Sensitive Methods to Detect Single-Stranded Nucleic Acids of Food Pathogens Based on Cell-Free Protein Synthesis and Retroreflection Signal Detection.
Sunjoo ChoiYe Seop ParkKyung Won LeeYu Jin ParkHee Ju JangDong-Myung KimTae Hyeon YooPublished in: Journal of agricultural and food chemistry (2024)
Cell-free protein synthesis (CFPS) has recently gained considerable attention as a new platform for developing methods to detect various molecules, ranging from small chemicals to biological macromolecules. Retroreflection has been used as an alternative signal to develop analytical methods because it can be detected by using a simple instrument comprising a white light source and a camera. Here, we report a novel reporter protein that couples the capability of CFPS and the simplicity of retroreflection signal detection. The design of the reporter was based on two pairs of protein-peptide interactions, SpyCatcher003-SpyTag003 and MDM2-PMI(N8A). MDM2-MDM2-SpyCatcher003 was decided as the reporter protein, and the two peptides, SpyTag003 and PMI(N8A), were immobilized on the surfaces of retroreflective Janus particles and microfluidic chips, respectively. The developed retroreflection signal detection system was combined with a previously reported CFPS reaction that can transduce the presence of a single-stranded nucleic acid into protein synthesis. The resulting methods were applied to detect 16S rRNAs of several foodborne pathogens. Concentration-dependent relationships were observed over a range of 10° fM to 10 2 pM, with the limits of detection being single-digit femtomolar concentrations. Considering the designability of the CFPS system for other targets, the retroreflection signal detection method will enable the development of novel methods to detect various molecules.
Keyphrases
- cell free
- label free
- loop mediated isothermal amplification
- nucleic acid
- real time pcr
- binding protein
- crispr cas
- high throughput
- amino acid
- gram negative
- working memory
- mass spectrometry
- small molecule
- single cell
- antimicrobial resistance
- machine learning
- quantum dots
- circulating tumor
- staphylococcus aureus
- circulating tumor cells
- convolutional neural network