DOI: 10.5061/dryad.6hdr7sr5z
title: Data for: Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding

description: The clinical potential of miRNA-based liquid biopsy has been largely
 limited by the heterogeneous sources in plasma and tedious assay
 processes. Here we develop a precise and robust one-pot assay called
 dual-surface-protein-guided orthogonal recognition of tumor-derived
 exosomes and in-situ profiling of microRNAs (SORTER) to detect
 tumor-derived exosomal miRNAs and enhance the diagnostic accuracy of
 prostate cancer (PCa). The SORTER utilizes two allosteric aptamers against
 exosomal marker CD63 and tumor marker EpCAM to create an orthogonal
 labeling barcode and achieve selective sorting of tumor-specific exosome
 subtypes. Furthermore, the labeled barcode on tumor-derived exosomes
 initiated targeted membrane fusion with liposome probes to import miRNA
 detection reagents, enabling in-situ sensitive profiling of tumor-derived
 exosomal miRNAs. With a signature of six miRNAs, SORTER differentiated PCa
 and benign prostatic hyperplasia with an accuracy of 100%. Notably, the
 diagnostic accuracy reached 90.6% in the classification of metastatic and
 non-metastatic PCa. We envision that the SORTER will promote the clinical
 adaptability of miRNA-based liquid biopsy.
description: Mean, SD, and LOD were
 calculated with standard formulas. Significance tests were obtained via a
 two-tailed Student’s t-test. The intensities of individual miRNA markers
 detected by the SORTER approach used min-max normalization. The PCa
 signature was calculated as the weighted sum of the normalized intensities
 of six miRNA markers by LDA, respectively. For binary classification, P
 values for pairwise comparisons were performed using a nonparametric,
 two-tailed Mann-Whitney U test. For ternary classification, the overall
 and group pair P values were determined using Kruskal-Wallis one-way ANOVA
 with post hoc Dunn’s test for pairwise multiple comparisons. Hierarchical
 clustering was performed for the analysis markers using the “pheatmap”
 package in the R language. ROC analyses were constructed for individual
 markers or marker combinations to evaluate the AUC, sensitivity and
 specificity, and accuracy of cancer diagnosis. The training cohort (n =
 42) was first analyzed to generate the discriminant function model, which
 was used to classify the patients in the validation cohort (n = 32). The
 optimal cutoff points were selected using Youden’s index based on the
 training cohort, which was applied to evaluate the sensitivity,
 specificity, and accuracy of the validation cohort. The t-distributed
 stochastic neighbor embedding (t-SNE) was performed using six markers as
 the input for binary classification (PCa and BPH). 
description: All statistical analyses were
 performed at 95% (P &lt; 0.05) CIs using OriginPro 2018, GraphPad
 Prism (v.8.0), and R software (version 4.1.2).
description: # Simultaneous subset tracing and miRNA profiling of tumor-derived
 exosomes via dual-surface-protein orthogonal barcoding [Article,
 https://doi.org/10.1126/sciadv.adi1556; Preprint,
 https://doi.org/10.21...3/rs.3.rs-2404819/v1] ## Description of the data
 and file structure (1) Fig.3B-Fluorescence\_spectra\_analysis.csv This
 document is the fluorescence spectra analysis of the orthogonal fusion
 between Tags-Lipo-DiO-DiI and 1× and 10× molar ratios Orth-Exo. The
 negative control is the stochastic fusion of Lipo-DiO-DiI and Exo.
 Tags-Lipo-DiO-DiI represents DNA tag-anchored liposomes (Tags-Lipo)
 double-labeled with the donor of 3,3’-dioctadecyloxacarbocyanine
 perchlorate (DiO, 501 nm) and the acceptor of 1,1’-dioctadecyl-3,3,3’,3’
 tetramethylindocarbocyanine perchlorate (DiI, 565 nm). Orth-Exo represents
 orthogonal barcode-anchored exosomes. (2)
 Fig.3C-Fluorescence\_kinetic\_analysis.csv This document is the
 fluorescence kinetic analysis of the target fusion between
 Tags-Lipo-DiO-DiI and Orth-Exo. The negative control is the stochastic
 fusion of Lipo-DiO-DiI and Exo. (3) Fig.3D-Fusion\_mixing\_analysis.csv
 This document is the fusion mixing analysis of Tags-Lipo-DiO-DiI and
 Orth-Exo at different temperatures. The negative control is the stochastic
 fusion of Lipo-DiO-DiI and Exo. (4)
 Fig.3F-Diameters\_of\_the\_fusion\_products.csv This document is the
 diameters of the fusion products determined by the dynamic light
 scattering method at different time intervals. (5)
 Fig.4B-Analysis\_of\_miR-21\_expression.csv This document is the
 fluorescence intensity analysis of miR-21 expression in orthogonal
 barcode-based benign prostatic hyperplasia cell (BPH-1) or PCa cells
 (LNCaP)-derived exosomes after incubation with DNA tag-anchored liposome
 probes (tag-Lipo@Au NFs) and Lipo@Au NFs, respectively. (6)
 Fig.4E-Quantifying\_exosomal\_miR-21.csv This document is the calibration
 curves for quantifying LNCaP-derived exosomal miR-21 spiked in PBS and
 extracellular vesicles (EVs)-depleted plasma (diluted by 100-folds in
 1×PBS). (7) Fig.4F-\_Expression\_levels\_of\_six\_miRNAs.csv This document
 is the radar plot for six miRNA markers from the four cell lines-derived
 exosomes, including three PCa cells (PC-3, LNCaP, and DU145) and one
 benign prostatic hyperplasia cell (BPH-1). (8)
 Fig.4G-miR-21\_analysis\_in\_plasma\_sample.csv This document is the
 miR-21 analysis in the fused vesicles after incubating with Tags-Lipo@Au
 NFs and Lipo@Au NFs in healthy and cancer plasma samples. (9)
 Fig.5.1-\_miRNAs\_expression\_in\_CD63\_EVs.csvThis document is the
 expression levels of six miRNAs in CD63+ EVs for distinguishing PCa
 patients (n = 20) from BPH controls (n = 10). The signal intensities were
 averaged over triplicate measurements of each sample and normalized by
 min-max normalization after the background subtraction. The identification
 of the CD63+EVs subpopulation was performed by single-target recognition
 of CD63 protein on a single-particle membrane, and their miRNA analysis
 was achieved by guided fusion of Lipo@Au NFs and CD63+EVs subpopulation.
 (10) Fig.5.2-miRNAs\_expression\_in\_EpCAM\_\_EVs.csvThis document is the
 expression levels of six miRNAs in EpCAM+ EVs for distinguishing PCa
 patients (n = 20) from BPH controls (n = 10). The signal intensities were
 averaged over triplicate measurements of each sample and normalized by
 min-max normalization after the background subtraction. The identification
 of the EpCAM+EVs subpopulation was performed by single-target recognition
 of EpCAM protein on a single-particle membrane, and their miRNA analysis
 was achieved by guided fusion of Lipo@Au NFs and EpCAM+EVs subpopulation.
 (11) Fig.5.3-miRNAs\_expression\_in\_CD63\_EpCAM\_\_EVs.csvThis document
 is the expression levels of six miRNAs in CD63+EpCAM+EVs using the SORTER
 assay for distinguishing PCa patients (n = 20) from BPH controls (n = 10).
 The signal intensities were averaged over triplicate measurements of each
 sample and normalized by min-max normalization after the background
 subtraction. (12) Fig.6-miRNAs\_expression\_in\_a\_training\_set.csv This
 document is the expression levels of six miRNAs in a training set
 involving age-matched patients with BPH (n = 18), mPCa (n = 11), and nPCa
 (n = 13). The signal intensities were averaged over triplicate
 measurements of each sample and normalized by min-max normalization after
 the background subtraction. (13)
 Fig.7-miRNAs\_expression\_in\_a\_validation\_set.csvThis document is the
 expression levels of six miRNAs in a validation set involving age-matched
 patients with 14 nPCa, 9 mPCa, and 9 BPH. The signal intensities were
 averaged over triplicate measurements of each sample and normalized by
 min-max normalization after the background subtraction. ## Code/Software
 All statistical analyses were performed at 95% (P &lt; 0.05) CIs using
 OriginPro 2018, GraphPad Prism (v.8.0), and R software (version 4.1.2).