Sensitive, Efficient Quantitation of <sup>13</sup> C-Enriched Nucleic Acids via Ultrahigh-Performance Liquid Chromatography–Tandem Mass Spectrometry for Applications in Stable Isotope Probing
Bibliographic record
Abstract
Stable isotope probing (SIP) of nucleic acids is a powerful tool for studying the functional traits of microbial populations within complex communities, but SIP involves a number of technical challenges. Many of the difficulties in DNA-SIP and RNA-SIP experiments can be effectively overcome with an efficient, sensitive method for quantitating the isotopic enrichment of nucleic acids. Here, we present a sensitive method for quantitating (13)C enrichment of nucleic acids, requiring a few nanograms of sample, and we demonstrate its utility in typical DNA-SIP and RNA-SIP experiments. All five nucleobases (adenine, guanine, cytosine, thymine, and uracil) were separated and detected by using ultrahigh-performance liquid chromatography-tandem mass spectrometry. We detected all isotopic species in samples with as low as 1.5 atom% (13)C above natural abundance, using 1-ng loadings. Quantitation was used to characterize the isotopic enrichment kinetics of cellulose- and lignin-based microcosm experiments and to optimize the recovery of enriched nucleic acids. Application of our method will minimize the quantity of expensive isotopically labeled substrates required and reduce the risk of failed experiments due to insufficient recovery of labeled nucleic acids for sequencing library preparation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".