Posttransfusion recovery, quality, and metabolism of short- and long-term stored platelets during controlled inflammation

Abstract

Platelet concentrates (PCs) are frequently used to prevent and treat bleeding in patients. However, their efficacy is reduced during inflammation as well as due to platelet storage lesion, including metabolomic shifts and changes in surface markers of stored PCs. This study aims to identify disparities between short- and long-term stored PCs during controlled inflammation, focusing on distinct metabolic pathways, alterations in surface markers and posttransfusion recovery (PTR). Twenty-four male participants received lipopolysaccharide or saline as control after an autologous transfusion of either short- (2 days) or long-term (7 days) stored PCs. Metabolomics and surface markers of these transfused PCs were assessed before transfusion using mass spectrometry and flow cytometry, respectively. Biotin-labeled platelets were used to assess surface markers after transfusion and determine PTR. Before transfusion, short-term stored PCs demonstrated increased glycolysis, pentose phosphate pathway activity, dense granule components (eg, serotonin, adenosine diphosphate, and epinephrine), and purine, arginine, and tryptophan metabolism. In contrast, long-term stored PCs exhibited elevated transsulfuration and taurine metabolism, along with higher levels of CD62P and CD63. During inflammation, a decreased PTR was found, particularly in long-term stored PCs. Higher expression of dense granule metabolite components and lower CD62P and lactate levels were correlated with improved PTR. Differences in metabolic pathways, surface markers, and PTR were identified between short- and long-term stored PCs in a controlled human experiment, suggesting a preference for the use of short-term stored PCs during inflammation. This trial was registered at the International Clinical Trials Registry Platform (https://trialsearch.who.int/) as #NL-OMON26852.

Graphical abstract

Figure 1.

Overview of the study. The study's time points are centrally presented, encompassing PTR and surface marker determination at the top, and metabolomic analyses at the bottom. BSA, bovine serum albumin; PBS, phosphate-buffered saline.

Figure 2.

Overview of PC metabolomics. (A) The partial least squares discriminant analysis (PLS-DA) scores plots of short and long-term stored PCs using components 1 and 2. The clustering pattern highlights distinct metabolic profiles influenced by storage duration, indicating potential biochemical shifts over time. (B) The variable importance in projection (VIP) scores from the PLS-DA model, highlighting the metabolites that had the greatest influence on the separation between short- and long-term stored samples. Higher VIP scores indicate metabolites that contributed most to the observed metabolic differences, potentially identifying key biomarkers affected by storage duration. (C) Pretransfusion heat map of all included metabolites. All data were categorized by storage duration (short- or long-term stored). Hierarchical clustering was used to group metabolites in clusters. AMP, adenosine 5′-monophosphate; ATP, adenosine triphosphate; GDP, guanosine diphosphate; GMP, guanosine monophosphate; GTP, guanosine triphosphate; IMP, inosine monophosphate.

Figure 3.

Pretransfusion (labeled or unlabeled) and posttransfusion (control or during inflammation) platelet surface markers as determined by flow cytometry. (A) Mean CD62P expression before transfusion was significantly higher in long-term stored platelets (8.62% ± 2.97%) than short-term stored PCs (4.21% ± 3.85%; P < .01). After biotinylation, long-term stored PCs exhibited a significantly higher expression of CD62P (14.10% ± 5.99%) than nonbiotinylated long-term stored PCs (P < .05), whereas the biotinylation of short-term stored PCs did not lead to a significant increase in CD62P expression (4.92% ± 2.13%). (B) Mean CD63 expression was significantly higher in long-term stored PCs (3.84% ± 2.26%) than short-term stored platelets (1.51% ± 0.87%; P < .01). The biotinylation process did not affect CD63 expression for both short- and long-term stored PCs. (C) Phosphatidylserine levels between short- and long-term stored PCs was not significantly different. (D-I) The line plot displays the mean and standard deviation of surface markers over time. Statistical comparisons were performed at each time point using an unpaired t test, alongside a 2-way repeated measures analysis of variance (ANOVA) to assess changes over time and between conditions. Significance levels are indicated as follows: ∗P < .05; ∗∗P < .01; ∗∗P < .001; ∗∗∗∗P < .0001; NS, not statistically significant.

Figure 4.

Pretransfusion (labeled or unlabeled) and posttransfusion (control or during inflammation) platelet surface markers stimulated with TRAP as determined by flow cytometry. (A-C) Surface markers were similar for both short- and long-term stored PCs after stimulation with TRAP. (D-I) The line plots show the mean and standard deviations of the percentage of activation and prophagocytic markers over time of transfused PCs up to 48 hours after transfusion. No major differences were found between short- and long-term stored PCs after stimulation with TRAP. At a few time points, CD63 expression was higher in short-term stored PCs (panel E). The line plot displays the mean and standard deviation of surface markers over time. Statistical comparisons were performed at each timepoint using an unpaired t test, alongside a 2-way repeated measures ANOVA to assess changes over time and between conditions. Significance levels are indicated as follows: ∗P < .05; ∗∗P < .01; ∗∗P < .001; ∗∗∗∗P < .0001; NS, not statistically significant.

Figure 5.

Survival and PTR of biotinylated platelets without and during inflammation. (A,D) Percentage of biotinylated platelets in circulation. (B,E) Percentage of biotinylated platelets related to the 10-minute measurement after transfusion. (C,F) Percentage of biotinylated platelets related to the 1-hour measurement after transfusion. The line plot displays the mean and standard deviation of recovery or PTR over time. Statistical comparisons were performed at each time point using an unpaired t test, alongside a 2-way repeated measures ANOVA to assess changes over time and between conditions. Significance levels are indicated as follows: ∗P < .05; ∗∗P < .01; ∗∗P < .001; ∗∗∗∗P < .0001; NS, not statistically significant.

Figure 6.

Metabolite and surface marker correlations in platelets. Correlation plot displaying all metabolites and surface markers of platelets from the PCs and 48-hour PTR (related to 1 hour). Red boxes indicate positive correlations, whereas blue boxes indicate negative correlations. AMP, adenosine 5′-monophosphate; ATP, adenosine triphosphate; GDP, guanosine diphosphate; GMP, guanosine monophosphate; GTP, guanosine triphosphate; IMP, inosine monophosphate.