The overuse of antibiotics plays a major role in the emergence and spread of multidrug-resistant bacteria. A molecularly targeted, specific treatment method for bacterial pathogens can prevent this problem by reducing the selective pressure during microbial growth. Herein, we introduce a nonviral treatment strategy delivering genome editing material for targeting antibacterial resistance. We apply the CRISPR-Cas9 system, which has been recognized as an innovative tool for highly specific and efficient genome engineering in different organisms, as the delivery cargo. We utilize polymer-derivatized Cas9, by direct covalent modification of the protein with cationic polymer, for subsequent complexation with single-guide RNA targeting antibiotic resistance. We show that nanosized CRISPR complexes (= Cr-Nanocomplex) were successfully formed, while maintaining the functional activity of Cas9 endonuclease to induce double-strand DNA cleavage. We also demonstrate that the Cr-Nanocomplex designed to target mecA-the major gene involved in methicillin resistance-can be efficiently delivered into Methicillin-resistant Staphylococcus aureus (MRSA), and allow the editing of the bacterial genome with much higher efficiency compared to using native Cas9 complexes or conventional lipid-based formulations. The present study shows for the first time that a covalently modified CRISPR system allows nonviral, therapeutic genome editing, and can be potentially applied as a target specific antimicrobial.

Measuring alterations in bacteria upon antibiotic application is important for basic studies in microbiology, drug discovery, clinical diagnosis, and disease treatment. However, imaging and 3D time-lapse response analysis of individual bacteria upon antibiotic application remain largely unexplored mainly due to limitations in imaging techniques. Here, we present a method to systematically investigate the alterations in individual bacteria in 3D and quantitatively analyze the effects of antibiotics. Using optical diffraction tomography, in-situ responses of Escherichia coli and Bacillus subtilis to various concentrations of ampicillin were investigated in a label-free and quantitative manner. The presented method reconstructs the dynamic changes in the 3D refractive-index distributions of living bacteria in response to antibiotics at sub-micrometer spatial resolution.

The healthcare industry is in dire need of rapid microbial identification techniques for treating microbial infections. Microbial infections are a major healthcare issue worldwide, as these widespread diseases often develop into deadly symptoms. While studies have shown that an early appropriate antibiotic treatment significantly reduces the mortality of an infection, this effective treatment is difficult to practice. The main obstacle to early appropriate antibiotic treatments is the long turnaround time of the routine microbial identification, which includes time-consuming sample growth. Here, we propose a microscopy-based framework that identifies the pathogen from single to few cells. Our framework obtains and exploits the morphology of the limited sample by incorporating three-dimensional quantitative phase imaging and an artificial neural network. We demonstrate the identification of 19 bacterial species that cause bloodstream infections, achieving an accuracy of 82.5% from an individual bacterial cell or cluster. This performance, comparable to that of the gold standard mass spectroscopy under a sufficient amount of sample, underpins the effectiveness of our framework in clinical applications. Furthermore, our accuracy increases with multiple measurements, reaching 99.9% with seven different measurements of cells or clusters. We believe that our framework can serve as a beneficial advisory tool for clinicians during the initial treatment of infections.

For appropriate treatments of infectious diseases, rapid identification of the pathogens is crucial. Here, we developed a rapid and label-free method for identifying common bacterial pathogens as individual bacteria by using three-dimensional quantitative phase imaging and deep learning. We achieved 95% accuracy in classifying 19 bacterial species by exploiting the rich information in three-dimensional refractive index tomograms with a convolutional neural network classifier. Extensive analysis of the features extracted by the trained classifier was carried out, which supported that our classifier is capable of learning species-dependent characteristics. We also confirmed that utilizing three-dimensional refractive index tomograms was crucial for identification ability compared to two-dimensional imaging. This method, which does not require time-consuming culture, shows high feasibility for diagnosing patients with infectious diseases who would benefit from immediate and adequate antibiotic treatment.

No abstract