A comprehensive CHO SWATH-MS spectral library for robust quantitative profiling of 10,000 proteins
Bibliographic record
Abstract
Sequential window acquisition of all theoretical fragment-ion spectra (SWATH) is a data-independent acquisition (DIA) strategy that requires a specific spectral library to generate unbiased and consistent quantitative data matrices of all peptides. SWATH-MS is a promising approach for in-depth proteomic profiling of Chinese hamster Ovary (CHO) cell lines, improving mechanistic understanding of process optimization, and real-time monitoring of process parameters in biologics R&D and manufacturing. However, no spectral library for CHO cells is publicly available. Here we present a comprehensive CHO global spectral library to measure the abundance of more than 10,000 proteins consisting of 199,102 identified peptides from a CHO-K1 cell proteome. The robustness, accuracy and consistency of the spectral library were validated for high confidence in protein identification and reproducible quantification in different CHO-derived cell lines, instrumental setups and downstream processing samples. The availability of a comprehensive SWATH CHO global spectral library will facilitate detailed characterization of upstream and downstream processes, as well as quality by design (QbD) in biomanufacturing. The data have been deposited to ProteomeXchange (PXD016047).
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".