Liquid Chromatographic and Mass Spectrometric Methods for Quantitative Proteomic Analysis from Single-Cell and Nanogram-Level Samples
Bibliographic record
Abstract
Liquid chromatography (LC) and mass spectrometry (MS) are two critical components in proteomics. Advances in methods for both LC and MS have significantly enhanced protein identification and quantifications of limited amounts of proteins, particularly at the picogram-to-nanogram level of proteins. In this study, we explored various LC conditions and MS platforms to optimize protein identification and quantification using data-independent acquisition (DIA). Our investigation focused on evaluating the sensitivity for protein identification, reproducibility of quantification, and robustness across multiple models, specifically focused on analyzing proteins at pico- to nanogram levels, with an emphasis on single-cell proteomics. We further applied our approach for the proteomic analysis of HeLa single cells. Overall, we identified and quantified over 6300 proteins at the single-cell level amount of peptides with a coefficient of variation (CV) of less than 20%, and detected up to 5000 proteins from isolated single HeLa cell samples. Finally, we analyzed docetaxel-treated and nontreated PC3 cells to reveal proteome changes at the single-cell level. This study provides a comprehensive technical evaluation for LC-MS methods in protein identification and quantification for analytical applications involving single-cell proteomics from the picogram to nanogram level of proteins.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".