Accurate Quantitative Proteomic Analyses Using Metabolic Labeling and High Field Asymmetric Waveform Ion Mobility Spectrometry (FAIMS)
Bibliographic record
Abstract
Stable isotope labeling by amino acids in cell culture (SILAC) is routinely used to profile changes in protein and peptide abundance across different experimental paradigms. As with other quantitative proteomic approaches, the detection of peptide isotopomers can be limited by the presence of interference ions that ultimately affect the quality of quantitative measurements. Here, we evaluate high field asymmetric waveform ion mobility spectrometry (FAIMS) to improve the accuracy and dynamic range of quantitative proteomic analyses using SILAC. We compared quantitative measurements for tryptic digests of isotopically labeled protein extracts mixed in different ratios using LC-MS/MS with and without FAIMS. To further reduce sample complexity, we also examined the improvement in quantitative measurements when combining strong cation exchange (SCX) fractionation prior to LC-MS/MS analyses. Using the same amount of sample consumed, analyses performed using FAIMS provided more than 30% and 200% increase in the number of quantifiable peptides compared to LC-MS/MS performed with and without SCX fractionation, respectively. Furthermore, FAIMS reduced the occurrence of interfering isobaric ions and improved the accuracy of quantitative measurements. We leveraged the application of FAIMS in phosphoproteomic analyses to profile dynamic changes in protein phosphorylation in HEK293 cells subjected to heat shock for periods up to 20 min. In addition to the enhanced phosphoproteomic coverage, FAIMS also provided the ability to separate phosphopeptide isomers that often coelute and can be misassigned in conventional LC-MS/MS experiments.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".