Multidimensional analysis of dental unit waterline biofilms: integration of morphologic observation and microbial diversity assessment.

Objectives: To comprehensively characterize dental unit waterline (DUWL) biofilms through integrated analysis combining scanning electron microscopy and high-throughput sequencing, examining structural features and microbial community composition across different tubing sections.

Method and materials: For each of the eight dental units with high-speed handpiece waterlines in continuous clinical service for ≥6 months at a university dental hospital, three 2-cm segments were collected from proximal, middle, and distal sections (total n=24). Scanning electron microscopy evaluated biofilm coverage percentage and morphology across 10 fields per specimen using a 9-grid quantification method. Microbial communities were analyzed using 16S rRNA gene V3-V4 region sequencing with subsequent bioinformatic processing through QIIME2. Statistical analyses included ANOVA, Kruskal-Wallis tests, and PERMANOVA to assess sectional differences.

Results: Scanning electron microscopy revealed a gradient of biofilm coverage increasing from proximal (31%) to middle (87%) and distal sections (nearly 100%). Structural features included partially detached biofilm fragments and distinctive bulging bacterial aggregates. Molecular analysis identified Proteobacteria as the predominant phylum, followed by Bacteroidetes and Firmicutes, with significant variations in community composition between sections. The distal section showed the highest microbial diversity, with significant differences observed in Chao1 richness (P =.025), observed species (P =.025), and ODS coverage (P =.024) between study groups. Principal coordinate analysis confirmed distinct clustering patterns among biofilm communities from different tubing sections.

Conclusion: This multidimensional analysis achieved its aim of comprehensively characterizing DUWL biofilms, establishing that heterogeneous distribution patterns warrant section-specific monitoring approaches for effective contamination control.