DNA Processor Modules Enabled Pattern Recognition of Extracellular Vesicles for Facile Cancer Diagnosis †

Qianyun Yang; Ruiyan Wang; Kun Wu; Di Li

doi:10.1002/cjoc.202300135

DNA Processor Modules Enabled Pattern Recognition of Extracellular Vesicles for Facile Cancer Diagnosis ^†

Qianyun Yang, Ruiyan Wang, Kun Wu, Di Li

School of Chemistry and Molecular Engineering

Research output: Contribution to journal › Article › peer-review

4 Scopus citations

Abstract

Extracellular vesicles (EVs) carry rich protein and nucleic acid information of host cells, thus, they are considered to be reliable biomarkers for cancer diagnosis. However, current EVs detection relies on technical expertise that requires special equipment to readout signals that prevent its point-of-care testing. In this study, we propose a Pattern Recognition of Molecular in Interest on Single EVs (PROMISE) strategy for clinical EVs detection. This strategy combines an aptamer-based DNA processor on single EVs, and a color-rendering enzyme to provide a visual output for naked-eyes enabled profiling. We demonstrate 100% accuracy in breast cancer discrimination. Furthermore, by utilizing thin-layer chromatography (TLC), we achieve a simultaneous screening of two types of cancers (breast and prostate cancer) in one sample. This PROMISE strategy could serve as a versatile platform for point-of-care EVs diagnosis. (Figure presented.).

Original language	English
Pages (from-to)	2269-2274
Number of pages	6
Journal	Chinese Journal of Chemistry
Volume	41
Issue number	18
DOIs	https://doi.org/10.1002/cjoc.202300135
State	Published - 15 Sep 2023

Keywords

Cancer diagnosis
DNA
Extracellular vesicles
Point-of-care
Protein
Single-molecule imaging

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1002/cjoc.202300135

Cite this

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title = "DNA Processor Modules Enabled Pattern Recognition of Extracellular Vesicles for Facile Cancer Diagnosis †",

abstract = "Extracellular vesicles (EVs) carry rich protein and nucleic acid information of host cells, thus, they are considered to be reliable biomarkers for cancer diagnosis. However, current EVs detection relies on technical expertise that requires special equipment to readout signals that prevent its point-of-care testing. In this study, we propose a Pattern Recognition of Molecular in Interest on Single EVs (PROMISE) strategy for clinical EVs detection. This strategy combines an aptamer-based DNA processor on single EVs, and a color-rendering enzyme to provide a visual output for naked-eyes enabled profiling. We demonstrate 100\% accuracy in breast cancer discrimination. Furthermore, by utilizing thin-layer chromatography (TLC), we achieve a simultaneous screening of two types of cancers (breast and prostate cancer) in one sample. This PROMISE strategy could serve as a versatile platform for point-of-care EVs diagnosis. (Figure presented.).",

keywords = "Cancer diagnosis, DNA, Extracellular vesicles, Point-of-care, Protein, Single-molecule imaging",

author = "Qianyun Yang and Ruiyan Wang and Kun Wu and Di Li",

note = "Publisher Copyright: {\textcopyright} 2023 SIOC, CAS, Shanghai, \& WILEY-VCH GmbH.",

year = "2023",

month = sep,

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doi = "10.1002/cjoc.202300135",

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T1 - DNA Processor Modules Enabled Pattern Recognition of Extracellular Vesicles for Facile Cancer Diagnosis †

AU - Yang, Qianyun

AU - Wang, Ruiyan

AU - Wu, Kun

AU - Li, Di

PY - 2023/9/15

Y1 - 2023/9/15

N2 - Extracellular vesicles (EVs) carry rich protein and nucleic acid information of host cells, thus, they are considered to be reliable biomarkers for cancer diagnosis. However, current EVs detection relies on technical expertise that requires special equipment to readout signals that prevent its point-of-care testing. In this study, we propose a Pattern Recognition of Molecular in Interest on Single EVs (PROMISE) strategy for clinical EVs detection. This strategy combines an aptamer-based DNA processor on single EVs, and a color-rendering enzyme to provide a visual output for naked-eyes enabled profiling. We demonstrate 100% accuracy in breast cancer discrimination. Furthermore, by utilizing thin-layer chromatography (TLC), we achieve a simultaneous screening of two types of cancers (breast and prostate cancer) in one sample. This PROMISE strategy could serve as a versatile platform for point-of-care EVs diagnosis. (Figure presented.).

AB - Extracellular vesicles (EVs) carry rich protein and nucleic acid information of host cells, thus, they are considered to be reliable biomarkers for cancer diagnosis. However, current EVs detection relies on technical expertise that requires special equipment to readout signals that prevent its point-of-care testing. In this study, we propose a Pattern Recognition of Molecular in Interest on Single EVs (PROMISE) strategy for clinical EVs detection. This strategy combines an aptamer-based DNA processor on single EVs, and a color-rendering enzyme to provide a visual output for naked-eyes enabled profiling. We demonstrate 100% accuracy in breast cancer discrimination. Furthermore, by utilizing thin-layer chromatography (TLC), we achieve a simultaneous screening of two types of cancers (breast and prostate cancer) in one sample. This PROMISE strategy could serve as a versatile platform for point-of-care EVs diagnosis. (Figure presented.).

KW - Cancer diagnosis

KW - DNA

KW - Extracellular vesicles

KW - Point-of-care

KW - Protein

KW - Single-molecule imaging

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