Split DNA Tetrahedron-Mediated Spatiotemporal-Hierarchy CRISPR Cascade Integrated with Au@Pt Nanolabels and Artificial Intelligence for a Cervical Cancer MicroRNA Bioassay

四面体 小RNA 级联 生物测定 清脆的 等级制度 DNA 计算生物学 生物 纳米技术 癌症研究 人工智能 计算机科学 化学 材料科学 遗传学 基因 结晶学 色谱法 市场经济 经济
作者
Xiaohui Zhan,Yujia Jiang,Zhiyu Li,Xiaoqin Hu,Fang Lan,Binwu Ying,Yao Wu
出处
期刊:ACS Nano [American Chemical Society]
标识
DOI:10.1021/acsnano.5c01376
摘要

The screening and monitoring of microRNAs as cancer molecular biomarkers is clinically significant, but traditional methods lack sufficient sensitivity, accuracy, and convenience. The CRISPR-colorimetric lateral flow assay (CLFA) integration offers a promising and efficient solution; however, cumbersome preamplification and poor quantification hinder clinical adoption. In this study, we developed a one-step isothermal CRISPR-Cas cascaded sensing system that is preamplification-free. At its core is a designed and selected split DNA tetrahedron activator, employing spatiotemporal-hierarchy mechanisms to precisely bidirectionally drive the kinetics of two Cas enzymes, accelerating the activation of Cas13a while delaying the initiation of Cas12a, to achieve optimal balance. This system enables ultrasensitive, single-step, single-tube, and rapid detection of a cervical cancer relative biomarker, microRNA-21, achieving a limit of detection of 38 aM with a broad linear range. The CRISPR system is further integrated with CLFA enhanced by ultrathin platinum-protected gold nanolabels (Au@Pt, also named Au@s-Pt), along with a smartphone equipped with dual convolutional neural network models (YOLO v5 and MobileNet v3), enabling more precise, rapid quantification of target miRNA. Using this integrated platform, miRNA-21 levels in cervical cancer and precancerous samples can be accurately quantified with approximately 30 min at low cost and without the need for large, sophisticated instruments, with results showing good concordance with quantitative real-time polymerase chain reaction. This platform provides an efficient, highly sensitive, user-friendly, and quantifiable point-of-care testing solution.
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