Metagenomics-based environmental surveillance on medical intensive care unit surfaces

Effective surveillance based on metagenomics is increasingly important in infection prevention. However, current workflows are insufficient for proper risk assessment. Upon evaluating and optimizing techniques, we recommend best practices and introduce a well-structured workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, and is quantitative. The workflow was developed using a representative microbiome sample which was created by aggregating 120 surface swabs collected from the medical intensive care unit at Rush University Medical Center. Liquid-liquid extraction, PMA treatment equipped with internal standards and absolute abundance profiling, qPCR, and a machine learning-based model are the recommended components for the comprehensive workflow, with whole-cell filtration and cultivation as optional accessories under particular circumstances. This workflow will contribute to more effective environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.