A multi-matrix workflow for isolation, characterization, and biobanking of antimicrobial- resistant bacteria from One Health surveillance samples

Abstract

Objectives Standardized protocols for the isolation of antimicrobial-resistant (AMR) bacteria are typically optimized for individual sample types, limiting comparability across One Health surveillance domains. Here, we describe a multi-matrix workflow for the isolation, characterization, and biobanking of AMR bacteria (Enterobacterales, Pseudomonadales and Staphylococcus aureus) from diverse One Health sample types, with the aim of enabling reproducible and comparable AMR surveillance across sectors and settings. Objective The workflow integrates common methodological principles, including enrichment media, defined combinations of selective and chromogenic agar plates, phenotype-based colony selection, antimicrobial susceptibility testing, and whole-genome sequencing–ready isolate preparation. A structured picking scheme enables the systematic selection of multiple isolates per phenotype, allowing resolution of within-sample and within-species diversity. The protocol is applicable to swabs, fecal material, water, soil, insects, and food samples and is designed to be scalable and adaptable to laboratories with varying levels of infrastructure. Results, application and demonstration Application of the workflow to samples of varying complexity demonstrates its capacity to recover multiple AMR bacterial species and distinct strains across matrices. More complex samples yielded a higher number of isolates, and the picker scheme enabled discrimination of phenotypically and genotypically distinct strains within individual samples, illustrating the protocol’s resolution and robustness. Conclusion This multi-matrix workflow provides a practical and reproducible approach for the isolation and biobanking of AMR bacteria across heterogeneous One Health samples. Its simplicity, adaptability, and cross-matrix comparability support its use in both high-throughput laboratories and resource-limited settings, thereby facilitating integrated AMR surveillance and comparative analyses at broader scales.