Isotopologue Ratios Identify 13C-Depleted, Methanotrophic Biomarkers in Complex Environmental Samples
Bibliographic record
Abstract
Summary Our study introduces a novel isotopologue-based approach for compound-specific isotope analysis (CSIA) using ultra-high-performance liquid chromatography–high-resolution mass spectrometry (UHPLC/HRMS), which extends d¹³C analysis to non-volatile, intact lipids typically inaccessible to conventional gas chromatography–isotope ratio mass spectrometry (GC/irMS). By leveraging the distinct imprint of ¹³C-depletion in isotopologue distributions, particularly the M1/M0 ratio, our method enables the reliable identification of ¹³C-depleted methanotrophic lipids in complex environmental systems. Method development, based on archaeol, a lipid amenable to both GC/irMS and UHPLC/HRMS, demonstrated a strong correlation (R = 0.94) between d¹³C values derived from our isotopologue-based approach and conventional GC/irMS. Subsequent application to lipid extracts from the Guaymas Basin, characterized by the widespread occurrence of anaerobic methane oxidation (AOM), proved highly effective in differentiating biomarkers strongly associated with methane-oxidizing archaea, such as intact archaeol derivatives, from those derived from other non-methanotrophic sources. These findings underscore the potential of isotopologue-based CSIA using UHPLC/HRMS as a powerful new tool for tracing methanotrophic microbial communities and methane cycling in modern and paleo-environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".