Back

A biosensor encompassing fusarinine C-magnetic nanoparticles and aptamer-red/green carbon dots for dual-channel fluorescent and RGB discrimination of Campylobacter and Aliarcobacter

Liu, W.; Chi, Z.

2023-02-23 bioengineering
10.1101/2023.02.22.529591 bioRxiv
Show abstract

The diarrhea pathogens Campylobacter and Aliarcobacter are similar in morphology and their resulting symptoms, making them difficult to be differentially diagnosed. Herein, we report a biosensor with two newly-synthesized modules to differentiate the genera-representative species of C. jejuni and A. butzleri. Module 1 was fusarinine C-decorated magnetic nanoparticles; module 2 consisted of C. jejuni-specific aptamer modified with red-emitting carbon dots (CDs) and A. butzleri-specific aptamer-modified green-emitting CDs. These two CDs had non-interfering spectra. Module 1 was used to selectively capture C. jejuni and A. butzleri from an un-cultured sample, and the specific CDs in module 2 would then recognize and bind to their counterpart bacteria when subjected to the collected module 1-bacteria complex. By measuring the fluorescence intensities from each CDs, the existence and abundance of each bacterium could be differentially indicated. This biosensor exhibited a wide detection range of up to 1 x 107 CFU/mL and the lowest limit of detection (LOD) of 1 CFU/mL, for each bacterium. Thus, the biosensor with dual-fluorescent channels facilitated a culture-independent, ultrasensitive and discriminative detection of C. jejuni and A. butzleri. Remarkably, this fluorescent detection could be transformed into RGB color indication to render the visual discrimination. After integrating the microfluidics, this biosensor offered RGB differentiation of the two bacteria in human stool or chicken broilers with a LOD of 5 CFU/mL and turnaround time of 65 min. This work suggested a new biosensor-based methodology for the discrimination of Campylobacter and Aliarcobacter in real samples.

Matching journals

The top 2 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.