Clinical Cytometry for Platelets and Platelet Disorders

Frelinger, Andrew L.; Spurgeon, Benjamin E. J.

doi:10.1016/j.cll.2023.04.008

Cited by 5 publications

(4 citation statements)

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“…Platelets are small multifunctional blood cells that circulate in a range of diverse and dynamic states [1]. Platelet phenotyping is common in cardiovascular research and may be helpful in clinical settings, as platelet glycoprotein levels can illuminate the efficacy of antiplatelet agents (e.g., aspirin and clopidogrel) and inform the diagnosis of bleeding disorders such as Glanzmann thrombasthenia and storage pool disease [2]. Whole blood phenotyping—made possible by flow cytometry—allows platelets to be studied in (patho)physiologic conditions and affords a rapid and convenient means of obtaining novel and statistically rich information on platelet biology [3].…”

Section: Introductionmentioning

confidence: 99%

“…CD62P is considered to be an inflammatory molecule because it mediates platelet adhesion to monocytes and stimulates cytokine secretion [13, 14]. It is ideal for assessing platelet activation in vitro and useful in clinical studies of cardiometabolic disorders and thrombocytopenia [2, 15]. PAC‐1 recognizes the active form of GPIIb/IIIa, which binds fibrinogen and mediates platelet aggregation [16].…”

Section: Introductionmentioning

confidence: 99%

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OMIP‐097: High‐parameter phenotyping of human platelets by spectral flow cytometry

Spurgeon,

Frelinger

2023

Cytometry Pt A

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Using spectral flow cytometry, we developed a 16‐color panel for analysis of platelet phenotype and function in human whole blood. The panel contains markers of clinical relevance and follows an optimized protocol for the high‐parameter phenotyping of (phosphatidylserine positive) procoagulant platelets. Inclusion of established markers, such as CD62P and PAC‐1, allows the subsetting of classic (proinflammatory and proaggregatory) phenotypes, while addition of novel markers, such as TLR9, allows the resolution of platelets with nonclassic functions. Multiple inducible (C3b, CD63, CD107a, CD154, and TLT‐1) and constitutive (CD29, CD31, CD32, CD36, CD42a, CD61, and GPVI) markers are also measurable, and we demonstrate the use of automatic gating for platelet analysis. The panel is widely applicable to research and clinical settings and can be readily modified, should users wish to tailor the panel to more specific needs.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OMIP‐097: High‐parameter phenotyping of human platelets by spectral flow cytometry

Spurgeon,

Frelinger

2023

Cytometry Pt A

Self Cite

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“…Flow cytometry with the inclusion of fluorescently-tagged antibodies is frequently used to investigate platelet protein expression and function as it requires only small volumes of blood and a relatively low number of platelets, making it ideal for analysis of clinical samples. However, the number of parameters that can be measured concurrently using this method is limited by overlap of fluorescent emission spectra, resulting in antibody panels that typically determine, at most, three to four markers in any one sample [30,31]. Spectral flow cytometry is a next-generation technique that allows the simultaneous measurement and discrimination of multiple fluorophores by evaluation of full emission spectral signatures.…”

Section: Introductionmentioning

confidence: 99%

“…(noting in this report that Blair et al . have subsequently published reports utilizing spectral flow cytometry[30,32]). In our studies we subjected our data to a powerful computational analytical approach employing machine learning to explore the potential existence of platelet sub-populations in the human circulation with a particular focus on young and old platelets.…”

Section: Introductionmentioning

confidence: 99%

Multi-parameter phenotyping of platelets and characterisation of the effects of agonists using machine learning

Vadgama,

Boot,

Allan

et al. 2023

Preprint

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BackgroundPlatelets are crucial for thrombosis and haemostasis, with their function driven by the expression of specialised surface markers. The concept of distinct circulating sub-populations of platelets has emerged in recent years, but their exact nature remains debatable. We reasoned that a more comprehensive characterisation of surface marker changes at rest and upon activation would be valuable in determining this.ObjectiveTo use a full spectrum flow cytometry-based panel, together with parameters of physical properties, to describe surface marker changes in healthy platelets at rest and on activation, and to observe how these responses differ according to platelet age.MethodsA 14-marker flow cytometry panel was developed and applied to vehicle- or agonist-stimulated platelet-rich plasma samples obtained from healthy volunteers, or to platelets sorted according to SYTO-13 staining intensity as an indicator of platelet age. Data were analysed using both user-led and independent approaches incorporating novel machine learning-based algorithms.ResultsThe assay detected changes in marker expression in healthy platelets, at rest and on agonist activation, that are consistent with the literature. Machine learning identified stimulated populations of platelets with high accuracy (>80%). Similarly, differentiation between young and old platelet populations achieved 76% accuracy, primarily weighted by FSC-A, CD41, SSC-A, GPVI, CD61, and CD42b expression patterns.ConclusionsOur findings provide a novel assay to phenotype platelets coupled with a robust bioinformatics and machine learning workflow for deep analysis of the data. This could be valuable in characterising platelets in disease.(240 words)EssentialsPlatelet function is directed by the expression of specialised surface markersCirculating platelet sub-populations are incompletely characterisedMulti-parameter spectral flow cytometry allows robust and comprehensive phenotyping of plateletsCoupling multi-parameter spectral flow cytometry with machine learning offers a powerful method to determine platelet sub-populations

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Multiparameter phenotyping of platelets and characterization of the effects of agonists using machine learning

Vadgama,

Boot,

Dark

et al. 2024

Research and Practice in Thrombosis and Haemostasis

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Clinical Cytometry for Platelets and Platelet Disorders

Cited by 5 publications

References 74 publications

OMIP‐097: High‐parameter phenotyping of human platelets by spectral flow cytometry

OMIP‐097: High‐parameter phenotyping of human platelets by spectral flow cytometry

Multi-parameter phenotyping of platelets and characterisation of the effects of agonists using machine learning

Multiparameter phenotyping of platelets and characterization of the effects of agonists using machine learning

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