Issue 13, 2025

A dCas9/sgRNA complex-mediated competitive assay for accurate and sensitive Pseudomonas aeruginosa analysis

Abstract

Pseudomonas aeruginosa (P. aeruginosa), a Gram-negative pathogenic bacterium, is one of the most common bacteria that causes severe infectious diseases. However, accurate and efficient detection of P. aeruginosa in clinical samples is a huge challenge. Therefore, in this study, we developed a Cas9 derivative (dCas9)/sgRNA-mediated competitive assay for the sensitive and precise characterization of genomic materials from P. aeruginosa. Our approach involved the identification of target genomic sequences using the dCas9/sgRNA complex, which occupied the “sensing probe” (SP) binding site, resulting in an increased availability of free SP. SP subsequently facilitated DNA polymerase/endonuclease-mediated signal cycles and signal production, enabling highly sensitive detection of P. aeruginosa. The proposed competitive assay demonstrated a robust linear response to P. aeruginosa within a concentration range from 10 CFU mL−1 to 106 CFU mL−1, leveraging numerous signal amplification processes and competitive target recognition while exhibiting robust anti-interference capacity. Compared with former strategies, the proposed competitive assay enabled the accurate detection of P. aeruginosa by directly identifying and binding genomic sequences, which could be easily extended to the detection of other bacteria by simply changing the sgRNA. In addition, the proposed approach exhibits significant clinical potential for early disease diagnosis owing to its excellent sensitivity and accuracy.

Graphical abstract: A dCas9/sgRNA complex-mediated competitive assay for accurate and sensitive Pseudomonas aeruginosa analysis

Supplementary files

Article information

Article type
Paper
Submitted
04 Dec 2024
Accepted
04 Mar 2025
First published
04 Mar 2025

Anal. Methods, 2025,17, 2791-2798

A dCas9/sgRNA complex-mediated competitive assay for accurate and sensitive Pseudomonas aeruginosa analysis

B. Wang, Z. Yu, Z. Zhang, Z. Zhu and Y. Song, Anal. Methods, 2025, 17, 2791 DOI: 10.1039/D4AY02194K

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