Multiomics signatures of hemolysis and vesiculation in stored red blood cells
6 affiliations
- 1Department of Biochemistry and Molecular Genetics, University of Colorado Denver – Anschutz Medical Campus, Aurora, CO, USA
- 2Omix Technologies Inc, Aurora, CO, USA
- 3Vitalant Research Institute, San Francisco CA, USA
- 4Department of Laboratory Medicine, University of California San Francisco, CA, USA
- 5RTI International, Pittsburgh, PA, USA
- 6University of British Columbia, Vancouver, British Columbia, Canada.
Abstract
Substantial donor-to-donor heterogeneity shapes red blood cell (RBC) storage biology, but the extent to which integrated donor and molecular measurements capture hemolysis and extracellular-vesicle (EV) phenotypes remains incompletely defined. We analyzed the tail-enriched REDS-III RBC-Omics recalled cohort using one storage-day-42 record from 649 donors. Demographic, complete blood count/ferritin, trace-element, metabolomic, lipidomic, and proteomic predictor blocks were benchmarked for three hemolysis and five EV outcomes. The molecular predictors and most outcomes were measured concurrently at day 42; change in total EVs was day 42 minus day 10. Ridge models used 10 repeats of five-fold outer cross-validation with five-fold inner tuning and fully fold-contained preprocessing and feature screening. Mean Pearson correlations were highest for osmotic hemolysis (r=0.63; full model), oxidative hemolysis (r=0.56; full model), RBC-derived CD235a+CD108- EVs (r=0.47; metabolomics), storage hemolysis (r=0.43; full model), and platelet-derived CD41a+CD62p- EVs (r=0.47; metabolomics). Change in total EVs and RBC-derived CD235a+CD108+ EVs were more modest (r=0.27 and 0.26, respectively), whereas total EV counts were not predicted above chance. Max-block permutation testing supported signal for seven outcomes (Benjamini-Hochberg q=0.006) but not total EVs (q=0.726). Elastic-net sensitivity analysis reproduced the principal ranking. Thus, day-42 storage phenotypes contain endpoint-specific distributed molecular signatures. These internally validated, cross-sectional results from a phenotype-enriched cohort do not establish prospective forecasting or transportability to routine donor populations; external validation is required.
Related
- Evaluation of the in vitro quality of X‐ray irradiated red cell concentrates processed and stored using di(2‐ethylhexyl) terephthalate plasticized blood bag sets
- Whole-Blood Transcriptome Profiling Revealed Gene Expression Signatures of Tourette Disorder
- Blood Cytokine Profiling Reveals Distinct Inflammatory Signatures in Phenotypes A and B of Polyendocrine Metabolic Ovarian Syndrome (PMOS; formerly PCOS).
- Blood metal signatures and their association with Alzheimer’s disease risk and cognitive decline in an Egyptian cohort
- Inflammatory Signatures from the Complete Blood Count and In-Hospital Mortality in Acute Myocardial Infarction
BibTeX
@article{dzieciatkowska2026multiomics,
title = {{Multiomics signatures of hemolysis and vesiculation in stored red blood cells}},
author = {Monika Dzieciatkowska and Daniel Stephenson and Travis Nemkov and Erika G. Marques de Menezes and Mars Stone and Gregory R. Keele and Grier P. Page and Steve Kleinman and Michael P. Busch and Philip J. Norris and Angelo D’Alessandro},
journal = {Blood red cells \& iron},
year = {2026},
month = oct,
doi = {10.1016/j.brci.2026.100142},
url = {https://doi.org/10.1016/j.brci.2026.100142},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Multiomics signatures of hemolysis and vesiculation in stored red blood cells
AU - Monika Dzieciatkowska
AU - Daniel Stephenson
AU - Travis Nemkov
AU - Erika G. Marques de Menezes
AU - Mars Stone
AU - Gregory R. Keele
AU - Grier P. Page
AU - Steve Kleinman
AU - Michael P. Busch
AU - Philip J. Norris
AU - Angelo D’Alessandro
PY - 2026
DA - 2026/10/06
JO - Blood red cells & iron
DO - 10.1016/j.brci.2026.100142
UR - https://doi.org/10.1016/j.brci.2026.100142
AB - Substantial donor-to-donor heterogeneity shapes red blood cell (RBC) storage biology, but the extent to which integrated donor and molecular measurements capture hemolysis and extracellular-vesicle (EV) phenotypes remains incompletely defined. We analyzed the tail-enriched REDS-III RBC-Omics recalled cohort using one storage-day-42 record from 649 donors. Demographic, complete blood count/ferritin, trace-element, metabolomic, lipidomic, and proteomic predictor blocks were benchmarked for three hemolysis and five EV outcomes. The molecular predictors and most outcomes were measured concurrently at day 42; change in total EVs was day 42 minus day 10. Ridge models used 10 repeats of five-fold outer cross-validation with five-fold inner tuning and fully fold-contained preprocessing and feature screening. Mean Pearson correlations were highest for osmotic hemolysis (r=0.63; full model), oxidative hemolysis (r=0.56; full model), RBC-derived CD235a+CD108- EVs (r=0.47; metabolomics), storage hemolysis (r=0.43; full model), and platelet-derived CD41a+CD62p- EVs (r=0.47; metabolomics). Change in total EVs and RBC-derived CD235a+CD108+ EVs were more modest (r=0.27 and 0.26, respectively), whereas total EV counts were not predicted above chance. Max-block permutation testing supported signal for seven outcomes (Benjamini-Hochberg q=0.006) but not total EVs (q=0.726). Elastic-net sensitivity analysis reproduced the principal ranking. Thus, day-42 storage phenotypes contain endpoint-specific distributed molecular signatures. These internally validated, cross-sectional results from a phenotype-enriched cohort do not establish prospective forecasting or transportability to routine donor populations; external validation is required.
SN - 3050-5984
ER -