Simultaneous Optical Detection of Multiple Bacterial Species Using Nanometer-Scaled Metal-Organic Hybrids

Rapid and Sensitive Quantification of Bacterial Viability Using Ratiometric Fluorescence Sensing

Bacterial viability assessment plays an important role in food-borne pathogen detection and antimicrobial drug development. Here, we first used GelRed as a DNA-binding stain for a bacterial viability assessment. It was found that live bacteria were able to exclude GelRed, which however could easily penetrate dead ones and be absorbed nonspecifically on the bacterial periplasm. Cations were used to reduce the nonspecific adsorption and greatly increase the red fluorescence ratio of dead to live bacteria. Combined with SYTO 9 (a membrane-permeable dye) for double-staining, a ratiometric fluorescent method was established. Using Escherichia coli O157:H7 as a bacteria model, the ratiometric fluorescent method can probe dead bacteria as low as 0.1%. A linear correlation between the ratiometric fluorescence and the theoretical ratio of dead bacteria was acquired, with a correlation coefficient R2 of 0.97. Advantages in sensitivity, accuracy, and safety of the GelRed/SYTO9-based ratiometric fluorescent method against traditional methods were demonstrated. The established method was successfully applied to the assessment of germicidal efficacy of different heat treatments. It was found that even 50 °C treatment could lead to the death of minor bacteria. The as-developed method has many potential applications in microbial researches, and we believe it could be expanded to the viability assessment of mammalian cells.

Simultaneous Electrochemical Detection of Multiple Bacterial Species Using Metal-Organic Nanohybrids

Organic metallic nanohybrids (NHs), in which many small metal nanoparticles are encapsulated within a conductive polymer matrix, are useful as sensitive electrochemical labels because the constituents produce characteristic oxidation current responses. Gold NHs, consisting of gold nanoparticles and poly(m-toluidine), and copper NHs, consisting of copper nanoparticles and polyaniline, did not interfere with each other in terms of the electrochemical signals obtained on the same electrode. Antibodies were introduced into these NHs to function as electrochemical labels for targeting specific bacteria. Electrochemical measurements using screen-printed electrodes dry-fixed with NH-labeled bacterial cells enabled the estimation of bacterial species and number within minutes, based on the distinct current response of the labels. Our proposed method achieved simultaneous detection of enterohemorrhagic Escherichia coli and Staphylococcus aureus in a real sample. These NHs will be powerful tools as electrochemical labels and are expected to be useful for rapid testing in food and drug-related manufacturing sites.

Python-assisted detection and photothermal inactivation of Salmonella typhimurium and Staphylococcus aureus on a background-free SERS chip

In this study, a background-free surface-enhanced Raman scattering (SERS) chip with a sandwich configuration was fabricated to enable reliable detection and photothermal inactivation of multiple bacteria. The SERS chip consists of a graphene-coated, phenylboronic-modified plasmonic gold substrate (pAu/G/PBA), and two aptamer-functionalized core (gold)-shell (Prussian blue/Poly-L-lysine and 4-mercaptobenzonitrile/polydopamine) SERS tags (Au@PB@PLL@Apt and Au@MB@PDA@Apt). The detection signals rely on the characteristic and nonoverlapping Raman bands of the SERS tags within the Raman-silent region (1800-2800 cm-1), where no background signals from the sample matrix are observed, leading to improved detection sensitivity and accuracy. Considering the relatively large size of bacteria (e.g., micron level), a rapid Raman mapping technique was chosen over conventional point-scan methods to achieve more reliable quantitative analysis of bacteria. This technique involves collecting and analyzing intensity signals of SERS tags from all the scattering points with an average ensemble effect, which is facilitated by the use of Python. As a proof-of-concept, model bacterium of Salmonella typhimurium and Staphylococcus aureus were successfully detected using the SERS chip with a dynamic range of 10-107 CFU/mL. Additionally, the SERS chip demonstrated successful detection of these bacteria in whole blood samples. Moreover, the photothermal effect of pAu/G led to efficient bacteria elimination, achieving approximately 100% eradication. This study integrated a background-free SERS chip with a Python-assisted rapid Raman mapping technique, resulting in a reliable, rapid and accurate method for detecting and eliminating multiple bacteria, which may provide a promising alternative for multiple screening of bacteria in real samples.

Holistic Prediction of AuNP Aggregation in Diverse Aqueous Suspensions Based on Machine Vision and Dark-Field Scattering Imaging

The localized surface-plasmon resonance of the AuNP in aqueous media is extremely sensitive to environmental changes. By measuring the signal of plasmon scattering light, the dark-field microscopic (DFM) imaging technique has been used to monitor the aggregation of AuNPs, which has attracted great attention because of its simplicity, low cost, high sensitivity, and universal applicability. However, it is still challenging to interpret DFM images of AuNP aggregation due to the heterogeneous characteristics of the isolated and discontinuous color distribution. Herein, we introduce machine vision algorithms for the training of DFM images of AuNPs in different saline aqueous media. A visual deep learning framework based on AlexNet is constructed for studying the aggregation patterns of AuNPs in aqueous suspensions, which allows for rapid and accurate identification of the aggregation extent of AuNPs, with a prediction accuracy higher than 0.96. With the aid of machine learning analysis, we further demonstrate the prediction ability of various aggregation phenomena induced by both cation species and the concentration of the external saline solution. Our results suggest the great potential of machine vision frameworks in the accurate recognition of subtle pattern changes in DFM images, which can help researchers build predictive analytics based on DFM imaging data.