Clinical Cytometry for Platelets and Platelet Disorders

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

Background

Platelet function is driven by the expression of specialized surface markers. The concept of distinct circulating subpopulations of platelets has emerged in recent years, but their exact nature remains debatable.

Objectives

To design a spectral flow cytometry-based phenotyping workflow to provide a more comprehensive characterization, at a global and individual level, of surface markers in resting and activated healthy platelets, and to apply this workflow to investigate how responses differ according to platelet age.

Methods

A 14-marker flow cytometry panel was developed and applied to vehicle- or agonist-stimulated platelet-rich plasma and whole blood samples obtained from healthy volunteers, or to platelets sorted according to SYTO-13 (Thermo Fisher Scientific) staining intensity as an indicator of platelet age. Data were analyzed using both user-led and independent approaches incorporating novel machine learning-based algorithms.

Results

The assay detected differences in marker expression in healthy platelets, at rest and on agonist activation, in both platelet-rich plasma and whole blood samples, that are consistent with the literature. Machine learning identified stimulated populations of platelets with high accuracy (>80%). Similarly, machine learning differentiation between young and old platelet populations achieved 76% accuracy, primarily weighted by forward scatter, cluster of differentiation (CD) 41, side scatter, glycoprotein VI, CD61, and CD42b expression patterns.

Conclusion

Our approach provides a powerful phenotypic assay coupled with robust bioinformatic and machine learning workflows for deep analysis of platelet subpopulations. Cleavable receptors, glycoprotein VI and CD42b, contribute to defining shared and unique subpopulations. This adoptable, low-volume approach will be valuable in deep characterization of platelets in disease.

Platelet function testing: Update on determinant variables and permissive windows using a platelet-count-based device

While there are various aspects of platelet biology that can be studied in the lab (i.e. adhesion, degranulation, integrin activation), the master test for platelet function is that which gives a measure of the platelet aggregation capacity upon stimulation with an agonist. Platelet function testing is necessary for the diagnosis of platelet disorders and the monitoring of patients receiving anti-platelet treatments. Furthermore, it becomes relevant in the quality control of platelet concentrates for transfusion purposes, especially considering the global concern about long term storage, other forms of storage (i.e. cryopreservation, lyophilization), and the impact of Pathogen Reduction Treatments (PRTs) on platelet performance upon transfusion. However, it has been acknowledged as technically difficult and demanding, since a fine platelet function test must be carried out under specific conditions. Still, there might be occasions that preclude the platelet function testing abiding to the gold standard requirements, thus, leaving us with the necessity to redefine which variables may condition or limit the analysis of platelet function testing. In the present manuscript, we test different variables (such as the anticoagulant used or the time elapsed since extraction) and the possibility to reconstitute blood prior to platelet function analysis. This study aims to provide windows of action at the diagnostics lab, especially when not all of the recommended procedures and conditions can be followed: for example, when a sample is sent from a long distance, when there is a limitation on blood extraction volume or when certain parameters (platelet count) preclude reliable test results.

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

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.