Unbiased DNA Pathogen Detection in Tissues: Real-World Experience With Metagenomic Sequencing in Pathology

Pathogen detection in formalin-fixed paraffin-embedded (FFPE) tissue remains challenging. We implemented metagenomic next-generation sequencing (mNGS) in our clinical diagnostic workflow to evaluate its feasibility, diagnostic yield, and pathogen spectrum in routine infectious pathology cases. Between November 2021 and April 2025, we analyzed 623 FFPE tissue samples using a low-depth mNGS workflow on the Thermo Fisher Ion Torrent platform with a CLC Genomics Workbench (Qiagen) bioinformatics pipeline. Our assay was designed to detect DNA pathogens. When possible, results were validated by orthogonal methods, including species-specific PCRs, 16S/internal transcribed spacer PCR, and immunohistochemistry on tissue sections. Among 623 samples analyzed, at least 1 potentially pathogenic and plausible microorganism was identified in 229 samples (36.8%), whereas 334 (53.6%) were negative and 60 (9.6%) were uninterpretable due to quality control failures or suspected contamination. Of the 229 positive samples, 145 (63.3%) involved bacteria, 37 (16.2%) viruses, 28 (12.2%) fungi, and 9 (3.9%) parasites; mixed infections with >1 pathogen were detected in 10 (4.4%) samples. The most frequently identified bacterial family was Mycobacteriaceae (n = 27), including Mycobacterium xenopi (n = 8), which is not routinely covered by syndromic multiplex PCR panels. Notable viral and fungal detections included a novel human circovirus and Coccidioides posadasii. Despite variable sample quality and DNA input, mNGS yielded reliable results in a wide range of tissue types. mNGS is a feasible, valuable addition to routine infectious pathology diagnostics, particularly in complex or inconclusive cases. The assay improved the diagnostic yield compared with conventional PCR, expanded the range of detectable pathogens, and proved robust even in low-quality FFPE samples. These results support broader adoption of mNGS in tissue-based pathogen diagnostics.