Platelet Phenotyping by Full Spectrum Flow Cytometry

Abstract

Platelets play key roles in hemostasis, immunity, and inflammation, and tests of platelet phenotype and function are useful in studies of disease biology and pathology. Full spectrum flow cytometry offers distinct advantages over standard tests and enables the sensitive and simultaneous detection of many biomarkers. A typical assay provides a wealth of information on platelet biology and allows the assessment of in vivo activation and in vitro reactivity, as well as the discovery of novel phenotypes. Here, we describe the analysis of platelets by full spectrum flow cytometry and discuss a range of controls and methods for interpreting results.

Preanalytical conditions for multiparameter platelet flow cytometry

“…In this example, most cells (95.35%) are CD62P positive, whereas only a small number of cells (4.65%) are low for expression of all markers. The data are more interesting when comparing fibrinogen positive (70.17%) and PS positive (28.1%) cells, as they are mutually exclusive in clusters 2, 4, and 15, but are both expressed in cluster 8, which reinforces recent observations by ourselves and others [ 6 , 10 , 58 , 59 ]. Ultimately, this is a limited example of both the analytical tools and applications available, but as multiparameter FFC panels develop in complexity the data generated by these methods will become exponentially richer and provide greater novelty and discovery.…”

“…Accordingly, there has been a parallel expansion and development of multidimensional analytical tools such as t-stochastic neighborhood embedded (tSNE) [ 3 ], spanning-tree progression analysis of density-normalized events (SPADE) [ 4 ], and flow self-organizing maps (flowSOM) [ 5 ]. Concurrent with advances in the wider field of single-cell analysis, platelet flow cytometry has seen various innovations including multidimensional FFC analysis [ [6] , [7] , [8] ], phosphoflow and barcoding [ 9 ], application of spectral FFC [ 10 ] and CyTOF with multidimensional analysis [ [11] , [12] , [13] ]. While the latter may show comparable efficacy to FFC [ 14 ] and the design of CyTOF assays has been recently described in full [ 15 ], it remains relatively inaccessible when compared with FFC, which is common in research and medical institutes worldwide.…”

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

“…(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.…”