Benchmarking transcriptional deconvolution methods for estimating tissue- and cell type-specific extracellular vesicle abundances

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 EVs transcriptome to a tissues/cell types 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.