design: At 6/24/48 hour post burn hpb  burned and unwounded larvae at matching developmental stages 3/4/5 dpf dpf were transferred to 35 mm dish containing calcium free PBS for 15 min and then anesthetized with tricaine methanesulfonate MS222  200 mg/L. A total of 150 fish were used for each time point by condition. Each dish of fish was digested with 2 mL digestion solution 0.25% trypsin  1mM EDTA in PBS at 28.5\u00b0C for 90 min with gentle pipetting every 10 min. Digestion was stopped by adding 200 mL digestion stop solution 1mM CaCl2  100% FBS. Dissociated cells were filtered with 40 mm cell strainers and centrifuged for 3 min at 3 000 rpm at 4 \u00b0C. Cell pellets were resuspended in PBS with 10% FBS plus DAPI 1mg/mL. Fluorescence activated cell sorting FACS was performed at University of Wisconsin Carbone Cancer Center Flow Lab using BD FACSAria. Targeted cells passed gating for cells versus debris  singles versus doublets  live versus dead and were high in either 488 nm/561 nm channel. Both dendra+ 488 nm channel and mCherry+ 561 nm channel cells were sorted into PBS with 10% FBS. Post sorting  cells were directly used for library construction in a Chromium controller at the Gene Expression Center of the University of Wisconsin Madison Biotechnology Center RRID: SCR 017757. Samples were processed with Chromium Single Cell Gene Expression Solution three prime v2 10X Genomics.", null, "pubmed:38922186", null, "Unwounded Larval Zebrafish  3 dpf", "GSM7982881", null, "source name:Whole blood|tissue:Whole blood|cell type:Macrophage|cell type:Neutrophil|genotype:WT|treatment:Unwounded|geo loc name:missing|collection date:missing", "Unwounded Larval Zebrafish  3 dpf", "FASTQ files were aligned to Zebrafish genome using cellranger version v3.1.1 Unwounded 4days  Burn 24 hours  v6.1.2 Unwounded 3days  Unwounded 5days  Burn 6hours  Burn 48hours UMI cutoffs per droplet were calcuated from cellranger and filtered feature matrices were used to generate a Seurat object V4 Assembly: GRCz11 danRer11 Supplementary files format and content: RNA count matrix", "Whole blood", "Larval zebrafish were wounded on the tail fin region at 3 days post feritlization using a handheld cauterizer.", "Dissociated cells were sorted using fluorescence activated cell sorting using BD FACSAria to separate neutrophils and macrophages. Cells were then loaded into the 10x chromium controller for GEM encapsulation and reverse transcription per manufacturer's instructions. Library construction was performed per 10x instructions with three prime v2 chemistry Each prepared library was sequenced on the NovaSeq using dual index sequencing", "Zebrafish embroys were collected from adult Tgmpx:dendra mpeg1.1:mCherry fish and kept in E3 medium with 1% methylene blue inside a 28.5\u02daC incubator.", "tissue:Whole blood|cell type:Macrophage|cell type:Neutrophil|genotype:WT|treatment:Unwounded", "GSM7982881", "GSM7982881: Unwounded Larval Zebrafish  3 dpf Danio rerio; RNA Seq", "GSM7982881 r1", "GSM7982881", "1", "Dissociated cells were sorted using fluorescence activated cell sorting using BD FACSAria to separate neutrophils and macrophages. Cells were then loaded into the 10x chromium controller for GEM encapsulation and reverse transcription per manufacturer's instructions. Library construction was performed per 10x instructions with three prime v2 chemistry Each prepared library was sequenced on the NovaSeq using dual index sequencing", null, "RNA-Seq", "TRANSCRIPTOMIC SINGLE CELL", "cDNA", "PAIRED", "ILLUMINA", "Illumina NovaSeq 6000", null, "SRP479046", null, "loader:fastq load.py", "WT-3dpf_S4_L002_R1_001.fastq.gz WT-3dpf_S4_L002_R2_001.fastq.gz", "fastq fastq", 7258692168.0, 62040104.0, "GSM7982881 r2", "0:28 1:89", "A:2013529524;C:1617959197;G:1704796898;T:1922004726;N:401823", 28, 89, null, null, 2013529524, 1617959197, 1704796898, 1922004726, 401823, "SRX22969958", "SRS19936059", "SRA1772557", "University of Wisconsin-Madison", "University of Wisconsin-Madison", 2, 0.00949, 0.92207, 0.00359, 0.18081, 0.99375, 0.83751, 0.37347, 0.55889, 28, 89, "T", "B", "sc-like readlen", "illumina", "novaseq_era", "unknown", "random_priming", "unknown", "sc", "single_cell_droplet", "10x", null, "United States", "2023-12-19", "Multi-stage", "Multi-stage", "Blood", "Hematopoietic 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": ["29186"], "units": {}, "query_ms": 8.979128004284576}