Fecal Aliquot Straw Technique (FAST) allows for easy and reproducible subsampling: assessing interpersonal variation in trimethylamine-N-oxide (TMAO) accumulation
Bibliographic record
Abstract
BACKGROUND: Convenient, reproducible, and rapid preservation of unique biological specimens is pivotal to their use in microbiome analyses. As an increasing number of human studies incorporate the gut microbiome in their design, there is a high demand for streamlined sample collection and storage methods that are amenable to different settings and experimental needs. While several commercial kits address collection/shipping needs for sequence-based studies, these methods do not preserve samples properly for studies that require viable microbes. RESULTS: We describe the Fecal Aliquot Straw Technique (FAST) of fecal sample processing for storage and subsampling. This method uses a straw to collect fecal material from samples recently voided or preserved at low temperature but not frozen (i.e., 4 °C). Different straw aliquots collected from the same sample yielded highly reproducible communities as disclosed by 16S rRNA gene sequencing; operational taxonomic units that were lost, or gained, between the two aliquots represented very low-abundance taxa (i.e., < 0.3% of the community). FAST-processed samples inoculated into germ-free animals resulted in gut communities that retained on average ~ 80% of the donor's bacterial community. Assessment of choline metabolism and trimethylamine-N-oxide accumulation in transplanted mice suggests large interpersonal variation. CONCLUSIONS: Overall, FAST allows for repetitive subsampling without thawing of the specimens and requires minimal supplies and storage space, making it convenient to utilize both in the lab and in the field. FAST has the potential to advance microbiome research through easy, reproducible sample processing.
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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 | 0.001 | 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 | 0.000 |
| 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".