Estimating absolute microbial abundances from metabarcoding anchored to cytometry data
Ser Giacomi, E.; Raut, Y.; McNichol, J.; Ribalet, F.; Tarran, G.; Hassler, C.; Dutkiewicz, S.; Fuhrman, J.; Follows, M.
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Over the past decades, metabarcoding and automated cell-counting approaches have greatly advanced our understanding of marine microbial communities. Metabarcoding provides high taxonomic resolution and comprehensive community characterization, typically as relative gene abundances, whereas flow cytometry provides absolute cell abundances but lower taxonomic coverage. Here, we assess whether concurrent flow-cytometry observations can calibrate metabarcoding data to derive absolute gene abundances across four basin-scale Atlantic and Pacific Ocean transects. We first show that flow-cytometry-anchored calibration reproduces absolute abundances of Prochlorococcus and Synechococcus with performance comparable (R2 = 0.87) to internal DNA standard-based quantification. For datasets lacking internal standards, the choice of cytometric "anchor" species introduces systematic offsets in absolute abundance estimates, although spatial patterns remain robust. These offsets may reflect underestimation of cytometric counts or variation in rRNA gene copy numbers among actively dividing cells. We therefore recommend the use of multiple anchors where possible to diagnose systematic uncertainty. Applying this framework, we derive absolute gene concentrations for diverse plankton taxa from compositional metabarcoding data. For taxa with known rRNA gene copy numbers, calibration further enables estimation of absolute cell concentrations. We also resolve ecotype-level absolute abundances of Prochlorococcus along a longitudinal temperature gradient, revealing ecological patterns not apparent from compositional or cytometric data alone. Our results demonstrate that calibrated metabarcoding provides a practical quantitative bridge between molecular and cytometric observations, yielding high taxonomic resolution together with absolute gene concentrations and quantified uncertainties.
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