Sensitive and Direct Analysis of Pseudomonas aeruginosa through Self-Primer-Assisted Chain Extension and CRISPR-Cas12a-Based Color Reaction

Pseudomonas aeruginosa (P. aeruginosa) is a common opportunistic Gram-negative pathogen that may cause infections to immunocompromised patients. However, sensitive and reliable analysis of P. aeruginosa remains a huge challenge. In this method, target recognition assists the formation of a self-primer and initiates single-stranded chain production. The produced single-stranded DNA chain is identified by CRISPR-Cas12a, and consequently, the trans-cleavage activity of the Cas12a enzyme is activated to parallelly digest Ag+ aptamer sequences that are chelated with silver ions (Ag+). The released Ag+ reacted with 3,3′,5,5′-tetramethylbenzidine (TMB) for coloring. Compared with the traditional color developing strategies, which mainly rely on the DNA hybridization, the color developing strategy in this approach exhibits a higher efficiency due to the robust trans-cleavage activity of the Cas12a enzyme. Consequently, the method shows a low limit of detection of a wide detection of 5 orders of magnitudes and a low limit of detection of 21 cfu/mL, holding a promising prospect in early diagnosis of infections. Herein, we develop a sensitive and reliable method for direct and colorimetric detection of P. aeruginosa by integrating self-primer-assisted chain production and CRISPR-Cas12a-based color reaction and believe that the established approach will facilitate the development of bacteria-analyzing sensors

Parameter configuration of residual block in conv1-conv5 group.

Parameter configuration of residual block in conv1-conv5 group.</p

The loss curves of different classification and detection algorithms varying with the iteration number.

The loss curves of different classification and detection algorithms varying with the iteration number.</p

Comparison of the detection results for different classification and detection algorithms.

Comparison of the detection results for different classification and detection algorithms.</p

Example of recognition results of northern leaf blight and common rust.

a. Northon leaf blight. b. Common rust.</p

S1 File -

(ZIP)</p

Profile of sample images for nine types of diseases.

Profile of sample images for nine types of diseases.</p

Multi-scale feature fusion method based on task design.

Multi-scale feature fusion method based on task design.</p

Attenuation factor introduction graph.

Attenuation factor introduction graph.</p