Monitoring storage induced changes in the platelet proteome employing label free quantitative mass spectrometry

Shelf life of platelet concentrates is limited to 5–7 days due to loss of platelet function during storage, commonly referred to as the platelet storage lesion (PSL). To get more insight into the development of the PSL, we used label free quantitative mass spectrometry to identify changes in the platelet proteome during storage. In total 2501 proteins were accurately quantified in 3 biological replicates on at least 1 of the 7 different time-points analyzed. Significant changes in levels of 21 proteins were observed over time. Gene ontology enrichment analysis of these proteins revealed that the majority of this set was involved in platelet degranulation, secretion and regulated exocytosis. Twelve of these proteins have been shown to reside in α-granules. Upon prolonged storage (13–16 days) elevated levels of α-2-macroglobulin, glycogenin and Ig μ chain C region were identified. Taken together this study identifies novel markers for monitoring of the PSL that may potentially also be used for the detection of “young” and “old” platelets in the circulation.

Supplementary data S1. Non-imputed LFQ values of significantly changes proteins during platelet storage. Data represents mean ± standard deviation (n=3), *: P < 0.05 compared to day 1  In order to confirm the platelet origin of the proteins of which levels were changed during storage, we compared our data to published datasets.
Rieckmann et al published a dataset including copy numbers of proteins identified in different primary human hematopoietic cell populations. 1 The top 20 most abundant proteins in neutrophils, monocytes, NK cells, T cells and B cells were compared to the proteins we identified to change in abundancy during storage. In neutrophils and monocytes, but not in other cell types, S100A9 was reported to be one of the most abundant proteins (based on copy number).
Contamination of leukocytes is a general problem in platelet proteomics, Zeiler et al distinguished the true murine platelet proteome from the contaminants by employing successive stages of purification. 2 By following the abundancy of proteins during these purification steps, contaminants were identified. In agreement with the paper of Rieckmann et al. S100A9 was identified as a potential contaminant.
The presence of S100A9 in platelets has been reported by mass spectrometry studies 3 , RNA-seq of platelets 4 and megakaryocytes 5 . Also functional studies on platelet derived S100A9 can be found in literature. 6,7 As platelet concentrates, after filtering with a leukocyte depletion filter contain only a maximum of 10,000 leukocytes, from which 10% is monocyte or granulocyte, we assume that S100A9 we identified in our study originates from platelets. In Supplementary data 4 we provide experimental proof that S100A9 is a bona vide component of platelets.
In the study of Zeiler et al, different histones were identified as contaminants. However, the histone identified in our current study, H2AFJ is identified as a true platelet protein in the dataset of Zeiler et al. Additionally, H2AJ has been detected in platelet microparticles. 8 As indicated in the Legend of Figure 1 peptides identified and quantified for H2A can be derived from multiple histone H2A variant. These variants include: H2AFJ, HIST1H2AJ, HIST1H2AH, HIST1H2AC, HIST3H2A, HIST1H2AD, HIST1H2AG, HIST1H2AB, HIST2H2AB and H2AFX. This issue is discussed in more detail in Supplementary data S4.
Besides H2AFJ we identified more histones, of which some have not been described to be present in platelets. Since no significant differences in levels of these histones were observed we did not include these data in the manuscript.
The efficiency of the use of a leukocyte reduction filter was tested by taking samples from a platelet concentrate (PC), before applying filtration, after one filtration step and after 2 filtration steps. Samples were analyzed employing LeucoCOUNT (CellQuestTM Pro software). Before filtering, 217.82 x 106 / L leukocytes were present in the PC. After 1 filtration step, only 2 events could be measured, and after an additional filtration step 1 event was measured (Table 1). These results indicate that the use of a leukocyte reduction filter very efficiently removes leukocytes from platelet concentrates. To study the efficiency of platelet washing and purification for lysate preparation and to confirm that leukocyte contamination did not affect our results, lysates of the samples before and after 1 and 2 filtration steps were prepared. To do this, the samples were spun down 20 min at 120 g. On the PRP 2 more washing steps were performed before the platelets were lysed in lysis buffer. Levels of S100A9, platelet factor 4 (PF4) and von Willebrand Factor (VWF) were measure employing ELISA (Fig.  S3). Levels of S100A9, PF4 and VWF were similar before and after filtration. As S100A9 is a highly abundant protein in leukocytes, these results indicate that the method used excludes leukocytes which were at first present in the unfiltered sample. This suggests that the platelet lysates which were used for the mass spectrometry analysis (which were obtained from PCs that were filtered once with a leukocyte reduction filter) contained little to no contamination of leukocytes. Based on these data S100A9 appears to be a bona fide component of platelets and its decline during storage suggest that it may be a useful marker for monitoring of development of the platelet storage lesion.