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

Abstract

Extracellular vesicles (EVs) contain cell-derived lipids, proteins and RNAs; however, determining 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 four 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 three methods were also concordant in their tissue- and cell-type-specific plasma EV abundance estimates. We identified driving factors for deconvolution accuracy and highlighted 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.

Keywords: cell‐conditioned medium; exosome; microvesicle; plasma; single‐cell RNA sequencing; transcriptome; urine.

FIGURE 1

Deconvolution accuracy across methods in pure EV samples from cell cultures. (a) Cell and EV RNA sequencing data from three prostate cancer cell lines DU145, LNCaP and PC3 (GSE183070) and a colorectal cancer (CRC, GSE121964) cell line (n = 3 cell and EV sample for each cell type). The cell RNA data was used to create a signature matrix for deconvolution of the cell‐derived EV RNA data. (b) Multidimensional scaling plot of the cell and EV samples showing cell type‐specific clustering. Circles indicate cells, and squares indicate EVs (n = 3 for each sample type). (c) Heatmaps of deconvolution results of the EV samples. The estimated EV abundance is the average of triplicates and is scaled between 0 and 1. The estimate for each sample is shown in Figure S1. The numbers are represented by the darkness of the colour, that is, 0 is white, and 1 is dark red. The upper left heatmap shows the true distribution. (d) Similarity to the true samples (upper left heatmap) is calculated as the Bray–Curtis index for all 12 samples, that is, four cell types and three replicates. The points indicate the mean Bray–Curtis index values and the error bars indicate SEM.

FIGURE 2

Deconvolution accuracy across methods in mixed DU145 and LNCaP EV samples. (a) Raw RNA sequencing reads from DU145 and LNCaP EV samples were downsampled and mixed in ratios of 100:0, 75:25, 50:50, 25:75 and 0:100. All mixed files contained 30 million reads. (b) Multidimensional scaling plots of true (square) and in silico mixed (circle) RNA sequencing reads show ratio‐specific clustering (n = 3 for each ratio). (c) DWLS deconvolution of DU145 and LNCaP EV samples estimates the in silico mixing ratio accurately. R indicates the Pearson correlation coefficient. The deconvolution estimates with the remaining 10 methods are shown in Figure S5a. (d) Raw RNA sequencing reads from DU145 and LNCaP EV samples were downsampled and mixed in ratios of 0:100, 0.5:99.5, 1:99, 2.5:97.5, 5:95, 7.5:92.5, 10:90, 90:10, 92.5:7.5, 95:5, 97.5:2.5, 99:1, 99.5:0.5 and 100:0. DWLS convolution was most accurate in estimating the low abundance in silico mixing ratios of DU145 and LNCaP EVs. R indicates the Pearson correlation coefficient. The deconvolution estimates of the remaining 10 methods are shown in Figure S5b. (e) Summary of Pearson correlation coefficients and Bray–Curtis index for deconvolution of DU145 and LNCaP in silico mixed EV samples for the 11 transcriptome deconvolution methods. The metrics are colour‐coded, as indicated in the legend.

FIGURE 3

Deconvolution accuracy of complex mixtures of DU145, LNCaP and PC3 EV samples. (a) Raw sequencing reads from DU145, LNCaP and PC3 EV samples were downsampled and mixed to yield 12 different samples with cell type‐specific EV content of 0%, 12.5%, 25%, 37.5% and 50%. (b) Compared to the true samples, DWLS, DWLSj, CIBERSORTx, DWLS OLS and DWLS SVR deconvolution methods accurately estimated the cell type‐specific EV content. The estimated EV abundance is the average of three in silico mixes. For visualization, the samples are ordered to show mixtures with an expected increase in the cell type‐specific EV content. Graphs with unsorted samples are shown in Figure S6.

FIGURE 4

Effect of missing cell types in the signature matrix. (a) In silico mixed EV samples were created by downsampling and mixing RNA reads from DU145 and physically mixed adipocyte and macrophage EV samples (GSE94155) in ratios of 100:0, 75:25, 50:50, 25:75 (n = 3 for each ratio). The samples were deconvoluted using the transcript signatures for DU145, LNCaP, PC3 and CRC cells. (b) The adipocyte and macrophage EV RNA did not significantly affect the cell type‐specific EV fraction estimated by DWLS. (c) Similarly, the cell‐specific EV estimated of the mixed samples were not significantly affected by adipocyte and macrophage EV RNA for DWLSj, CIBERSORTx, DWLS OLS, DWLS SVR, EV‐origin, dtangle and MuSiC deconvolution methods; however, the presence of 50% or more adipocyte and macrophage EV RNA changed cell type‐specific EV estimates for Bisque, DeconRNASeq and MuSiC nnls deconvolution methods.

FIGURE 5

Benchmarking the deconvolution accuracy for cell line‐derived EV samples across methods. (a) Overall benchmarking scores of DWLS deconvolution of the pure, the downsampled, and the in silico mixed EV samples with expected cell types‐specific EV content > or ≤10%, respectively, and complex mixtures. RMSE indicates root mean square error. Correlations for the deconvolution of the in silico mixed DU145 and LNCaP EV samples are shown as 1‐PCC, where PCC indicates the Pearson Correlation Coefficient. (b) Overall benchmarking of the remaining 10 transcriptome deconvolution methods.

FIGURE 6

Deconvolution of tissue‐ and kidney‐cell type‐specific EV abundances in urine across methods. (a) Deconvolution of urine EV samples from the exoRbase cohort (n = 16 samples) by the 11 deconvolution methods. The deconvolution methods’ estimated tissue/organ source is colour‐coded according to the legend. (b) Pearson correlation coefficients (R² indicated by colour) between the 11 deconvolution methods’ estimates of tissue‐specific urine EV abundance. (c) Estimated tissue‐specific EV abundance and (d) Pearson correlation coefficients (R² indicated by colour) between the 11 deconvolution methods’ estimates of the urine EVs from the Dwivedi et al. (2023) cohort (n = 72). (e) Heatmaps showing the estimated abundances of kidney epithelial cell type‐specific urine EV abundances in the exoRbase (top) and Dwivedi (bottom) cohorts by the 11 deconvolution methods. The estimated EV abundance is the average of all samples in the respective cohorts and is scaled between 0 and 1. The numbers are represented by the darkness of the colour, that is, 0 is white and 1 is dark orange. (f) Correlation between the DWLS method's deconvolution of urine samples from the exoRbase and Dwivedi cohorts. R indicates the Pearson correlation coefficient. The correlations for the remaining 10 deconvolution methods are shown in Figure S11. PT, proximal tubule; PC, principal cell; IC, intercalated cell; DCT, distal convoluted tubule; cTAL, cortical thick ascending limb of the loop of Henle's; CNT, connecting tubule.

FIGURE 7

Deconvolution of tissue‐specific EV abundances in plasma across methods. (a) Estimated tissue‐specific plasma EV abundance of plasma EV samples (n = 118, GEO accession numbers GSE100206 and GSE133684) by the 11 deconvolution methods. The tissues/organs are colour‐coded according to the legend. (b) Correlation between the individual person's tissue‐specific plasma EV abundance estimated by DLWS and (top panel) DWLSj, (middle panel) CIBERSORTx and (bottom panel) DWLS OLS. R indicates the Pearson correlation coefficient. (c) Heatmap showing the estimated abundances of peripheral blood mononuclear cells‐derived plasma EVs by the 11 deconvolution methods. The estimated EV abundance is the average of all samples in the cohorts and is scaled between 0 and 1. The numbers are represented by the darkness of the colour, that is, 0 is white and 1 is dark red. The correlation between the 11 deconvolution methods estimates is shown in Figure S13.