Evaluation of a ‘plug and play’ nanoflow liquid chromatography system for MS-based proteomic characterization of clinical FFPE specimens
Bibliographic record
Abstract
INTRODUCTION: Proteomic analysis of formalin-fixed paraffin-embedded (FFPE) tumor tissue specimens has gained interest in the last 5 years due to technological advances and improved sample collection, as well as biobanking for clinical trials. The real-world implementation of clinical proteomics to these specimens, however, is hampered by tedious sample preparation steps and long instrument acquisition times. AREAS COVERED: To advance the translation of quantitative proteomics into the clinic, we are comparing the performance of the leading commercial nanoflow liquid chromatography (nLC) system (based on literature reviews), the Easy-nLC 1200 (Thermo Fisher Scientific, Waltham, MA, U.S.A.), to the Evosep One HPLC (Evosep Biosystems, Odense, Denmark). We measured FFPE-tissue digests from 21 biological replicates with a similar gradient on both of the LC systems while keeping the on-column amount (1 µg total protein) and the single-shot data-dependent acquisition-based MS/MS method constant. EXPERT OPINION: Overall, the Evosep One facilitates robust and sensitive high-throughput sample acquisition, making it suitable for clinical MS. We found the Evosep One to be a useful platform for positioning mass spectrometry-based proteomics in the clinical setting. The clinical application of nLC/MS will inform clinical decision-making in oncology and other diseases.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".