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

Collaborative representation-based classification of microarray gene expression data

<div><p>Microarray technology is important to simultaneously express multiple genes over a number of time points. Multiple classifier models, such as sparse representation (SR)-based method, have been developed to classify microarray gene expression data. These methods allocate the gene data points to different clusters. In this paper, we propose a novel collaborative representation (CR)-based classification with regularized least square to classify gene data. First, the CR codes a testing sample as a sparse linear combination of all training samples and then classifies the testing sample by evaluating which class leads to the minimum representation error. This CR-based classification approach is remarkably less complex than traditional classification methods but leads to very competitive classification results. In addition, compressive sensing approach is adopted to project the high-dimensional gene expression dataset to a lower-dimensional space which nearly contains the whole information. This compression without loss is beneficial to reduce the computational load. Experiments to detect subtypes of diseases, such as leukemia and autism spectrum disorders, are performed by analyzing the gene expression. The results show that the proposed CR-based algorithm exhibits significantly higher stability and accuracy than the traditional classifiers, such as support vector machine algorithm.</p></div

The average accuracy of CRC for leukemia dataset with reduced dimensionality.

<p>The average accuracy of CRC for leukemia dataset with reduced dimensionality.</p

The average accuracy of CRC for breast cancer dataset with reduced dimensionality.

<p>The average accuracy of CRC for breast cancer dataset with reduced dimensionality.</p

Leave-one-out cross validation (CV) classification results.

<p>Leave-one-out cross validation (CV) classification results.</p

The average accuracy of CRC for DLBCL dataset with reduced dimensionality.

<p>The average accuracy of CRC for DLBCL dataset with reduced dimensionality.</p

Running time of classification on the microarray database.

<p>Running time of classification on the microarray database.</p

Gene data sets used in this study.

<p>Gene data sets used in this study.</p

Gene expression data.

<p>Gene expression data.</p

10-fold CV classification results.

<p>10-fold CV classification results.</p