{"title":"Mapping Organism-wide Single Cell mRNA Expression Linked to Extracellular Vesicle Biogenesis, Secretion, and Cargo.","authors":"Thomas J LaRocca, Daniel S Lark","doi":"10.1093/function/zqaf005","DOIUrl":null,"url":null,"abstract":"<p><p>Extracellular vesicles (EVs) are functional lipid-bound nanoparticles trafficked between cells and found in every biofluid. It is widely claimed that EVs can be secreted by every cell, but the quantity and composition of these EVs can differ greatly among cell types and tissues. Defining this heterogeneity has broad implications for EV-based communication in health and disease. Recent discoveries have linked single-cell EV secretion to the expression of genes encoding EV machinery and cargo. To gain insight at single-cell resolution across an entire organism, we compared the abundance, variance, and co-expression of 67 genes involved in EV biogenesis and secretion, or carried as cargo, across >44 000 cells obtained from 117 cell populations in the Tabula Muris. Our analysis provides both novel holistic and cell population-specific insight into EV biology. The highest overall expression of EV genes occurs in secretory cells of the pancreas and perhaps more surprisingly, multiple non-neuronal cell populations of the brain. We find that the most abundant EV genes encode the most abundant EV cargo proteins (tetraspanins and syndecans), but these genes are highly differentially expressed across functionally distinct cell populations. Expression variance identifies dynamic and constitutively expressed EV genes while co-expression analysis reveals novel insights into cell population-specific coordination of expression. Results of our analysis illustrate the diverse transcriptional regulation of EV genes which could be useful for predicting how individual cell populations might communicate via EVs to influence health and disease.</p>","PeriodicalId":73119,"journal":{"name":"Function (Oxford, England)","volume":" ","pages":""},"PeriodicalIF":5.1000,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11931722/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Function (Oxford, England)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/function/zqaf005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"CELL BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Extracellular vesicles (EVs) are functional lipid-bound nanoparticles trafficked between cells and found in every biofluid. It is widely claimed that EVs can be secreted by every cell, but the quantity and composition of these EVs can differ greatly among cell types and tissues. Defining this heterogeneity has broad implications for EV-based communication in health and disease. Recent discoveries have linked single-cell EV secretion to the expression of genes encoding EV machinery and cargo. To gain insight at single-cell resolution across an entire organism, we compared the abundance, variance, and co-expression of 67 genes involved in EV biogenesis and secretion, or carried as cargo, across >44 000 cells obtained from 117 cell populations in the Tabula Muris. Our analysis provides both novel holistic and cell population-specific insight into EV biology. The highest overall expression of EV genes occurs in secretory cells of the pancreas and perhaps more surprisingly, multiple non-neuronal cell populations of the brain. We find that the most abundant EV genes encode the most abundant EV cargo proteins (tetraspanins and syndecans), but these genes are highly differentially expressed across functionally distinct cell populations. Expression variance identifies dynamic and constitutively expressed EV genes while co-expression analysis reveals novel insights into cell population-specific coordination of expression. Results of our analysis illustrate the diverse transcriptional regulation of EV genes which could be useful for predicting how individual cell populations might communicate via EVs to influence health and disease.