Data for: Simultaneous subset tracing and miRNA profiling of tumor-derived exosomes via dual-surface-protein orthogonal barcoding
Data files
Oct 05, 2023 version files 30.47 KB
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Fig.3B-Fluorescence_spectra_analysis.csv
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Fig.3C-Fluorescence_kinetic_analysis.csv
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Fig.3D-Fusion_mixing_analysis.csv
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Fig.3F-Diameters_of_the_fusion_products.csv
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Fig.4B-Analysis_of_miR-21_expression.csv
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Fig.4E-Quantifying_exosomal_miR-21.csv
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Fig.4F-_Expression_levels_of_six_miRNAs.csv
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Fig.4G-miR-21_analysis_in_plasma_sample.csv
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Fig.5.1-_miRNAs_expression_in_CD63_EVs.csv
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Fig.5.2-miRNAs_expression_in_EpCAM__EVs.csv
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Fig.5.3-miRNAs_expression_in_CD63_EpCAM__EVs.csv
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Fig.6-miRNAs_expression_in_a_training_set.csv
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Fig.7-miRNAs_expression_in_a_validation_set.csv
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README.md
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Abstract
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.
README: 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 < 0.05) CIs using OriginPro 2018, GraphPad Prism (v.8.0), and R software (version 4.1.2).
Methods
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).
Usage notes
All statistical analyses were performed at 95% (P < 0.05) CIs using OriginPro 2018, GraphPad Prism (v.8.0), and R software (version 4.1.2).