In this issue of Blood, D’Alessandro et al1 identify a ferroptosis-like mechanism that regulates the breakdown of stored red blood cells (RBCs) in mice and humans, providing potential insights into the heterogeneity of transfusion outcomes.

The authors applied genetic approaches, including quantitative trait loci (QTL) analysis, to answer why there is so much heterogeneity in the hemolysis of stored RBCs. They also leveraged 2 key resources: the JAX Diversity Outbred (J:DO) mice and the Recipient Epidemiology and Donor Evaluation Study Red Blood Cell (REDS RBC) Omics clinical cohort.

J:DO mice were bred specifically to generate a mouse model that more closely represents the complexity and diversity of human populations. The Complex Trait Consortium recognized that human health problems are caused by diseases with multifaceted etiologies and that traditional inbred mice do not accurately represent the genetic diversity of humans. Eight founder strains (A/J, C57BL/6J, 129S1/SvlmJ, NOD/ShiLtJ, NZO/HILtJ, CAST/EiJ, PWK/PhJ, WSB/EiJ) were crossed since 2005.2 The authors used stored RBCs from 350 J:DO mice from the 34th generation to identify that Steap3 is the driver of RBC posttransfusion recovery in mice. They confirmed this result showing that the gain-of-function Steap3 allele from FVB/J mice when bred to the C57BL/6J mice caused higher lipid peroxidation and thus a decrease in posttransfusion recovery.

Over 400 metabolite associations were identified using metabolite and lipid QTL in the stored RBCs from the J:DO mice. These data are available via an online interactive portal accessible through the article. The QTL analysis showed that the levels of oxylipins, including prostaglandins, eicosanoid and octadecadienoic hydroxy- and hydroxy-peroxides (HETEs, HODEs and HPETEs), correlated with increased lipid peroxidation and a decrease in posttransfusion recovery, and long-chain unsaturated fatty acids were protective against a decline in posttransfusion recovery.

The authors next validated their murine findings using the REDS RBC Omics cohort which is a large data set comprising behavioral, genetic, and biochemical characteristics of blood donors, which have been linked to the clinical outcomes of the patients transfused with their donated red cells.3 The authors analyzed metabolites in end-of-storage blood (42 days) from 13,091 index and 643 recalled donors. The author also identified HETEs and HODEs as the top correlates for hemolysis, consistent with the results observed in the mice. Expanding on their findings in mice, the authors identified single-nucleotide polymorphisms (SNPs) in STEAP3 were associated with hemolysis or oxylipins levels in end-of-storage blood. SNPs in EPHX2, LPCAT3, and FADS1/2 were also associated with changes in oxylipins levels.

Unfortunately, the authors could not clearly validate the potential clinical relevance of the STEAP3 SNPs that were identified using their vein-to-vein transfusion database. However, they did find that homozygous SNPs in TP53 and LPCAT3 were associated with lower hemoglobin increments, a measure of transfusion efficacy. These results indicate that genetic changes can alter transfusion efficacy.

D’Alessandro et al have combined a wealth of genetic and metabolic analyses to describe a ferroptosis-like mechanism as a novel cause of hemolysis in stored blood. The inclusion of pharmacological inhibition of ferroptosis in stored blood cells from FVB/J mice followed by posttransfusion recovery analysis would have been a valuable proof of principle.

Building on these data, a potential translational outcome of this work may be the use of pharmacological inhibition of ferroptosis in stored blood to potentially extend the storage length of blood, thereby enhancing blood supplies.4 

Conflict-of-interest disclosure: S.-R.P. reports advisory board participation, receiving speaker’s fees, and performing consultancy work for CSL Vifor; performing consultancy work for ITL Biomedical and GiveWell; and having a noncompensated role as director of the WHO Collaborating Centre for Anaemia Detection and Control. R.S. declares no competing financial interests.

1.
D’Alessandro
A
,
Keele
GR
,
Hay
A
, et al
.
Ferroptosis regulates hemolysis in stored murine and human red blood cells
.
Blood
.
2025
;
145
(
7
):
765
-
783
.
2.
Chesler
EJ
,
Miller
DR
,
Branstetter
LR
, et al
.
The Collaborative Cross at Oak Ridge National Laboratory: developing a powerful resource for systems genetics
.
Mamm Genome
.
2008
;
19
(
6
):
382
-
389
.
3.
Endres-Dighe
SM
,
Guo
Y
,
Kanias
T
, et al
.
Blood, sweat, and tears: Red Blood Cell-Omics study objectives, design, and recruitment activities
.
Transfusion
.
2019
;
59
(
1
):
46
-
56
.
4.
Roberts
N
,
James
S
,
Delaney
M
,
Fitzmaurice
C
.
The global need and availability of blood products: a modelling study
.
Lancet Haematol
.
2019
;
6
(
12
):
e606
-
e615
.
Sign in via your Institution