Development of Mass Spectrometry-Based Shotgun Method for Proteome Analysis of 500 to 5000 Cancer Cells
Bibliographic record
Abstract
A shotgun proteome analysis method and its performance for protein identification from 500 to 5000 cells are described. Sample preparation, which was done in one tube, involved the use of a surfactant (NP-40) for cell lysis, followed by acetone precipitation of the proteins. The resulting protein pellet was washed with cold acetone to remove remaining surfactant, and the pellet was then solubilized in NH(4)HCO(3). After trypsin digestion of the proteins, the digest was analyzed by the use of nanoflow liquid chromatography (LC) quadrupole time-of-flight mass spectrometry (QTOF MS). Sample injection and gradient speed in running LC QTOF MS were optimized. It was shown that this method could identify an average (n = 3) of 167 +/- 21, 237 +/- 30, 491 +/- 63, and 619 +/- 59 proteins from 500, 1000, 2500, and 5000 MCF-7 breast cancer cells, respectively. To demonstrate the potential use of this method for generating proteome profile from circulating tumor cells (CTCs) isolated from human blood, a healthy human blood sample was spiked with MCF-7 cells, and this mixture was processed and then subjected to antibody tagging of the MCF-7 cells. The tagged cells were sorted and collected using flow cytometry. The proteome profiles of small numbers of cells isolated in this way were found to be similar to those of the original MCF-7 cells, suggesting the possibility of the use of this method for cell typing of CTCs.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".