From Gene Copies to Cell Numbers: Advancing Quantitative Approaches in Protistan Ecology Using Digital PCR

Quantifying abundances of unicellular eukaryotes (protists) remains a central challenge in microbial ecology, as methodological differences can strongly influence abundance estimates and ecological interpretation. Although molecular tools have thus far greatly improved our understanding of protists, high rRNA gene copy numbers limit quantitative inferences. Digital PCR (dPCR) has emerged as a promising tool for absolute quantification, yet its application for unicellular eukaryotes and its comparability to established cell-based methods remain insufficiently explored. Here, we develop species-specific dPCR assays for two important freshwater ciliates (Urotricha castalia and Urotricha pseudofurcata) and establish gene copy number correction factors to enable highly accurate quantitative abundance estimates. We assess assay performance using controlled laboratory experiments and apply the approach to environmental samples, directly benchmarking dPCR against catalyzed reporter deposition-FISH (CARD-FISH). Under controlled conditions, dPCR and CARD-FISH yielded comparable accuracy, with dPCR showing superior precision. In field applications, method-dependent differences emerged, reflecting both methodological constraints and biological variability. Notably, dPCR provided an overall higher sensitivity, enabling robust detection of low-abundance taxa. Our results highlight dPCR as a scalable and sensitive approach that, when combined with appropriate correction strategies, represents a significant step towards more reliable molecular quantification of protists. At the same time, differences between methods underscore the value of integrating molecular and microscopy-based approaches. We propose that combining dPCR with tools such as CARD-FISH can offer complementary insights into protist population dynamics. Such integrative frameworks provide a powerful path forward for improving abundance estimates and advancing quantitative microbial ecology.