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Introduction

Platelet numbers, platelets in hemostasis, platelet–leukocyte interactions, platelets in inflammation and infection, platelets in resolution, other functions of platelets, conclusions, disclosures, platelets in inflammation and resolution.

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Andreas Margraf , Alexander Zarbock; Platelets in Inflammation and Resolution. J Immunol 1 November 2019; 203 (9): 2357–2367. https://doi.org/10.4049/jimmunol.1900899

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Platelets have long been known for their role in hemostasis. In this, platelet adhesion and activation leads to the formation of a firm thrombus and thus the sealing of a damaged blood vessel. More recently, inflammatory modes of function have been attributed to these non–nuclei-containing cellular fragments. Interaction with leukocytes, secretion of proinflammatory mediators, and migratory behavior are some of the recent discoveries. Nonetheless, platelets also have anti-inflammatory potential by regulating macrophage functions, regulatory T cells, and secretion of proresolving mediators. This review summarizes current knowledge of platelet functions with a special focus on inflammation and resolution of inflammation.

Organism homeostasis has to be guaranteed at all times by providing specific security measures. The hematopoietic system contains cellular populations that exhibit immune defense and hemostatic functions, sealing vascular damage and combating invasion of pathogens. The role of platelets in hemostasis is well established, and these cells are the first responders in hemostatic clot formation, involving the adhesion and activation of platelets at a site of vascular injury. Nevertheless, these small cellular fragments have been shown to feature a much wider variety of functions than previously recognized ( Fig. 1 ).

The destiny of a platelet in inflammation and resolution. Bacterial invasion results in the recruitment and activation of immune cells, including platelets. Equally, a vascular injury results in platelet activation and depending on the site and sort of injury (e.g., endothelial denudation versus atherosclerotic plaque formation), an inflammatory interplay. An evolving thrombus as well as recruited destructive/harmful immune cells (e.g., neutrophils) need to be removed, and the executed inflammatory or hemostatic reaction has to be stopped and resolved for tissue homeostasis and unimpaired blood flow. Platelets contribute to the resolution of inflammation by a multitude of factors, including interaction and modification of T cells and macrophages. Platelet production and sequestration are equally affected by inflammatory processes.

Platelets are small (mean platelet volume: ∼8.9 fl in humans, 4.3 fl in mice) anucleate fragments that are derived from megakaryocytes ( 1 , 2 ). They were first described by Schultze in 1865 and subsequently by Bizzozero in 1882 ( 3 ). Humans contain ∼150 to 400 × 10 9 platelets per liter, whereas mice contain drastically higher amounts of platelets (∼1000 × 10 9 platelets per liter), which has to be taken into consideration when interpreting data from animal models ( 4 – 6 ). Platelet production occurs within the bone marrow, and recent data suggest that megakaryocytic cells and platelet production can also be observed in the lung ( 7 ). Platelets arise from megakaryocytes, and their production (thrombopoiesis) is regulated among others by thrombopoietin (THPO) ( 8 , 9 ). Regulation of platelet counts relies on a delicate feedback mechanism in which binding of THPO to its receptor on both platelets and megakaryocytes leads to reduced plasma levels, whereas a reduction in systemic platelet counts results in reduced scavenging of THPO and thus increased plasma values. Platelet numbers are affected by a variety of factors, such as inherent disease phenotypes or inflammatory complications. It has been shown that sepsis might impact platelet numbers ( 10 ), and a reduction in the number of platelets during sepsis is associated with a worse outcome ( 11 ). This observation might be caused by the fact that an increased inflammatory response induces more platelet adhesion in the microcirculation ( 12 ). Interestingly, also other factors, such as age ( 13 – 15 ), gender ( 16 , 17 ), and circadian rhythms ( 18 , 19 ), are known to affect platelet numbers and functions.

Platelets circulate within the blood stream, executing their functions, and they can be removed in the spleen (Ab-mediated) and liver (desialylation dependent, Fc-independent) or, under certain conditions, also in lung and brain and can be phagocytosed in a vWF-dependent manner by macrophages ( 20 – 25 ).

Following an endothelial injury, platelets adhere to the subendothelial layer, are activated, aggregate, and ultimately form a firm thrombus, occluding the injured site to prevent leakage of intravascular blood into the extravasal compartment, thus executing barrier functions. Each step relies on specific surface receptors. Adhesion under high shear is mainly mediated by the binding of GpIb/V/IX on platelets to collagen-bound vWF, thereby facilitating the interaction of GpVI with collagen. This GpVI–collagen interaction can then result in platelet activation ( 26 ). Interestingly, GpVI is also capable of binding to fibrin, leading to further GpVI-mediated platelet activation and thrombus stabilization ( 27 ). Platelets can furthermore adhere to collagen via GpIa/IIa ( 28 ), and GpIIb/IIIa is also capable of binding to vWF ( 29 , 30 ). Binding of activators (e.g., thrombin to GpIb/V/IX and PARs, collagen to GpVI, ADP to P2Y receptors, thromboxane A2 to thromboxane receptor) leads to intracellular signaling events and a shape change of the formerly discoid platelets into an either fried-egg (lamellipodia) or star-like shape (filopodia) ( 31 – 33 ). Activation of platelets also induces a conformational change of the GpIIb/IIIa integrin to a high affinity conformation, production of thromboxane A2, and mobilization and release of intracellular granules (α- and dense granules) which contain secondary mediators, including vWF, ADP, calcium, epinephrine, histamine, RANTES/CCL5, Pf4/CXCL4, and serotonin ( 34 ).

Immunothrombosis.

Platelets are functionally multifaceted cells ( Fig. 1 ), capable of interacting with various cell types, including leukocytes. Interactions of platelets with leukocytes have been observed in both hemostasis and immune defense, resulting in the creation of the term “immunothrombosis” ( Fig. 2 ). This term exemplifies the interaction of platelets with other immune cells, as well as plasmatic coagulation components, resulting in thrombus formation, possibly to protect the host organism and restrict the infection to the local environment, as has been nicely summarized by Gaertner and Massberg ( 35 ). This concept was recently challenged; in a model of Salmonella Typhimurium infection, formed thrombi contained only limited amounts of bacteria in vivo, contradicting the notion of a thrombus as a major bacterial-capturing site throughout the whole body ( 36 , 37 ). Rather, an organ or pathogen specificity was detected in the formation of thrombi in this study ( 37 ).

Platelet receptors from hemostasis to resolution. Platelets participate in various tasks within the organism, including hemostasis, immunothrombosis, inflammation, and resolution of inflammation. Platelet receptors appear to contribute to not just one singular process but rather a multitude of overlapping and not clearly separable processes. This image contains selected receptors.

Mechanisms of platelet–leukocyte interaction.

The interaction of platelets with leukocytes is of utmost relevance in the regulation of both inflammatory and hemostatic processes. After activation, platelets express P-selectin on their cell surface, which can interact with the selectin receptor PSGL-1 expressed on leukocytes. In an inflammatory setting, platelets influence leukocyte emigration by capturing leukocytes at specific extravasation sites, thus facilitating tissue infiltration in a PSGL-1/P-selectin–dependent manner, somewhat paving the path to extravasation ( 38 ). Indeed, neutrophils actively search for activated platelets to engage in a PSGL-1–mediated signaling event ( 39 ). Similarly, also in a sterile liver injury model, platelets provide a route for neutrophil extravasation in a GpIIb/IIIa dependent manner ( 40 ).

Platelets may also interact with other cells (including adjacent platelets, leukocytes, and endothelial cells) not only via direct receptor-mediated cell–cell interaction but also by binding to fibrinogen via GpIIb/IIIa ( Fig. 2 ). Activated platelets, expressing the high affinity conformation of the GpIIb/IIIa integrin receptor, are capable of binding to fibrinogen, leading to crosslinking with neutrophils via their surface integrin receptor Mac-1 ( 41 ). The leukocyte integrin Mac-1 can also directly interact with the platelet receptor GpIbα and thus influence thrombosis ( 42 ). Such platelet–leukocyte interaction can be observed in venous thrombosis, implicating a leukocyte-rich thrombus formation and showing the interaction of the hemostatic system with the immune system. Indeed, venous thrombosis depends on the interplay of monocytes, platelets, plasmatic coagulation, and neutrophils ( 43 ).

Platelet–monocyte interaction.

Platelet–monocyte interactions are not only important in venous thrombosis. In rheumatoid arthritis, platelet–monocyte complex formation is increased and platelets can induce a proinflammatory phenotype in monocytes in a CD147, PSGL-1, EP1/EP2, and COX-2–dependent manner ( 44 – 46 ). This interaction also assists in the adhesion of monocytes to the endothelium ( 47 ) and even an upregulation and activation of β1 and β2 integrins on monocytes, thus supporting proinflammatory recruitment of monocytes ( 48 ).

Notwithstanding, platelets when opsonized with IgG and interacting with monocytes are also capable of promoting a phenotype change toward IL-10–producing regulatory monocytes, emphasizing the bipolar character of platelets in the propagation of inflammation on one hand, but also control and resolution of inflammation on the other ( 49 ).

Platelet–leukocyte interplay in infection.

During pulmonary infection, platelets regulate ICAM-1 expression on endothelial cells via a shuttling process in which the PSGL-1/P-selectin–mediated platelet–neutrophil aggregate formation evolves in GpIb–Mac1-induced extracellular vesicle–mediated shuttling of arachidonic acid from neutrophils to platelets. This induces platelet thromboxane synthesis, and the thromboxane subsequently leads to the upregulation of endothelial ICAM-1 ( 50 ). This process is needed for neutrophils to competently penetrate an organ, the lung in this study, and combat invading pathogens. Indeed, leukocyte infiltration, bacterial burden and survival in a model of Escherichia coli induced pneumonia are platelet dependent ( 50 ). Comparable results were obtained in a model of GpVI-deficient mice in Klebsiella pneumoniae –induced pulmonary inflammation ( 51 ). Additionally, during bacterial infection, antimicrobial peptides, such as LL-37/CRAMP, are released from activated platelets. LL-37 may preactivate platelets and facilitate not only thrombus formation but also platelet–leukocyte complex formation ( 52 , 53 ).

Interestingly, the formation of platelet–neutrophil complexes in the lung depends critically on platelet expressed inositol hexakisphosphate kinase 1 (IP6k1) ( 54 ). In addition, CD40L expressed on platelets is a key regulator of platelet–leukocyte complex formation and influences regulatory T cell recruitment in atherosclerosis ( 55 ). Not only in the lung, but also in the kidney, is platelet–leukocyte interplay of utmost relevance, as platelet P-selectin is needed for competent infiltration of leukocytes into outer and inner medulla of the kidney in an ischemia-reperfusion injury model ( 56 ). A codependence on platelets and neutrophils in bacterial infection has also been observed in the liver, in which platelets migrate in order to collect bacteria and present it to neutrophils for subsequent phagocytosis ( 57 ). Neutrophils are capable of releasing neutrophil extracellular traps (NETs). In the setting of a bacterial infection, these NETs may capture and kill bacteria ( 58 ). The platelet–bacteria interaction in lung and liver indeed depends on TLR4 and results in formation of NETs ( 59 ). Even though the platelet–NETosis axis appears to be helpful in bacterial capturing, it may subsequently induce thrombin activation and intravascular coagulation in sepsis, possibly leading to multiorgan failure ( 60 ).

Platelet–leukocyte complexes and NETs in sterile inflammation.

Another clinically relevant immune complication relying on NET formation and often appearing in an inflammatory context is heparin induced thrombocytopenia. This platelet factor 4 (Pf4)–dependent pathologic condition results in platelet–neutrophil aggregation, formation of thrombi, and a reduction in platelet numbers. The formation of thrombi could be blocked by inhibiting of NET formation, whereas thrombocytopenia was unaffected by this, emphasizing the complexity of platelet–neutrophil interplay ( 61 ).

Leukocyte activation by secreted mediators.

Aside from direct receptor-mediated interactions as mentioned above, platelets are capable of activating leukocytes via secreted mediators, such as C-X-C-motif chemokines or the damage-associated molecular pattern HMGB-1. Platelet-derived HMGB-1 results in NETosis, induces monocyte accumulation, influences thrombotic diseases ( 62 – 64 ), and mediates bacterial clearance in a cecal ligation and puncture model ( 65 ). Intriguingly, activated platelets themselves can bind HMGB-1 on their surface via the receptor of advanced glycation endproducts (RAGE) and TLR-4, and HMGB-1 is capable of enhancing the activation of platelets without directly activating the platelets on its own ( 66 ).

Termination of inflammation.

With regard to the termination of the inflammatory process, in vitro data suggest a platelet-mediated delay of human neutrophil apoptosis ( 67 ). Platelet Pf4 diminishes neutrophil apoptosis in a model of arterial occlusion ( 68 ), and leukocyte apoptosis is associated with increased presence of platelet–leukocyte complexes ( 69 ).

Role of THPO.

Inflammation impacts platelet consumption, function, and production ( 70 ). Sepsis is associated with thrombocytopenia and changes in THPO levels ( 10 ). At the same time, thrombocytopenia on admission is associated with increased mortality in septic intensive care unit patients ( 71 ). THPO is known to activate platelets, induce formation of platelet–leukocyte aggregates, and increase the fMLP-mediated reactive oxygen species release of neutrophils ( 72 , 73 ). Blocking of THPO leads to a reduction in organ damage in experimental sepsis ( 74 ), exemplifying that a loss of platelets, which is associated with increased THPO levels, is directly linked to outcome and organ protection in inflammation.

Platelet activity in sepsis.

Aside from platelet numbers and its consequences on THPO levels, platelet activity is also affected throughout the course of inflammation. Although spontaneous platelet activity could be observed in severely septic patients, stimulus-dependent ex vivo aggregation of these platelets was significantly reduced ( 75 ). The dysfunctional aggregation of these platelets correlates with the severity of sepsis, and interestingly, a reduction in PAC-1 binding, detecting the high affinity conformation of GpIIb/IIIa, was observed in one study, even though a possible baseline difference that might be in line with a preactivation of these platelets was not reported ( 76 ). Indeed, another study showed that sepsis occurrence correlates with the fibrinogen-binding response of isolated platelets from patients, exemplifying an increased fibrinogen binding of circulating platelets in patients with risk for sepsis ( 77 ). Even though conflicting data exists with regard to platelet aggregation, overall, clinical data demonstrate a hyperreactive phenotype of platelets in septic patients ( 78 , 79 ). Also, an animal study using a septic mouse model suggested a prothrombotic, hyperreactive phenotype of platelets with the occurrence of platelet-rich thrombi and organ damage. Increased P-selectin exposure and fibrinogen binding as well as platelet–leukocyte complex formation appear in this model within the first 24–48 h. Also, animals following cecal ligation and puncture showed an increased thrombus formation rate compared with sham mice ( 12 ).

Platelet–bacteria interaction.

During bacterial infection, platelets may interact directly with bacteria via binding of different bacterial components to multiple receptors on platelets, including TLR-4, FcγRIIa, GpIIb/IIIa, and other receptors ( 80 ). This interaction evokes, on one hand, an activation response and platelet aggregation ( 81 ); on the other hand it leads to bacterial capturing, bundling, and migration of platelets ( 57 ). Consequently, inflammatory cells are attracted and platelets assist in bacterial clearance by presenting bacteria to neutrophils for inducing phagocytic elimination. Interestingly, bacteria caught by Kupffer cells in the liver are enclosed by platelets that are recruited in a GpIb, vWF, and GpIIb-dependent manner, impacting bacterial clearance and host survival ( 82 ). Platelets are additionally capable of releasing antimicrobial molecules themselves, such as the aforementioned LL37 ( 53 ), thus impacting host defense. Of note, platelets can directly kill Pf4-opsonized Gram-negative bacteria, such as E. coli ( 83 ). Microbicidal properties of Pf4 are also visible in Plasmodium falciparum infection, in which Pf4 lyses the digestive vacuole of the parasite inside of infected erythrocytes ( 84 , 85 ).

Viral infections and inflammasome.

Aside from direct bactericidal properties, macrophage phagocytosis is enhanced by platelets, not via direct interaction but through IL-1β ( 86 ). For further immune-defense–related activity, platelets express MHC class I and are capable of antigenic protein uptake, Ag-presentation, and coactivation of T cells ( 87 ). In viral infections, such as influenza infection, platelets engulf viral particles and release complement factor 3, which ultimately results in neutrophil aggregation and neutrophil DNA release, leading to host defense but also possible microthrombotic complications ( 88 ). Viral infections, more specifically dengue and influenza virus infections, also lead to the upregulation of IFITM3 in human platelets and megakaryocytes, and IFITM3 in megakaryocytes mediates the secretion of antiviral proteins, thus restricting dengue virus infection ( 89 ).

Following dengue infection, the NACHT, LRR and PYD domains-containing protein 3 (NLRP3) inflammasome is assembled, resulting in increased release of IL-1β, which subsequently augments vascular permeability in dengue-infected subjects ( 90 ). Activation of platelet NLRP3 has also been observed in cecal ligation and puncture–mediated sepsis, showing increased levels of IL-1β as well as IL-18 in lungs and kidneys of septic rats ( 91 ). NLRP3 is further implicated in the activation of platelets and in vitro thrombus formation together with BTK ( 92 ), and it has been shown to affect the GpIIb/IIIa outside–in signaling response ( 93 ). Nonetheless, it is worth noting that the inflammasome-related IL-1 cytokine release by platelets is under debate, as it has been suggested that for example leukocyte contamination can lead to false positive results ( 94 , 95 ).

Platelets in sterile inflammation.

Aside from pathogen-related inflammation, platelets are also of relevance in sterile inflammation. With regard to the aforementioned NLRP3 inflammasome, an upregulation of platelet NLRP3 could be observed in a model of hindlimb ischemia in a TLR4-dependent manner, ultimately triggering platelet aggregation ( 96 ). In another sterile inflammatory condition, in nonalcoholic steatohepatitis, platelet-dependent liver damage was observed ( 97 ). Mechanistically, the authors showed that platelet GpIbα, α-granule release, and hyaluronan–CD44-mediated interaction with Kupffer cells is needed for development of nonalcoholic steatohepatitis and subsequent hepatocellular carcinoma. In addition, another study investigating a model of experimental autoimmune encephalomyelitis showed the importance of platelet GpIbα for pathogenesis of experimental autoimmune encephalomyelitis by influencing leukocyte CNS infiltration. Thus, it appears that as a general mechanism, GpIbα serves as an “inflammatory” receptor, playing an important role for organ damage during sterile inflammation ( 98 ).

Fascinatingly, this same receptor might not solely serve as an inflammatory receptor by, for example, impacting the formation of platelet–leukocyte aggregates, but it appears to also be responsible for the suppression of TNF-α secretion from monocytes and inhibition of Mac-1 upregulation on neutrophils at later time points (24 h), possibly coinciding with the onset of a resolution phase in systemic inflammation ( 99 ). Also, in immune-complex–mediated inflammation, platelets on one hand assist in neutrophil activation, leading to organ invasion and damage, and on the other hand, in sharp contrast, show protective effects in that they seal extravasation sites of leukocytes and thus help in restoration of vascular integrity and barrier function, protecting against bleeding complications and exemplifying a “bipolar” character of platelets throughout inflammation ( 100 ). However, platelets also contribute to the inflammatory response of other nonbacterial diseases. In allergic inflammation, platelets are involved in the pathophysiology of the disease and can be found in lung tissue ( 101 ). The relocation of platelets into the lung suggests that allergens may, to some extent, induce migration of platelets ( 102 ). Such relocation of platelets is likely to affect overall blood-inherent platelet numbers and thus possibly THPO levels. Of note, in a smaller study investigating allergic asthma patients it was shown that these patients have a higher platelet count ( 103 ), once again linking platelet production to inflammation. However, another group reported a decreased mean platelet volume but no clear correlation with platelet numbers ( 104 ). Fascinatingly, platelets themselves even promote allergic asthma progression in a CD40L-dependent manner, inhibiting regulatory T cells and supporting (proallergic) Th2 responses ( 105 ). Also, platelet exocytosis, mediated by Munc13-4, was recently associated with the development of allergic airway inflammation ( 106 ), and platelets expressing the FcγRIIa even mediate anaphylaxis, the most severe allergic complication ( 107 ). It was observed that platelets from allergic patients show an activated phenotype, visible by increased plasma levels of Pf4 in asthma patients, increased platelet–eosinophil complex formation, serotonin release, and higher soluble P-selectin levels ( 108 – 111 ). Nonetheless, this platelet phenotype leaves the patients with slight hemostatic defects, resulting in prolonged bleeding time and reduced collagen- and ADP-dependent platelet aggregation ( 112 ). This exemplifies either a contradictory inflammatory/hemostatic platelet environment in which platelets are inflammatory active but hemostatically impaired or possibly a state of hemostatic exhaustion due to the chronic activation of platelets ( 101 ).

Platelet-derived extracellular vesicles in immune regulation.

Platelets are capable of releasing shedding-related membrane microvesicles (spontaneous releasates termed ectosomes) and exocytosis-related vesicles of smaller magnitude, exosomes ( 113 ). Platelet microvesicles exhibit surface molecules related to platelets, such as GpIb or GpIIb/IIIa, and thus can interact with a multitude of cells. Within leukocytes, platelet-derived extracellular vesicles preferentially interact with granulocytes and monocytes, mostly CD14 ++ CD16 + monocytes in whole blood ( 114 ). Platelet-derived microvesicles can capture and activate circulating neutrophils and facilitate neutrophil–endothelial cell interactions, thus acting in a proinflammatory manner ( 115 ). Also, these microvesicles are described to deposit RANTES on activated endothelial cells which—again in a proinflammatory manner—subsequently attracts monocytes ( 116 ). Furthermore, these small vesicles bind to smooth muscle cells, which leads to increased SMC migration, proliferation, and monocyte adhesion to vesicle-treated smooth muscle cells ( 117 ). In contrast, in an anti-inflammatory mode of function, microvesicles can also induce the differentiation of naive CD4+ T cells into regulatory T cells ( 118 ) and affect macrophage phenotypes (reduced TNF-α and IL-10 release by activated macrophages, induction of TGF-β release) and also impact differentiation of monocytes to immature dendritic cells, for example leading to reduced phagocytic activity ( 119 ). The smaller exosomes also contain micro RNAs that regulate endothelial cell signaling pathways, such as Wnt signaling ( 120 ), with Wnt/β-catenin signaling being known for reducing immune cell infiltration in a model of experimental autoimmune encephalomyelitis ( 121 ).

Platelet–endothelial cell interactions.

Inflammation leads to activation of endothelial cells. Platelets are known to roll on activated endothelial cells in a P-selectin/GpIb-dependent manner ( 122 – 125 ), whereas the expression and thus contribution of PSGL-1 on platelets to platelet–endothelial interactions has been critically discussed ( 126 , 127 ). Interaction of platelets with injured endothelial cells is additionally mediated by PECAM-1 ( 128 – 130 ). The main effects of such interaction appear to be, on one hand, the regulation of endothelial barrier and leakage as described above, whereas on the other hand, presence of platelets on endothelial cells facilitates capturing and subsequent tissue infiltration of inflammatory cells.

Platelets and the complement system.

Platelets do also interact with the complement system. The complement system relies on three activation cascades, namely the classical (activated by Ag-complexed IgM or IgG molecules binding to C1), alternative (by spontaneous low-level hydrolysis of C3), and lectin pathway (pathogen-associated-molecular pattern–lectin-pathway recognition molecule binding). Platelets contain multiple complement factors, including a form of C3 that differs from plasmatic C3. This specific C3 isoform can contribute to and enhance complement activation upon bacterial infection ( 131 ). Platelets also express various complement receptors, such as C3aR and C5aR, and complement binding evokes an activation response in platelets ( 131 ). Additionally, activated platelets can also activate the complement cascade in return ( 132 , 133 ), amplifying this cascade. Interestingly, C3-deficient mice show a reduction in venous thrombus formation with decreased platelet deposition and also reduced in vitro platelet activation, whereas C5 deficiency does not impact platelet activation but rather affects tissue factor expressed by myeloid cells, which may induce fibrin formation and thus influences venous thrombus formation ( 134 ). Also within the lectin pathway, platelets affect the coagulation system: platelets activate MASP-1, which in return can exert a thrombin-like activity, again linking platelet-dependent coagulation and complement-mediated inflammatory responses ( 135 , 136 ).

Chronic inflammation.

When focusing on inflammation, it is important to distinguish between acute and chronic inflammatory conditions, as platelets appear to change their role in an acute setting from proinflammatory to proresolving, whereas in chronic inflammation this switch is lacking. Indeed, preliminary data from a mouse model used to mimic a chronic inflammation–associated IKK2 activation showed an increased mean platelet volume and increased basal P-selectin surface expression, whereas in vitro platelet aggregation was reduced. The authors thus speculate that chronic inflammation might result in a fatigued or “exhausted” phenotype of platelets and megakaryocytes ( 137 ), an observation that is in accordance with the above-mentioned “exhausted” phenotype in chronically ill asthmatic patients. Nonetheless, a study on patients with chronic urticaria suggested an increased P2Y12 expression on platelets from patients compared with controls and also an increased activation response, as measured by soluble P-selectin and platelet aggregation ( 138 , 139 ). Also in psoriasis, increased mean platelet volume, platelet distribution width, soluble P-selectin levels, and platelet aggregation were observed in patients compared with controls contrasting the notion of an “exhausted” phenotype and instead pointing toward a more distinguished regulation in platelet activation patterns ( 140 ). These findings are most likely impacted by the underlying disease phenotype with regard to affected organ, cytokine/chemokine milieu, and duration and severity of the disease.

Inflammation, once provoked, has to be controlled and limited in time and spatial dimensions. Unhindered inflammation can lead to severe organ damage (compare sepsis) or chronification. Control of inflammation together with an active resolution phase is thus needed to restore tissue physiology and remove now-unwanted (apoptotic) inflammatory cells and debris, implicating an interplay between macrophages, platelets, lymphocytes, extracellular matrix components, and progenitor cells ( 141 ). Interestingly, platelets have anti-inflammatory properties. Platelets are known to interact with and enhance responses of regulatory T cells, resulting in increased IL-10 levels ( 142 , 143 ). Regulatory T cells are needed to support macrophage efferocytosis via secretion of IL-13 during resolution of inflammation ( 144 ). Also, activated platelets themselves are known to modulate macrophages toward an anti-inflammatory phenotype with increased release of IL-10 and reduced secretion of TNF-α ( 145 ). With regard to platelet–inflammatory cell interaction, platelet CLEC-2 and podoplanin inhibit leukocyte infiltration into arthritic joints and thus synovial inflammation and even influence the resolution of autoimmune arthritis in mice ( 146 ). Equal results of an anti-inflammatory mode of function were also obtained in a septic inflammation model in which platelet specific CLEC-2 deletion resulted in worsened organ injury ( 147 ). As mentioned before, platelets also induce formation of NETs. Even though these DNA releasates are mostly regarded as proinflammatory, they are equally known to limit inflammation by degradation of cytokines and chemokines, such as IL-10, IL-6, MCP-1, MIP-1α and β, IL-1β, and TNF ( 148 ), even though these results were debated controversially ( 149 ).

Platelets can directly interact with macrophages, leukocytes, and lymphocytes but also secret proresolving signals, so-called specialized proresolving mediators (SPMs) ( Fig. 3 ). These lipoxin mediators are known to be organ protective or influence organ restoration in different disease models, among others abrogating neutrophil infiltration ( 150 , 151 ). Prominent resolution mediators are, for example, Resolvin D1/D2, Resolvin E1, and maresin 1. Maresin-like lipid mediators are produced by platelets, as well as leukocytes ( 152 ). Interestingly, platelets express the SPM receptors ChemR32 (Resolvin E1 receptor), GPR32 (Resolvin D1 receptor), and ALX (lipoxin A4 receptor), and platelet stimulation by maresin 1 leads to a phenotype change in which proinflammatory mediator release from platelets is inhibited, but aggregation in response to ADP and spreading on fibrinogen are enhanced ( 153 , 154 ). Also, Resolvin D1, and 17-HDHA, a Resolvin precursor, lead to increased platelet aggregation upon ADP stimulation ( 153 ). Contrasting data exist with regard to effects of Resolvins on P-selectin surface mobilization in human platelets, as one study found no difference when Resolvin D1 was used in combination with ADP, whereas Resolvin E1 in ADP stimulation in another study led to reduced P-selectin surface mobilization, showing a differential interplay of resolution and platelet activation ( 153 , 155 ). The interaction of platelets with neutrophils even results in the biosynthesis of maresin 1 in a neutrophil-dependent manner and thus impacts organ damage and restoration in a murine HCl-induced lung injury model, exerting protective effects ( 156 ). This finding elicits a dual, somewhat contrasting, role of both platelets and neutrophils in the onset (harmful neutrophil invasion) and termination/resolution of inflammation. Interestingly, also in a model of venous thrombus resolution, which heavily relies on platelet–leukocyte interplay, lack of PECAM-1 provoked an insufficient resolution, although both endothelial and platelet PECAM-1 appear to be implicated in this ( 157 ). Consequently, platelets have also been observed to influence organ regeneration in a model of partial hepatectomy in mice. In this study, treatment with a von-Willebrand factor Ab or usage of vWF-deficient mice abolished platelet influx into the liver and dampened regeneration of the remaining liver tissue. This was observed by a decrease in the number of proliferating hepatocytes and a reduction in liver-to-body weight ratio hinting to the potential of platelets not only for inflammatory cell clearance but for also re-establishing of organ integrity ( 158 ). In summary, the versatility of platelets during onset and resolution of inflammation is far from being fully understood. The precise mechanisms and actions leading to a switch from proinflammatory recruitment of neutrophils to an anti-inflammatory termination of neutrophil influx and the recruitment and priming of resolving regulatory T cells and macrophages with the release of proresolving mediators remain unclear. One unanswered question so far is whether there is a platelet-intrinsic resolution program that can be activated during the course of inflammation, or if it is a purely passive reaction based on changes of outside mediators and the different priming of the platelets.

Platelet identity. Platelets were originally functionally characterized by their hemostatic potential. However, other functions and a phenotype change can also be observed. Multiple functional regulators are produced, released, or presented by activated platelets and differentially affect organism homeostasis.

As platelets seal an injury, it is apparent that they are crucial for barrier functions. This becomes of utmost importance in tissue in which there is not only one schematic stimulus (e.g., vessel wall injury leading to hemostasis), but when multiple hits are combined (such as septic inflammatory bleeding complications). In such setting, the true potential and capability of platelets are needed and challenged. Aside from the above-mentioned functions, platelets are capable of influencing a number of other processes, emphasizing its multipurpose potential. Platelet CLEC-2 is crucial for (lymph-) angiogenesis and also lung development ( 159 , 160 ). Interestingly, although platelets are important for developmental processes, at early fetal stages they appear to be functionally impaired, also leading to decreased platelet–leukocyte interaction in vivo ( 13 ). Already at this very early stage, the question thus arises of whether a “fully functioning” platelet with regard to its hemostatic potential is really needed, or if an intrinsic shift toward specific platelet functions and phenotypes perseveres. With further regard to the ITAM receptor CLEC-2, podoplanin-expressing tumor cells interact with platelet-expressed CLEC-2 ( 161 , 162 ), and this interaction evolves in the production of proteases, leading to facilitated metastasis ( 163 ). In addition, platelets are also involved in wound healing ( 164 ) by releasing pro- and antiangiogenic mediators ( 165 ) and SDF-1, which impacts progenitor cell recruitment ( 166 ). Platelets induce differentiation of endothelial progenitor cells ( 167 ), and platelet-derived growth factor affects neointima formation and smooth muscle cell proliferation, which could be of equal importance in tissue and organ regeneration and restructuring ( 168 ). With regard to restructuring of tissue, platelets furthermore contain inhibitors of metallopeptidases (TIMP1, 2, and 4), as well as matrix metalloproteinases (MMPs) and disintegrin metalloproteinases (ADAMs), which are likely to contribute to disease related tissue remodeling and reorganization, for example in atherosclerosis ( 169 , 170 ). Interestingly, MMP-2 was shown to impact soluble CD40L levels ( 171 ), which in turn are associated with various inflammatory processes, such as monocyte adhesion and migration and endothelial barrier breakdown ( 55 , 172 – 174 ).

Platelets are versatile cells undergoing a drastic renaissance of scientific interest. Whereas previous research focused on the role of platelets as merely hemostatic components, it is now clear that these small cellular fragments are equally needed for inflammatory reactions, angiogenesis, wound healing, and resolution of inflammation ( Fig. 3 ).

Possible current therapeutic interventions targeting platelet activation, production, or surface receptors thus may be re-evaluated with regard to inflammatory complications or interventions during the resolution process of such. Also, administration of platelet concentrates has to be carefully re-evaluated and adapted to each patient with regard to its inflammatory background and phenotype.

This work was supported by Interdisciplinary Centre for Clinical Research Muenster Grant SEED12/18 (to A.M.), Deutsche Forschungsgemeinschaft Grants KFO342/1, SCHA1238/7-1, ZA428/14-1, ZA428/12-1, and INST211/604-2, and Interdisciplinary Centre for Clinical Research Muenster Grant Za2/001/18 (to A.Z.).

neutrophil extracellular trap

NACHT, LRR and PYD domains-containing protein 3

platelet factor 4

thrombopoietin.

The authors have no financial conflicts of interest.

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Essay on Blood Platelets: Top 5 Essays | Cells | Protoplasm | Biology

Here is an essay on ‘Blood Platelets’ for class 8, 9, 10, 11 and 12. Find paragraphs, long and short essays on ‘Blood Platelet’ especially written for school and college students.

Essay on Blood Platelets

Essay Contents:

• Essay on the Methods of Counting of Blood Platelets

Essay # 1. Introduction to Blood Platelets:

Platelets are non-nucleated round or oval, biconvex discs having various sizes and covered by unit membrane. The average size is 2.5 µ. But bigger forms (4 – 5µ) are also seen. In ordinary blood film the platelets are gen­erally not seen separately but in clumps. If ant agglutinant (EDTA) is applied, in the blood prior to drawing of film then the same can be seen separately in light microscope.

In light microscope generally two components of platelets are seen in the stained slide. One is the clear ground substance—the hyalomere (hyalos=glass, meros=part) that is stained very faintly and the other is the deeply stained central portion—the chromatomere or granulomere.

Electron microscopic studies of platelets stained with glutaraldehyde followed by osmium tetroxide reveal additional components other than two components mentioned above. It is also claimed that the surrounding membrane of each platelet is covered by a thin film of carbohydrate.

In electron microscope the hyalomere is seen to consist of homogeneous fine granular materials. These hyalomeres in the periphery of platelets were also found to contain microtubules and microfilaments.

The micro­tubules probably give the ovoid structure of platelets and the microfilaments are presumably associated with microtubules. The microfilaments contain thrombosthenin which can contract like actin and myosin in muscle. This contractile element—the thrombosthenin is responsible for change of the shape of platelets.

Similarly, under electron microscope, the chromatomere is seen to contain numerous components.

Essay # 2. Components of Blood Platelet:

These components (Fig. 4.14) are as follows:

i. Alpha Granules:

These granules are oval or sometimes round in shape and having diameter, 0.2 µ and length 0.3 µ to 0.4 µ. These granules are often seen enclosed in a mem­brane. A rounded dense osmophilic area is often present in these granu­lar matrices of the organelle.

The function of α-granules is still uncertain. It is claimed that it has got lysosomal function which is im­portant in platelet release reaction and aggregation, in clot resolution or in the platelet’s phagocytic ca­pacity. Whether these granules are the sources of the platelet factor-3 are not clear.

ii. Mitochondria:

These are 2-3 in number and are clearly seen in a thin section of platelets.

iii. Sydersomes:

These are iron-containing (Ferritin) vesicles. These are not seen very frequently.

iv. Very Dense Granules (Serotonin-Containing Granules):

These types of granules have been demonstrated by Silver & Gardner in rabbit’s platelets. These granules have got diameter 0.05 µ to 0.13 µ and are surrounded by a unit membrane. These granules have been described to contain serotonin. Because these granules are osmophilic and as serotonin (5-HT) is also highly osmophilic. Furthermore following treatment with reserpine, the relative concentration of the osmophilic granules in these organelles is decreased.

v. Glycogen Granules:

These granules are also distributed in certain parts of the platelets.

vi. Ribosomes:

These are generally seen in newly formed platelets.

vii. Systems of Tubules and Vesicles:

These are of two types. One is the surface-connecting system and the other is the dense-connecting system. The surface-connecting systems communicate with the surface of platelets and are concerned with phagocytosis. The dense-connecting system originates from the Golgi apparatus of the megakaryocyte. This system does not communicate with the surface of the platelets.

The average life of platelets is about 5 to 9 days. They are destroyed in the spleen and other reticulo-endo­thelial cells.

Essay # 3. Functions of Blood Platelets :

i. Initiate Blood Clotting:

When blood is shed, the platelets disintegrate and liberate thromboplastin which activates prothrombin into thrombin.

ii. Repair Capillary Endothelium:

While in the circulation, the platelets adhere to the damaged endothelial lining of the capillaries and thus bring about a speedy repair. It is known that the capillary walls, being very delicate, are easily damaged and unless these weak spots are quickly mended, the vessels will break at these spots and capillary bleeding will take place. When the platelet count falls (below 50,000 per cu. mm) such capillary bleeding occurs.

iii. Haemostatic Mechanism:

This process seems to play by the dual functions of platelets such as agglutina­tion and coagulation. The cessation of blood flow from ruptured blood vessels takes place through simul­taneous coagulation and agglutination by platelets.

iv. Hasten Clot Retraction:

Speed of clot retraction (syneresis) is directly proportional to the number of platelets present and this retraction process is dependent upon the thrombosthenin (contractile protein of platelets) in presence of ATP and magnesium ions.

v. When Platelets Disintegrate, 5-Hydroxytryptamine and Histamine are Liberate:

5-hydroxytryptamine (5-HT) has vasoconstrictor effect and helps in haemostatic mechanism.

vi. Contain Some Substances which are like ABO Blood Antigens:

Platelets may be agglutinated by specific antisera and in the presence of complement, lysis may occur.

In this disease there is diminution of platelets in the blood. Haemorrhage occurs beneath the skin and mucous membrane. The appearance of lesions varies with the type of purpura, the duration of lesions, and the acuteness of the onset.

The colour is first red, becoming gradually darker, then purple, fading to a brownish yellow. It may result in permanent pigmentation or it may disappear in course of 2 or 3 weeks but the damaged capillary endothelium is not repaired. The coagulation time remains normal but the bleeding time is prolonged. The clot does not retract.

Essay # 4. Properties of Blood Platelets :

Not exactly known. Platelets possess protein and a considerable amount of phospholipid, much of which seems to be cephalin.

Characteristic properties are:

(a) Sticking to water-wettable surface or otherwise rough surface (injured or diseased endothelium, etc.),

(b) Easy clumping, and

(c) Easy disintegration and thus liberation of thrombokinase.

Total Number and its Variations :

The average number of platelets present per cubic mm of blood is about 250,000 to 450,000. Fairly rapid chang­es in number take place from day to day and even from one part of the day to another. Roughly speaking, those physiological conditions, which alter the total count of leucocytes, also alter the platelet count in the same direction.

Essay # 5. Methods of Counting of Blood Platelets :

For avoidance of clumping, an antiagglutinating agent, EDTA (ethylenediamine tetra-acetic acid) is gener­ally used during counting of platelets. This can be done in ordinary light microscope by direct or indirect method. In ordinary method, a drop of antiagglutinating substance is poured over the clear surface of the skin and a pin puncture is made through the drop.

The blood that comes out is mixed up with the antiag­glutinating agent and an ordinary blood film is made through it. The platelet count is made along with the number of R.B.C. present in each field. It is counted as the number of platelets present per 100 R.B.C. in each field. If the erythrocyte count per cubic mm is made then the value of platelets number can be worked out from the above ratio.

In direct method the platelet count is made by counting the same along with R.B.C. in counting chamber. In this method, measured amount of blood and measured amount of antiagglutinant along with a dye are taken in the pipette and are thoroughly mixed. The platelets and R.B.C. present are counted in a counting chamber.

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• Published: 11 June 2021

Assessment of a complete and classified platelet proteome from genome-wide transcripts of human platelets and megakaryocytes covering platelet functions

• Jingnan Huang 1 , 2 ,
• Frauke Swieringa 1 , 2   na1 ,
• Fiorella A. Solari 2   na1 ,
• Isabella Provenzale 1 ,
• Luigi Grassi 3 ,
• Ilaria De Simone 1 ,
• Constance C. F. M. J. Baaten 1 , 4 ,
• Rachel Cavill 5 ,
• Albert Sickmann 2 , 6 , 7   na1 ,
• Mattia Frontini 3 , 8   na1 &
• Johan W. M. Heemskerk 1   na1  

Scientific Reports volume  11 , Article number:  12358 ( 2021 ) Cite this article

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• Cardiovascular biology
• Cardiovascular diseases
• Cell biology
• Haematological diseases
• Molecular medicine

Novel platelet and megakaryocyte transcriptome analysis allows prediction of the full or theoretical proteome of a representative human platelet. Here, we integrated the established platelet proteomes from six cohorts of healthy subjects, encompassing 5.2 k proteins, with two novel genome-wide transcriptomes (57.8 k mRNAs). For 14.8 k protein-coding transcripts, we assigned the proteins to 21 UniProt-based classes, based on their preferential intracellular localization and presumed function. This classified transcriptome-proteome profile of platelets revealed: (i) Absence of 37.2 k genome-wide transcripts. (ii) High quantitative similarity of platelet and megakaryocyte transcriptomes (R = 0.75) for 14.8 k protein-coding genes, but not for 3.8 k RNA genes or 1.9 k pseudogenes (R = 0.43–0.54), suggesting redistribution of mRNAs upon platelet shedding from megakaryocytes. (iii) Copy numbers of 3.5 k proteins that were restricted in size by the corresponding transcript levels (iv) Near complete coverage of identified proteins in the relevant transcriptome (log2fpkm > 0.20) except for plasma-derived secretory proteins, pointing to adhesion and uptake of such proteins. (v) Underrepresentation in the identified proteome of nuclear-related, membrane and signaling proteins, as well proteins with low-level transcripts. We then constructed a prediction model, based on protein function, transcript level and (peri)nuclear localization, and calculated the achievable proteome at ~ 10 k proteins. Model validation identified 1.0 k additional proteins in the predicted classes. Network and database analysis revealed the presence of 2.4 k proteins with a possible role in thrombosis and hemostasis, and 138 proteins linked to platelet-related disorders. This genome-wide platelet transcriptome and (non)identified proteome database thus provides a scaffold for discovering the roles of unknown platelet proteins in health and disease.

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Introduction

Platelets are generated in the bone marrow as cell fragments from hematopoietic stem cells that are differentiated into megakaryocytes. In the circulating, the mature platelets control many blood-related processes both in health and disease. These functions extend from blood vessel-lymph separation and maintenance of vascular integrity to allowing hemostasis, promoting arterial thrombosis, regulating inflammatory, immune and infection processes; and even facilitating tumor progression 1 , 2 . The ultrastructure and the protein/RNA composition of a platelet, determined during their ontogenesis, allows the execution of all these functions. However, comparative studies of the molecular composition and structure of platelets in relation to their functions and megakaryocytic origin are still missing.

Although platelets do not contain a nucleus, they are equipped with mitochondria, several types of storage granules and multiple intracellular membrane structures, including endoplasmic reticulum (smooth and rough), a likely rudimentary Golgi apparatus, lysosomes, peroxisomes and endosomes 3 , 4 , 5 . Characteristic large invaginations, designated as open canicular or dense tubular system, make up ~ 1% or the cell volume and are filled with blood plasma components. A well-developed actin-myosin and tubulin cytoskeleton is required for proplatelet formation, micro- organization of the membrane structures, and mediates activation-dependent structural changes 6 , 7 , 8 , 9 . Whether the full repertoire of metabolic enzymes is present in platelets is still unclear, while the glucose metabolism is well-developed 10 , 11 . Furthermore, the ribosomal mRNA translation machinery is retained as well as elements of protein processing and trafficking and a repertoire of proteolytic processes in the proteasome 12 , 13 . Overviews point to a battery of receptors and channels, multiple adaptor molecules and small molecule GTP-binding proteins (G-proteins), and large protein kinase and phosphatase networks 2 , 14 .

Human genetic studies supported by mouse models show that hundreds and possibly thousands of platelet-expressed proteins contribute to thrombosis and hemostasis 15 . We reasoned that assembling the complete (quantitative) proteome and transcriptome of human platelets can provide a much better understanding of the molecules that determine platelet structure and functions in health and disease. As earlier platelet proteomes, reported in single articles, are limited in the numbers of identified proteins 16 , 17 , 18 , there is a need to integrate multiple proteomic studies based on the same methodology. While the number of genes detected in available transcriptomes of platelets and megakaryocytes are a magnitude higher 19 , 20 , 21 , these do not extend to the whole genome. Here, we combined multiple proteomes with the genome-wide RNA database of platelets and megakaryocytes generated by the Blueprint consortium 22 , 23 , and integrated these into a platelet structure and function- based protein classification system, for defining the full platelet proteome. Detailed analysis of this database provided novel insights into the structure–function relations of platelets.

Function-based classification of platelet proteins in merged proteome

Considering that the previously published (phospho)proteomics profiles of highly purified platelets from 22 healthy subjects in 6 cohorts were generated by the same analytical workflow 24 , 25 , 26 , 27 , 28 , 29 , we decided to integrate these datasets (Suppl. Figure  1 A). Primary sources of these datasets are listed in Table 1 . The resulting, merged human platelet proteome—one of the largest described so far—contained a total of 5,211 identified proteins, of which 80% were present in at least 2 cohorts (Suppl. Datafile 2 ). For 3,629 of these proteins, also copy numbers per platelet were present. In order to obtain a useful knowledgebase, we then categorized these proteins into 21 classes, based on intracellular localization and function (Fig.  1 A). For an objective classification, we used a dichotomous decision scheme together with human UniProt-KB assignments regarding the supposed primary location and/or function of that protein (Fig.  1 B). Highest fractions of identified proteins were seen in the following classes (Suppl. Figure  1 B): C 20 (transcription & translation, n  = 488 proteins), C 12 (other metabolism, n  = 475), C 18 (signaling & adaptor proteins, n  = 471), C 11 (mitochondrial proteins, n  = 455), and C 10 (membrane receptors & channels, n  = 327). Distribution profiles of the 3,629 proteins with copy numbers (Suppl. Figure  1 C) showed highest abundance and gene expression levels of the classes: C 01 (cytoskeleton actin- myosin), C 07 (glucose metabolism) and C 04 (cytoskeleton receptor-linked). This clustering analysis hence underscored the importance in platelets of signaling, mitochondrial and cytoskeletal proteins 2 .

Classification scheme and decision tree for gene and protein assignment to 21 function classes. Assignment was based on primary subcellular localization of the protein and its assumed function according to UniProt-KB. ( A ) Class numbering in alphabetical order. ( B ) Hierarchical decision tree.

Relevant genome-wide transcriptomes of platelets and megakaryocytes

Based on well-purified human platelet and megakaryocyte preparations, the Blueprint consortium 30 , 31 has recently generated one of the largest databases with genome-wide, quantitative information on a total of 57.8 k transcripts in either cell type (Fig.  2 , for source see Table 1 ). Examination of the distribution pattern of all gene-linked transcripts indicated that 37.2 k of these were essentially absent (log2fpkm 0.02–0.03 ± 0.03, mean ± SD) in platelets (Fig.  3 A) and megakaryocytes (Fig.  3 B). The residual presence of ~ 20 k expressed transcripts supports earlier analyses of the comparative transcriptomes of blood cells 19 . We then combined these Blueprint datasets with the combined proteome data to come to a draft full platelet proteome.

Dataflow of numbers of transcripts of proteome proteins. Relevant transcripts were defined as those of log2fpkm ≥ 0.20. Identified proteins refer to proteins present in the combined proteome from six cohorts. Non-identified proteins refer to proteins with relevant transcript levels in the combined PLT and MGK transcriptome. Data from validation cohort are also indicated.

Histograms of RNA levels in transcriptome of platelets (PLT) or megakaryocytes (MGK). ( A , B ) Distribution of all 57,289 genome-wide transcripts. ( C , D ) Distribution of all relevant transcripts (log2fpkm ≥ 0.20) for PLT ( n  = 17,629) or MGK ( n  = 16,843). ( E , F ) Distribution of protein-coding transcripts, as identified in the proteome, for PLT ( n  = 5,030) or MGK ( n  = 4,882). Levels of RNA expression (log2fpkm) were binned as < 0.20, 0.20–0.50, 0.50–1.00, 1.00–2.00, etc. For flow of numbers of transcripts and proteins, see Fig.  2 .

Based on a low threshold of log2fpkm ≥ 0.20 for relevant expression levels (see below), we obtained a defined set of 20.4 k transcripts, which was taken to assemble the relevant transcriptomes for platelets (17.6 k) and megakaryocytes (16.8 k). Comparison between cell types gave a same distribution pattern ( p  > 0.10, χ 2 ) for platelets and megakaryocytes (Fig.  3 C,D). Filtering for transcripts of the 5.2 k identified platelet proteins, again resulted in similar distribution patterns (Fig.  3 E,F). In either cell type, the lower level transcripts (log2fpkm < 1.00) were under-represented in comparison to the unfiltered genome-wide distribution ( p  = 0.049, χ 2 ).

Correlational analysis learned that the platelet and megakaryocyte transcriptomes were highly correlated; this was the case for both the 57.3 k genome-wide transcripts (log2fpkm ≥ 0.00, R = 0.85, β > 0.99) and the 20.4 k transcripts with relevant expression levels in either/both cell types (log2fpkm ≥ 0.20, R = 0.75, β > 0.99; Suppl. Figure  2 A, B). This markedly revealed high similarity of the RNA species composition in human platelets and megakaryocytes. Concerning different RNA biotypes, this correlation remained high, when extracting only the protein-coding genes (14.8 k, R = 0.75, β > 0.99), but it reduced for the 3.8 k RNA genes and 1.9 k pseudogenes (R = 0.43–0.54) (Suppl. Figure  2 C-E).

For justification of the relevant transcript threshold for protein expression, we reduced this further from log2fpkm 0.20 to 0.15; this resulted in inclusion of no more than 8 extra proteins from the combined proteome, half of it being plasma-derived proteins and the other half with minimal copy numbers. This indicated that log2fpkm of 0.20, although arbitrary, provides a reasonable cutoff value for transcripts resulting in measurable proteins.

Using the combined knowledgebase of platelets and megakaryocytes, we assessed which of the 20.4 k expressed transcripts (log2fpkm ≥ 0.20) were also present in the 5.2 k platelet proteome (Fig.  2 ). It appeared that the majority of proteins had relevant transcription levels. In 19 of the 21 protein function classes only 1.6% of the protein transcripts were below the cut-off (77/4,907 with log2fpkm 0.04 ± 0.05, mean ± SD, n  = 19) (Table 2 ). However, in the classes C 02 (cytoskeleton intermediate) and C 17 (secretory proteins), percentages of below cut-off were much higher, amounting to 58% and 24%, respectively.

Given the analysis above, we considered that the combined platelet and megakaryocyte transcriptome (either log2fpkm ≥ 0.20) may provide the most extensive list of mRNAs that can be translated into proteins. To evaluate this, we performed the same analysis as above for the platelet-only transcriptome. This resulted in a number of 'false' assignments of 181 (Table 2 ). For the megakaryocyte-only transcriptome data, this number increased to 329. Accordingly, the combined list of relevant platelet and megakaryocyte transcripts appeared to provide the best overlap with the proteomics dataset. By confining to proteins with relevant mRNA expression, the identified platelet proteome was therefore set at 5,050 proteins.

Comparison of (non-)identified parts of the platelet proteome

We then reasoned that starting from the genome-wide transcriptome of platelets and megakaryocytes (log2fpkm ≥ 0.20), it was possible to construct a 'full' theoretical platelet proteome and compare this with the identified platelet proteins. By thus comparing the identified proteins with the transcripts of protein-coding genes, we could calculate the remaining, non-identified part of the proteome at 9,721 proteins, i.e. 66% of all mRNA transcripts (Suppl. Figure  3 A). Based on this analysis, the majority of the 14.8 k proteins in the theoretical proteome was still absent in the current platelet proteomes. A similar number of 14.3 k was obtained when only including the relevant transcripts of platelets (Suppl. Figure  3 B,C).

Detailed examination of the genes for which no protein products were detected revealed marked differences between function classes (Fig.  4 A,B). Highest numbers and percentages of transcripts of the 'missing' proteins were obtained for: C 20 (transcription & translation, n  = 1,795), C 21 (uncharacterized and other proteins, n  = 1,683), C 13 (other nuclear proteins, n  = 1,269), C 10 (membrane receptors & channels, n  = 1,112), C 17 (secretory proteins, n  = 583), and C 18 (signaling & adapter proteins, n  = 561). This prompted us to investigate the reasons for these inter-class differences in coverage of the identified proteome.

Transcript distribution of identified and not identified proteins in the platelet proteome per function class. Examined were all relevant protein-coding transcripts (log2fpkm ≥ 0.20) of the combined relevant PLT/MGK transcriptome, with separation of identified proteins ( n  = 5,050) and not identified proteins ( n  = 9,721). For full data, see Suppl. Figure  3 . ( A ) Numbers of transcripts numbers per function class. ( B ) Percentage distribution of transcripts per function class.

Restraining factors for a complete platelet proteome

Acknowledging current mass-spectrometry limitations (see Suppl. Methods), we hypothesized that absence of mRNA products can be explained by three restraining factors: (i) low protein copy number, (ii) low mRNA level, and/or (iii) retaining of a protein in the megakaryocyte perinuclear region. The annotated platelet and megakaryocyte transcriptome knowledgebase allowed us to estimate these restraining factors.

The relation between platelet copy numbers and transcript levels is still unclear 32 , 33 . To reassess this issue, we compared the relevant Blueprint transcriptome (log2fpkm ≥ 0.20) with the 3.5 k proteins with known copy numbers. Correlative scatter plots showed a marked triangular pattern (Fig.  5 A,B). This pattern indicated that the abundance of a protein was restricted by, but was not otherwise dependent of the transcript level. Given the high similarity of the platelet and megakaryocyte transcriptomes, this implied that the megakaryocytic mRNA levels in fact maximized the extent of protein expression in platelets.

Comparison of protein copy numbers with mRNA levels and class-based analysis. ( A , B ) Protein copy numbers compared per gene to transcript levels (log2fpkm) for datasets of platelets (PLT, n  = 3,519) ( A ) or megakaryocytes (MGK, n  = 3,442). ( B ) Note triangular space, with low-abundance proteins (< 500 copies/platelet) were normalized to 150 copies. ( C , D ) Over-representation of protein function classes in quantitative proteome-transcriptome space per predefined area (I–V). Area I is considered to represent a condition of high translation (high mRNA level) and high transcription (high copy number); area II of high translation and low transcription; area III of low translation and transcription, and area IV an intermediate condition. Area V represents proteins without relevant transcript levels in PLT. Transcriptome-proteome triangle with analyzed areas ( C ). Enlarged space indicating function classes (C 01 -C 21 ) with significant over-representation per area. Statistics in Suppl. Table 1 .

To examine this further, we defined five regions in the proteome- transcriptome space, labeled as areas I-V (Fig.  5 C). For each of 3.5 k quantified proteins, we performed a modeling analysis per function class in Matlab. This modelling revealed that—regardless of the use of platelet or megakaryocyte plots—several classes were significantly over-represented ( p  = 10 −2 to 10 −10 ) in some of these areas (Suppl. Table 1 ). As illustrated in Fig.  5 D, for area I (high copy number and high mRNA), four classes were over-represented (i.e., cytoskeletal and glucose- metabolism proteins, p  < 10 –2 ). For the areas II and III with low copy numbers ('low translation'), six and three classes were over-represented, respectively ( e.g . , signaling-related, proteasomal, transcriptional and mitochondrial proteins). Thus, the classes accumulating in areas II-III appeared to be enriched in proteins with low copy numbers, irrespective of their corresponding transcript levels. Area V (low transcript levels) was enriched in keratin-like and secretory proteins (classes C 02 and C 17 ); and area IV of medium mRNA levels contained most of the remaining classes.

To categorize the low-level mRNAs, we examined the transcript level distributions per class, in which we separated the identified and non-identified parts of the theoretical proteome. Overall, the majority of the identified proteins showed relatively high corresponding transcript levels, regardless of their function class (Fig.  6 A). On the other hand, the low-level mRNAs (log2fpkm 0.20–1.00) were enriched in the non-identified proteome (median p  = 0.0005) (Fig.  6 B). This held for 12 out of 21 classes, where transcripts of non-identified proteins appeared to be of a lower level.

Distribution profile of relevant transcripts of per protein function class. For the relevant platelet transcriptome ( n  = 17,629), heatmaps were constructed of percentual distribution of transcript levels per function class (rainbow colors; blue = low, red = high). ( A ) Heatmap for transcripts of identified proteins ( n  = 5,030). ( B ) Heatmap for transcripts of non-identified proteins ( n  = 9,267); furthermore RNA genes ( n  = 2,480) and pseudogenes ( n  = 852). Expression levels (log2fpkm) were binned as 0.20–0.50, 0.50–1.00, 1.00–2.00, etc. For numbers of transcripts, see Suppl. Figure  3 .

To examine the low-level transcripts in these 12 classes, we searched for common elements ( n  ≥ 10) in protein names. Examples are: for C 01 : 'actin' or 'myosin'; for C 03 : 'centromere', 'centrosomal' or 'dynein'; for C 06 : 'AP1-3 complex subunit', 'Golgi' or 'trafficking protein particle' (Table 3 ). Close examination showed that, for all 12 classes with > 20% low-level mRNAs, the same > 20% also applied for elements of the non-identified proteome (Suppl. Table 2 ). As apparent from the listed most abundant transcripts of elements in almost all classes, the non-identified protein segments contained multiple isoforms or subunits of complexes that were also present in the identified segments, although the former had lower-level mRNAs (Table 3 ). Furthermore, sets of proteins seemed to be missing in almost all elements.

As a third restraining factor, we examined protein retainment in the megakaryocyte, by reasoning that in particular (peri)nuclear proteins will not move into a shedding proplatelet. This applied for the classes C 20 (transcription & translation), C 13 (other nuclear proteins) and C 03 (cytoskeleton microtubule), containing multiple centromere/mitotic spindle proteins (Fig.  6 A). Hence, these three classes were listed as providing additional explanation for low identification in the proteome (Suppl. Table 2 ).

Prediction model of the total platelet proteome

We then established an matrix for determining the three restraining factors per class (Fig.  7 A). This matrix was then used to calculate weighted mean values of the fractions of identified proteins grouped per factor. The fractions of identified proteins for (i) low copy number, (ii) low mRNA > 20%, and (iii) retainment in megakaryocytes, amounted to 43%, 45% and 20%, respectively. For all other classes, the average fraction of identified proteins was 65% (Fig.  7 A). By ratioing, this resulted in correction factors (0.66, 0.69 and 0.31, respectively) for class predictions of the likeliness that additional proteins would appear in an enlarged proteome (Fig.  7 B).

Restraining factors per function class and prediction model of full platelet proteome. Analysis of non-identified proteins ( n  = 9,721) from the relevant, combined PLT/MGK transcriptome per function class. Full dataset is provided in Suppl. Table 2 . ( A ) Fraction of identified proteins in green. Well-identified classes with fractions > 0.55 labeled as ID. Indicated in red are each of three restraining factors per class: (i) over- represented low copy number (areas II-III in Fig.  5 D), (ii) low mRNA level (area V, LM = low mRNA > 45%); (iii) retainment in megakaryocyte (peri)nucleus upon platelet shedding. Bottom: means of identified fractions (weighted for the presence of multiple factors); and correction factor in comparison to 'well-identified'. ( B ) Based on identified proteins ( n  = 5,050), modelled prediction of increased identification of missing proteins per class at higher proteomic detection. Shown per class are fractions of total relevant transcripts (heatmapped), and total expected proteins (bottom line). ( C ) Validation of prediction model based on novel proteome with 5,341 identified proteins.

Summarizing, the prediction model indicated a greatly enlarged size of the platelet proteome up to 10 k proteins at a 1- or twofold higher detection efficacy. Markedly, apart from a consistent underrepresentation of classes of (peri)nuclear proteins (C 03 , C 13 , C 20 ), the model also predicted that a poor detection of proteins in the classes: C 10 (membrane receptors & channels), C 17 (secretory proteins), and C 21 (uncharacterized & other proteins).

Proteome model validation

For validation of the model, we performed a new proteomic analysis with pooled platelets from 30 healthy subjects and the newest mass spectrometers. The obtained proteome included 4,389 of the previously identified proteins with relevant transcripts, as well as 954 previously not identified proteins (Fig.  2 ; details in Suppl. Datafile 3 ). Of additional 139 proteins without relevant transcript levels (log2fpkm < 0.20), the majority of 70% again appeared in C 02 (intermediate cytoskeleton, n  = 15, 11%) and C 17 (secretory proteins, n  = 81, 58%). This underscored the earlier observation that keratins and plasma proteins are present in the proteome of platelet samples.

Concerning the 954 novel obtained proteins, only small fraction of 3.8% showed low transcript levels with log2fpkm 0.20–1.00. Heatmap representation showed an similar distribution profile for all classes (Suppl. Figure  4 ). Markedly, inclusion of the novel proteins agreed with the prediction model for the majority of classes (Fig.  7 C). Interestingly, higher than expected were the novel proteins for C 20 (transcription & translation, additional 139 proteins) and C 13 (other nuclear proteins n  = + 121); lower were those of C 09 (membrane proteins, n  = + 7).

Coverage of genes associated with hemostasis and thrombosis

To further establish the clinical relevance of these datasets, we incorporated the identified proteome set into a Reactome-based protein–protein interaction network (267 core proteins and 2,679 new nodes) that was constructed to identify the roles of platelet and coagulation proteins in thrombosis and hemostasis 15 . As shown in Fig.  8 , this network incorporated 1.3 k of the identified proteins (median protein copies 2,200, median transcript level log2fpkm 4.97), as well as a set of 1.1 k proteins/transcripts (median log2fpkm 1.97) not present in the combined proteome (Fig.  8 A,B). Importantly, of the latter set, 172 proteins were obtained in the proteome of the validation cohort.

Network-based potential roles of (non)identified proteins in platelet proteome in arterial thrombosis and hemostasis. Using a published meta-analysis of mouse genes in thrombosis and bleeding, the network was built in Cytoscape, containing 267 core genes (bait nodes), 2679 new nodes, connected by 19.7 k interactions 15 . ( A ) Redrawn network visualization with color-coded proteins identified (green) or not identified (red) in the platelet proteome, with relevant transcript levels (node size, log2fpkm). Names are listed of 40 proteins with highest mRNA expression levels. ( B ) Distribution profile of (non)identified proteins with transcript levels (median copy numbers, median log2pkm). No mRNA = below relevant threshold. Attribute lists are given in Suppl. Datafile 4 .

To further establish the coverage for platelet-related disorders, we extracted the databases Online Mendelian Inheritance in Man (OMIM) 34 and Bloodomics 23 in combination with a recent overview paper 35 for genes associated with bleeding, thrombocythemia or thrombophilia. This resulted in 138 genes, of which 9 were absent in the platelet transcriptome but present in the proteome (coagulation factor and other plasma proteins), and 5 were absent in both (Table 4 ). For the remaining set of 124 genes, transcript levels (log2fpkm 4.58 ± 3.70, mean ± SD) and copy numbers (22.8 ± 73.0 k) in platelets were relatively high. Markedly, the majority of these 124 genes encoded for proteins in the classes C 10 (membrane receptors and channels, n  = 22), C 17 (secretory proteins, n  = 19), C 20 (transcription & translation, n  = 12), C 18 (signaling & adapter proteins, n  = 10), with a lower presence in the other classes. In accordance with the network analysis, it is likely that many still unknown gene products link to a platelet quantitative or qualitative traits, and hence to bleeding or thrombosis. The near complete coverage of the theoretical platelet proteome for known hemostatic pathways was also checked in the Reactome database (not shown).

In this paper, we integrated in a functional way the human platelet proteome, using data from six cohorts established in the same institute, with the recently composed genome-wide, > 57 k platelet and megakaryocyte transcriptomes from the Blueprint consortium 30 . By UniProt-aided categorization of all relevant transcripts (set at log2fpkm ≥ 0.20) into 21 protein function classes, we were able to generate a first full proteomic map of the sub-cellular, metabolic and signaling molecules in an average human platelet. Importantly, this analysis also provide a reference list of 37.2 k transcripts according to our lists are not or hardly expressed in platelets.

Overall, the manuscript covers six major novel aspects: (i) for the first time we established the full or theoretical platelet proteome based on a state-of-the-art genome-wide platelet and megakaryocyte transcriptome; (ii) using > 57 k transcripts we identified an unexpected high similarity of the quantitative platelet and megakaryocyte transcriptomes (including RNA gene transcripts), in spite of a weak correlation between the protein and transcript levels, providing insight into the distribution of RNA species upon platelet shedding; (iii) based on the systematic protein classification, the collected data provide molecular understanding of the complexity of platelet structures and functions; (iv) based on the established theoretical proteome, we developed and also validated a prediction model for identifying missing proteins in the current proteome sample sets; (v) the combined datasets offer better understanding of protein adhesion and uptake of plasma proteins by platelets; (vi) the combination of quantitative transcriptomes and (partly) quantitative proteomes completes our knowledge of the roles of > 100 genes and proteins in diseases not limited to thrombosis and hemostasis.

Correlational analysis of the 20 k expressed transcripts in platelets and/or megakaryocytes indicated an overall high similarity between the transcriptomes of the two cell types. This particularly held for the 14.8 k transcripts of protein-coding genes (R = 0.75), while the correlation was lower for the 3.8 k RNA genes and 1.9 k pseudogenes (R = 0.43–0.54). Although inter-individual differences are expected, our findings indicate that the majority of mRNA species evenly spread from megakaryocytes to the formed proplatelets, with limited degradation during platelet ageing. The aberrant transcript profiles of pseudogenes and RNA genes, which in general were more abundant in megakaryocytes, may be due to retention or to enhanced degradation of such shorter RNA forms 36 . In agreement with our findings, also other authors presenting smaller-size and not genome-wide datasets (3.5 k proteins and 5.5 k mRNAs), have reported a low correlation between platelet protein and transcript levels 37 , 38 . This lack of correlation however does exclude a role of altered mRNA and protein levels in platelet-related diseases 21 .

Based on the composition of the genome-wide transcriptomes of platelets and megakaryocytes, we calculated that the current proteome of 5,050 expressed proteins misses approximately 66% of the expected translation products. Highest percentages of missing proteins were seen in the classes C 20 (transcription & translation 79%), C 21 (uncharacterized proteins 79%), C 13 (other nuclear proteins 86%), C 10 (membrane receptors & channels 78%), C 17 (secretory proteins 72%), and C 18 (signaling & adapter proteins 55%). Especially low-level mRNAs (log2fpkm 0.20–1.00) appeared to be missing in the identified proteome, likely giving rise to only low copy numbers of proteins.

Proteomic technologies have been well developed, since the publication of the first draft human proteome, which revealed 17.3 k gene products and 4.1 k protein N-termini 39 . Accordingly, the present set of 5.0 k identified platelet proteins is higher than earlier published proteomes, e.g. of mouse platelets of 4.4 k proteins with copy numbers 40 , or of the semi-quantitative 3.5–4.8 k proteins in human platelets 38 , 41 . Smaller size published platelet sub-proteomes are a 0.1 k secretome 42 , and a 1.0 k sheddome 43 . Regarding platelet transcriptomes, which are more uniformly to construct, other authors have published a similar 20 k size with 16 k transcripts at > 0.3 fpkm 44 .

As a check of the present concept—starting from genome-wide platelet and megakaryocyte transcriptomes to determine the theoretical proteome—we evaluated the proteomes reported in three papers, using the current GeneCards gene designations. The proteomes of platelets from Dengue patients 45 or from platelet concentrates 46 were found to contain 93.1% (1,769/1,901) and 98.4% (2,466/2,505) proteins that were present in our protein database. Proteins without relevant transcripts were quite low, 2.1% and 0.1%, respectively. A paper analyzing the proteomes from cord blood and adult peripheral blood platelets 47 showed lower overlap of 79.9% (3,950/4,941) with the current proteome, supplemented with 16.4% proteins with relevant transcripts and 3.7% (183/4,941) without relevant transcripts in dataset. For the last fraction, it is unclear if residual presence of neonatal transcripts contributes to this higher percentage.

In platelet proteomics, the detection of proteins from blood plasma or other blood cells is a continuous point of attention. Our analysis based on highly purified, washed platelet preparations indicated the invariable present presence of plasma proteins. This can be explained by the fact that platelets exhibit an extensive open canicular system (estimated at 1 vol%) in open contact with the plasma, and furthermore also endocytose plasma proteins. The list includes 73 proteins classified as C 17 (secretory proteins) without corresponding mRNAs, of which at least fibrinogen and β2-glycoprotein 1 are known to be taken up by platelets 48 . Of note, fibrinogen levels are greatly reduced in the proteome of patients with Glanzmann's thrombasthenia, lacking integrin αIIbβ3. At the other hand, we find that multiple 'plasma proteins' can also be expressed by platelets themselves. Hence, even with the development of quality checks of 'plasma contamination', it may be difficult to rate many secretory proteins as platelet or non-platelet.

Apart from the inevitable presence of plasma proteins in platelet preparations, also other conditions may influence the obtained platelet protein composition. One relevant condition is that of macro-thrombocytopenia (e.g., Bernard-Soulier syndrome), often resulting in more fragile platelets, where obtaining of the high quality platelet preparation is a challenge. Another factor is emperipolesis, such as engulfment of hematopoietic cells by megakaryocytes in malign disorders, also affecting the platelet proteome.

To explain the missing of proteins in the identified proteome, we considered three restraining factors: (i) low protein copy number, (ii) low mRNA level, and (iii) protein retainment in the megakaryocyte perinuclear region. By estimating these restraining factors per protein function class, we calculated the technically achievable proteome of ~ 10 k proteins. The assumption is that improved technical developments will generate larger size proteomes (Suppl. Methods).

For validation of the function class-based prediction model of the remaining part of the proteome, we generated an additional proteomic set, which revealed 1.0 k new proteins in the predicted classes, of which 97% with relevant transcript levels. Interestingly, nuclear-related proteins were more frequently present than was predicted, thus pointing to a more prominent incorporation of (peri)nuclear proteins in megakaryocyte-shed platelets than was anticipated.

The function class-based analysis of (non)identified platelet proteome, based on relevant transcript levels (log2fpkm ≥ 0.20) as well as the listing of 37.2 k genome-wide not expressed transcripts provides novel and detailed information on the presence of protein isoforms, subunits of complexes and metabolic, protein processing and signaling pathways (see Table 3 ). For instance, regarding the apoptosis-related Bcl/Bax proteins (C 18 ) involved in platelet clearance 49 , the isoforms BNIP2, BCL2L1 (BCL-XL or BIM), BAD and BAK1 are present in the current proteome, while also the transcripts of BLC7B, BCL9 and BCL2 are highly expressed. As another example, regarding the glycosyl transferases (C 16 ) and epimerases (C 12 ) implicated in the surface glycosylation pattern and thereby in platelet survival time 50 , prominently present in the proteome (transcriptome) are GALM, GALE, GNE, C1GALT1 and B4GALT1/3/4/5/6, while C1GALT1C1 (COSMC) is only lowly transcribed.

In this Covid-19 era, our list also provides information on ACE2, BSG and TMPRSS2. In platelets and megakaryocytes, ACE2 expression levels appear to be very low (log2fpkm 0.00–0.03), similar to the levels in other blood cells ( https://blueprint.haem.cam.ac.uk/bloodatlas ). On the other hand, BSG (basigin) with high transcript levels is present in the platelet proteome, but not the marginally expressed TMPRSS2.

Both network analysis and OMIM-based evaluation of the genes/proteins known to contribute to platelet count, hemostasis and thrombosis showed high coverage by the current platelet proteome and transcriptome dataset. Since still little is known of many of the proteins, the list of 20 k transcripts reveals a wealth of novel information on proteins that will influence platelet structure and function. Knowledge for understanding disease processes is still limited, as prior work from our and other labs describe only small-size alteration in platelet (phospho)proteomes of patients with Scott ( ANO6 ) 27 or Glanzmann ( ITGA2B ) 48 disorders or with pseudohypoparathyroidism ( GNAS ) 28 . Altogether, this underscores that our approach to define a complete platelet proteome provides a valuable scaffold for further exploring and understanding platelet traits in and beyond thrombosis and hemostasis.

The current approach to define a classified full or theoretical platelet proteome from transcriptomes of platelets and megakaryocytes offers new insights into platelet composition and function, but also has limitations. As discussed above, platelets and megakaryocytes can bind and incorporate proteins from plasma, extracellular matrix or other cells, where the corresponding transcripts can be missing. In case of low transcript levels, copy numbers of proteins in platelets can be too low to be detected by mass spectrometric techniques (for detailed discussion on technical limitations, see supplementary methods). Furthermore, the source (individual healthy, diseased subject) and purification method of platelets and megakaryocytes can influence the specific composition of proteome and transcriptome, especially regarding the more rare molecules. It is noted here, that a subset of proteins expressed at very low copy numbers may be relevant for platelet ontogenesis, but have limited impact on platelet functions.

Earlier analyses indicated that the platelet proteome from healthy subjects is quite stable with < 15% of changes 51 . Similarly, the global platelet proteomes from the few patients, extensively studied so far—such as Albright hereditary osteodystrophy, Glanzmann or Scott syndrome patients—showed only minor changes compared to that of control subjects 27 , 28 , 48 . the technical abilities to study this in the future is made in the revised discussion (page 16). In the near future, with the use of roboting techniques allowing higher throughput analysis of large sample sets and with the application of stable isotope markers 17 , we expect to know more on the variable part of the platelet proteome in health and disease.

Subject cohorts and platelet samples

Washed, purified blood platelets were obtained in the same laboratories from six cohorts of healthy control donors, anonymized for medical-ethical reasons after informed consent. For each cohort, platelet samples were freshly isolated from anticoagulated blood by first collecting platelet-rich plasma, and removing plasma by a double wash step. Contamination was < 0.02% for red blood cells and leukocytes, presence of plasma about 1 vol%. Raw proteomic data per cohort are provided in the following papers. Cohort 1 ( n  = 3) in Burkhart et al . 24 , cohort 2 ( n  = 3) in Beck et al . 25 , cohort 3 ( n  = 3) in Beck et al . 26 , cohort 4 ( n  = 2) in Solari et al . 27 , cohort 5 ( n  = 8) in Swieringa et al . 28 , and cohort 6 ( n  = 3) in Lewandrowski et al . 29 . Platelets were always derived from anonymous healthy donors, due to ethics restrictions also not revealing age or sex. New experimental work was approved by the Ethics Committee of Maastricht University and Maastricht University Medical Centre 28 .

The genome-wide Blueprint gene expression data were generated from platelets obtained from venous blood ( n  ≥ 3 per transcript, NHS Blood and Transplant healthy blood donors), and depleted from leukocytes 23 , 31 . Primary data are public accessible via https://blueprint.haem.cam.ac.uk/mRNA/ or htt ps://blueprint/haem.cam.ac.uk/bloodatlas/. 31 . Purity of platelets was checked by Sysmex, hemocytometer and from transcriptional signatures. Culturing of megakaryocytes ( n  ≥ 3 per transcript) from cord blood, and check by flow cytometry (CD41 and CD42 double-positive) were as described 19 . Blood samples from healthy volunteers were obtained after full informed consent according to the Declaration of Helsinki.

In all reported studies, platelet lysates were analyzed according to a common bottom-up mass-spectrometry proteomics approach in the same laboratory. Experiments details are in the original papers 24 , 25 , 26 , 27 , 28 , 29 . Briefly, purified lysed platelets were subjected to a filter-aided sample preparation or ice-cold ethanol precipitation procedure. Isolated proteins were then trypsin-digested in guanidinium HCl or urea and (triethyl) ammonium bicarbonate (incubated over night at 37 °C). For global proteome analysis, complex peptide mixtures were fractionated by high-pH reversed phase chromatography (pH 6 or 8). For detection and quantification of platelet phospho-peptides, an enrichment procedure was included using TiO 2 beads, followed by hydrophilic interaction liquid chromatography (HILIC) fractionation. Fractions of peptides or phosphopeptides were analyzed by nano-liquid chromatography (LC)-MS/MS using QExactive (QStar Elite) and Orbitrap Velos mass spectrometers. Raw data were processed with Proteome Discoverer, SearchGui and Peptide Shaker implemented with Mascot and Sequest and X!Tandem search algorithms. Spectra were searched against a human UniProt-KB database. For database versions, see the original papers 24 , 25 , 26 , 27 , 28 , 29 . In all cases, a false discovery rate (FDR) of 1% was set.

Primary data deposits and links

Primary datasets were downloaded per proteome cohort via the website links of Table 1 , also providing information on the deposited spectral datasets. In cohort one ( n  = 3 subjects), relative protein abundance levels 52 were determined in combination with a protein abundance estimate to give protein copy numbers per platelet 51 . In brief, protein copy numbers were assessed based on a normalized spectral abundance factor (NSAF) method. First, absolute quantification information was obtained from a set of 24 reference proteins (providing reference copy numbers), which then was used to correct NSAF indexes and was extrapolated to copy numbers of remaining proteins with known NSAF values.

In cohorts 2–5 ( n  = 3, 3, 2, 8 subjects, respectively), additional proteins were obtained without copy numbers, obtained from either global proteome analysis and/or phosphoproteome analysis 25 , 26 , 27 , 28 . In cohort 6 ( n  = 3 subjects), platelet membrane proteins were identified 29 . Presence of individual proteins per cohort is indicated in Suppl. Datafile 2 .

Proteome tabling construction

The summative identified proteins with or without copy numbers, derived from global proteome or sub-proteome/enrichment (phospho-proteins or membrane proteins) analysis, were all checked in UniProt-KD (consulted January 2019—January 2020) and listed per corresponding gene (GeneCards). If no match between UniProt-KD assignment and gene name was found, additional gene databases were consulted (Biomart, Ensembl).

Transcriptomes

Genome-wide quantitative data of 57,849 transcripts assessed in human platelets and human megakaryocytes were established via a guided procedure by the Blueprint consortium 23 , 31 . For link to sources, see Table 1 . For establishing relevant transcription levels, we used an arbitrary, low expression cut-off of log2fpkm ≥ 0.20, which included lowly abundant transcripts, to include all theoretical proteins presumably with very low levels (Suppl. Datafile 1 ).

Functional classification of protein-coding and other transcripts

The knowledge bases GeneCards (consulted January 2019—January 2020) was used to primarily separate protein-coding genes, RNA genes and pseudogenes. GeneCards provides comprehensive information on the annotated and predicted human genes, integrating gene-centered data from ~ 150 web sources 53 . Gene annotation was performed for all 20,425 gene transcripts (out of 57,849) with log2fpkm ≥ 0.20 in platelets and/or megakaryocytes.

For all relevant transcripts of protein-coding genes (log2fpkm ≥ 0.20), a supervised classification procedure was developed to combine the corresponding proteins into function classes. The classification was hierarchical, according to a yes/no decision tree (Fig.  1 ), instructed by the EMBL UniProt-KB knowledgebase (visited January 2019–January 2020) 54 . UniProt-based decisions were based on the general description in Uniprot-KB of the (putative) protein's intracellular location and cellular function. Priority order of decision assignment was according to classical cell biology, i.e. from central' to 'peripheric: nucleus → mitochondria → endoplasmic reticulum and Golgi apparatus → cell → other cellular vesicles (lysosomes, peroxisomes, endosomes, secretory vesicles) → (plasma) membrane interactions → cytoskeleton structures → cytosolic protein types. When no relevant information was available, proteins were classified as 'Uncharacterized and other proteins'. Note that (assumed) extracellular proteins were classified as secretory proteins, as these are considered to be released into the blood plasma by gland cells.

Area analysis of proteome-transcriptome space

For the matrix of 3,626 proteins with information on copy numbers and transcript levels in platelets (log2fpkm × 1000), a rectangular triangle was obtained, in which five areas (I-V) were pre-defined as follows. Top right corner, I ( x  = 100,000, y  = 8, x -radius = 0.4, n  = 58 PLT); top left corner, II ( x  = 1000, y  = 8, x -radius = 0.3, n = 776 PLT), bottom left corner, III ( x  = 1000, y  = 0.75, x -radius = 0.3, n  = 137 PLT); middle of triangle, IV ( x  = 5000, y  = 4, x -radius = 0.4, n  = 928 PLT), and all below the triangle, V ( x  = 600–200,000, y  = 0.6–10.2, n  = 185 PLT). For each dot (protein) in the matrix, using Matlab the distance (in log space) was determined to each of the predefined areas; and recordings were made as in/out. Subsequently, for the proteins per function class, p -values of over-representation in pre-defined areas were calculated, employing a native Matlab function.

Proteome prediction modelling

For prediction of the 'missing' (non-identified) part of the platelet proteome, we generated a model that was based on the definition, per protein class of three restraining factors: (i) low protein copy number, (ii) low mRNA level, and (iii) protein retainment in megakaryocytes upon proplatelet formation. Therefore, per function class, the fraction of non-identified proteins was calculated from all transcripts with log2fpkm ≥ 0.20 in platelets and/or megakaryocytes, with an arbitrary setting of well-identified classes having < 45% 'missing proteins'. Classes with low copy numbers were obtained from the proteome-transcriptome matrix (over-representation in areas II and III); or when no other explanation for low identification was present. Classes with low mRNA levels were also taken from the proteome-transcriptome space (over-representation in area V); or when the transcript fraction with log2fpkm 0.20–1.00 was > 22.5% (arbitrary set at half of 45%). Classes with supposed protein retainment in megakaryocytes came from handbook knowledge, i.e. the 'nuclear classes' C 13 and C 20 ; and furthermore C 3 -cytoskeleton microtubule, given the retainment of mitotic spindle and centromere structures. Mean restraining factors were calculated from the averages of non-identified proteins in the corresponding classes. See further Suppl. Methods. Coverage of hemostatic pathways was checked in the Reactome database 55 .

Model validation using extended novel proteome

To validate our model, platelet samples were collected as above from 30 healthy subjects, digested with trypsin, and analyzed by liquid chromatography-mass spectrometry. See further Suppl. Methods. Mass spectrometry proteomics data were deposited to the ProteomeXchange Consortium via the PRIDE partner repository 56 with the dataset identifier PXD022011 (username: [email protected]; password: 7BeFQOxP).

Bioinformatics and statistics

Statistical comparison was by probability analysis in Excel (Mann–Whitney U-test or Student t-test for continuous variables). Distribution profiles were compared by a χ 2 test. Values of p <0.05 were considered significant.

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Acknowledgements

JH, IP and IDS are supported by the European Union’s Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement TAPAS No. 766118. JH is enrolled in a joint PhD program of the Universities of Maastricht and Santiago de Compostela (Spain); IP and IDS are enrolled in a joint PhD program of the Universities of Maastricht and Reading (UK). MF is supported by the British Heart Foundation (FS/18/53/22863). Research support by the Ministerium für Innovation, Wissenschaft und Forschung from Nordrhein-Westfalen, the Cardiovascular Centre (HVC) of Maastricht University Medical Centre + , the Centre for Molecular Translational Medicine (INCOAG, MICRO-BAT), the German Federal Ministry of Education and Research (BMBF 01EO1503) and the Deutsche Forschungsgemeinschaft (ZA 639/4-1 and JU 2735/2-1).

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These authors contributed equally: Frauke Swieringa, Fiorella A. Solari, Albert Sickmann, Mattia Frontini and Johan W. M. Heemskerk.

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Department of Biochemistry, CARIM, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands

Jingnan Huang, Frauke Swieringa, Isabella Provenzale, Ilaria De Simone, Constance C. F. M. J. Baaten & Johan W. M. Heemskerk

Leibniz-Institut Für Analytische Wissenschaften-ISAS-E.V, Dortmund, Germany

Jingnan Huang, Frauke Swieringa, Fiorella A. Solari & Albert Sickmann

Department of Haematology, University of Cambridge, National Health Service Blood and Transplant (NHSBT), Cambridge Biomedical Campus, Cambridge, UK

Luigi Grassi & Mattia Frontini

Institute for Molecular Cardiovascular Research (IMCAR), University Hospital RWTH, Aachen, Germany

Constance C. F. M. J. Baaten

Department of Data Science and Knowledge Engineering, FSE, Maastricht University, Maastricht, The Netherlands

Rachel Cavill

Medizinische Fakultät, Medizinische Proteom-Center, Ruhr-Universität Bochum, Germany

Albert Sickmann

Department of Chemistry, College of Physical Sciences, University of Aberdeen, Aberdeen, UK

Institute of Biomedical & Clinical Science, College of Medicine and Health, University of Exeter Medical School, Exeter, UK

Mattia Frontini

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F.S., F.A.S., I.P. and I.D.S. analyzed and interpreted data and revised the manuscript; F.A.S., L.G., R.C., C.B., A.S., M.F. provided essential tools and revised the paper; J.H., M.F. and J.W.H. designed research, analyzed and interpreted data and wrote the paper.

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Correspondence to Jingnan Huang or Johan W. M. Heemskerk .

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Supplementary information 1. datafile 1. genome-wide transcriptome and identified proteome of plt and mgk, supplementary information 2. datafile 2. identified proteins in cohorts 1-6, supplementary information 3. datafile 3. validation proteome and newly identified proteins, supplementary information 4. datafile 4. nodes of protein interaction network of t&h, supplementary information 5. supplementary methods, figures and tables, rights and permissions.

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Huang, J., Swieringa, F., Solari, F.A. 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 (2021). https://doi.org/10.1038/s41598-021-91661-x

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DOI : https://doi.org/10.1038/s41598-021-91661-x

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Achieving hemostasis: piling up platelets changes everything

The platelet signaling network is an integrating engine, is any of this clinically relevant, acknowledgments, platelets and hemostasis: a new perspective on an old subject.

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Lawrence F. Brass , Scott L. Diamond , Timothy J. Stalker; Platelets and hemostasis: a new perspective on an old subject. Blood Adv 2016; 1 (1): 5–9. doi: https://doi.org/10.1182/bloodadvances.2016000059

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This is an exciting time for clinicians and scientists interested in platelet biology. Improved imaging methods allow platelets to be observed in action in animal models in real time at ever greater resolution. Expanding proteomic and genetic data sets lend themselves to better understanding platelet activation. New gene editing methods make it easier, faster, and less expensive to test new ideas using transgenic animal models. Combining systems biology approaches with computational methods encourages a broader perspective on platelet activation and makes it possible to develop ideas in silico that can then be tested in vivo. One result has been an opportunity to revisit prevailing wisdom about the hemostatic response, extending and occasionally refuting what has come before.

Systems biology is the study of complex interactions, some of whose properties can be understood only when multiple cells or multiple pathways are considered. Here we will consider 2 examples in which improved methods and a systems-oriented approach have provided insights into the most basic of platelet functions: participation in the hemostatic response to injury. The first example considers the ways in which the simple act of piling up of platelets at a site of injury helps to calibrate the hemostatic response by altering the environment in which platelet activation occurs. The second example considers how individual signaling events within platelets form an integrated network whose properties emerge from the individual pathways.

Penetrating injuries trigger platelet activation by the local accumulation of platelet agonists. Some agonists, such as collagen, are stationary; others, such as thrombin, adenosine diphosphate (ADP), and thromboxane A2 (TxA 2 ) are mobile. Platelet activation is commonly considered with an agonist-centric perspective, but this perspective omits the impact of the local environment, which changes rapidly as platelets and fibrin accumulate. Recent evidence suggests that formation of a hemostatic thrombus first promotes and then limits platelet activation by providing a sheltered environment in which agonists can accumulate. Thus, there is a reciprocal, rather than a unidirectional, relationship between platelet activation and thrombus structure ( Figure 1A ). Because this relationship emerges as platelets pile up, it is worth considering how it happens.

A systems view of the hemostatic response to penetrating injuries . High-resolution confocal fluorescence microscopy studies performed in mice show that the hemostatic structure has a characteristic architecture whose properties emerge as platelets accumulate, altering local conditions. (A) Presence of a core of highly activated, densely packed platelets overlaid with a shell of loosely packed, less activated platelets and the transition zone that exists between them. (B) Manner in which soluble platelet agonists such as thrombin, ADP, and TxA 2 form concentration gradients radiating from the site of injury. (C) Distribution of the agonists is determined in part by differences in transport rates in the narrowing gaps between platelets, gaps whose dimensions decrease as clot retraction proceeds.

Although platelet behavior has been studied for over a century, recent advances in intravital imaging pioneered by the Furie laboratory 1-3   and others 4-9   have made it possible to observe the hemostatic response in mice in real time at high resolution. Those studies show that platelet activation in this setting is heterogeneous. Although some platelets change shape, secrete their granule contents, and become procoagulant, others display only minimal external signs of activation. The result is a gradient of platelet activation with a core of fully activated platelets, a shell of less activated platelets, and a transition zone between them ( Figure 1A ). 7  

Among the properties that distinguish the core from the shell is packing density, which is greater in the core. 7   Tight packing slows the movement of soluble molecules in the gaps between platelets, which shrink as the thrombus retracts. 10,11   The core is where most of the fibrin is found and where clot retraction would be expected to have the greatest impact ( Figure 1B ). As packing density increases, transport becomes dominated by diffusion rather than convection, slowing movement to an even greater extent ( Figure 1C ). 10-12  

Regional differences in packing density also affect the distribution of platelet agonists. The result is the appearance of concentration gradients in which the distribution of each agonist is also affected by its physical properties and binding to other molecules. Individual platelets are exposed to combinations of agonists whose concentrations vary over time ( Figure 1B ). Submaximal concentrations of multiple platelet agonists can have additive or even synergistic effects. 13   Thrombin is the main driver of full platelet activation in the thrombus core. TxA 2 and ADP are primarily drivers for the thrombus shell. 7,14   The impact of packing density is demonstrated by studies showing a mutation in α IIb β 3 that impairs clot retraction decreases thrombin activity and reduces platelet activation. 11,15   Studies performed in silico extend the observational studies and provide hypotheses that can be tested in vivo and in vitro. 10,16,17  

Most of the studies summarized in Figure 1 were performed in the mouse microvasculature using a laser or a sharpened probe to make small holes in arterioles and venules. To what extent are the results applicable to people? Human platelets cannot readily be studied in vivo. However, when studied in a microfluidics device that incorporates collagen, tissue factor, and the transmural pressure drop that normally occurs following vascular injury, human platelets form an inner core of fully activated platelets overlaid by a shell of less activated platelets just as mouse platelets do in vivo. 18  

What about events in arteries and veins, rather than arterioles and venules? Primarily for technical reasons, high-resolution imaging studies have largely been limited to the microvasculature. However, there has been progress. 9,19-21   More work needs to be done, but the initial message appears to be the same. In both settings, the piling up of platelets changes everything by producing a local environment in which agonists accumulate.

Most of what is known about the platelet signaling network was worked out one pathway at a time. The first part of this essay shows that platelets within a growing hemostatic mass are exposed to combinations of agonists, any of which may be present below optimal concentrations. Agonist receptors are not generic. Each agonist has a unique receptor set that can couple to the platelet signaling network in different ways ( Figure 2 ). 22   Thrombin, for example, activates 2 members of the protease-activated receptor family on human platelets, PAR1 and PAR4, allowing it to signal through the heterotrimeric G proteins, G q , G 12 , and, directly or indirectly in platelets, G i2 . PAR1 produces a quick burst of signaling; PAR4 a more sustained response. ADP activates P2Y 1 and P2Y 12 , the latter coupled to G i2 and the former to G q . Signals mediated by G q activate phospholipase Cβ, leading to increased cytosolic Ca 2+ , activation of Rap1b, and, ultimately, to the activation of α IIb β 3 . 23,24   G i2 inhibits cyclic adenosine monophosphate (cAMP) formation, activates Akt, and promotes integrin activation by inhibiting Rap1b inactivation. 25   Once α IIb β 3 has been activated, integrin-dependent signaling promotes clot retraction, increasing packing density and slowing solute transport.

Integrating the platelet signaling network to obtain an optimal response . Although platelet signaling pathways were originally described one at a time, they form a closely regulated network that both promotes and limits the hemostatic response. The figure focuses on events downstream of the G protein–coupled receptors for thrombin, ADP, and TxA 2 . It illustrates 2 GTP-dependent switching points or nodes in the network, crosstalk between pathways, and the presence of regulatory loops that affect information flow through the nodes. The green and red boxes summarize transgenic mouse models associated with gain or loss of function, respectively. References are in the text.

The platelet signaling network makes possible a measured response to agonists in part because of feedback loops and nodes within the network where signaling pathways converge. Examples include G q , G i2 , and Rap1b ( Figure 2 ). The activity state of each of these is determined by whether they are bound to guanosine triphosphate (GTP) or guanosine diphosphate (GDP), the GDP-bound state being inactive. In effect, these are on/off switches. Replacement of GDP with GTP is promoted by a guanine nucleotide exchange factor (GEF), which for G q and G i2 is an agonist-occupied receptor and for Rap1b is CalDAG-GEF1. Restoration of the inactive state is accelerated by a GTPase activating proteins (GAP). For G q and G i2 the primary GAPs in platelets are RGS10 and RGS18. 26-28   For Rap1b, the primary GAP is Rasa3. 25,29  

Network integration occurs in part by regulating the balance of GEF and GAP activity. The availability of RGS10 and RGS18 is regulated by spinophilin (SPL), which sequesters both in resting platelets, and by 14-3-3γ, which binds RGS proteins in activated platelets. 26,30   Dissociation of SPL/RGS complexes occurs after a brief delay, creating a negative feedback loop when platelets are activated by thrombin or TxA 2 . 26   As an example of pathway convergence, dissociation of the SPL/RGS complex also occurs when endothelium-derived PGI 2 suppresses platelet activation by raising platelet cAMP levels ( Figure 2 ). 31   For Rap1b, regulation occurs at the level of Rasa3, whose ability to act as a GAP is inhibited by signaling downstream of G i2 . 25,32   Rap1b 33   and CalDAG-GEF1, 34,35   like spinophilin, are targets for cAMP-dependent phosphorylation.

How can the relative contributions of these regulatory events be assessed? One way is with transgenic mice ( Figure 2 ). Deletion of G i2 α 36,37   or G q α 38   produces a loss of function phenotype, as does deleting spinophilin or introducing a missense mutation in spinophilin that mimics the effects of cAMP-dependent phosphorylation. 26,31   In contrast, deleting either RGS18 27,39   or RGS10 (Peisong Ma and L.F.B., unpublished observations, 2015), or introducing a mutation in G i2 α that makes it resistant to RGS proteins, 7,40   produces a gain of function. These effects are not of equivalent magnitude: deleting G q causes spontaneous bleeding, but deleting G i2 does not. 36-38   Neither gain-of-function mutation appears to cause spontaneous thrombosis. The RGS-insensitive G i2 mutation causes expansion of the thrombus shell without affecting the size of the core. 7  

Mutations at the level of Rap1b are equally informative about network integration. Deleting Rap1b causes a loss of function phenotype with a prolonged tail bleeding time and increased time to occlusion. 41   Deleting CalDAG-GEF1 also causes a loss of function 42   as do CalDAG-GEF1 mutations in humans. 43,44   Deleting Rasa3 causes severe thrombocytopenia, bleeding, and increased embryonic and perinatal lethality. 25,29   The thrombocytopenia is believed to be due to spontaneous platelet activation and shortened platelet survival. 25   These observations speak to the importance of Rasa3 at the Rap1b network integration point.

In summary, recent studies show that platelets possess an integrated signaling network rather than a collection of independent pathways. Packing density and therefore transport rates help determine agonist distribution and concentration. Activity at network nodes determines how large the hemostatic mass will grow.

There are several ways that the 2 examples cited here can inform decision making by hematologists, cardiologists, and pharmaceutical companies. First, they provide a context to better understand why platelets express receptors for so many different agonists. Second, they suggest that the strengths and limitations of some commonly used antiplatelet agents reflect not only their half-lives, affinities, and off-rates, but also where they work on the platelet signaling network and how well they penetrate thrombus structure. For example, observational studies performed in vivo suggest that widely prescribed P2Y 12 antagonists impair hemostasis and reduce recurrent thrombotic events by destabilizing the thrombus shell with comparatively little impact on the thrombus core, at least in the microcirculation where these studies were performed. Finally, the data suggest that tests of on-treatment platelet function in patient taking antiplatelet agents need to be designed to better reproduce the complex conditions that the observational studies show exists within a growing thrombus. Computational studies that recapitulate platelet accumulation and transport may prove helpful in this regard, especially as the simulations become even more refined. 10,17,45-50  

This work was supported by National Institutes of Health, National Heart, Lung and Blood Institute grants P01 HL40387, P01 HL120846, and R01 HL103419.

Contribution: All authors contributed to the ideas expressed in the manuscript.

Conflict-of-interest disclosure: The authors declare no competing financial interests.

Correspondence: Lawrence F. Brass, University of Pennsylvania, Room 815 BRB-II, 421 Curie Blvd, Philadelphia, PA 19104; e-mail: [email protected] .

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Platelets as messengers of early-stage cancer

Siamack sabrkhany.

1 Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands

Marijke J. E. Kuijpers

2 Department of Biochemistry, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands

Mirjam G. A. oude Egbrink

Arjan w. griffioen.

3 Angiogenesis Laboratory, Cancer Center Amsterdam, Department of Medical Oncology, VU University Medical Center, Amsterdam UMC, Amsterdam, The Netherlands

Platelets have an important role in tumor angiogenesis, growth, and metastasis. The reciprocal interaction between cancer and platelets results in changes of several platelet characteristics. It is becoming clear that analysis of these platelet features could offer a new strategy in the search for biomarkers of cancer. Here, we review the human studies in which platelet characteristics (e.g., count, volume, protein, and mRNA content) are investigated in early-stage cancer. The main focus of this paper is to evaluate which platelet features are suitable for the development of a blood test that could detect cancer in its early stages.

Introduction

Cancer is one of the foremost causes of death worldwide [ 1 ]. Over the past decades, researchers have been focused on the discovery of new approaches for improved therapy of cancer, ultimately aiming for cure. It is known that an earlier detection of cancer will profoundly improve the success of patient treatment and enhance overall survival [ 2 – 4 ]. However, early detection of cancer is notoriously difficult and efficient blood-based biomarkers are hardly available. Up to now, many blood-based sources of biomarkers, such as plasma, serum and circulating RNA/DNA, tumor cells, or exosomes/microparticles, have been exploited in the search for the ideal biomarker allowing detection of cancer at its earliest stages [ 5 ]. Remarkably, platelets have long been neglected in blood biomarker research [ 6 ], in spite of the growing evidence that platelets are important in the development and progression of cancer [ 7 – 9 ]. Platelets have been shown to possess an important biological role at several stages of malignant disease, such as angiogenesis [ 10 ], cell proliferation [ 11 ], cell invasiveness, and metastasis [ 12 , 13 ]. In addition, there are indications that inhibition of platelet function has an inhibitory effect on tumor growth and that this increases overall survival of patients [ 14 – 17 ].

The interaction between platelets and cancer is evidently reciprocal [ 18 ]. Platelets have a stimulatory effect on cancer progression [ 7 , 8 ], while at the same time, the presence of a malignant disease affects multiple platelet characteristics and functions. For example, malignant tumors have been shown to increase platelet numbers and hijack platelet functions in order to fuel cancer progression [ 19 ]. Moreover, there are several promising studies showing that platelet features from patients with cancer are already altered in early stages of malignant disease [ 20 – 24 ]. Hence, the use of platelet characteristics is expected to provide an innovative strategy in the search for biomarkers of early-stage cancer [ 9 ]. In the current paper, we will briefly review the mechanisms by which platelets stimulate cancer progression and present an overview of literature describing the effect of cancer presence on platelet activation, count, volume, mRNA/protein content, and function, as well as whether these features could be used as biomarkers of early-stage cancer.

Platelets promote angiogenesis, tumor growth, and metastasis

Cancerous tumors are also viewed as wounds that never heal [ 25 ], because they induce local and systemic coagulation and platelet adhesion, activation, aggregation, and secretion [ 26 – 28 ]. During the past decades, it has become increasingly clear that tumors can use platelets to stimulate angiogenesis and induce cancer cell proliferation and metastasis (Fig. ​ (Fig.1) 1 ) [ 7 , 10 , 29 ].

Platelets promote tumor angiogenesis cancer growth and metastasis. (1) The prothrombotic tumor microenvironment induces platelet adhesion, activation, and secretion in the (angiogenic) blood vessels within the tumor. (2) Platelet secretome and microparticles induce tumor angiogenesis, vessel stabilization, cancer cell proliferation, and resistance to apoptosis. At the same time, platelets sequester proteins and mRNA from the tumor (and become so-called tumor-educated platelets). (3) Tumor cell-platelet aggregate formation shields circulating tumor cells from the immune system. (4) Platelets support metastatic niche formation by inducing the expression of adhesion molecules and recruitment of stromal cells at potential metastatic sites. (5) Platelet secretome increases tumor cell invasiveness by inducing epithelial-mesenchymal transition (EMT) and inhibiting local immune response

The composition of the tumor vasculature is different from the normal vasculature [ 30 , 31 ]. Blood vessels in a tumor are characterized as immature and leaky, due to the exposure to the tumor microenvironmental milieu. Hence, the tumor endothelium can be discontinuous, providing prothrombotic conditions via the expression of thrombotic proteins, e.g., tissue factor, collagen, von Willebrand factor, podoplanin, and integrins [ 32 – 36 ]. Eventually, these factors, combined with the low blood flow, create a thrombophilic tumor microenvironment [ 25 , 37 ]. This prothrombotic state initiates platelet adhesion and activation, followed by secretion of the rich granule content of platelets within the tumor [ 10 ].

Platelet granules contain an enormous amount of bioactive growth factors, e.g., chemokines, cytokines, and matrix metalloproteinases (MMPs). Upon secretion, these molecules can support tumor growth and progression, mainly via, but not limited to, induction of tumor angiogenesis [ 10 , 38 ]. This rate-limiting step in cancer growth is intricately regulated by a plethora of growth factors and the involvement of tumor cells and different tumor stroma cells (e.g., endothelial cells, pericytes, smooth muscle cells, fibroblasts, platelets, and a variety of mature and immature immune cells) [ 39 ]. The role of platelets in this process is slowly becoming elucidated [ 7 , 10 , 40 – 43 ]. By secreting their bioactive molecules within the tumor microenvironment, platelets not only play an important role in the regulation of tumor angiogenesis but also in vascular stabilization and integrity [ 41 – 45 ], as well as resistance to therapy [ 46 , 47 ].

It has been described that the secretome and microparticles released from activated platelets increase proliferation of several cancer cell lines (e.g., ovarian and hepatocellular carcinoma cancer cells) [ 11 , 48 ]. In addition, platelets are able to increase resistance to mitochondrial apoptosis in cancer cells [ 41 , 42 , 49 ]. At the same time, platelets support tumor cell survival and transport in the circulation once these cells detach from the primary tumor site. To this end, platelets are able to attach to circulating tumor cells via several adhesion receptors, which results in the formation of tumor cell-platelet aggregates [ 50 ]. Exploiting this mechanism, circulating tumor cells are able to protect themselves from immune surveillance [ 50 , 51 ]. Platelets aggregated to the tumor cell surface also support adhesion to the endothelium of potential metastatic sites [ 52 ]. Platelet-vessel wall interaction induces the expression of multiple adhesion molecules on the luminal side of the vessels which increases recruitment of stromal cells (e.g., monocytes) [ 51 , 53 ]. In addition, CD97 expressed on tumor cells results in platelet activation and secretion of platelet-derived mediators, such as ATP, lysophosphatidic acid, and TGF-β, that disrupt endothelial junctions and thereby increase tumor cell invasiveness and extravasation into healthy tissue [ 12 , 54 ]. Furthermore, TGF-β secreted from platelets and direct platelet-tumor cell contact activates the TGF-β/Smad- and the NF-κB pathways in malignant cells [ 12 ]. This stimulates enhanced tumor cell aggressiveness by epithelial-mesenchymal transition (EMT) [ 12 , 13 , 52 ]. Platelets can induce cancer cells to upregulate mesenchymal markers such as SNAIL, vimentin, fibronectin, and MMP-9, and stimulate downregulation of E-cadherin, which is a fundamental step in EMT [ 12 , 55 ]. At the same time, TGF-β downregulates the local immune response, for example, by inhibiting the expression of NKG2D, the major receptor of several MHC class I homologs, on natural killer cells [ 13 , 56 ].

Platelet inhibition reduces cancer progression

As tumors use (activated) platelets to boost tumor angiogenesis, tumor growth, and metastasis (Fig. ​ (Fig.1), 1 ), it has been suggested that targeting platelets may result in inhibition of cancer progression. Indeed, reduction of platelet count in tumor-bearing mice reduced tumor angiogenesis, tumor growth, and metastasis [ 41 , 42 , 57 – 59 ]. Besides their stimulatory effect on tumor growth, platelets are critical at stabilizing the tumor vasculature and preventing intratumoral hemorrhage [ 41 , 43 ]. In addition, multiple in vivo studies have shown that treatment with aspirin or clopidogrel, both inhibitors of platelet aggregation, reduces tumor angiogenesis and tumor growth, as well as cancer progression in tumor-bearing mice [ 17 , 44 , 60 – 63 ]. Furthermore, large epidemiological studies suggested that daily intake of aspirin at low anti-platelet doses reduces the risk of development of several types of cancer [ 14 , 16 ]. Also, a prospective cohort study revealed that daily aspirin intake after the diagnosis of colorectal cancer decreased cancer-specific death and overall mortality [ 64 ]. Although the exact mechanism underlying this effect of aspirin is still poorly understood, several recent studies suggest that inhibition of platelets by aspirin reduced their ability to induce cancer cell proliferation through modulation of the c-MYC oncoprotein and inhibition of platelet-derived COX-1/thromboxane A2 [ 17 , 26 ]. However, the effect of aspirin on tumor growth could also be due to its inhibitory effect on COX-2 expressed on tumor cells [ 65 , 66 ]. Large randomized placebo-controlled prospective human studies investigating the effect of platelet inhibition on overall survival of patients with cancer are ongoing, and may provide further clinical evidence of the protective properties of anti-platelet agents against cancer [ 67 ].

Over the past few years, there are also an increasing number of studies indicating that certain anticancer therapies, specifically tyrosine kinase inhibitors (TKIs), reduce platelet count, as well as platelet activation [ 68 – 70 ]. This class of drugs is widely used for the clinical management of a variety of cancer types, mostly in combination with more conventional treatment strategies. It is therefore assumed that next to the direct effect of these drugs on tumor cells, they may lend part of their activity indirectly via the inhibition of platelet function.

Tumor cells influence platelet characteristics

Activated platelets stimulate cancer progression at different stages, making platelets an attractive target in the battle against cancer. At the same time, the presence of a tumor has a major influence on platelet characteristics, possibly through effects at the level of the megakaryocytes. These characteristics include platelet count, volume, protein and mRNA content, and activation state; responses at the level of these features make platelets an interesting new target for blood biomarker research for the detection of early-stage cancer [ 9 ].

Platelet count

The association between platelet count and malignant disease is well recognized. In the presence of occult cancer, platelet production can be heavily increased in response to various tumor-derived and systemic factors (Fig. ​ (Fig.2) 2 ) [ 7 , 19 ]. Platelets are produced in the bone marrow through the formation of proplatelets by terminally differentiated megakaryocytes [ 36 ]. In vivo studies have demonstrated that tumor-bearing mice have increased serum levels of megakaryocyte-stimulating factors, such as IL-6, M-CSF, and SDF-1α [ 71 ]. Also in patients, plasma levels of G-CSF, GM-CSF, and IL-6 were found to be elevated, resulting in enhanced platelet production and paraneoplastic thrombocytosis [ 72 ]. Pucci et al. described platelet factor-4 (PF-4) as a cancer-enhancing endocrine signal-stimulating bone marrow megakaryopoiesis, which is associated with a decreased survival of lung cancer patients [ 73 ]. However, the chicken-or-the-egg question is still unanswered: either an increase in thrombopoietic factors leads to an increase in platelet count with subsequent stimulation of tumor growth, or an already aggressive tumor secretes thrombopoietic factors, which results in an increase in platelet count. Nonetheless, increased levels of thrombopoietic factors and elevated numbers of platelets are often observed in patients with cancer.

Heat map and supervised cluster analysis of protein expression data from platelets of early-stage lung or head of pancreas cancer (HoP cancer) patients compared to healthy sex- and age-matched controls. Cancer type-specific supervised cluster analysis clearly separates the platelet proteome of patients with early-stage lung cancer ( a ) or head of pancreas cancer ( b ) from healthy controls. Reprinted from [ 10 ] with permission

We and others have demonstrated a clear correlation between platelet count and the presence of cancer [ 19 , 23 , 74 ]. Also, there is accumulating evidence that thrombocytosis (i.e., a platelet count above 400 × 10 9 platelets/L) is an independent predictor of poor prognosis in various types of cancer [ 19 ]. However, the value of platelet count as a biomarker of early-stage cancer is still unclear. A large clinical study with approximately 140,000 patients showed that almost 40% of patients with idiopathic thrombocytosis (i.e., without inflammatory disease or iron deficiency) exhibit some form of occult malignancy [ 75 ]. In addition, a recent systematic review of studies performed in a primary care setting suggests that thrombocytosis is a marker of increased risk of cancer presence in adults older than 40 years [ 76 ]. In this study, the incidence of cancer in the year following the assessment of thrombocytosis was 11.6% in males and 6.2% in females, as compared to 4.2 and 2.2% in males and females with a normal platelet count, respectively. Interestingly, in this study, thrombocytosis was the strongest predictor of lung and colorectal cancer. While the studies above suggest that thrombocytosis could be a risk factor of undiagnosed cancer in primary care setting, it is not specified whether these cancer diagnoses concerned early- or late-stage cancer. Overall, platelet count may be a good predictor of poor prognosis in patients with cancer; however, platelet count as a single measurement is probably not sufficient for detection of early-stage cancer.

Platelet volume

Platelet volume is established during platelet formation, where preplatelets are transformed to proplatelets [ 77 ]. Platelet volume (normal range: 9.4–12.3 fL) is genetically determined and is fairly stable over the lifetime of healthy individuals [ 78 ], but it can vary during a wide range of diseases. Changes in mean platelet volume (MPV) have been demonstrated in cardiovascular disease, cerebrovascular disease, peripheral artery disease, Crohn’s disease, and colitis [ 79 – 83 ]. In addition, from studies by us and others, it is becoming increasingly clear that MPV is also affected during malignant diseases [ 23 , 84 – 87 ]. Electron microscopy has revealed that platelets of patients with ovarian tumors have more mitochondria and significantly smaller microtubules compared to platelets from healthy individuals [ 88 ]. We demonstrated that MPV is elevated in patients with lung or head of pancreas cancer [ 23 ]. Furthermore, a recent meta-analysis confirmed that the MPV of treatment-naïve patients with malignant tumors was significantly higher compared to the MPV of healthy individuals, which generally seemed to normalize after treatment [ 84 ].

Over the past years, accumulating evidence suggests that the platelet population within one individual is heterogenous in platelet structure and activation properties [ 78 , 89 ]. This heterogeneity is most likely the result of differences during thrombopoiesis (e.g., by dissimilar megakaryocyte proplatelet formation), imbalanced platelet priming, environmental conditions and platelet ageing [ 90 ]. Therefore, changes in MPV could be a reflection of proinflammatory and/or prothrombotic conditions where thrombopoiesis is affected by inflammatory cells and bioactive molecules such as IL-6 and C-reactive protein [ 91 ]. It is suggested that the largest platelets in an individual are associated with increased platelet aggregation, beta-thromboglobulin secretion, thromboxane synthesis, and metabolic and enzymatic activity [ 92 , 93 ]. Consequently, larger platelets can be assumed to be more reactive, as compared to smaller platelets as suggested in patients with coronary artery disease [ 92 ]. This increased reactivity is, among others, due to an increase in copy numbers of integrin αIIbβ3 and glycoprotein (GP) Ibα on larger platelets [ 94 , 95 ]. This is in agreement with other studies, where an increase in platelet activation is demonstrated in patients with cancer [ 23 , 69 , 96 ]. In addition, several retrospective and prospective studies suggest that larger platelets are more reactive and that patients with large platelets are at higher risk for thrombotic events [ 97 – 99 ]. It is important to realize, however, that platelet volume is not the only factor determining platelet reactivity to stimuli [ 78 , 90 ].

Thus far, the impact of MPV in patients with malignant tumors is not fully understood.

There are several studies, which reveal that high MPV is a predictor of poor prognosis in various types of cancer [ 85 , 100 – 102 ]. However, the role of MPV as an early messenger of cancer presence seems to be limited, as it is mostly affected in later stages of the disease [ 86 , 87 , 100 , 103 ]. In addition, while in most studies the differences in MPV between patients with advanced cancer and controls are significant, the changes in platelet volume between patients and controls are very small. For example, MPV has been shown to be significantly higher in patients with gastric or endometrium cancer compared to healthy controls [ 86 , 104 ]. However, MPV was 8.31 fL (range 7.53–9.09) or 7.8 fL (range 6.2–11.3) in patients with gastric or endometrium cancer, respectively, whereas in healthy controls, MPV was 7.85 fL (range 7.4–8.3) and 7.2 fL (range 1.6–14.9), respectively. These small differences can be easily masked by a wide range of variables that can impact MPV. Therefore, MPV as a single measurement does not seem to be a good marker to distinguish patients with early-stage cancer from healthy individuals.

Platelet protein content

Platelets transport a vast amount of bioactive proteins in their granules, including growth factors, chemokines, and proteases, which they can secrete upon activation [ 10 ]. In vivo studies with mice bearing human tumors have demonstrated that platelets are able to sequester proteins that are secreted from tumors [ 71 ]. This resulted in higher concentrations of tumor-derived factors (such as TGF-β, MCP-1, RANK, and TIMP-1) inside platelets due to the presence of cancer [ 71 ]. In addition, platelets of tumor-bearing mice were observed to contain increased levels of thrombospondin-1 (TSP-1), which is highly correlated to tumor progression. After resection of the tumor, these levels decreased to baseline [ 105 ]. Upregulation of proteins, such as TSP-1 and platelet factor 4 (PF4), inside platelets could even be used to detect clinically undetectable tumors (< 1 mm 3 ) in mice [ 105 , 106 ].

Multiple studies in humans have also demonstrated changes in platelet protein content in patients with cancer (Table ​ (Table1). 1 ). However, part of these results are derived from indirect measurements where the platelet content was not measured but calculated ( Platelet content = Serum concentration − plasma concentration Platelet count in whole blood ) from serum and plasma concentrations. Nevertheless, there are also studies where bioactive factors were directly measured in isolated platelets from patients with cancer and compared to platelets of healthy individuals [ 20 , 23 , 107 ]. Peterson et al. demonstrated elevated concentrations of VEGF, PF4, and PDGF in platelets of patients with colorectal cancer [ 20 ]. In addition, they showed that these changes in platelets provided a statistically significant discrimination between patients and age-matched healthy controls [ 20 ]. While approximately half of the patients included in this study had early-stage (I–II) colorectal cancer, the small sample size prevented detection of a correlation between cancer stage and platelet content. In our study of 2017 [ 23 ], we demonstrated a significant increase in concentrations of VEGF and PDGF in platelets of patients with early-stage (I–II) lung cancer compared to platelets of a healthy sex- and age-matched control group. In platelets of patients with late-stage (III–IV) lung cancer, we observed a decrease in PF4, CTAPIII, and TSP-1 as compared to the control group, which was similar to a previous study performed in patients with advanced cancer [ 107 ]. In patients with head of pancreas cancer only, VEGF was elevated in platelets as compared to controls [ 23 ]. Thus, changes in platelet protein content are consistently observed in patients with cancer. Interestingly, the platelet changes appear to be tumor-type-dependent as demonstrated by multiple other studies (Table ​ (Table1). 1 ). The combination of changes in multiple platelet characteristics allowed cancer type-specific discrimination between early-stage cancer patients and healthy controls. For this purpose, we used the data from our study to develop a multivariable diagnostic model for both lung and head of pancreas cancer, including platelet count, volume, protein content, and activation status [ 23 ]. This model appeared to be able to distinguish patients with early-stage lung or head of pancreas cancer from healthy sex- and age-matched controls [ 23 ]. Overall, the above studies suggest that a combination of platelet measurements could be used to distinguish patients with early- or late-stage cancer from healthy individuals.

Expression of proteins in platelets of patients with different cancer types compared to control

Expression (increased(↑), decreased(↓), not changed(~)) of proteins in platelets of patients with lung (LC), colorectal (CRC), breast (BC), glioblastoma (GBM), head of pancreas (HoP), hepatocellular (HCC), prostate (PC) cancer or mix of various cancers (VC) compared to a (healthy) control group. Empty spaces indicate a lack of suitable research

Ang-1 angiopoietin-1, bFGF basic fibroblast growth factor, CTAPIII connective tissue activating peptide-III, GSH-S glutathione synthetase, HGF hepatocyte growth factor, PDGF platelet-derived growth factor, PF4 platelet factor-4, TGF-β transforming growth factor beta, TSP-1 trombospondin-1, VEGF vascular endothelial growth factor

The changes in platelet content are not limited to the abovementioned proteins. Several years ago, Klement and colleagues demonstrated a substantial change in the platelet proteome of tumor-bearing mice [ 108 ]. In 2018, we have performed the first study in humans, in which 139 differentially expressed proteins were found in platelets of patients with early-stage lung or head of pancreas cancer as compared to healthy individuals of comparable age and gender [ 22 ]. Furthermore, surgical removal of the malignant tumor resulted in normalization of the platelet proteome [ 22 ]. Interestingly, we found that molecular content changes in head of pancreas cancer were more pronounced and showed a different profile as compared to the lung cancer platelet proteome, which supports the notion of tumor-type dependency of platelet changes (Fig. ​ (Fig.2). 2 ). Multiple potential platelet-derived biomarkers of early-stage cancer were identified by the comparison of the platelet proteome in cancer patients with that of sex- and age-matched healthy controls [ 22 ].

Several mechanisms underlie the changes in platelet proteome of patients with malignant disease (Fig. ​ (Fig.1). 1 ). Proteins present in platelets are either synthesized by megakaryocytes that produce the platelets, or are absorbed by megakaryocytes and/or platelets themselves from the blood [ 105 , 108 , 109 ]. The concept of tumor-educated platelets (TEPs) assumes that platelets can directly take up bioactive factors from tumor cells, while residing temporarily in the tumor [ 21 , 22 ]. As a consequence, the platelet proteome mirrors the tumor-based changes and the concentration of potential tumor reporter proteins can be considerably higher inside platelets than in plasma. This enables more accurate measurement, as compared to quantification in plasma, making platelets an attractive source of biomarker discovery [ 20 , 23 , 108 ]. Several studies even suggest that in the presence of a growing tumor, changes inside platelets precede alterations in plasma [ 20 , 105 , 106 , 108 ]. This implies that measurements of potential biomarkers inside platelets could be more sensitive for detection of early-stage cancer than plasma measurements.

Platelet mRNA content

The platelet mRNA profile is currently emerging as a new potential source in cancer biomarker research [ 21 , 24 , 110 ]. Platelets do not contain a nucleus and, therefore, the mRNA transcripts either originate from megakaryocytes during platelet synthesis [ 36 , 111 ] or are absorbed from the blood during circulation and/or interaction with tumor or other cell types [ 112 ]. Several mechanisms have been described that determine the platelet mRNA profile. Nilsson et al. demonstrated tumor-derived mRNA transfer (mutant EGFRvIII) from cancer cells to blood platelets of healthy individuals in vitro [ 110 ]. In addition, they showed that platelets isolated from glioma or prostate cancer patients also contained the cancer-associated mRNA biomarkers EGFRvIII and PCA3. Most likely, platelets take up these mRNAs via microvesicle-dependent endocytosis [ 113 ], but a microvesicle-independent mechanism is also possible [ 114 ]. In addition, induction of platelet activation by external stimuli (e.g., collagen or bioactive molecules secreted by a tumor) stimulates maturation of resident pre-mRNAs in platelets and results in RNA splicing in platelets during activation [ 115 ].

The potential of using platelet mRNA as a diagnostic tool in patients with cancer was emphasized by a study of Best et al., who discovered changes in platelet mRNA profiles in cancer patients [ 21 ]. In this study, the platelet mRNA profile of patients with several types of cancer was compared to the platelet mRNA profile of healthy individuals. This study demonstrated that the platelet mRNA profile was affected in almost all cancer patients. A combination of changed mRNAs allowed good discrimination between cancer patients and healthy individuals with high sensitivity (96%) and specificity (92%). An important limitation of this study was the nature of the control group that was clearly younger and had a different gender distribution than most of the cancer groups. This is especially important as we and others have shown that changes in platelet characteristics are correlated with age and sex [ 23 , 116 ]. In a follow-up study, Best et al. used a particle swarm optimization–enhanced algorithm to select an mRNA biomarker panel that could distinguish patients with NSCLC (non-small cell lung cancer) from healthy individuals [ 24 ]. A panel of 1000 genes resulted in accurate detection of early- and late-stage NSCLC when compared to an unmatched control group. Matched evaluation for age and smoking status (panel of 830 genes) also resulted in a statistically significant discrimination of patients with NSCLC from healthy controls. Unfortunately, the latter analysis was not subdivided in early- and late-stage cancer. Overall, platelet-derived mRNA seems to be a sensitive and promising new tool in the search for biomarkers of early-stage cancer. Further studies are still needed to validate these encouraging results.

Early detection of cancer allows a significant improvement of overall survival of patients. During the past years, platelet characteristics are emerging as a promising source for biomarkers of cancer. This review summarizes and discusses studies in which platelet features (e.g., count, volume, protein, and mRNA profile) are explored in early-stage as well as late-stage cancer. Platelet count and volume appear to have a more prognostic value and their importance as biomarkers of early-stage cancer remains to be demonstrated. However, a combination of platelet count, volume, and selected platelet-derived proteins could be used to discriminate patients with early-stage cancer from healthy individuals. Most promising data are from patient studies illustrating vast changes in platelet proteome and mRNA content. These changes occur already in early stages of cancer and illustrate that the platelet content could be a rich source of potential biomarkers. In addition, these profiles could be exploited to provide information on the organ of origin, which could provide a lead for clinical follow-up diagnostics to confirm the tumor location.

Future large-scale studies are needed in general population or in people who have a genetic disposition to acquire certain types of cancer (e.g., patients with mutation in BRCA1/2 or Lynch syndrome). These individuals have a very high lifetime risk of developing cancer and are therefore frequently screened with invasive and/or imaging techniques to detect potential tumors. These patients are therefore an exceptional group to study, to investigate whether platelet changes precede clinically detectable tumors.

Authors’ contributions

All authors contributed equally.

This work was supported by the KWF Cancer Society VUMC-2018-11651 (to AWG) and Kootstra Talent Fellowship CD.18.0008 (to SS).

Declarations

The authors declare no conflict of interest.

No ethical approvals are required for this review.

Informed consent is not required for this review.

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