Comprehensive proteomic analysis of protein changes during platelet storage requires complementary proteomic approaches
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it