The cellular response to extracellular vesicles is dependent on their cell source and dose
Data files
May 19, 2023 version files 27.59 MB
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CSRmousenormdata.Rdata
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human_CSR_raw_count.txt
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Raw_figure_data.xlsx
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README.md
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Abstract
Extracellular vesicles (EV) have been established to play important roles in cell-cell communication and have shown promise as therapeutic agents. However, we still lack a basic understanding of how cells respond upon exposure to EVs from different cell sources at various doses. Thus, we treated fibroblasts with EVs from twelve different cell sources at doses between 20 and 200,000 per cell, analyzed their transcriptional effects, and functionally confirmed the findings in various cell types in vitro, and in vivo using single-cell RNA-sequencing. Unbiased global analysis revealed EV dose to have a more significant effect than cell source, such that high doses downregulated exocytosis and upregulated lysozomal activity. However, EV cell source-specific responses were observed at low doses, and these reflected the activities of the EV’s source cells. Finally, we assessed EV-derived transcript abundance and found that immune cell-derived EVs were most associated with recipient cells. Together, this study provides important insight into the cellular response to EVs.
EV uptake experiments
Five thousand human primary fibroblasts (NIGMS Human Genetic Cell Repository #GM08402) were seeded into flat bottom 96-well plates and incubated with 90ul full medium with 10ul PBS-HAT buffer containing 1e5, 1e6, 1e7, 1e8 or 1e9 EVs for 24 hours. Cells were then washed twice with PBS and analyzed.
RNA-sequencing
Cell or EV RNA was extracted (32) and precipitated as previously described (3) by incubating 500ul TRI Reagent (Sigma), adding 100ul chloroform, and shaking vigorously. After a 15 minute incubation, samples were centrifuged at 12,000 x g for 15 minutes at 4C and 300ul of aqueous phase was mixed with 300ul isopropanol, 30ul of 3M sodium acetate, and 1ul Pellet Paint (Merck) and incubated overnight at –20C. The next morning, samples were centrifuged at 20,000 x g for 30 minutes at 4C, the pellets were washed two times with 700ul of 70% ethanol, before drying and resuspending in 15uL elution buffer (Qiagen). RNA concentrations were measured using Qubit RNA High Sensitivity Assay (Thermo Fischer Scientific) and 2ng were used to generate full length cDNA by Smart-seq2, which utilizes an oligo dT primer (23). 50bp single end reads were sequenced on a HiSeq3000 (Illumina), converted to fastq using bcl2fastq, adapters trimmed using Trim Galore, and the resulting reads aligned to the ENSEMBL human transcriptome GRCh37 or ENSEMBL mouse transcriptome GRCm39 using Tophat 2.1.1. To generate the normalized count matrix, DEseqDataSetFromMatrix and estimateSizeFactors were applied from the DEseq2 package in R (30).
Clustering, differential expression and gene ontology, and overlap enrichment analysis
Variable genes above six normalized counts and transcriptome mapping was performed as previously described (28,29). Briefly, Euclidian distances between samples were derived from tSNE analysis using the Rtsne package and maps were assembled by applying force directed connections between each sample and its 25 nearest neighbors for Figure 1-2 and 40 nearest neighbours for Figure 4. Infomap clustering and visualizations were produced using the igraph package in R. Differential expression between groups indicated was performed using the Deseq2 package in R. Genes with an adjusted p-value below 0.05 were separated based on whether they were upregulated or downregulated and analyzed using panther.org complete biological processes statistical overrepresentation test version 10.5281/zenodo.4081749, released 2020-10-09. A control gene set was constructed from all up- and downregulated genes graphed together and the fold enrichments displayed for each individual group were calculated by dividing their enrichment by that of the same term in the control group. Gene overlap enrichment was performed as: (# overlapping genes)/(# genes in group one)*(# genes in group two)(29)
R and R Studio, with required packages listed in the scripts.
