run_metadata: 36248

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36248	SRR33873805	SRX29085352	SRS25297140	SRP590547	PRJNA1272846	Organism wide contributions to systemic skeletal muscle repair in larval zebrafish.	GSE299146	Transcriptome Analysis	We developed a systemic muscle injury model in zebrafish. Single cell transcriptomic analysis of muscle and non muscle tissues revealed that systemic and local muscle injuries elicit distinct cellular molecular responses both quantitatively and qualitatively. These studies suggest that large and small scale muscle injuries activate different regenerative programs resulting in either systemic or local repair. Overall design: To characterize the regenerative responses and signaling pathways activated post systemic or local muscle injury we generated Tgactc1b:NTR mCherry transgenic zebrafish lines.At 4 dpf transgenic larvae were treated with MTZ overnight to systemically injure all myofibers or were subjected to local mechanical injury needlestick of 2 3 somites.Uninjured and systemically injured larvae were collected at 1 2 and 4 xxx post injury. Mechanically injured larvae were collected at 2 xxx post injury. Sequencing results were compared between uninjured systemically injured and mechanically injured siblings.				larval trunk 1dpi Negative Control	GSM9034427		source name:Trunk\|tissue:Trunk\|genotype:Tgactc1b:NTR mCherry\|treatment:Fish Water\|batch:11/4/2024\|geo loc name:missing\|collection date:missing	larval trunk 1dpi Negative Control	post sequencing the Illumina output was processed using the CellRanger v8.0.1 pipeline to generate gene barcode count matrices. A custom reference genome was made with the “cellranger mkref” 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 “cellranger count” 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 “cellranger count” was used for downstream analysis. Datasets were integrated and analyzed using Seurat v4.4.0 package with R v4.4.2Stuart etal. 2019; Team 2024. 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 usingcommonly used QC matrixes Ilicic et al. 2016. Cells having a unique number of genes between 200 and 6500 mitochondrial gene percentage <5 and total number of reads UMIs between 300and 35000 were used for downstream processing. Each dataset was independently normalizedand scaled using the “SCTransform” function which is an improved method for normalization that performs a variance stabilizing transformation using negative binomial regression Hafemeisterand 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; Haghverdiet al. 2018. Integration features were selected based on the top 6000 highly variable features using “SelectIntegrationFeatures” function nfeatures = 6000 which was used as input for the“anchor.features” argument of the “FindIntegrationAnchors” function. PCA analysis was performed on the 6000 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 “FindNeighbors” and “FindClusters” 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 cell clustering using “FindClusters” function. The resolution parameter res = 0.3 that determines the granularity of clusteringwas selected by visually inspecting clusters with resolutions ranging between 0.1 and 2.0 as well as clustree graph 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 “RunUMAP” function Becht et al. 2018. Data was graphed using different plot functions such as “DimPlot” “VlnPlot” “FeaturePlot” “Dotplot” and “DoHeatmap” to view the cell cluster identity and marker gene expression. Cell proportions were extracted using the “table” and “prop.table” functions. Differential gene expression for individual clusters was identified using Wilcoxon rank sum test in the “FindAllMarkers” function. Marker genes detected in at least 25% of the clustered cells and had a positive average log2FC were reported. Muscle cells identified from the complete dataset were subclustered using the “subset” function for subcluster analysis. The muscle subset was again normalized and scaled using the “SCTransform” function with glmGamPoi method Ahlmann Eltze and Huber 2021. Fifty principal components were used and the resolution parameter was set to 0.4. Downstream analysis was done as described above for the integrated analysis. We used a zebrafish single atlas Sur et al. 2023 to generate a database of markers for each tissue and cell type Table S1. The differentially expressed markers of each cluster were crossreferenced with our compiled database using our “DE Marker Scoring” algorithm Saraswathy et al. 2024. For every matching marker gene one point was given to the respective cluster under the column name with matching cell identity. Iteration over every marker gene was performed to generate a scoring matrix with varying points for each cluster against the different cell identities compiled in the database Table S2 sheet: scoring. The “phyper” function in R was then used to calculate one tailed binomial probabilities using hypergeometric distribution for the total score obtained from each cluster against each cell identity in the database Table S2 sheet: Binomial probability. −log10 of probability values were obtained for plotting the heatmap Table S2 sheet:−log10P. The resulting values were scaled from 0 to 100 and plotted as a heatmap using GraphPad prism. Each cluster was given an identity based on the maximum −log10 p score obtained in the heatmap. The top DE markers of clusters with ambiguous scores were manually annotated using the “ident genes” from the zebrafish single atlas Sur et al. 2023 and literature search McKellar et al. 2021. Top DE markers generated for each cluster is given in Table S3 sheet:topDEmarkers.all.clusters “RenameIdents” function was used to assign identity to each cluster. To confirm the assigned cluster identities enrichment of classical markers of respective cell types were tested using Dot plot. The R package CellChat v2.1.2 was used to evaluate regenerative cell cell interactions post local and systemic muscle injury Jin et al. 2021. CellChat models the probability of cell cell communication by integrating our gene expression data with a database of known interaction between signaling ligands receptors and their cofactors CellChatDB. The RNA data was used to create CellChat object using “createCellChat” function followed by the recommended preprocessing functions with default parameters for the analysis of individual datasets. Truncated mean method with 10% trimmed observation was used to compute average gene expression per cell group for the complete dataset. The default trimean method was used to compute average gene expression for the CellChat analysis. CellChatDB.zebrafish was used to infer cell cell communication. All categories of ligand receptor interactions in the database were used in the analysis. Communications involving less than 10 cells were excluded. The “netAnalysis computeCentrality” function was used to calculate network centrality scores at each time point. Functions such as “netVisual circle” “netAnalysis contribution” “netVisual aggregate” “netVisual bubble” “netAnalysis signalingRole heatmap” and “netAnalysis signalingRole scatter” were used to generate different plots used in this paper. Assembly: zebrafish reference genome GRCz11 and the sorted Gene Transfer Format file v4.3.2 from the improved zebrafish transcriptome annotation Lawson et al. 2020. Supplementary files format and content: Compressed Filtered Feature and Barcode files as .tsv file and count matrices in .mtx file format.	Trunk	Fish were treated with 12 hours of metronidazole or were stuck locally with a needle	15 larvae per cohort were dissected to remove the head caudal fin and internal organs and then processed as described Farnsworth DB 459 2020. For tissue lysis 500μL of dissociation buffer 2ug/ul Collagenase P 2mM CaCl2 0.2 ul DNaseI 0.25% Trypsin in 1X PBS was added to the samples and incubated at 28 °C until samples were visually dissociated 15min. Dissociation was stopped with equal volume of stop buffer 10% FBS 0.001mM EDTA in 1X PBS. Samples were centrifuged at 750rcf for 7min at 4°C the supernatant was discarded and the pellet was resuspended in 100μL of resuspension buffer 1% FBS 2mM CaCl2 1X Penicillin/Streptomycin in DMEM. The suspension was strained through 20μm cell strainer Pluriselect USA 43 10020 40 centrifuged once 500rcf 1 min 4°C to pass cells through the strainer and centrifuged a second time 750rcf 7 min 4°C to pellet the cells. The cells were resuspended in 100μL of resuspension buffer centrifugated 750rcf 7min 4°C and the pellet was resuspended in 50μL 0.04% BSA in PBS. 5μl of cells were then combined with 5μl of Hoechst Invitrogen H3570 incubated for 10min and transferred to a hemocytometer Bulldogbio NC1731934 to determine cell concentration. An additional 5μl of cells were incubated with Trypan blue Sigma T8154 for 5min transferred to a hemocytometer and counted to assess cell viability. Samples with > 70% viability were then submitted for single cell sequencing. For snRNA seq 30 µl of isolated nuclei at a concentration of 1000 nuclei/µl was submitted to Genome Technology Access Center at McDonnel Genome Institute of Washington University. Two biological replicates of each genotype 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’ 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’ 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.	Larval zebrafish of 4 dpf underwent drug treatments or needle stick injuries for muscle ablation	tissue:Trunk\|genotype:Tgactc1b:NTR mCherry\|treatment:Fish Water\|batch:11/4/2024	GSM9034427	GSM9034427: larval trunk 1dpi Negative Control; Danio rerio; RNA Seq	GSM9034427 r1	GSM9034427	1	15 larvae per cohort were dissected to remove the head caudal fin and internal organs and then processed as described Farnsworth DB 459 2020. For tissue lysis 500μL of dissociation buffer 2ug/ul Collagenase P 2mM CaCl2 0.2 ul DNaseI 0.25% Trypsin in 1X PBS was added to the samples and incubated at 28 °C until samples were visually dissociated 15min. Dissociation was stopped with equal volume of stop buffer 10% FBS 0.001mM EDTA in 1X PBS. Samples were centrifuged at 750rcf for 7min at 4°C the supernatant was discarded and the pellet was resuspended in 100μL of resuspension buffer 1% FBS 2mM CaCl2 1X Penicillin/Streptomycin in DMEM. The suspension was strained through 20μm cell strainer Pluriselect USA 43 10020 40 centrifuged once 500rcf 1 min 4°C to pass cells through the strainer and centrifuged a second time 750rcf 7 min 4°C to pellet the cells. The cells were resuspended in 100μL of resuspension buffer centrifugated 750rcf 7min 4°C and the pellet was resuspended in 50μL 0.04% BSA in PBS. 5μl of cells were then combined with 5μl of Hoechst Invitrogen H3570 incubated for 10min and transferred to a hemocytometer Bulldogbio NC1731934 to determine cell concentration. An additional 5μl of cells were incubated with Trypan blue Sigma T8154 for 5min transferred to a hemocytometer and counted to assess cell viability. Samples with > 70% viability were then submitted for single cell sequencing. For snRNA seq 30 µl of isolated nuclei at a concentration of 1000 nuclei/µl was submitted to Genome Technology Access Center at McDonnel Genome Institute of Washington University. Two biological replicates of each genotype 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.		RNA-Seq	TRANSCRIPTOMIC SINGLE CELL	cDNA	PAIRED	ILLUMINA	Illumina NovaSeq 6000		SRP590547			LIB065814-DIL01_22CNHTLT4_S90_L008_R1_001.fastq.gz LIB065814-DIL01_22CNHTLT4_S90_L008_R2_001.fastq.gz	fastq fastq	96199623993.0	537428067.0	GSM9034427 r1	0:28 1:151	A:26836117761;C:21626512077;G:22419696915;T:25306734573;N:10562667	28	151			26836117761	21626512077	22419696915	25306734573	10562667	SRX29085352	SRS25297140	SRA2144659	Johnson Lab, Developmental Biology, Washington University in St.Louis	Johnson Lab, Developmental Biology, Washington University in St.Louis	2	0.01026	0.90959	0.00375	0.1621	0.99019	0.80945	0.41304	0.54052	28	151	T	B	sc-like readlen	illumina	novaseq_era	unknown	cdna_unspecified	unknown	sc	single_cell_droplet	10x		United States	2025-06-06	Larval	Larval	Trunk	Surface Structure