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Identification of platelet function defects by multi-parameter assessment of thrombus formation.

de Witt SM, Swieringa F, Cavill R, Lamers MM, van Kruchten R, Mastenbroek T, Baaten C, Coort S, Pugh N, Schulz A, Scharrer I, Jurk K, Zieger B, Clemetson KJ, Farndale RW, Heemskerk JW, Cosemans JM - Nat Commun (2014)

Bottom Line: Three types of thrombus formation can be identified with a predicted hierarchy of the following receptors: glycoprotein (GP)VI, C-type lectin-like receptor-2 (CLEC-2)>GPIb>α6β1, αIIbβ3>α2β1>CD36, α5β1, αvβ3.Application with patient blood reveals distinct abnormalities in thrombus formation in patients with severe combined immune deficiency, Glanzmann's thrombasthenia, Hermansky-Pudlak syndrome, May-Hegglin anomaly or grey platelet syndrome.We suggest this test may be useful for the diagnosis of patients with suspected bleeding disorders or a pro-thrombotic tendency.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.

ABSTRACT
Assays measuring platelet aggregation (thrombus formation) at arterial shear rate mostly use collagen as only platelet-adhesive surface. Here we report a multi-surface and multi-parameter flow assay to characterize thrombus formation in whole blood from healthy subjects and patients with platelet function deficiencies. A systematic comparison is made of 52 adhesive surfaces with components activating the main platelet-adhesive receptors, and of eight output parameters reflecting distinct stages of thrombus formation. Three types of thrombus formation can be identified with a predicted hierarchy of the following receptors: glycoprotein (GP)VI, C-type lectin-like receptor-2 (CLEC-2)>GPIb>α6β1, αIIbβ3>α2β1>CD36, α5β1, αvβ3. Application with patient blood reveals distinct abnormalities in thrombus formation in patients with severe combined immune deficiency, Glanzmann's thrombasthenia, Hermansky-Pudlak syndrome, May-Hegglin anomaly or grey platelet syndrome. We suggest this test may be useful for the diagnosis of patients with suspected bleeding disorders or a pro-thrombotic tendency.

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Clustering of thrombus formation at 52 microspots using eight outcome parameters.Whole blood from control subjects was perfused over arrays of microspots in a parallel-plate flow chamber for 3.5 min at 1,600 s−1. Numbering of coatings with different adhesive proteins or peptides as in Fig. 1. Recorded phase-contrast images were analysed for morphological score, integrated feature size and platelet deposition (surface area coverage). Following in situ DiOC6 labelling, fluorescence images were recorded to assess stable platelet adhesion (during blood flow) and thrombus volume (after blood flow). Thrombi were poststained to determine fibrinogen binding (FITC-labelled anti-fibrinogen mAb), P-selectin expression (FITC-anti-CD62P mAb), and procoagulant activity (AF647-annexin A5). Mean values of the parameters (n=5–7, thrombus size: n=4–6) were normalized from 0–10, and arranged by unsupervised hierarchical cluster analysis. (a) Clustered heatmap for 52 different surfaces (columns) and eight measurement parameters (rows). Clustering of surfaces revealed three different types of thrombus formation, (b) Robustness of data set, assessed by bootstrapping randomizations of all data with Pvclust. Shown are the pro forma clusters obtained, using approximately unbiased (AU) P-values of 90 and 95, indicative for a strong fit.
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f4: Clustering of thrombus formation at 52 microspots using eight outcome parameters.Whole blood from control subjects was perfused over arrays of microspots in a parallel-plate flow chamber for 3.5 min at 1,600 s−1. Numbering of coatings with different adhesive proteins or peptides as in Fig. 1. Recorded phase-contrast images were analysed for morphological score, integrated feature size and platelet deposition (surface area coverage). Following in situ DiOC6 labelling, fluorescence images were recorded to assess stable platelet adhesion (during blood flow) and thrombus volume (after blood flow). Thrombi were poststained to determine fibrinogen binding (FITC-labelled anti-fibrinogen mAb), P-selectin expression (FITC-anti-CD62P mAb), and procoagulant activity (AF647-annexin A5). Mean values of the parameters (n=5–7, thrombus size: n=4–6) were normalized from 0–10, and arranged by unsupervised hierarchical cluster analysis. (a) Clustered heatmap for 52 different surfaces (columns) and eight measurement parameters (rows). Clustering of surfaces revealed three different types of thrombus formation, (b) Robustness of data set, assessed by bootstrapping randomizations of all data with Pvclust. Shown are the pro forma clusters obtained, using approximately unbiased (AU) P-values of 90 and 95, indicative for a strong fit.

Mentions: Replicate measurements of blood perfusion experiments over all 52 coated microspots and using different fluorescent labels (n≥4 donors per condition and label) resulted in detailed insight into the contribution of each surface to thrombus formation (see wall chart in Supplementary Fig. 2). Standardized analysis of microscopic (fluorescence) images provided the following parameters of thrombus formation: morphological score, integrated feature size, stable platelet adhesion, fibrinogen binding, P-selectin expression, overall platelet deposition, thrombus volume and procoagulant activity. Unsupervised hierarchical cluster analysis of all data (52 microspots, 8 parameters) revealed separation into three patterns of thrombus formation, indicated as types I–III (Fig. 4a). Surfaces producing type I thrombi consisted of single-protein coatings causing limited adhesion of few platelets. Type II thrombi mostly formed on surfaces co-coated with vWF or vWF-BP causing deposition of multiple platelets, single or in small aggregates, and showing limited activation (fibrinogen binding, P-selectin expression). Type III thrombi formed on several combined surfaces giving rise to large aggregates of platelets, high in activation markers. With the exception of collagen I (which binds vWF from plasma), type III thrombi only appeared at double- or triple-coated surfaces containing vWF, vWF-BP and/or laminin combined with rhodocytin or (GPO)n peptides. Robustness of the unsupervised cluster analysis was checked by data re-sampling and rebuilding the tree by 10,000 randomizations with an approximately unbiased P-value of 90, indicative of a strong fit (Fig. 4b).


Identification of platelet function defects by multi-parameter assessment of thrombus formation.

de Witt SM, Swieringa F, Cavill R, Lamers MM, van Kruchten R, Mastenbroek T, Baaten C, Coort S, Pugh N, Schulz A, Scharrer I, Jurk K, Zieger B, Clemetson KJ, Farndale RW, Heemskerk JW, Cosemans JM - Nat Commun (2014)

Clustering of thrombus formation at 52 microspots using eight outcome parameters.Whole blood from control subjects was perfused over arrays of microspots in a parallel-plate flow chamber for 3.5 min at 1,600 s−1. Numbering of coatings with different adhesive proteins or peptides as in Fig. 1. Recorded phase-contrast images were analysed for morphological score, integrated feature size and platelet deposition (surface area coverage). Following in situ DiOC6 labelling, fluorescence images were recorded to assess stable platelet adhesion (during blood flow) and thrombus volume (after blood flow). Thrombi were poststained to determine fibrinogen binding (FITC-labelled anti-fibrinogen mAb), P-selectin expression (FITC-anti-CD62P mAb), and procoagulant activity (AF647-annexin A5). Mean values of the parameters (n=5–7, thrombus size: n=4–6) were normalized from 0–10, and arranged by unsupervised hierarchical cluster analysis. (a) Clustered heatmap for 52 different surfaces (columns) and eight measurement parameters (rows). Clustering of surfaces revealed three different types of thrombus formation, (b) Robustness of data set, assessed by bootstrapping randomizations of all data with Pvclust. Shown are the pro forma clusters obtained, using approximately unbiased (AU) P-values of 90 and 95, indicative for a strong fit.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4109023&req=5

f4: Clustering of thrombus formation at 52 microspots using eight outcome parameters.Whole blood from control subjects was perfused over arrays of microspots in a parallel-plate flow chamber for 3.5 min at 1,600 s−1. Numbering of coatings with different adhesive proteins or peptides as in Fig. 1. Recorded phase-contrast images were analysed for morphological score, integrated feature size and platelet deposition (surface area coverage). Following in situ DiOC6 labelling, fluorescence images were recorded to assess stable platelet adhesion (during blood flow) and thrombus volume (after blood flow). Thrombi were poststained to determine fibrinogen binding (FITC-labelled anti-fibrinogen mAb), P-selectin expression (FITC-anti-CD62P mAb), and procoagulant activity (AF647-annexin A5). Mean values of the parameters (n=5–7, thrombus size: n=4–6) were normalized from 0–10, and arranged by unsupervised hierarchical cluster analysis. (a) Clustered heatmap for 52 different surfaces (columns) and eight measurement parameters (rows). Clustering of surfaces revealed three different types of thrombus formation, (b) Robustness of data set, assessed by bootstrapping randomizations of all data with Pvclust. Shown are the pro forma clusters obtained, using approximately unbiased (AU) P-values of 90 and 95, indicative for a strong fit.
Mentions: Replicate measurements of blood perfusion experiments over all 52 coated microspots and using different fluorescent labels (n≥4 donors per condition and label) resulted in detailed insight into the contribution of each surface to thrombus formation (see wall chart in Supplementary Fig. 2). Standardized analysis of microscopic (fluorescence) images provided the following parameters of thrombus formation: morphological score, integrated feature size, stable platelet adhesion, fibrinogen binding, P-selectin expression, overall platelet deposition, thrombus volume and procoagulant activity. Unsupervised hierarchical cluster analysis of all data (52 microspots, 8 parameters) revealed separation into three patterns of thrombus formation, indicated as types I–III (Fig. 4a). Surfaces producing type I thrombi consisted of single-protein coatings causing limited adhesion of few platelets. Type II thrombi mostly formed on surfaces co-coated with vWF or vWF-BP causing deposition of multiple platelets, single or in small aggregates, and showing limited activation (fibrinogen binding, P-selectin expression). Type III thrombi formed on several combined surfaces giving rise to large aggregates of platelets, high in activation markers. With the exception of collagen I (which binds vWF from plasma), type III thrombi only appeared at double- or triple-coated surfaces containing vWF, vWF-BP and/or laminin combined with rhodocytin or (GPO)n peptides. Robustness of the unsupervised cluster analysis was checked by data re-sampling and rebuilding the tree by 10,000 randomizations with an approximately unbiased P-value of 90, indicative of a strong fit (Fig. 4b).

Bottom Line: Three types of thrombus formation can be identified with a predicted hierarchy of the following receptors: glycoprotein (GP)VI, C-type lectin-like receptor-2 (CLEC-2)>GPIb>α6β1, αIIbβ3>α2β1>CD36, α5β1, αvβ3.Application with patient blood reveals distinct abnormalities in thrombus formation in patients with severe combined immune deficiency, Glanzmann's thrombasthenia, Hermansky-Pudlak syndrome, May-Hegglin anomaly or grey platelet syndrome.We suggest this test may be useful for the diagnosis of patients with suspected bleeding disorders or a pro-thrombotic tendency.

View Article: PubMed Central - PubMed

Affiliation: Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Universiteitssingel 50, 6229 ER Maastricht, The Netherlands.

ABSTRACT
Assays measuring platelet aggregation (thrombus formation) at arterial shear rate mostly use collagen as only platelet-adhesive surface. Here we report a multi-surface and multi-parameter flow assay to characterize thrombus formation in whole blood from healthy subjects and patients with platelet function deficiencies. A systematic comparison is made of 52 adhesive surfaces with components activating the main platelet-adhesive receptors, and of eight output parameters reflecting distinct stages of thrombus formation. Three types of thrombus formation can be identified with a predicted hierarchy of the following receptors: glycoprotein (GP)VI, C-type lectin-like receptor-2 (CLEC-2)>GPIb>α6β1, αIIbβ3>α2β1>CD36, α5β1, αvβ3. Application with patient blood reveals distinct abnormalities in thrombus formation in patients with severe combined immune deficiency, Glanzmann's thrombasthenia, Hermansky-Pudlak syndrome, May-Hegglin anomaly or grey platelet syndrome. We suggest this test may be useful for the diagnosis of patients with suspected bleeding disorders or a pro-thrombotic tendency.

Show MeSH
Related in: MedlinePlus