(J-391) SLIM-FISH: Simplified Laser Induced Microfluidic Fluorescence In-Situ Hybridization Based Antibiotics Susceptibility Test and Sub-5 Minute Pathogen Identification

Introduction:: Overusing broad-spectrum antibiotics in hospitals due to the slow process of traditional bulk assays pathogen identification and antibiotics susceptibility testing raised severe public health concerns threatening the effective treatment of bacterial infections. Nucleic Acid Amplification Tests (NAATs) and amplification-free technologies such as conventional Fluorescence In Situ Hybridization (FISH) assay were developed though they are also strained by increased risks of contamination or poor sensitivity.[1] To counter these issues, we present the Simplified Laser Induced Microfluidic Fluorescence In-Situ Hybridization (SLIM-FISH) based pathogen identification and antibiotics susceptibility test (ID&AST). The innovative assay and detection platform enables rapid pathogen ID under 5 minutes and AST under 60 minutes with high sensitivity and shows consistency with the current medical standards.

Materials and Methods:: To Perform SLIM-FISH ID, the reaction mixture consisted of 100 nM 16S rRNA targeting PNA Probes in a custom hybridization buffer consisting of 50mM NaCl, 25 mM Tris- HCl pH 8.0, and 0.5% polyvinylpyrrolidone The reaction mixture is then mixed with the collected sample to be tested with a 1:1 ratio before thermal permeation using a thermal cycler (Thermal Electron Corporation HBPX 110) at 95 °C for 2 minutes, followed by single-cell resolution Laser-Induced Fluorescent (LIF) detection system coupled with a polydimethylsiloxane microfluidic detection chip (Fig. b). A similar process was performed for AST, but antibiotics were added to the reaction mixture, and a short incubation process was added before the LIF detection.

Results, Conclusions, and Discussions:: The thermal permeation partially porouslize target pathogen membrane enables PNA probe internalization and shows a stronger fluorescent signal than the background (Fig. a&c). In the presence of antibiotics, cell count in susceptible strains would not increase before and after incubation, whereas that of resistant strains would increase (Fig. d). When both E. coli and Universal probes are used, both probes will be internalized by E. coli but only the universal probe will enter non-E. coli bacteria (Fig. e). The assay dynamic range is determined, showing high relativity between our assay and the number of theoretically detectable cells with a detection limit as low as 10^3 CFU/ml (Fig. f). A tree machine learning algorithm is trained and used, showing 98.7% accuracy (Fig. g).
Based on the presented results, we determined the mechanism of the SLIM-FISH based pathogen ID AST assay and demonstrated SLIM-FISH couples with high-resolution detection LIF system and machine learning algorisms can significantly accelerate and simplify the conventional clinical bulk assays while further improving detection sensitivity and consistency with clinical AST standard.
Despite SLIM-FISH demonstrating good compatibility with most gram-negative bacteria, we recognize the specificity of some can be improved since many gram-negative bacteria shares similar 16S rRNA. In future studies, we will design specific PNA probes for those bacteria and adapt the SLIM-FISH with microvalve-based high throughput automated streamline testing platforms, enabling automated plug-and-play combinatorial AST.

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References (Optional): : Reference: [1] Kauskih, A. et al., (2017) Biosensors and Bioelectronics. https://doi.org/10.1016/j.bios.2017.06.006