EUVIMEDEuropean Health Evidence
Uhr 10/10Sources Journal Tree
Easy Demo

Lokaler Crossref-Datenbestand · journal-article

CHAI-sEV: A Programmable Hairpin-Driven Cas9 Platform with AI Integration for Dual-Protein Profiling and Intelligent Classification of Tumor-Derived Small Extracellular Vesicles

Haoyu Shen, Xinyu Pan, Yan Wu, Bing Chen, Xiaolei Zhou, Linbo Qiao, Clement Yaw Effah, Jing Zhou, Junying Yu, Zhenzhong Zhang, Xiaonan Yang, Yongjun Wu, Lihua Ding

Analytical Chemistry · 2026

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Abstract Multiplexed protein profiling of tumor-derived small extracellular vesicles (TsEVs) requires signal-conversion strategies that are sensitive, low-background, and orthogonal. Collateral-cleavage CRISPR assays based on Cas12a or Cas13a provide efficient amplification but can compromise single-pot multitarget detection, whereas the sequence-specific cleavage of Cas9 remains underexplored for converting vesicle-surface protein recognition into orthogonal amplified outputs. Here, we report CHAI-sEV, a programmable Cas9-mediated trigger-release strategy based on PAM-bearing hairpin-locking probes (PHLPs) for dual-protein profiling of TsEVs. Each PHLP integrates Cas9 recognition and signal conversion into a single molecular substrate, in which sgRNA-guided Cas9 cleavage unlocks a sequestered CHA activator to initiate orthogonal signal amplification. After EGFR- or PD-L1-binding aptamers label TsEVs, engineered aptamer tails recruit specific Cas9-sgRNA complexes to the vesicle surface, enabling protein-specific PHLP activation and amplified fluorescence readout. This Cas9-programmed trigger-release mechanism enables dual-channel TsEV protein detection without relying on nonspecific collateral cleavage. Under optimized conditions, CHAI-sEV achieved detection limits of 162 and 328 particles/μL for EGFR and PD-L1. In plasma samples, CHAI-sEV differentiated lung cancer patients from benign-lung-disease patients and healthy controls and showed decreased EGFR and PD-L1 signals in post-treatment samples. As proof-of-concept extensions, the CHAI-sEV was further coupled to an IGZO-FET electrical readout and an exploratory machine-learning analysis combining dual-protein signals with clinical features. Overall, PHLPs provide a substrate-design principle that converts Cas9 cleavage into amplification-compatible and orthogonally multiplexable biosensing outputs, enabling sensitive and multiplexed protein profiling of TsEVs and offering potential for extension to diverse molecular targets.

Bibliografischer Nachweis

Publikationsdaten

Autor:innen
Haoyu Shen, Xinyu Pan, Yan Wu, Bing Chen, Xiaolei Zhou, Linbo Qiao, Clement Yaw Effah, Jing Zhou, Junying Yu, Zhenzhong Zhang, Xiaonan Yang, Yongjun Wu, Lihua Ding
Quelle
Analytical Chemistry
Publikation
2026-01-01
Band / Ausgabe
Nicht angegeben
Seiten
Nicht angegeben
ISSN / ISBN
0003-2700, 1520-6882
Zitationen
0 laut Crossref
Referenzen
0 hinterlegt

Zitieren

Zitierfähiger Nachweis

Haoyu Shen, Xinyu Pan, Yan Wu, Bing Chen, Xiaolei Zhou, Linbo Qiao, Clement Yaw Effah, Jing Zhou, Junying Yu, Zhenzhong Zhang, Xiaonan Yang, Yongjun Wu, Lihua Ding (2026). CHAI-sEV: A Programmable Hairpin-Driven Cas9 Platform with AI Integration for Dual-Protein Profiling and Intelligent Classification of Tumor-Derived Small Extracellular Vesicles. Analytical Chemistry. https://doi.org/10.1021/acs.analchem.6c05200
RIS BibTeX CSL-JSON

Kontext

Themen, Förderung und Nutzung

Lizenzhinweise: Lizenz 1 · Lizenz 2 · Lizenz 3