On-site detection of multiple extracellular antibiotic resistance genes using SERS

Extracellular antibiotic resistance genes (eARGs) exacerbate the propagation of antimicrobial resistance in the environment and pose great threats to human health. On-site identification and quantification of multiple eARGs at trace level are an urgent need. Herein, we designed a core-satellite platform based on surface enhanced Raman scattering (SERS) to simultaneously detect multiple eARGs (sul1, tetA, and bla(TEM)) in the field without PCR amplification. The detection limits of 0.66 (sul1), 0.66 (tetA), and 0.13 aM (bla(TEM)) were obtained in one SERS measurement which evidenced its ultra-high sensitivity. The multivariate machine learning model with random forest algorithm exhibited good regressions between the actual and predicted concentrations of eARGs fragments (R-sul1(2) = 0.985, R-tetA(2) = 0.993, R-blaTEM(2) = 0.994, p < 0.05). The whole process is within 40 min from sample collection to the acquisition of the SERS spectra, realizing an efficient analysis. This SERS strategy successfully quantified the eARGs in wastewater treatment plants, cattle and aquaculture farms, and groundwater, and the results are comparable to droplet digital PCR assays. Our study opens a new avenue for multiple eARGs surveillance on-site.