An integrated protocol based on workflows of imaged capillary isoelectric focusing (icIEF) for in-depth protein heterogenous characterization: High-efficient fractionation and online mass spectrometry detection
Bibliographic record
Abstract
We present an integrated protocol for studying the charge heterogeneity of therapeutic proteins combining two workflows: imaged capillary isoelectric focusing (icIEF) with fractionation and online mass spectrometry (MS) detection on a single platform. This protocol enables both intact MS-based protein charge variant characterization and in-depth peptide mapping of collected fractions via high-performance liquid chromatography (HPLC) tandem mass spectrometry. Through systematic methodology validation, the platform is demonstrated to be robust, with step-by-step method development, standardized operating procedures (SOPs), exceptional reproducibility, and high sensitivity. Notably, this protocol facilitates icIEF-UV separation followed by either a fractionation scheme or MS online detection within a single platform, providing simplified workflows with reduced reagent consumption compared to traditional techniques. Embracing the ''Green Chemistry'' concept, the protocol addresses key challenges in biopharmaceutical discovery, quality control, and manufacturing, promoting sustainability throughout the process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".