Filter-Assisted ICP-MS Tumor Liquid Biopsy Enabled by Dual-Target-Regulated Functional DNA Nanospheres Cascade Amplification.

IF 10.7 2区 材料科学 Q1 CHEMISTRY, PHYSICAL
Yiman Wu, Yue Wang, Li Yan, Ke Huang, Kai Chang, Piaopiao Chen
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引用次数: 0

Abstract

An ultrasensitive ICP-MS aptasensor is developed utilizing a label-free, simple filter membrane-assisted separation technique combined with nucleic acid signal amplification for the analysis of circulating tumor cells (CTCs) in lung cancer clinical samples. The approach is based on the high-affinity interaction between aptamers and PD-L1 and mucin 1, which are overexpressed on the cell surface, in conjunction with functional Y-DNA nanospheres and catalytic hairpin assembly amplifications, enabling the simultaneous detection of two proteins. Additionally, a four-armed nanostructure with significant spatial site resistance is self-assembled by introducing streptavidin with biotinylated-hairpin structures, improving the separation efficiency of the filter membrane. This structural design enables the effective isolation of biotin-T-Hg2+-T and biotin-C-Ag+-C from free Hg2+ and Ag+, facilitating highly sensitive dual-protein detection via ICP-MS. The limits of detection reached ag mL-1 levels for proteins and single-cell levels for A549 cells. CTCs are extracted from whole blood samples of lung cancer patients within 45 min through a simple centrifugation procedure. Quantification of CTCs is performed in 37 clinical samples, demonstrating results consistent with clinical diagnoses. The assay exhibits a specificity of 100% and a sensitivity of 94.5%.

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来源期刊
Small Methods
Small Methods Materials Science-General Materials Science
CiteScore
17.40
自引率
1.60%
发文量
347
期刊介绍: Small Methods is a multidisciplinary journal that publishes groundbreaking research on methods relevant to nano- and microscale research. It welcomes contributions from the fields of materials science, biomedical science, chemistry, and physics, showcasing the latest advancements in experimental techniques. With a notable 2022 Impact Factor of 12.4 (Journal Citation Reports, Clarivate Analytics, 2023), Small Methods is recognized for its significant impact on the scientific community. The online ISSN for Small Methods is 2366-9608.
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