Frontiers in microbiology · 2026
Unveiling pathogens and contaminants: refining metagenomics for clinical diagnostics.
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Ibañez-Lligoña Marta, Colomer-Castell Sergi, Campos Carolina, González-Camuesco Álvaro, Llauradó Arnau, Garcia-Larroy Jorgina, Sánchez-Tejerina Daniel, Rando-Segura Ariadna, Andrés Cristina, Esperalba Juliana, Nadal Patricia, Ferrer Roser, Cortese Maria Francesca, Tabernero David, Gregori Josep, Riveiro-Barciela Mar, Ruiz-Cobo Juan Carlos, Ruiz Agustin, Del Barco Ester, Buti Maria, Goya Maria, Antón Andrés, Cano Amanda, Juntas-Morales Raúl, Quer Josep
Аннотация
INTRODUCTION: Shotgun metagenomic sequencing (mNGS), an untargeted approach that sequences all nucleic acids in a sample, has emerged as a powerful tool for pathogen detection and genome characterization. However, its implementation in clinical diagnostics remains limited due to technical challenges such as contamination and reduces sensitivity, especially in low-biomass samples.
METHODS: We applied mNGS to 144 clinical samples representing chronic infections, acute infections, and respiratory co-infections. To address contamination, we established a framework integrating negative controls, lab-specific contaminant watchlists, and computational filtering. Viral detection performance and genome recovery were assessed across sample types and viral loads.
RESULTS: Viral load was shown to be the primary determinant of sensitivity, with reliable recovery achieved only at higher titers. Our framework substantially improved contamination management, reducing false-positive signals and enhancing viral genome recovery. mNGS enabled the detection of clinically relevant co-infections and refined viral classification beyond targeted diagnostics, while also revealing the substantial risk of spurious detections in the absence of contamination-aware workflows.
DISCUSSION: These findings define practical sensitivity thresholds for clinical mNGS and underscore the need for contamination-aware workflows, particularly for low-biomass samples, while providing an open-source contaminants watchlist that enhances reliability and utility of clinical metagenomics.
METHODS: We applied mNGS to 144 clinical samples representing chronic infections, acute infections, and respiratory co-infections. To address contamination, we established a framework integrating negative controls, lab-specific contaminant watchlists, and computational filtering. Viral detection performance and genome recovery were assessed across sample types and viral loads.
RESULTS: Viral load was shown to be the primary determinant of sensitivity, with reliable recovery achieved only at higher titers. Our framework substantially improved contamination management, reducing false-positive signals and enhancing viral genome recovery. mNGS enabled the detection of clinically relevant co-infections and refined viral classification beyond targeted diagnostics, while also revealing the substantial risk of spurious detections in the absence of contamination-aware workflows.
DISCUSSION: These findings define practical sensitivity thresholds for clinical mNGS and underscore the need for contamination-aware workflows, particularly for low-biomass samples, while providing an open-source contaminants watchlist that enhances reliability and utility of clinical metagenomics.