An rRNA hybridization-based approach for rapid and accurate identification of diverse fungal pathogens

Invasive fungal infections are a global threat for which early diagnosis is critical for patient outcomes, but current diagnostic measures remain notoriously slow. Here, we extend our multiplexed, hybridization-based rRNA-targeted strategy for rapid, sensitive pathogen identification, previously designed for diverse bacteria and Candida species, to identify diverse fungal pathogens. We created a set of 91 probes targeting 86 medically relevant fungal species, designed to recognize regions of differential conservation across taxonomic groupings, from class- to species-specific probes. We assessed assay performance across a Training Set of 93 clinical isolates spanning 32 species of common fungal pathogens across 18 genera, with Pearson correlations of probeset reactivity profiles identifying the pathogen at the species, genus, and family level with 83%, 94%, and 95% accuracy, respectively, in a leave-one-out analysis. We developed a new classifier on this Training Set, using taxonomic categories to select progressively more informative probes at each taxonomic level. After optimization, we assessed performance on an independent Validation Set of 54 clinical isolates spanning the same species as the Training Set, with 91%, 94%, and 98% at the species, genus, and family levels, respectively. We piloted our assay on formalin-fixed paraffin-embedded (FFPE) tissue, demonstrating rapid, culture-independent fungal identification from this high-value clinical sample type, often the sole specimen available. The assay requires <30 min hands-on time (or <65 min from FFPE tissue), returning results in <8 h from cultured specimen on an RNA detection platform available in clinical laboratories.IMPORTANCETimely identification of fungal pathogens is critical for patient outcomes. Here we describe a multiplexed hybridization-based assay targeting rRNA that capitalizes on the high sequence conservation and abundance of rRNA to generate unique reactivity profiles that serve as "fingerprints" of diverse fungal pathogens. By comparing these fingerprints to a Training Set of known isolates, this novel assay enables accurate identification of 32 species of pathogenic fungi from crude lysates of cultured clinical isolates.