Comprehensive proteomic analysis of protein changes during platelet storage requires complementary proteomic approaches
Bibliographic record
Abstract
BACKGROUND: Proteomics methods may be used to analyze changes occurring in stored blood products. These data sets can identify processes leading to storage-associated losses of blood component quality such as the platelet (PLT) storage lesion (PSL). The optimal strategy to perform such analyses to obtain the most informative data sets, including which proteomics methods, is undefined. This study addresses relative differences among proteomics approaches to the analysis of the PLT storage lesion. STUDY DESIGN AND METHODS: Changes to the PLT proteome between Days 1 and 7 of storage were analyzed with three complementary proteomic approaches with final mass spectrometry analysis: two-dimensional (2D) gel electrophoresis/differential gel electrophoresis (DIGE), isotope tagging for relative and absolute quantitation (iTRAQ), and isotope-coded affinity tagging (ICAT). Observed changes in concentration during storage of selected proteins were confirmed by immunoblotting. RESULTS: In total, 503 individual proteins changed concentration over a 7-day storage period. By method, a total of 93 proteins were identified by 2D gel/DIGE, 355 by iTRAQ, and 139 by ICAT. Less than 16 percent of the 503 proteins, however, were identified by not more than at least two proteomic approaches. Only 5 proteins were identified by all approaches. Membrane protein changes were not reliably detected with 2D gel/DIGE methods. CONCLUSION: Although proteomics analyses identified many storage-associated protein changes, these varied significantly by method suggesting that a combination of protein-centric (2D gel or DIGE) and peptide-centric (iTRAQ or ICAT) approaches are essential to acquire adequate data. The use of one proteomics method to study changes in stored blood products may give insufficient information.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".