Rapid identification of health care-associated infections with an integrated fluorescence anisotropy system

Abstract

Health care-associated infections (HAIs) and drug-resistant pathogens have become a major health care issue with millions of reported cases every year. Advanced diagnostics would allow clinicians to more quickly determine the most effective treatment, reduce the nonspecific use of broad-spectrum antimicrobials, and facilitate enrollment in new antibiotic treatments. We present a new integrated system, polarization anisotropy diagnostics (PAD), for rapid detection of HAI pathogens. The PAD uses changes of fluorescence anisotropy when detection probes recognize target bacterial nucleic acids. The technology is inherently robust against environmental noise and economically scalable for parallel measurements. The assay is fast (2 hours) and performed on-site in a single-tube format. When applied to clinical samples obtained from interventional procedures, the PAD determined the overall bacterial burden, differentiated HAI bacterial species, and identified drug resistance and virulence status. The PAD system holds promise as a powerful tool for near-patient, rapid HAI testing.

Keywords: Biosensors; antibiotics; antimicrobials; drug resistance; fluorescence anisotropy; healthcare–associated infections; nanotechnology; nucleic acid testing; superbugs.

Fig. 1. PAD system.

(A) Assay procedure. Bacteria are lysed, and total RNA is extracted. Following the RT-PCR amplification, samples containing amplicons and DNA polymerase are incubated with an all-in-one master mix that has the detection key and the reporter. The resulting fluorescence anisotropy of the sample is then measured. (B) Photograph of a disposable RNA extraction cartridge made in plastic. The device has an RNA extraction chamber packed with glass beads (inset). (C) Photograph of a portable system for fluorescence anisotropy detection. Four separate optical cubes can be plugged into an electronic base station. (D) PAD measurement is controlled through a custom-designed application in a smartphone. The PAD device and the smartphone communicate via Bluetooth.

Fig. 2. Optical detection design.

(A) Schematic of an optical cube. The optical excitation module has an LED, a linear polarizer, and a focusing lens. The emission light is measured by a pair of detector sets, each consisting of a lens, a polarization filter, and a photodiode (PD). (B) Circuit diagram. The on-board computer controls the entire system and communicates with a smartphone. To enhance the signal-to-noise ratio (SNR), the system uses the optical lock-in detection scheme. The intensity of the excitation light is amplitude-modulated, and the resulting emission intensities are mixed with the carrier frequency. The dotted box indicates an optical cube. BPF, band-pass filter; LPF, low-pass filter; AMP, amplifier. (C) The lock-in method significantly improved the signal-to-noise ratio (630 times, 28 dB). (D) The accuracy of the PAD was benchmarked against a commercial plate reader. The measured values show excellent agreements (R² = 99%). Experiments were performed in triplicate, and the data were displayed as means ± SD. Horizontal and vertical error bars were from the plate reader and the PAD measurements, respectively.

Fig. 3. Universal bacteria detection.

(A) The universal capture key (UNI key) detects a conserved bacterial sequence (N = A for Escherichia, Klebsiella, and Acinetobacter; N = T for Pseudomonas and Staphylococcus). The reporter is composed of a primer, a template, and FAM-DNA. (B) Five different HAI pathogens (10⁶ CFU/ml) were detected with the PAD. The signal levels were statistically identical among concentration-matched bacterial samples. (C) Detection sensitivity. Samples with different bacterial concentrations (E. coli, 10^–1 to 10⁸ CFU/ml) were prepared through serial dilution. The limit of detection (LOD) was at the level of a single bacterium. The threshold was set at 3× SD above background signal of the sample without bacteria. All experiments were performed in triplicate and the graphs are displayed as means ± SD.

Fig. 4. Assay optimization for POC operation.

(A) Schematic illustration of UDG-mediated control on carryover contamination. Uracil-containing carryover contaminant was specifically broken down by UDG, which allows for the amplification of the true target DNA only. (B) Uracil-containing contaminants (10⁷ copies) were added to all samples. Contaminated samples produced high signal even in the absence of target bacteria. When samples were treated with UDG, the false-positive signal was eliminated. (C) The PAD reagents were lyophilized to facilitate their transport and extend their shelf life. After 4 weeks of storage in ambient condition, the reagents were used for bacterial detection. No difference was observed between fresh and lyophilized reagents. Bacterial samples in (B) and (C) contained E. coli (10⁶ CFU/ml). All experiments were performed in triplicate, and the data are displayed as means ± SD. RT, room temperature.

Fig. 5. Bacteria typing with PAD.

(A) A set of detection keys specific for HAI pathogens was designed (HAI keys). An Escherichia key is shown as an example. (B) The specificity of HAI keys was tested. The signal was high only in the presence of the target species even in the mixture of other bacterial species. An example of E. coli (10⁶ CFU/ml) detection is shown. (C) Heat map of Δr values obtained for HAI detection. Bacterial concentration was 10⁶ CFU/ml. (D) Detection keys for antibiotic resistance and virulence (ARV keys) were designed for further typing. Two types of methicillin-resistant S. aureus (MRSA; 10⁶ CFU/ml), health care–associated MRSA (HA-MRSA) and community-acquired MRSA (CA-MRSA), were identified by targeting the specific regions in mecA and PVL genes. Three pathogens [methicillin-sensitive S. aureus (MSSA), E. coli, and P. aeruginosa; 10⁶ CFU/ml] were included as controls. All experiments were performed in triplicate. The heat map displays mean values and the bar graphs display means ± SD.

Fig. 6. Clinical application of PAD for HAI detection.

(A) Nine samples from different patients were processed by the PAD for bacterial load (UNI), presence of the HAI species (HAI), and resistance/virulence status (ARV). (B) The clinical samples were also tested by a clinical pathology laboratory (culture and qPCR). The PAD and pathology reports agreed with each other.