Combinatorial Click Chemistry Labeling to Study Live Human Gut-Derived Microbiota Communities
Bibliographic record
Abstract
Gut bacteria were shown to exert pivotal effects on health and disease. However, mechanistic studies of gut bacterial communities are limited due to the lack of technologies for real-time studies on live bacteria. Here, we developed COMBInatorial cliCK-chemistry (COMBICK) labeling on human gut-derived bacteria, both aerobic and anaerobic strains, to enable dynamic tracing of live, heterogeneous bacterial communities on the strain level, including clinical isolates of the Enterobacteriaceae family. We further show that COMBICK labeling is applicable on anaerobic bacterial strains directly isolated from stool. In COMBICK, the number of labeled bacteria that can be simultaneously differentiated increases exponentially depending on the availability of fluorophores and machine capabilities. This method allows real-time studies of bacterial communities from a variety of ecosystems, and can significantly advance mechanistic research in the microbiome field.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".