Assessment of the impact of different fecal storage protocols on the microbiota diversity and composition: a pilot study
Bibliographic record
Abstract
BACKGROUND: Fecal samples are currently the most commonly studied proxy for gut microbiota. The gold standard of sample handling and storage for microbiota analysis is maintaining the cold chain during sample transfer and immediate storage at - 80 °C. Gut microbiota studies in large-scale, population-based cohorts require a feasible sample collection protocol. We compared the effect of three different storage methods and mock shipment: immediate freezing at - 80 °C, in 95% ethanol stored at room temperature (RT) for 48 h, and on blood collection card stored at RT for 48 h, on the measured composition of fecal microbiota of eight healthy, female volunteers by sequencing the V4 region of the 16S rRNA gene on an Illumina MiSeq. RESULTS: Shared operational taxonomic units (OTUs) between different methods were 68 and 3% for OTUs > 0.01 and < 0.01% mean relative abundance within each group, respectively. α and β-diversity measures were not significantly impacted by different storage methods. With the exception of Actinobacteria, fecal microbiota profiles at the phylum level were not significantly affected by the storage method. Actinobacteria was significantly higher in samples collected on card compared to immediate freezing (1.6 ± 1.1% vs. 0.4 ± 0.2%, p = 0.005) mainly driven by expansion of Actinobacteria relative abundance in fecal samples stored on card in two individuals. There was no statistically significant difference at lower taxonomic levels tested. CONCLUSION: Consistent results of the microbiota composition and structure for different storage methods were observed. Fecal collection on card could be a suitable alternative to immediate freezing for fecal microbiota analysis using 16S rRNA gene amplicon sequencing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".