FISH, Chips, and Clicks: Integrating FISH, Digital PCR, and AI Microscopy for Filamentous Bacteria Identification in Wastewater

Filamentous bacteria significantly influence activated sludge performance, yet reliable identification remains challenging in routine municipal laboratory settings. Traditional microscopy provides morphological information but can be limited by analyst subjectivity and the lack of species-level identification. Building on our core microscopy program rather than replacing it, the RWF Laboratory is conducting a proof-of-concept study that incorporates fluorescence in situ hybridization (FISH), digital PCR (dPCR), and AI assisted image analysis for enhanced filament identification.
Mixed liquor samples from wastewater in our laboratory are routinely examined using phase-contrast microscopy to support process control and operations. In this study, we propose supplementing this traditional approach with commercially available FISH probes targeting filament groups of operational relevance, including Microthrix, Nocardia, and Thiothrix. Complementary dPCR assays will be developed for sensitive, species-level detection and quantification of target genetic markers, featuring low method detection limits (MDLs) and high specificity to differentiate specific microbial groups, such as chlorine-resistant filaments prevalent in activated sludge. In addition, digital micrographs generated from phase-contrast microscopy will be processed using a machine-learning image-recognition model trained on annotated sludge images to identify filament structures and estimate relative abundance. This proof-of-concept effort highlights a practical model for integrating molecular and AI-enabled tools into everyday wastewater microscopy programs. By generating preliminary performance insights across FISH, dPCR, and machine-learning image analysis, this work provides a foundation for future validation, method refinement, and wider adoption among municipal laboratories seeking higher specificity, faster analyst training, and safer workflows without abandoning classical microscopy expertise.