Skip to main content
NCBI home page
Scientific Reports logoLink to Scientific Reports
. 2024 Nov 26;14:29381. doi: 10.1038/s41598-024-80632-7

Identification of new bioactive molecules in platelet preparation, storage, and transfusion reactions for improved transfusion management

Anne-Claire Duchez 1,2,✉, Charles-Antoine Arthaud 1,2, Marie-Ange Eyraud 1,2, Amélie Prier 1,2, Marco Heestermans 1,2, Hind Hamzeh-Cognasse 2, Fabrice Cognasse 1,2
PMCID: PMC11599570  PMID: 39592728

Abstract

Platelet concentrates (PCs) intended for transfusion contain bioactive molecules that can be considered Biological Response Modifiers (BRMs), mainly originating from plasma regardless of the preparation process. During storage, NGAL and GDF-15 levels increase in single donor apheresis platelet concentrates (SDA-PC), whereas in buffy coat platelet concentrates (BC-PC), the levels of MIP1α, MCP-3, and HSAA increase, and GDF-15 levels decrease. These molecules, primarily released by leukocytes, may contribute to adverse reactions (ARs) following a PC transfusion. Notably, in SDA-PC or BC-PC transfusions that result in ARs, the levels of NGAL, HSAA, and GDF-15 are significantly elevated, while the levels of MDC and CX3CL1 are significantly reduced compared to transfusions without ARs. These biomarkers could potentially serve as predictors for PCs-induced ARs.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-80632-7.

Subject terms: Predictive markers, Platelets

Key points

• New BRMs identified in SDA-PC induced AR : NGAL, HSAA, GDF-15, MDC, CX3CL1, with IL-13, MIP1α in BC-PC induced AR.

• Composition in BRM significantly different between SDA-PC and BC-PC (increased ADAMTs13, NGAL, MDC, GDF-15; decreased CX3CL1).

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-80632-7.

Introduction

Platelets are small anucleate cells continuously produced from megakaryocytes, primarily in the bone marrow and also in the lungs1–3. They are involved not only in hemostasis but also in inflammation and immune responses, contributing to various pathophysiological processes4–7. Platelet functions can be regulated by receptors responsive to BRMs produced by the platelets themselves7–9. However, in the vascular circulation under pathophysiological conditions, several bioactive molecules released from leukocytes, the endothelium, and platelets can disturb platelet functions10–13. The stimulation, suppression, or disruption of immune homeostasis may be responsible for the adverse effects of BRMs. Activation of the immune system by various immune cells, including macrophages, monocytes, lymphocytes, and natural killer cells, leads to the massive release of cytokines such as IL-1, TNF-alpha, IFN-gamma, IL-6, and IL-8, often causing symptoms including fever, chills, muscle pain, weakness, loss of appetite, nausea, vomiting, diarrhea, and rash.

In the context of transfusion, cytokines are among the bioactive molecules known to be involved in inflammation, with e.g. IL-1 and IL-6, contributing to transfusion-related ARs. Several studies have measured the levels of IL-1, IL-6, IL-8, TNFα, IFNγ, and TGFβ in different types of platelet concentrates, such as Single Donor Apheresis Platelet Concentrates (SDA-PC), Platelet-Rich Plasma (PRP), and Buffy Coat Platelet Concentrates (BC-PC)14. Some of these cytokines could be involved in ARs following PC transfusion15–17.

Bioactive molecules such as cytokines, chemokines, and proteases can influence the activation status of platelets, endothelial and circulating cells. In the context of transfusion, whole blood, PC, or plasma intended for transfusion should be thoroughly characterized to avoid adverse transfusion reactions. We decided to measure HSAA, CX3CL1, GDF-15, MDC, MIP1α, MCP-3, NGAL, IL-13 and ADAMTs13 for their implication in inflammation and coagulation process. HSAA is a potential inhibitor of platelet function and is involved in endothelial dysfunction18,19. In contrast, CX3CL1 is capable of activating platelets20, and ADAMTs13 is a protease specific for von Willebrand factor (vWF), which is important in clot formation. Additionally, GDF-15, MDC, MIP1α, MCP-3, NGAL, and IL-13 are involved in inflammatory and immunological processes21,22.

In this study, our aim was to investigate the levels of several biological molecules and elucidate their potential involvement in adverse reactions following a PC transfusion. We evaluated several biological molecules known to not be produced by platelets themselves but detected in platelet concentrates. We observed modulation of these cytokine concentrations during the storage of SDA-PC and BC-PC. Additionally, we noted changes in these molecule levels in the event of adverse reactions in SDA-PC and BC-PC.

Methods

Ethic statement

This study complies with all relevant ethical regulations. The study was approved by the Institutional Ethics Review Board of the French Blood Establishment (EFS, DC-2019–3803 & AC-2020-3959). The research was performed in accordance with the Declaration of Helsinki.

SDA-PC and Buffy Coat platelet concentrate (BC-PC) were obtained from “Etablissement Français du Sang (EFS) Auvergne-Rhone-Alpes” with 9,206 volunteers enrolled between March 2013 and February 2016 giving their informed consent23. At the time of the collection, the PCs dedicated for transfusion were not treated with pathogen inactivated compounds. The residual SDA-PCs or BC-PC transfused were collected.

A total of 3,549 SDA-PC and 5,090 BC-PC samples were collected. There were 61 adverse reactions reported in BC-PC transfusions, whereas 79 adverse reactions were reported in SDA-PC transfusions.

More information on the platelet concentrate collections, including storage times and the exact number of samples per category, is provided in Table 1. However, we did not have access to clinical data regarding the patient history of those who received the transfusions. This includes information on their diseases and comorbidities, the number of blood product transfusions (particularly platelet concentrates), duration of hospitalization, and the purpose of the transfusion (whether prophylactic or therapeutic).

Table 1.

Platelet concentrate with or without adverse reaction along the storage time.

Platelet concentrate SDA-PC BC-PC
Adverse Reaction No AR AR No AR AR
Storage time (days) [1–3] [3–5] [1–3] [3–5] [1–3] [3–5] [1–3] [3–5]
n 1630 1919 19 60 2909 2181 27 34
Open in a new tab

Sample preparation

SDA-PCs and BC-PCs were collected as described previously17,24. Briefly, blood was collected on ACD-A using Trima, a continuous-flow cell separator (Gambro BCT, Lakewood, CO, USA). The SDA-PCs was automatically resuspended in 35% autologous donor plasma and 65% platelet additive solution (PAS-D, Intersol, Fenwal, La Châtre, France; or PAS-E, SSP+, MacoPharma, Mouveaux, France) and stored at 22 ± 2 °C with gentle rotation and shaking (60 rpm) for a maximum of 5 days (after collection was completed) before being issued for transfusion.

The BC-PC were prepared by pooling 5 ABO and RhD identical BC obtained by separation after whole blood centrifugation (Optipress, Baxter Healthcare Corporation). The pooled Buffy Coats were resuspended in 35% of Platelet Additive Solution (PAS-D) and immediately leuko-reduced by filtration25. BCs were then stored at 22˚C under gentle agitation (60 RPM) for < 5 days before being issued for transfusion.

SDA-PC contained 5.5 × 103± 0.9 platelet/mm3 whereas BC-PC contained 4.6 × 103 ± 0.5 platelet/mm3.

The leftovers of transfused PCs were collected. To remove platelets, the leftover PC was centrifuged (402 × g; 10 min), after which the supernatants were aliquoted and frozen at − 80˚C until further use.

Luminex

To detect and quantify the level of CX3CL1, MDC, MCP-3, we used HCYTOMAG-60 K-04 from Merck Milipore. To detect and quantify the level of MIP1α and IL13, we used HCYTOMAG-60 K-02 from Merck Milipore. To detect and quantify GDF-15, NGAL, SAA and ADAMTs13, we used HCVD2MAG-67 K-05 from Merck Milipore. The analysis was performed with Bio-Plex 200 Systems (Biorad).

Statistical analysis

Multiple comparisons were performed 2-way-ANOVA and Mann Whitney test. P-values of 0.05 and lower are considered statistically significant (* p < 0.05, ** p < 0.01, ***p < 0.001 and **** p < 0.0001). Statistical analysis and Spearman correlation were carried out using GraphPad version 6 (GraphPad Software, La Jolla California USA).

Results

Modulation of bioactive molecule concentrations is dependent on storage and platelet concentrate preparation

We first evaluated bioactive molecule concentrations in SDA-PC stored for different time periods ([1–3] and [3–5] days). Only two molecules levels, NGAL and GDF-15, increased significantly (20.7 ± 12 vs. 21.7 ± 9.1 and 0.15 ± 0.0001 vs. 0.16 ± 0.0001, respectively) during longer storage, whereas the majority of molecule levels increased without significance (IL-13, ADAMTs13, MIP1α, MCP-3, MDC, HSAA) or decreased without significance (CX3CL1) (Fig. 1A). The proportion of bioactive molecules levels measured, is modulated through storage. ADAMTs13 is the most abundant molecule (44.6% at storage time [1–3] and 51.8% at storage time [3–5]), followed by HSAA (53.3% at [1–3] and 45.9% at [3–5]) and NGAL (1.7% at [1–3] and 1.95% at [3–5]) (Fig. 1B). NGAL and MIP1α concentrations are not correlated with storage time. Indeed, spearman correlation test highlighted significant p value (p = 0.0052 and 0.0152 respectively), with r factor positive and negative, respectively ( r = 0.08420 and − 0.09522) (Fig. 1C).

Fig. 1.

Fig. 1

Open in a new tab

Evaluation of bioactive molecules in Single Donor Apheresis along the storage time. (A) Bar graphs representing the mean ± SEM of concentration of bioactive molecule levels in SDA-PC along the storage [1–3] days and [3–5] days. Mann Whitney test, * p < 0,05 (B) Pie chart representing the distribution of the molecules evaluated in SDA-PC. (C) Correlation matrix (Spearman) between storage time (ST) with bioactive molecule levels. Red and black dot represented significative positive and negative correlation respectively.

We also evaluated bioactive molecule levels in BC-PC during storage. With longer storage, the levels of IL-13, MIP1α, NGAL, MCP-3, MDC, and HSAA increased (Fig. 2A), with a significant increase for MCP-3, and HSAA in BC-PC (271.7 ± 114 vs. 289.4 ± 118.8; and 682.3 ± 306.6 vs. 744 ± 614, respectively). However, during the storage time of BC-PC, the levels of GDF-15 decreased, with a significant decrease for GDF-15 (0.17 ± 0.04 vs. 0.16 ± 0.04) (Fig. 2A). The proportion of bioactive molecules levels in BC-PC followed the same trend as SDA-PC (Fig. 2B). MIP1α & GDF-15 and MCP-3 & MDC correlated with storage time, with negative and positive significant correlations, respectively (Fig. 2C).

Fig. 2.

Fig. 2

Open in a new tab

Evaluation of bioactive molecules in Buffy Coat along the storage time. (A) Bar graphs representing the mean ± SEM concentration of bioactive molecules in BC-PC along the storage [1–3] days and [3–5] days. Mann Whitney test, * p < 0,05 (B) Pie chart representing the distribution of the molecules evaluated in BC-PC. (C) Correlation matrix (Spearman) between storage time (ST) avec bioactive molecule levels. Red and black dot represented significative positive and negative correlation respectively.

We then compared bioactive molecule levels in SDA-PC and BC-PC during storage. We observed significant difference of all bioactive molecule level between SDA-PC and BC-PC (Supplemental Fig. 1). NGAL, MCP-3, HSAA, MDC, IL-13, and ADAMTs13 levels were higher in BC-PC compared to SDA-PC, whereas MIP1α, CX3CL1, and GDF-15 levels were lower in BC-PC compared to SDA-PC (Supplemental Fig. 1). ADAMTs13 and NGAL were significantly more abundant in BC-PC compared to SDA-PC at both [1–3] and [3–5] days of storage (Fig. 3A), with a fold change of 2.5 for NGAL (Fig. 3B). CX3CL1 is significantly less abundant in BC-PC than SDA-PC at both [1–3] and [3–5] days of storage (Fig. 3A). However, GDF-15 levels were significantly different between SDA-PC and BC-PC at [1–3] days of storage (Fig. 3A), with a fold change of 2.5 (Fig. 3B). MCP-3 and MDC levels were significantly different between SDA-PC and BC-PC at [3–5] days of storage (Fig. 3A).

Fig. 3.

Fig. 3

Open in a new tab

Comparison between SDA-PC and BC-PC content in bioactive molecules along the storage time. (A) Bar graphs representing the mean ± SEM concentration of bioactive molecules in SDA-PC and BC-PC along the storage [1–3] days and [3–5] days. 2-ways ANOVA test, * p < 0,05; ** p < 0,01; *** p < 0,001; **** p < 0,0001 (B) Heat Map representing the fold change BC-PC on SDA-PC for each molecules.

Adverse reactions following single donor apheresis platelet concentrate and the role of biologic response modifiers

ARs following transfusion of SDA-PC may involve several BRMs. In our study, ARs associated with SDA-PC primarily included chills, rash, fever, and tachycardia (Supplemental Fig. 2A). Interestingly, the occurrence of these symptoms appears to be linked to the storage time of SDA-PC: chills occurred 4 times with shorter storage duration and 25 times with longer storage; rash occurred 6 times after [1–3] days of storage and 29 times after [3–5] days; fever was more frequent with longer storage (16 occurrences vs. 2 with shorter storage), and distress symptoms only appeared with longer storage (Supplemental Fig. 2B).

We sought to understand if specific bioactive molecules could contribute to these symptoms. We observed a significant modulation of MDC, CX3CL1, HSAA, and MIP1α between SDA-PC with and without chills and distress symptoms (Supplemental Fig. 2C, D). CX3CL1 and MIP1α were modulated between SDA-PC with and without rash syndrome (Supplemental Fig. 2E), and only CX3CL1 was modulated in cases of fever symptoms following SDA-PC transfusion (Supplemental Fig. 2F).

Interestingly, several bioactive molecule levels were higher in SDA-PC associated with ARs (IL-13, ADAMTs13, MIP1α, NGAL, MCP-3, and HSAA) or lower (MDC, GDF-15, and CX3CL1) compared to levels in SDA-PC without AR (Supplemental Fig. 3). Additionally, MDC, MCP-3, MDC, HSAA, and GDF-15 levels were significantly modulated across storage times ([1–3] and [3–5] days) between SDA-PC with and without AR after transfusion (Fig. 4A). The levels of MCP-3, MDC, HSAA, and GDF-15 between AR and non-AR cases presented fold changes of 1.5, -1.5, 2.5, and − 1.5, respectively (Fig. 4B). MIP1α, NGAL and CX3CL1 were only significantly modulated between AR and non-AR cases at days [3–5] and [1–3] days respectively, after SDA-PC transfusion (Fig. 4A), presenting fold changes of 1.5 and − 2.5, respectively (Fig. 4B).

Fig. 4.

Fig. 4

Open in a new tab

Comparison between AR and no-AR content in bioactive molecules along the storage time in SDA-PC. (A) Bar graphs representing the mean ± SEM concentration of Biological response modifiers (BRM) in SDA-PC and SDA-PC with AR occurrence along the storage [1–3] days and [3–5] days. 2-ways ANOVA test, * p < 0,05; ** p < 0,01; *** p < 0,001; **** p < 0,0001 (B) Heat Map representing the fold change AR on no AR SDA-PC for each BRMs. (C) Pie Chart representing the distribution of the BRMs evaluated in SDA-PC with AR. (D) Correlation matrix (Spearman) between adverse reactions following SDA-PC transfusion (symptoms) with BRMs levels. Red and black dot represented significative positive and negative correlation respectively.

Moreover, HSAA was the most abundant bioactive molecule in SDA-PC associated with AR (85%), followed by ADAMTs13 (13.8%) and NGAL (1.5%) (Fig. 4C). We also observed several correlations between cytokine levels and AR symptoms (Fig. 4D). Spearman correlation analysis revealed positive and significant correlations between MCP-3 levels and distress symptoms, hypotension, hypertension, and pulmonary edema. CX3CL1, ADAMTs13, and NGAL levels were positively correlated with rash symptoms of AR (Fig. 4D).

Adverse reactions following buffy coat platelet concentrate and the role of biologic response modifiers

ARs following transfusion of BC-PC may involve several BRMs. In our study, ARs associated with BC-PC primarily included chills, fever, and rash (Supplemental Fig. 4A). Symptoms of AR appeared to correlate with the storage time of BC-PC: chills occurred 16 times with shorter storage duration and 21 times with longer storage; rash occurred 4 times after [1–3] days of storage and 13 times after [3–5] days; fever symptoms were equivalent between short and long storage times, occurring 7 times in each period (Supplemental Fig. 4B). Interestingly, we did not observe significant modulation of any biological molecules for chills, distress, rash, and fever symptoms (Supplemental Fig. 4C-F).

Several molecules were higher in BC-PC associated with AR (IL-13, MIP1α, NGAL, MCP-3, HSAA, and GDF-15) and lower (ADAMTs13, MDC, and CX3CL1) compared to BC-PC without AR occurrence (Supplemental Fig. 5). However, MIP1α, HSAA, and CX3CL1 levels were significantly modulated across storage times ([1–3] and [3–5] days) between BC-PC with and without AR after transfusion (Fig. 5A). Interestingly, the fold changes between AR and non-AR cases were 5, 5, and − 2.5 for MIP1α, HSAA, and CX3CL1, respectively (Fig. 5B). Additionally, we observed that IL-13, NGAL, and MDC levels were significantly modulated during early storage ([1–3] days) between BC-PC with and without AR following transfusion (Fig. 5A), with fold changes of 1.5, 0, and − 1.5, respectively (Fig. 5B). GDF-15 levels were significantly modulated only during longer storage ([3–5] days) between BC-PC with and without AR following transfusion (Fig. 5A), with a fold change of approximately 1.5 (Fig. 5B). Lastly, ADAMTs13 did not show modulation between storage time and AR (Fig. 5A), with a fold change of about 0 between AR and non-AR cases (Fig. 5B). The most abundant bioactive molecules in BC-PC were HSAA, ADAMTs13, and NGAL (Fig. 5C). However, no correlations were observed between bioactive molecule levels and AR symptoms (Fig. 5D), but positive and negative correlations were noted between storage time and the levels of bioactive molecules (Fig. 5D).

Fig. 5.

Fig. 5

Open in a new tab

Comparison between AR and no-AR content in bioactive molecules along the storage time in BC-PC. (A) Bar graphs representing the mean ± SEM concentration of BRMs in BC-PC and BC-PC with AR occurrence along the storage [1–3] days and [3–5] days. 2-ways ANOVA test, * p < 0,05; ** p < 0,01; *** p < 0,001 (B) Heat Map representing the fold change SAR on no SAR BC-PC for each BRMs. (C) Pie Chart representing the distribution of the BRMs evaluated in BC-PC with AR. (D) Correlation Matrix of BRMs levels and AR symptoms in BC-PC with AR (spearman correlation). Red and black dot represented significative positive and negative correlation respectively.

Discussion

In platelet concentrates transfusion, Buffy Coat Platelet Concentrate (BC-PC) is the most frequently transfused compared to SDA-PC, in many countries including France, with better yield and fewer adverse reactions following transfusion. However, the composition of biomolecules has not been extensively investigated. Previous studies have evaluated bioactive molecules such as IL-1, IL-6, IL-8, and TGF-β9,15, in SDA-PC and BC-PC with or without occurrence of AR. In our study, we evaluated bioactive molecules that are not generated by platelets, in SDA-PC and BC-PC with or without adverse reactions following platelet concentrate transfusion (Fig. 6).

Fig. 6.

Fig. 6

Open in a new tab

Graphical abstract of our study.

Bioactive molecule levels during storage and processing of platelet concentrates (PC)

In our study, we observed that the compositions of bioactive molecules in SDA-PC and BC-PC are not equivalent (Supplemental Fig. 1). Most molecules detected were present at higher levels in BC-PC compared to SDA-PC, except for MIP1α, GDF-15, and CX3CL1 (Fig. 3A). Comparatively, ADAMTs13, NGAL, MCP3, MDC, and GDF-15 levels increased during storage and between SDA-PC and BC-PC, whereas CX3CL1 levels decreased between no AR and AR (Figs. 4A, 5A and 6). Specifically, during the storage of SDA-PC, NGAL and GDF-15 levels increased (Figs. 1 and 6), while MIP1α, MCP-3, and HSAA levels increased, and GDF-15 decreased during the storage of BC-PC (Figs. 2 and 6).

We attempted to explain these differences based on platelet concentrations in SDA-PC versus BC-PC. SDA-PC contained 5.5 × 103± 0.9 platelet/mm3 whereas BC-PC contained 4.6 × 103± 0.5 platelet/mm3, a significant difference as determined by the Mann-Whitney test. However, these molecules are not only released by platelets but also by leukocytes, hepatocytes, and endothelial cells26–28. Platelets could release MIP1α and GDF-1529–31, and since GDF-15 is more predominant in SDA-PC compared to BC-PC, this could explain the significant differences in GDF-15 levels (Fig. 3).

The differences in molecule levels between SDA-PC and BC-PC could also be attributed to the amount of plasma added during preparation. Despite effective leukocyte depletion, the molecules detected could originate from plasma and possibly from extracellular vesicles (EVs) produced by mother cells. Indeed, EVs could carry these molecules or be sites for their production. Interestingly, few studies have highlighted that EVs, not only from platelets, are present in SDA-PC32–35. These EVs could release these different biomolecules. These EVs could be release during the leukofiltration, during the process of SDA-PC or BC-PC, and present in abundance in the PC. The role of EVs should be investigated further in future studies.

Biologic response modifiers (BRMs) show distinct differences between SDA-PC transfusions with and without adverse reactions (AR)

SDA-PC transfusions can induce adverse reactions ranging from mild to severe in recipients (see Supplemental Fig. 2). However, these reactions have become less frequent in early times. Comparing cases with and without AR following SDA-PC reveals significant differences in the expression of measured molecules (see Supplemental Fig. 2)17. Small difference may not be the unique factor involved in adverse reactions. In other words, a single molecule may not be solely responsible for the adverse reaction; rather, it could be a combination of several molecules contributing to the effect. Nonetheless, even slight variations in the composition of the PC whether related to known or unknown factors, can influence its overall function and impact the recipient’s reaction. Levels of NGAL, MCP-3, HSAA are consistently higher in AR cases compared to those without AR, whereas MDC and CX3CL1 levels exhibit an inverse pattern (Fig. 4).

Notably, HSAA levels do not correlate with specific AR symptoms (Fig. 4D) however there is a significative association between HSAA levels in SDA-PC with or without AR, for chill and distress symptoms (Supplemental Fig. 2CD), yet their increase during storage suggests potential involvement in the AR process. HSAA is released during acute inflammation from hepatocytes and endothelial cells26,27, promoting fibrin amyloid formation36 and contributing to endothelial dysfunction18. It also interacts with platelets26, inhibiting aggregation19 and facilitating platelet adhesion to the endothelium37. We could speculate on HSAA involvement in AR following a PC, because this shows the largest difference between AR and non-AR transfusions. It would be of interest to investigate this molecule in an animal model, to elucidate its role in PC transfusion. Chill symptoms correlate with MDC and NGAL levels (Fig. 4D), while distress symptoms appear linked to MCP-3, IL-13, and MIP1α (Fig. 4D).

Given the differences observed in several molecules between AR and non-AR cases, we categorize these molecules as Biological Response Modifiers (BRMs). Comparison of SDA-PC and BC-PC regarding AR occurrence reveals increased levels of HSAA, GDF-15, and NGAL in AR cases, suggesting a potential protective role of MDC and CX3CL1 in the context of AR.

Biologic response modifiers (BRMs) show distinct differences between BC-PC transfusions with and without adverse reactions (AR)

BC-PC is the most commonly transfused platelet component, yet it can induce adverse reactions following transfusion (see Supplemental Fig. 4). Significant modulation of all measured bioactive molecules occurs between cases with and without AR in BC-PC (see Supplemental Fig. 5). Levels of IL-13, MIP1α, NGAL, GDF-15 and HSAA are consistently higher in AR cases as compared to those without AR, whereas MDC and CX3CL1 levels exhibit an inverse pattern (Fig. 5) Interestingly, there was no observed correlation between the levels of bioactive molecules measured and AR symptoms (see Fig. 5), suggesting that individual BRMs may not singularly influence specific symptoms, but rather a combination of several BRMs in BC-PC may contribute to inducing an adverse reaction.

Biologic response modifiers (BRMs) show differences between SDA-PC and BC-PC transfusions with and without adverse reactions (AR)

The modulation patterns of ADAMTs13 and GDF-15 in BC-PC with or without AR (see Supplemental Fig. 5) differ from those observed in SDA-PC (see Supplemental Fig. 3). Specifically, ADAMTs13 levels are lower in AR cases, whereas GDF-15 levels are higher in AR cases compared to non-AR cases in BC-PC—an opposite trend from SDA-PC observations. However, the observation regarding the potential protective role of MDC and CX3CL1 seen in SDA-PC is also evident in BC-PC (see Fig. 5 and Supplemental Fig. 5). CX3CL1 is involved in biological processes such as hemostasis through binding to the von Willebrand receptor glycoprotein Ib38 and integrins αvβ3 and αIIbβ338. It also plays a role in leukocyte adhesion to endothelium39 and platelet activation and adhesion20, indicating its potential significance in post-transfusion adverse reactions related to inflammation and thrombosis. Moreover, MDC induces platelet activation40,41 and regulates Th2 and Treg responses42.

In this study, we evaluated several biological molecules known to not be produced by platelets themselves but detected in platelet concentrates. We observed modulation of these cytokine concentrations during the storage of SDA-PC (NGAL, GDF-15) and BC-PC (MIP1α, MCP-3, HSAA, GDF-15). Additionally, we noted changes in these molecules in the event of adverse reactions in SDA-PC (NGAL, MDC, HSAA, GDF-15, CX3CL1) and BC-PC (IL-13, MIP1α, NGAL, MDC, HSAA, GDF-15, CX3CL1).

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1 (1.2MB, docx)

Acknowledgements

The authors wish to thank all the volunteer donors who contributed samples for the study. We would also like to thank the medical staff at the Etablissement Français du Sang Auvergne-Rhone-Alpes, Saint-Etienne, France for their technical support throughout this study.

Author contributions

FC designed the study, supervised the research, secured funding and obtained approval from the ethics committee; ACD analysed data, wrote and review the manuscript. CAA, AP, MAE, HHC, MH conducted research.

Funding

This work was supported by grants from Etablissement Français du Sang (EFS) – the French National Blood Establishment - the Association “Les Amis de Rémi” Savigneux, France and the ANR-HEASY_PLAT Program (ANR-22-CE17-0063).

Data availability

Any additional information required to reanalyze the data reported in this work paper is available from the lead contact (anne-claire.duchez@efs.sante.fr) upon request.

Declarations

Conflicts of interest

The authors have no conflict of interests to declare.

Footnotes

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Tyagi, T. et al. A guide to molecular and functional investigations of platelets to bridge basic and clinical sciences. Nat. Cardiovasc. Res.1, 223–237. 10.1038/s44161-022-00021-z (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 2.van der Meijden, P. E. J. & Heemskerk, J. W. M. Platelet biology and functions: new concepts and clinical perspectives. Nat. Rev. Cardiol.16, 166–179. 10.1038/s41569-018-0110-0 (2019). [DOI] [PubMed] [Google Scholar]
  • 3.Lefrancais, E. et al. The lung is a site of platelet biogenesis and a reservoir for haematopoietic progenitors. Nature544, 105–109. 10.1038/nature21706 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 4.Boilard, E. et al. Platelets amplify inflammation in arthritis via collagen-dependent microparticle production. Science327, 580–583. 10.1126/science.1181928 (2010). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 5.Linge, P., Fortin, P. R., Lood, C., Bengtsson, A. A. & Boilard, E. The non-haemostatic role of platelets in systemic lupus erythematosus. Nat. Rev. Rheumatol.14, 195–213. 10.1038/nrrheum.2018.38 (2018). [DOI] [PubMed] [Google Scholar]
  • 6.Li, S. et al. The dynamic role of platelets in cancer progression and their therapeutic implications. Nat. Rev. Cancer. 24, 72–87. 10.1038/s41568-023-00639-6 (2024). [DOI] [PubMed] [Google Scholar]
  • 7.Scherlinger, M., Richez, C., Tsokos, G. C., Boilard, E. & Blanco, P. The role of platelets in immune-mediated inflammatory diseases. Nat. Rev. Immunol.23, 495–510. 10.1038/s41577-023-00834-4 (2023). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Cognasse, F. et al. Platelets as key factors in inflammation: focus on CD40L/CD40. Front. Immunol.13, 825892. 10.3389/fimmu.2022.825892 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 9.Brown, G. T., Narayanan, P., Li, W., Silverstein, R. L. & McIntyre, T. M. Lipopolysaccharide stimulates platelets through an IL-1beta autocrine loop. J. Immunol.191, 5196–5203. 10.4049/jimmunol.1300354 (2013). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 10.Bester, J. & Pretorius, E. Effects of IL-1beta, IL-6 and IL-8 on erythrocytes, platelets and clot viscoelasticity. Sci. Rep.6, 32188. 10.1038/srep32188 (2016). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 11.Davizon-Castillo, P. et al. TNF-alpha-driven inflammation and mitochondrial dysfunction define the platelet hyperreactivity of aging. Blood134, 727–740. 10.1182/blood.2019000200 (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 12.Limb, G. A., Webster, L., Soomro, H., Janikoun, S. & Shilling, J. Platelet expression of tumour necrosis factor-alpha (TNF-alpha), TNF receptors and intercellular adhesion molecule-1 (ICAM-1) in patients with proliferative diabetic retinopathy. Clin. Exp. Immunol.118, 213–218. 10.1046/j.1365-2249.1999.01067.x (1999). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Pignatelli, P. et al. Tumor necrosis factor-alpha as trigger of platelet activation in patients with heart failure. Blood106, 1992–1994. 10.1182/blood-2005-03-1247 (2005). [DOI] [PubMed] [Google Scholar]
  • 14.Wadhwa, M. et al. Cytokine levels in platelet concentrates: quantitation by bioassays and immunoassays. Br. J. Haematol.93, 225–234. 10.1046/j.1365-2141.1996.4611002.x (1996). [DOI] [PubMed] [Google Scholar]
  • 15.Wadhwa, M., Seghatchian, J. & Thorpe, R. Are cytokines in platelet concentrates responsible for febrile transfusion reactions? Transfus. Sci.18, 367–371. 10.1016/S0955-3886(97)00033-7 (1997). [DOI] [PubMed] [Google Scholar]
  • 16.Stack, G. & Snyder, E. L. Cytokine generation in stored platelet concentrates. Transfusion34, 20–25. 10.1046/j.1537-2995.1994.34194098597.x (1994). [DOI] [PubMed] [Google Scholar]
  • 17.Nguyen, K. A. et al. A computerized prediction model of hazardous inflammatory platelet transfusion outcomes. PLoS One. 9, e97082. 10.1371/journal.pone.0097082 (2014). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 18.You, K. et al. Neutralizing serum amyloid a protects against sinusoidal endothelial cell damage and platelet aggregation during acetaminophen-induced liver injury. Biochem. Biophys. Res. Commun.639, 20–28. 10.1016/j.bbrc.2022.11.079 (2023). [DOI] [PubMed] [Google Scholar]
  • 19.Sayinalp, N. et al. Protein C inhibitor and serum amyloid A in immune thrombocytopaenic purpura. J. Int. Med. Res.32, 62–65. 10.1177/147323000403200110 (2004). [DOI] [PubMed] [Google Scholar]
  • 20.Schafer, A. et al. Novel role of the membrane-bound chemokine fractalkine in platelet activation and adhesion. Blood103, 407–412. 10.1182/blood-2002-10-3260 (2004). [DOI] [PubMed] [Google Scholar]
  • 21.Wei, C. T. et al. Elevated plasma level of neutrophil gelatinase-associated lipocalin (NGAL) in patients with breast cancer. Int. J. Med. Sci.18, 2689–2696. 10.7150/ijms.58789 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Rossaint, J., Vestweber, D. & Zarbock, A. GDF-15 prevents platelet integrin activation and thrombus formation. J. Thromb. Haemost. 11, 335–344. 10.1111/jth.12100 (2013). [DOI] [PubMed] [Google Scholar]
  • 23.Cognasse, F. et al. Platelet soluble CD40-ligand level is associated with transfusion adverse reactions in a mixed threshold-and-hit model. Blood130, 1380–1383. 10.1182/blood-2017-03-773945 (2017). [DOI] [PubMed] [Google Scholar]
  • 24.Duchez, A. C. et al. Lipidomic analysis of differently prepared platelet concentrates in additive solution during storage. Blood Transfus.10.2450/2022.0144-22 (2022). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Vucic, M., Stanojkovic, Z., Antic, A., Vucic, J. & Pavlovic, V. Evaluation of platelet activation in leukocyte-depleted platelet concentrates during storage. Bosn J. Basic. Med. Sci.18, 29–34. 10.17305/bjbms.2017.2321 (2018). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Urieli-Shoval, S. et al. Adhesion of human platelets to serum amyloid A. Blood99, 1224–1229. 10.1182/blood.v99.4.1224 (2002). [DOI] [PubMed] [Google Scholar]
  • 27.Simoes, E. et al. Sex dimorphism in inflammatory response to obesity in childhood. Int. J. Obes. (Lond). 45, 879–887. 10.1038/s41366-021-00753-1 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Klinger, M. H. et al. Immunocytochemical localization of the chemokines RANTES and MIP-1 alpha within human platelets and their release during storage. Int. Arch. Allergy Immunol.107, 541–546. 10.1159/000237097 (1995). [DOI] [PubMed] [Google Scholar]
  • 29.Supernat, A. et al. Transcriptomic landscape of blood platelets in healthy donors. Sci. Rep.11, 15679. 10.1038/s41598-021-94003-z (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Huang, J. et al. Assessment of a complete and classified platelet proteome from genome-wide transcripts of human platelets and megakaryocytes covering platelet functions. Sci. Rep.11, 12358. 10.1038/s41598-021-91661-x (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Rowley, J. W. et al. Genome-wide RNA-seq analysis of human and mouse platelet transcriptomes. Blood118, e101–111. 10.1182/blood-2011-03-339705 (2011). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Tripisciano, C. et al. Different potential of Extracellular vesicles to support thrombin generation: contributions of phosphatidylserine, tissue factor, and Cellular Origin. Sci. Rep.7, 6522. 10.1038/s41598-017-03262-2 (2017). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Gamonet, C. et al. Processing methods and storage duration impact extracellular vesicle counts in red blood cell units. Blood Adv.4, 5527–5539. 10.1182/bloodadvances.2020001658 (2020). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 34.De Paoli, S. H. et al. Structural analysis of platelet fragments and extracellular vesicles produced by apheresis platelets during storage. Blood Adv.8, 207–218. 10.1182/bloodadvances.2023011325 (2024). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Marcoux, G. et al. Microparticle and mitochondrial release during extended storage of different types of platelet concentrates. Platelets28, 272–280. 10.1080/09537104.2016.1218455 (2017). [DOI] [PubMed] [Google Scholar]
  • 36.Page, M. J. et al. Serum amyloid A binds to fibrin(ogen), promoting fibrin amyloid formation. Sci. Rep.9, 3102. 10.1038/s41598-019-39056-x (2019). [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Siman-Tov, R. et al. Elevated serum amyloid A levels contribute to increased platelet adhesion in COVID-19 patients. Int. J. Mol. Sci.2310.3390/ijms232214243 (2022). [DOI] [PMC free article] [PubMed]
  • 38.dos Meyer, S. et al. The CX3C chemokine fractalkine mediates platelet adhesion via the Von Willebrand receptor glycoprotein ib. Blood117, 4999–5008. 10.1182/blood-2011-02-335471 (2011). [DOI] [PubMed] [Google Scholar]
  • 39.Schulz, C. et al. Chemokine fractalkine mediates leukocyte recruitment to inflammatory endothelial cells in flowing whole blood: a critical role for P-selectin expressed on activated platelets. Circulation116, 764–773. 10.1161/CIRCULATIONAHA.107.695189 (2007). [DOI] [PubMed] [Google Scholar]
  • 40.Abi-Younes, S., Si-Tahar, M. & Luster, A. D. The CC chemokines MDC and TARC induce platelet activation via CCR4. Thromb. Res.101, 279–289. 10.1016/s0049-3848(00)00402-3 (2001). [DOI] [PubMed] [Google Scholar]
  • 41.Rosa, A. et al. WASp controls oriented migration of endothelial cells to achieve functional vascular patterning. Development14910.1242/dev.200195 (2022). [DOI] [PMC free article] [PubMed]
  • 42.Li, Q. et al. Regulation of Th1/Th2 and Th17/Treg by pDC/mDC imbalance in primary immune thrombocytopenia. Exp. Biol. Med. (Maywood). 246, 1688–1697. 10.1177/15353702211009787 (2021). [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplementary Material 1 (1.2MB, docx)

Data Availability Statement

Any additional information required to reanalyze the data reported in this work paper is available from the lead contact (anne-claire.duchez@efs.sante.fr) upon request.

Articles from Scientific Reports are provided here courtesy of Nature Publishing Group

RESOURCES