Development of an Absolute Quantification Method for hERG Using PRM with Single Isotopologue in-Sample Calibration
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The human ether-à-go-go-related gene (KCNH2)-encoded protein hERG constitutes the α subunit of the Kv11.1 channel and contributes to the I kr current, which plays an important role in the cardiac action potential. Genetically and xenobiotically triggered malfunctions of hERG can cause arrhythmia. The expression of hERG in various study systems was assessed mainly as the fold change relative to the corresponding control. Here, we developed a simple and sensitive quantitation method using targeted mass spectrometry, i.e., the parallel reaction monitoring approach, to measure the absolute quantity of hERG in copy number. Such measurements do not require controls, and the obtained values can be compared with similar results for any other protein. To effectively avoid matrix effects, we used the heavy-match-light (HML) in-sample calibration approach that requires only a single isotopologue to achieve copy-number quantitation. No significant difference was observed in the results obtained by HML and by the classic standard addition in-sample calibration approach. Using four proteotypic peptides, we quantified the average number of copies of hERG in the HEK293T heterologous expression system as 3.6 ± 0.5 × 10 6 copies/cell, i.e., 1 million copies/cell for the fully assembled Kv11.1 channel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".