Multimodal capture-antibody-independent lateral flow immunoassay based on AuNF-PMBA for point-of-care diagnosis of bacterial urinary tract infections

Bacterial urinary tract infections cause huge challenges to public health and require rapid diagnostic methods, while lateral flow immunoassay (LFIA) belongs to the most known point-of-care detection technology. Capture-antibody (CA) is used for labeling targets in traditional LFIA, yet the production of antibodies is time-consuming and expensive. In this study, we reported a multimodal CA-independent LFIA (MCI-LFIA) method for rapid diagnosis of bacterial urinary tract infections with high flexibility and accuracy. The assay employs p-mercaptophenylboronic acid-modified Au nanoflower (namely AuNF-PMBA nanomaterials) as multifunctional labels, which possess colorimetric-Raman-photothermal properties and outstanding bacteria capture ability. Take Escherichia coli (E. coli) as model bacteria, the limit of detection (LOD) of this MCI-LFIA for E. coli was 10(3 )cfu/mL by naked-eye, which is three orders of magnitude lower than conventional CA-dependent sandwich method. For quantitative detection, LOD was 10(3) cfu/mL in colorimetric mode, 10(2) cfu/mL in Raman mode and 10(2) cfu/mL in photothermal mode. Significantly, the developed AuNF-PMBA-based MCI-LFIA successfully detected target pathogens in clinical E. coli-positive human urine samples within 45 min with high accuracy. This diagnostic platform may be easily adapted for detection of other target pathogens for bacterial diseases in point-of-care settings with good reproducibility and excellent specificity.

Automated summary

This study developed a multimodal CA-independent LFIA method for rapid diagnosis of bacterial urinary tract infections. By using AuNF-PMBA nanomaterials as multifunctional labels, this method achieved high sensitivity and accuracy. The results showed that the LOD of this method in naked-eye and quantitative modes was lower than the conventional CA-dependent sandwich method. Additionally, successful detection of target pathogens in clinical samples was achieved using this diagnostic platform.