Highly accurate and sensitive absolute quantification of bacterial strains in human fecal samples
Bibliographic record
Abstract
Next-generation sequencing (NGS) approaches have revolutionized gut microbiome research and can provide strain-level resolution, but these techniques have limitations in that they are only semi-quantitative, suffer from high detection limits, and generate data that is compositional. The present study aimed to systematically compare quantitative PCR (qPCR) and droplet digital PCR (ddPCR) for the absolute quantification of Limosilactobacillus reuteri strains in human fecal samples and to develop an optimized protocol for the absolute quantification of bacterial strains in fecal samples. Using strain-specific PCR primers for L. reuteri 17938, ddPCR showed slightly better reproducibility, but qPCR was almost as reproducible and showed comparable sensitivity (limit of detection [LOD] around 104 cells/g feces) and linearity (R2 > 0.98) when kit-based DNA isolation methods were used. qPCR further had a wider dynamic range and is cheaper and faster. Based on these findings, we conclude that qPCR has advantages over ddPCR for the absolute quantification of bacterial strains in fecal samples. We provide an optimized and easy-to-follow step-by-step protocol for the design of strain-specific qPCR assays, starting from primer design from genome sequences to the calibration of the PCR system. Validation of this protocol to design PCR assays for two L. reuteri strains, PB-W1 and DSM 20016 T, resulted in a highly accurate qPCR with a detection limit in spiked fecal samples of around 103 cells/g feces. Applying our strain-specific qPCR assays to fecal samples collected from human subjects who received live L. reuteri PB-W1 or DSM 20016 T during a human trial demonstrated a highly accurate quantification and sensitive detection of these two strains, with a much lower LOD and a broader dynamic range compared to NGS approaches (16S rRNA gene sequencing and whole metagenome sequencing). Based on our analyses, we consider qPCR with kit-based DNA extraction approaches the best approach to accurately quantify gut bacteria at the strain level in fecal samples. The provided step-by-step protocol will allow scientists to design highly sensitive strain-specific PCR systems for the accurate quantification of bacterial strains of not only L. reuteri but also other bacterial taxa in a broad range of applications and sample types.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".