Proteomic analysis of red blood cells from donors exhibiting high hemolysis demonstrates a reduction in membrane‐associated proteins involved in the oxidative response
Bibliographic record
Abstract
BACKGROUND The development of hemolysis during ex vivo hypothermic storage is multifaceted. Standardization of collection and production processes is used to minimize variability in biologics manufacturing and to maximize product quality. However, the influence of various donor characteristics on product quality is often difficult to evaluate and to control. Using a proteomic approach, we aimed to decipher relevant donor characteristics that may predict red blood cell (RBC) quality during storage. STUDY DESIGN AND METHODS Ten healthy volunteer donors exhibiting repeated high hemolysis at outdate (>0.8%; RBCHH) and 10 age‐ and sex‐matched control donors (RBCCtrl) were studied. Common quality variables were measured on Days 5, 14, 21, 28, and 42 of storage. Protein profiles of hemoglobin‐depleted membrane fractions from RBCHH and RBCCtrl donors were analyzed using a quantitative proteomics approach based on iTRAQ (isobaric tags for relative and absolute quantitation). RESULTS Time‐dependent lesion development was apparent in both donor populations. RBCHH exhibited reduced 2,3‐bisphosphoglycerate levels (p < 0.001) and morphologic score (p < 0.001), but displayed elevated hemolysis level (p < 0.001), RBC‐derived microvesicle formation (p < 0.001), and mean corpuscular fragility (p < 0.001) compared to RBCCtrl, indicating notable differences at the membrane between the two donor populations. Proteomic findings revealed a significant reduction in the level of proteins involved in oxidative response pathways at early time points in RBCHH compared to that of RBCCtrl. CONCLUSION The recruitment of these candidate proteins might be part of a response mechanism altered in RBCHH donors and therefore may be useful as a donor screening tool.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".