Data for: Sensitive and specific detection of tumor-derived exosomes using nanopore-crystal microchips
Data files
Jan 11, 2024 version files 35.12 KB
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Fig.3e-Fluorescence_enhancement_factor.csv
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Fig.3f-Simulated_reflection_spectra.csv
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Fig.3h-Simulated_fluorescence_emission_power_curves.csv
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Fig.4d-Capture_efficiency_of_anti-CD81-based_NC-Chip_and_Solid-Chip.csv
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Fig.4e-Detectionand_Solid-Chip_dynamic_range_for_quantifying_NCL_expression_on_PANC-1_Exo.csv
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Fig.4f-NC-Chip_for_detection_of_NCL_expression.csv
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Fig.4g-Calibration_curves_for_detection_of_NCL_expression.csv
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Fig.4h-Radar_plot_showing__expression_of__eight__proteins_from_one_normal_and_seven_tumor_cell_lines.csv
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Fig.4i-1-Correlation_of_expression_of_eight_proteins_on_unpurified_Exo.csv
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Fig.4i-2-Correlation_of_expression_of_eight_proteins_on_UC-purified_Exo.csv
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Fig.4j-NC-Chip_assessment_of_tExos_in_PR_control_and_two_PC_patient_plasma.csv
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Fig.5-Integrative_phenotyping_of_pancreatic_cancer_exosomes_in_a_training__cohort.csv
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Fig.6-Validation_of_the_NC-Chip_for_PC_diagnosis.csv
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README.md
Abstract
Tumor-derived exosomes (tExos) have emerged as promising circulating biomarkers for early cancer diagnosis. However, the sensitivity and specificity of existing assays often limit the clinical translation of tExos. In this study, we present a highly versatile microfluidic platform, termed the nanopore-crystal microchip (NC-Chip), for specific isolation and ultrasensitive detection of tExos in as little as 0.5 μL of plasma samples from pancreatic cancer (PC) patients. The NC-Chip incorporates a herringbone-patterned hierarchical porous hydrogel scaffold, enabling fluid manipulation, size exclusion, immunoaffinity capture, and signal amplification. These integrated features significantly enhance the sensitivity and specificity of tExos assays in complex clinical scenarios. Utilizing an eight-protein signature, the NC-Chip can distinguish patients with pancreatitis and non-metastatic PC with 100% accuracy in the training cohort and 94.9% accuracy in the validation cohort. This platform is sensitive, specific, inexpensive, and only needs small-volume samples, offering a powerful exosome-based liquid biopsy tool for early PC diagnosis.
README: README:Sensitive and specific detection of plasma tumor-derived exosomes using nanopore-crystal microchips
Description of the data and file structure
(1) Fig.3e-Fluorescence_enhancement_factor.csv
This document is the enhancement factor of fluorescent FDG, Cyanine3, and Tex-red inside Flat-Chip, Solid-Chip, and NC-Chip. Before use, the FDG solution at FDG: β-Gal ratio of 1:1 was created and reacted for 30 min at 37 °C in the dark. The enhancement factor was calculated by ∆E,exp = (IE−Ix)/(I0−Ix), where IE represents the intensity on the NC-Chip or Solid-Chip on the herringbone pattern, I0 represents the intensity on the Flat-Chip channel, and Ix represents the background correction. The FDG represents fluorescein-di-β-d-galactopyranoside. The β-Gal represents β-galactosidase. The NC-Chip represents nanopore-crystal microchip. The Solid-Chip represents solid herringbone chip. The Flat-Chip represents an assay chip on clean glass slides.
(2) Fig.3f-Simulated_reflection_spectra.csv
This document is the simulated reflection spectra of NC-Chip excited by plane light. The N represents the layer of nanopore-crystal structure.
(3) Fig.3h-Simulated_fluorescence_emission_power_curves.csv
This document is the simulated fluorescence emission power curves of a dipole on a glass substrate (Flat chip) and nanopore-crystal structure (NC-Chip). A single dipole was employed to simulate the generated fluorescence of FDG.
(4) Fig.4d-Capture_efficiency_of_anti-CD81-based_NC-Chip_and_Solid-Chip.csv
This document is the comparison capture efficiency of anti-CD81-based NC-Chip and Solid-Chip using DiO-stained PANC-1 Exo (1.0×109 particles mL-1) in PBS and extracellular vesicles-depleted plasma (100-fold dilution). The anti-CD81-based NC-Chip and Solid-Chip represents the NC-Chip and Solid-Chip that was modified by CD81 monoclonal antibodies. The DiO represents 3,3'-dioctadecyloxacarbocyanine perchlorate. The PANC-1 Exo represents PANC-1 cell-derived exosomes.
(5) Fig.4e-Detectionand_Solid-Chip_dynamic_range_for_quantifying_NCL_expression on_PANC-1_Exo.csv
This document is the detection dynamic range for quantifying nucleolin (NCL) expression on PANC-1 Exo by the NC-Chip and Solid-Chip. The c represents the concentration of exosomes.
(6) Fig.4f-NC-Chip_for_detection_of_NCL_expression.csv
This document is the NC-Chip for detection of NCL expression on hTERT-HPNE cell-derived exosomes (hTERT-HPNE Exo) and PANC-1 Exo (1.0 × 109 particles mL-1) spiked in both PBS and 100-fold diluted extracellular vesicles (EV)-depleted plasma. Control experiment for PBS or 100-fold diluted EV-depleted plasma.
(7) Fig.4g-Calibration_curves_for_detection_of_NCL_expression.csv
This document is the calibration curves for detecting the NCL expression on PANC-1 Exo spiked in PBS and 100-fold diluted EV-depleted plasma.
(8) Fig.4h-Radar_plot_showing _expression_of eight proteins_from_one_normal_and _seven_tumor_cell_lines.csv
This document is the radar plot showing expression of eight proteins from one normal hTERT-HPNE- and seven tumor PC cell lines (CFPAC-1-, PANC-1-, MIAPaCa-2-, SW1990, Panc 08.13, PaTu 8988t, and BxPC-3)-derived exosomes.
(9) Fig.4i-1-Correlation_of_expression_of_eight_proteins_on_unpurified_Exo.csv
This document is the correlation of expression of eight proteins on unpurified Exo derived from PANC-1-, MIAPaCa-2-, and SW1990 cell lines by NC-Chip. The unpurified Exo represents a cell-depleted culture medium.
(10) Fig.4i-2-Correlation_of_expression_of_eight_proteins_on_UC-purified_Exo.csv
This document is the correlation of expression of eight proteins on ultracentrifugation (UC)-purified Exo derived from PANC-1-, MIAPaCa-2-, and SW1990 cell lines by NC-Chip.
(11) Fig.4j-NC-Chip_assessment_of_tExos_in_PR_control_and_two_PC_patient_plasma.csv
This document is the NC-Chip assessment of eight protein expression levels on Tumor-derived exosomes (tExos) directly from one pancreatitis (PR) control and two pancreatic cancer (PC) patient plasma samples.
(12) Fig.5-Integrative_phenotyping_of_pancreatic_cancer_exosomes_in_a_training _cohort.csv
This document is the integrative phenotyping of pancreatic cancer exosomes in a training cohort. A training set of 28 age-matched healthy donors (HD), 26 PR, 40 non-metastatic pancreatic cancer (nPC), and 25 metastatic pancreatic cancer (mPC). After removing the background, the signal intensities for each sample were averaged over three measurements and then normalized using min-max. The NA represents represents the word "information not available."
(13) Fig.6-Validation_of_the_NC-Chip_for_PC_diagnosis.csv
This document is the validation of the NC-Chip for PC diagnosis. A validation set of 11 HD, 26 PR, 33 nPC, and 20 mPC participants. After removing the background, the signal intensities for each sample were averaged over three measurements and then normalized using min-max.
Methods
The mean, SD, and LOD were computed utilizing established standard formulas. A student’s t-test with two tails was employed to assess significance. The intensities of individual protein markers measured by the NC-Chip were normalized using Min-Max. The normalized intensities of eight protein markers were weighted together to create the PC signature by LDA. The nonparametric, two-tailed Mann-Whitney U test for binary classification was used to determine P values for pairwise comparisons. With a post-hoc Dunn’s test for pairwise multiple comparisons, a Kruskal-Wallis one-way ANOVA was used to determine the overall and group pair P values for ternary classification. To evaluate the AUC, sensitivity, specificity, and accuracy of PC diagnosis, ROC analyses were developed for individual markers or marker combinations. The discriminant function model was initially constructed using data from the training cohort, and then it was applied to classify patients in the validation cohort. The validation was conducted using non-blinded samples. The validation cohort’s sensitivity, specificity, and accuracy were evaluated after the optimal cutoff values were selected based on the training cohort using Youden’s index. Using OriginPro 2018, GraphPad Prism (v.8.0), and R software (version 4.1.2), all statistical analyses were performed with 95% confidence intervals (P < 0.05).