PANAMA enabled high sensitivity dual nanoflow LC/MS metabolomics and proteomics analysis
Bibliographic record
Abstract
Summary High sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient to prevent nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on robust solid phase micro-extraction step for routine sample clean-up and bioactive molecule enrichment. Our method, termed PANAMA, improves compound resolution and detection sensitivity without compromising depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens including biofluids, cell line and tissue samples. It generates high quality, information rich metabolite-protein datasets while bypassing the need for specialized instrumentation. Motivation The ability to routinely, sensitively and reproducibly analyze both cellular proteins and metabolite mixtures from the same biospecimens can enhance the discovery of biomolecules associated with basic biochemical processes and pathobiological states. Yet existing mass spectrometry-based profiling methods rely on specialized protocols and duplicated instrumentation platforms, resulting in increased time, sample consumption and costs. We sought to generate an effective platform for both metabolomic and proteomic studies on the same samples by enabling nanoflow liquid chromatography for small molecules. The resulting approach was extensively optimized and benchmarked to provide in depth molecular coverage, along with improved chromatographic separations, sensitivity and reliability as compared to existing methods. The cost benefit ratio of PANAMA is substantial because the platform bypasses the need for specialized instrumentation stemming from incompatible procedures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".