DOI: 10.6084/m9.figshare.28794242.v1
title: <b>Engineered Muscle-Derived Extracellular Vesicles Boost Insulin Sensitivity and Glucose Regulation</b>

description: <b>Description of the data and file structure</b>EVs were harvested from conditioned medium of scaffolds grown engineered muscle tissue.EVs and conditioned medium was analyzed by Tandam LS mass-spectrometry,EVs miRNA libraries were preordered by Qiagen qia-seq kit and sequenced by illumina with Novaseq kits.Files and variablesFile: datasets_to_upload.zip<b>Description:</b> datasets for multiomic analysis of EVs and conditioned medium from WT and overexpressing GLUT4 engineered muscles.<b>AFM _size_quantification.xlsx</b><b>Description:</b> size measurements by AFM of WT (first sheet) or G4OE (second sheet) derived EVs of pictures in 4/10 micrometer resolution.max H (m) - max height of particleprojA(m^2) - projected surface area of particlesurfA (m^2) - surface area of particleeqR (m) - adjusted radius of particleVol(m^3) - volume of particle<b>Cond_medium_proteomics.xlsx</b><b>Description:</b> values of proteins levels in conditioned medium after maxquant quantification. columns include the protein full name, uniprot id, gene symbol followed by identification parameters of each protein (converge, number of peptides and unique peptides detected, molecular weight). row levels quantification and their log2 levels correspondingly of 3 WT and GLUT4 repeats. Followed by abundancy in sample by category, number of peptides in each repeat and GO-terms enrichmant analysis.<b>EVs_proteomics.xlsx</b><b>Description:</b> values of proteins levels in EVs after maxquant quantification. columns include the protein full name, uniprot id, gene symbol followed by identification parameters of each protein (converge, number of peptides and unique peptides detected, molecular weight). row levels quantification and their log2 levels correspondingly of 3 WT and GLUT4 repeats. Followed by abundancy in sample by category, number of peptides in each repeat and GO-terms enrichmant analysis.<b>EVs_qc_boxplot_df_GLUT_log2.csv</b><b>Description:</b> Proteins found in MISEV2018 categories from G4OE derived EVs for QC of content this include category, mean level across repeats, and name of general category.<b>FC_proteomics_EVs_mat.csv</b><b>Description:</b> Fold change between WT and G4OE EVs proteins with gene name, their fold change (WT-G4OE) and the corrected FDR significance.<b>GLUT4_depleted_Terms.csv</b><b>Description:</b> Gene set enrichment analysis of proteins found to be depleted in G4OE derived EVs (higher in WT) columns represent the set by which enrichment was tested, which term, genes overlap from the term, significance and corrected FDR, the odds ratio as tested by “enrichr” , “enrichr” combind score, and genes names found in term.<b>GLUT4_enriched_Terms.csv</b><b>Description:</b> Gene set enrichment analysis of proteins found to be enriched in G4OE derived EVs (lower in WT) columns represent the set by which enrichment was tested, which term, genes overlap from the term, significance and corrected FDR, the odds ratio as tested by “enrichr”, “enrichr” combind score, and genes names found in term.<b>GLUT4_EV_miRNA_data.xlsx</b><b>Description:</b> small RNA sequencing alignment by Geneglobe of Qiaseq results in raw UMI counts of all repeats of WT and G4OE derived EVs.Summary - description of sequencing runs and alignment in general followed by general quantification of small RNA groupsmiRNA-piRNA - quantification of miRNAs and piRNAs found in each repeat of WT/G4OE derived EVstRNA - quantification of tRNAs found in each repeat of WT/G4OE derived EVsmRNA - quantification of mRNAs found in each repeat of WT/G4OE derived EVsunmappable reads that were not aligned to the genome<b>DE_miRs_EVs_GLU4_vs_WT.csv</b><b>Description:</b> differential expression analysis of WT-G4OE derived miRNAs by DEseq2. This includes miRNA name, its corrected mean expression across all repeats and conditions, the log2 fold change, and its standard deviation, the wald statistics, its p-value and the corrected FDR.<b>miRTargetLink 2.0.csv</b><b>Description:</b> list of known and predicted miRNAs targets of miRNAs, miR-16-5p, miR-122-5p and miR-486-5p as downloaded from miRTargetLink version 2.0. this includes the mRNA target, its corresponding miRNA, experimental evidence/ prediction, the paper source, the experiment checking the miRNA-mRNA relationship and the paper PMID.<b>miR_path_target_enrichment.csv</b><b>Description:</b> KEGG pathway enrichment analysis results of shared mRNA targets of miRNAs miR-16-5p, miR-122-5p and miR-486-5p ranked by their interaction score defined in our paper. this includes the pathway name, the enrichment p-value, number of genes found in the term and number of miRNAs targeting these genes<b>Code/software</b>Data were analyzed using R-V4.0.4, Python-V3.9.2 and GraphPad software. miRNA analyses were run in R-V4.0.4 Differential expression analysis was conducted using the “DEseq2” package and corrected for multiple hypotheses by FDR. Proteomic analyses were run with Python-V3.9.2 miRNA and protein figures were plotted using R