Method for absolute quantification of short chain fatty acids via reverse phase chromatography mass spectrometry
Bibliographic record
Abstract
Short chain fatty acids (SCFAs; including acetate, propionate, and butyrate) are an important class of biological molecules that play a major role in modulating host-microbiome interactions. Despite significant research into SCFA-mediated biological mechanisms, absolute quantification of these molecules in their native form by liquid chromatography mass spectrometry is challenging due to their relatively poor chromatographic properties. Herein, we introduce SQUAD, an isotope-based strategy for absolute quantification of SCFAs in complex biological samples. SQUAD uses aniline derivatization in conjunction with isotope dilution and analysis by reverse-phase liquid chromatography mass spectrometry. We show that SQUAD enables absolute quantification of biologically relevant SCFAs in complex biological samples with a lower limit of detection of 40 nM and a lower limit of quantification ranging from 160 nM to 310 nM. We observed an intra- and inter-day precision under 3% (relative standard deviation) and errors in intra- and inter-day accuracy under 10%. To demonstrate this quantification strategy, we analyzed SCFAs in the caecal contents of germ free versus conventionally raised specific pathogen free (SPF) mice. We showed that acetate was the most abundant SCFA in both types of mice and was present at 200-fold higher concentration in the SPF mice. We also illustrated the use of our quantification strategy in in vitro microbial cultures from five different species of bacteria grown in Mueller Hinton media. This study illustrates the diverse SCFA production rates across microbial taxa with acetate production serving as one of the key differentiating factors across the species. In summary, we introduce an isotope dilution strategy for absolute quantification of aniline-dativized SCFAs and illustrate the utility of this approach for microbiome research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".