反褶积
电池类型
生物信息学
胞外囊泡
细胞外小泡
计算生物学
转录组
生物
细胞
生物系统
化学
细胞生物学
计算机科学
微泡
基因表达
生物化学
算法
小RNA
基因
作者
Jannik Hjortshøj Larsen,I Jensen,Per Svenningsen
标识
DOI:10.1101/2024.02.27.582268
摘要
Abstract Extracellular vesicles (EVs) contain cell-derived lipids, proteins, and RNAs; however, the challenge to determine the tissue- and cell type-specific EV abundances in body fluids remains a significant hurdle for our understanding of EV biology. While tissue- and cell type-specific EV abundances can be estimated by matching the EV’s transcriptome to a tissue’s/cell type’s expression signature using deconvolutional methods, a comparative assessment of deconvolution methods’ performance on EV transcriptome data is currently lacking. We benchmarked 11 deconvolution methods using data from 4 cell lines and their EVs, in silico mixtures, 118 human plasma, and 88 urine EVs. We identified deconvolution methods that estimated cell type-specific abundances of pure and in silico mixed cell line-derived EV samples with high accuracy. Using data from two urine EV cohorts with different EV isolation procedures, four deconvolution methods produced highly similar results. The four methods were also highly concordant in their tissue-specific plasma EV abundance estimates. We identified driving factors for deconvolution accuracy and highlight the importance of implementing biological knowledge in creating the tissue/cell type signature. Overall, our analyses demonstrate that the deconvolution algorithms DWLS and CIBERSORTx produce highly similar and accurate estimates of tissue- and cell type-specific EV abundances in biological fluids.
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