Amplicon sequencing with internal standards yields accurate picocyanobacteria cell abundances as validated with flow cytometry
Bibliographic record
Abstract
Abstract To understand ecosystem state and function, marine microbial ecologists seek measurements of organismal abundance and diversity at high taxonomic resolution. Conventional flow cytometry accurately estimates microbial cell abundance but only discerns broad groups with distinct optical properties. While amplicon sequencing resolves more comprehensive diversity within microbiomes, it typically only provides relative organismal abundances within samples, not absolute abundance changes. Internal genomic standards offer a solution for absolute amplicon-based measures. Here, we spiked genomic standards into plankton samples from surface seawater, gathered at 46-km intervals along a cruise transect spanning the southern California Current System and the oligotrophic North Pacific Subtropical Gyre. This enabled evaluation of the absolute volumetric gene copy abundances of 16S rRNA amplicon sequence variants (amplified with 515Y-926R universal primers, quantitatively validated with mock communities) and cell abundances of picocyanobacteria with known genomic 16S copy numbers. Comparison of amplicon-derived cell abundances of Prochlorococcus and Synechococcus with flow cytometry data from nearby locations yielded nearly identical results (slope = 1.01; Pearson’s r = 0.9942). Our findings show that this amplicon sequencing protocol combined with genomic internal standards accurately measures absolute cell counts of marine picocyanobacteria in complex field samples. By extension, we expect this approach to reasonably estimate volumetric gene copies for other amplified taxa in these samples.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".