Evaluating long-term stool preservation methods for maximizing the recovery of viable human fecal microbiota
Bibliographic record
Abstract
The gut microbiome plays a fundamental role in human health, prompting efforts to catalog and preserve its diversity across human populations. While DNA sequencing dominates microbiome research, cultivation remains essential for mechanistic studies and therapeutic development. However, best practices for long-term stool preservation remain limited. Here, we compared the stability of eight cryopreservation treatments for maintaining viable stool microbiota over a 1-y storage period at -80 °C (freezer) or at -196 °C (liquid nitrogen) using samples from infants, children, and adults. Combining cultivation on six media with 16S rRNA sequencing, we show that ultralow-temperature cryopreservation has a minimal impact on microbiota diversity compared to fresh cultures. Standard glycerol preservation and simple snap-freezing performed comparably to more complex and costly protocols, with all cultured samples retaining donor-specific microbiota profiles after long-term cryopreservation. The lack of strong treatment-specific effects on microbiota composition suggests a shared microbial response to freeze‒thaw stress favoring taxa with broad environmental tolerance. Our findings offer practical, low-cost strategies for stool biobanking. Importance: The cultivation of bacterial taxa from complex communities, such as those in fecal samples, is essential for mechanistic studies and the development of microbiota-based therapeutics, including defined consortia and individual probiotic strains. Such cultivation efforts typically rely on previously stored samples; however, systematic knowledge regarding long-term preservation strategies that ensure the viability and regrowth of constituent bacterial taxa remains limited. In this study, we systematically evaluated 16 distinct cryopreservation conditions to assess their efficacy in maintaining bacterial viability. Our results show that conventional glycerol-based preservation and simple snap-freezing are comparable in performance to more elaborate and cost-intensive protocols. Moreover, we identified the duration of sample transport prior to freezing as a critical determinant of post-thaw bacterial recovery. These findings provide valuable data on the relative effectiveness of various preservation methods and support the use of low-cost, easily implementable strategies that are particularly suitable for application in resource-limited settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".