Off-Line Two-Dimensional Liquid Chromatography with Maximized Sample Loading to Reversed-Phase Liquid Chromatography-Electrospray Ionization Tandem Mass Spectrometry for Shotgun Proteome Analysis
Bibliographic record
Abstract
We demonstrate a strategy of maximizing the performance of reversed-phase (RP) liquid chromatography (LC) tandem mass spectrometry (MS/MS) for efficient shotgun proteome analysis by optimizing the sample loading to the instrument in an off-line two-dimensional (2D) LC tandem MS platform. To determine the quantity of peptides present in a proteome digest or fractionated peptides from strong-cation exchange (SCX) separation, an automated system based on RPLC with a rapid step solvent gradient for peptide elution and ultraviolet (UV) detection was developed. This system also allowed the purification of the peptides by removing salts and other impurities present in a sample. It was found that controlling the amount of peptides injected into a RPLC MS/MS system was critical to achieve the maximum efficiency in peptide and protein identification. With the use of off-line 2D-LC-MS/MS, peptide fractions from the first dimension of separation were desalted and quantified, followed by injecting the optimal amount of the sample into RPLC-MS/MS for peptide sequencing. The application of this strategy was demonstrated in the proteome profiling of breast cancer MCF-7 cells. From the analysis of 28 SCX fractions with each injecting 1 microg of sample into a 75 mum x 100 mm C18 column interfaced to a quadrupole/time-of-flight mass spectrometer, a total of 2362 unique proteins or protein groups were identified with a false positive peptide identification rate of 0.19%, as determined by target-decoy proteome sequence searches. Replicate 2 h runs of individual fractions with the exclusion of precursor ions of peptides already identified in the first runs resulted in the identification of an additional 549 unique proteins or protein groups with a false positive identification rate of 0.60%. This example illustrated that off-line 2D-LC-MS/MS, with maximal sample injection to the RPLC-MS, is an effective method for shotgun proteome analysis. Finally, the advantages and limitations of this method, compared to other methods, are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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 teacher head, 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".