A total of 30 participants were enrolled in the study, including 10 young (28.1 ± 1.9 years), 10 middle-aged (60.4 ± 2.2 years) and 10 old individuals (74.2 ± 3.0 years), with 5 males and 5 females in each group. The schematic of this study is shown in Supplementary Figure S1. The inclusion criteria were as follows: (1) age >18 years and signed informed consent form; (2) no mental disorders; and (3) no history of alcohol or drug abuse. The exclusion criteria included acute medical treatment or hospitalization within the first 3 months prior to blood collection; the chronic use of medications (e.g., aspirin and clopidogrel) and dietary supplements (e.g., fish oil and evening primrose oil) that may affect platelet function; the presence of severe diseases, including acute heart, liver, kidney disease, blood disease, or respiratory failure; and poorly controlled hypertension, diabetes or hyperlipidaemia. The characteristics of the subjects are shown in Table 1.

The blood was collected from subjects through peripheral venipuncture into vacuum tubes containing 3.2% sodium citrate. The platelet-rich plasma (PRP) was obtained by centrifugation at 200 × g for 10 min within one hour after blood collection, and the upper 2/3 of the PRP was placed in a new tube. The PRP was then centrifuged at 300 × g for 10 min in the presence of acid-citrate-dextrose (ACD) and 10 μg prostaglandin I₂ sodium salt (Aladdin, Shanghai, China), washed twice with modified Tyrode's buffer (138 mM NaCl, 3.3 mM NaH₂PO₄▪2H₂O, 1 mM MgCl₂, 2.9 mM KCl, 5.5 mM glucose, and 20 mM HEPES) to obtain purified platelet samples and stored at −80°C.

Protein extraction and peptide preparation were conducted by Novogene Co., Ltd. Briefly, platelet samples were completely lysed with DB lysis buffer [8 M urea, 100 mM triethylammonium bicarbonate (TEBA), pH 8.5], followed by 5 min of ultrasonication on ice. The lysate was centrifuged at 12,000 × g for 15 min at 4°C, and the supernatant was added to 1 M DL-dithiothreitol to react for 1 h at 56°C; the sample was subsequently alkylated with sufficient iodoacetamide for 1 h at room temperature in the dark followed by incubation in an ice bath for 2 min. Each protein sample was taken and the volume was made up to 100 μl with DB lysis buffer, trypsin and 100 mM TEAB buffer were added, sample was mixed and digested at 37°C for 4 h. Then, trypsin and CaCl₂ were added and the samples were digested overnight. Formic acid was mixed with the digested sample, the sample was adjusted to a pH under 3, and the sample was centrifuged at 12,000 × g for 5 min at room temperature. The supernatant was slowly loaded onto the C18 desalting column, washed with washing buffer (0.1% formic acid, 3% acetonitrile) 3 times, and then elution buffer (0.1% formic acid, 70% acetonitrile) was added. The eluents of each sample were collected and lyophilized.

Washed platelets were obtained using the same method as in 2.2 section. Whole cell lysates were separated by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (PAGE) and transferred to polyvinylidene membranes. The membranes were incubated overnight at 4°C with specific primary antibodies (diluted to 1:1000) and then incubated with anti-mouse or anti-rabbit antibodies (diluted to 1:5000). The bands were exposed using electrochemiluminescent reagent and the EvolutionCapt system (Vilber Lourmat), and quantified using ImagePro Plus software. Monoclonal anti-macrophage migration inhibitory factor (MIF) antibody, polyclonal anti-mannan binding lectin serine peptidase (MASP) 1 antibody, monoclonal anti-superoxide dismutase (SOD) 1, Monoclonal anti-calumenin antibody were purchased from Zen-bioscience (Chengdu, China). Monoclonal anti-factor H antibody were purchased from Huaan Biotechnology (Hangzhou, China). Monoclonal anti-autophagy-related 7 (ATG7) and monoclonal anti-actin antibody were purchased from ABclonal (Wuhan, China).

The levels of SOD and other biochemical indexes in plasma were detected by KingMed Diagnostics (Guangzhou, China).

DIA MS analysis was conducted by Novogene Co., Ltd. (Beijing, China). LC-MS/MS spectra were searched using an EASY-nLCTM 1,200 UHPLC system coupled with an Orbitrap Q ExactiveTM HF-X mass spectrometer (Thermo Fisher, Germany) operating in DIA mode. Data analysis and visualization of DIA data were conducted by Novogene Co., Ltd. using the Proteome Discoverer 2.2 (PD 2.2, Thermo Fisher Scientific) platform, Biognosys Spectronaut v. 9.0, and R statistical framework. MS2-based label-free quantification was carried out by analysing DIA raw data using Biognosys Spectronaut v.9.

The Spectronaut-Pulsar further filtered the retrieval results, and peptide spectrum matches (PSMs) with credibility of more than 99% were identified PSMs. The identified protein contains at least 1 unique peptide. The identified PSMs and proteins were retained and analyzed with a false discovery rate (FDR) of no more than 1.0%. The proteins whose quantitation significantly differed between the experimental and control groups were evaluated by t-test [p < 0.05 and |fold change (FC)| > 1.5] were defined as differentially expressed proteins (17, 18). R (R-3.4.3) with the heatmap gplots package was used for C-means cluster analysis, heatmap drawing, and KEGG enrichment analysis. Metascape (http://metascape.org) was used for pathway and functional analyses. Cytoscape 3.9.1 and the STRING (v10; https://string-db.org/) plug-in were used for visual analysis of the protein-protein interaction (PPI) network. Gene set enrichment analysis (GSEA) 3.0 was used to conduct GSEA. Enrichment dot bubbles and bars with colour gradients were plotted by https://www.bioinformatics.com.cn (last accessed on 20 Feb 2023), an online platform for data analysis and visualization. The flow chart was drawn by Figdraw.

In this study, we conducted proteomic analysis of platelet samples from 10 young, 10 middle-aged, and 10 elderly adults. A total of 44,756 peptides were detected, and 5,142 proteins were identified. Platelet proteomic analysis across different age groups demonstrated significant alterations in the protein profile of platelets from older individuals (Figure 1A; Supplementary Figure S2). Specifically, 332 differentially expressed proteins were upregulated (p < 0.05) and 104 were downregulated (p < 0.05) in the old group compared with the young group (Figure 1B), 227 differentially expressed proteins were upregulated (p < 0.05) and 54 were downregulated (p < 0.05) in the old vs. middle groups (Figure 1C), and 60 differentially expressed proteins were upregulated (p < 0.05) and 33 were downregulated (p < 0.05) in the middle vs. young groups (Figure 1D). Thirty-four differentially expressed proteins overlapped in both the middle vs. young and the old vs. young groups (Figure 1E). Enrichment analysis revealed that the overlapping 34 differentially expressed proteins, including beta-globin gene (HBB), human beta-defensins (HBD), TUBAL3, TUBB2B, and inter-alpha-trypsin Inhibitor Heavy chain 3 (ITIH3), were associated with “megakaryocyte development and platelet production” (Figure 1G). One hundred eighty-seven differentially expressed proteins overlapped in both the old vs. young and the old vs. middle groups (Figure 1F). The top enriched terms among the shared proteins included “complement activation”, among others (Figure 1H). These whole-proteome data reveal the differentially expressed proteins and the pathways involved in ageing. The list of differential proteins and overlapped proteins can be found in Supplementary Data Sheet 1.

To explore the dynamic changes in the platelet proteome during ageing, we performed cluster and PPI network analyses among the young, middle, and old populations. Proteins within cluster 1 (n = 238) showed a steady trend in the young and middle groups but significantly increased in the old group (Figure 2A). The enriched pathways among these proteins were most associated with the initial triggering of complement (Figure 2D). Proteins within cluster 2 (n = 137) displayed a significant age-dependent increasing trend, progressing from the young to the middle-aged and further to the old populations. (Figure 2B); the phagosome pathway was strongly enriched in these proteins (Figure 2D). Protein expression in cluster 3 (n = 91) showed an age-related decreasing trend in the middle-aged and old groups (Figure 2C); these proteins were mainly involved in haemostasis, neutrophil degranulation, and cellular detoxification based on pathway enrichment analysis (Figure 2D). The PPI network based on the molecular complex detection (MCODE) algorithm analysis is shown in Figure 2E, which identified seven functional clusters. The above results suggested the potential functional changes in platelets during ageing. The list of proteins from clusters 1–3 can be found in Supplementary Data Sheet 2.

Based on the analysis above, the largest difference in the platelet protein expression profile was seen between the elderly and young groups. To better elucidate the effect of age on the platelet proteome, we analysed the differences in the proteome between the elderly and young groups. As shown in Figure 3A, the enriched processes were “platelet degranulation”, and others. The proteins associated with platelet degranulation are shown in Table 2. PPI network analysis was performed on the differentially expressed proteins in the old group compared with the young group (Figure 3B). KEGG enrichment analysis showed seven signalling pathways in Figure 3C, most of which are associated with platelet activity and function (19, 20). Furthermore, GSEA revealed that the calcium signalling pathway and the NF-κB signalling pathway were enriched in the old group (Figures 3D,E), which is consistent with the hyperactivity of platelets in elderly populations.

Based on the GSEA, we found that the apoptosis and autophagy pathways were negatively enriched in the old group (Figures 3F,G), which indicated that the apoptosis and autophagy functions of platelets in the elderly were decreased. In this study, we observed a significant downregulation of ATG7, as a conventional autophagy-related gene, consistent with previous research findings (Supplementary Figure S3) (21).

The expression levels of platelet degranulation-related proteins SOD1 and MIF, as well as autophagy-related protein ATG7, were assessed using western blot analysis in an independent cohort of young (n = 7, aged 30.0 ± 5.4 years) and elderly (n = 7, aged 77.1 ± 5.3 years) individuals (Figure 4A). The results revealed a significant decrease in the levels of ATG7, SOD1, and MIF in the elderly group (p < 0.05). The findings are consistent with the results obtained from DIA MS analysis. Moreover, we assessed the levels of SOD in plasma samples obtained from both young and elderly cohorts, which revealed a significant decline in SOD concentrations within the elderly group (p < 0.05) (Figure 4B).

Based on the analysis above, differences in platelet proteins across various age groups may be associated with complement activation. The complement system is an important noncellular factor in the rapid response to tissue damage or inflammation that helps maintain integrity and prevent serious damage to the body. There are three pathways of complement activation, including the classical pathway, the alternative pathway and the lectin pathway. C4 is an important factor in the classical pathway, and the MASP family plays a key role in the lectin pathway (22). Factor H is the negative regulator of the alternative pathway that prevents complement consumption via uncontrolled alternative pathway activation on the surface of platelets (23). In this study, we observed a general upregulation of C4, MASP1, MASP2, and factor H expression in platelets among the elderly population (Figures 5A–E). Moreover, the expression levels of factor H and MASP1 were assessed using western blot analysis in both young and elderly groups (Figure 5F). The results revealed a significant increase in the levels of factor H and MASP1 (p < 0.05), which is consistent with the results obtained from DIA MS analysis.