PB0773 - Single-platelet immunophenotyping of temporally-defined sub-populations in mice

Background: Circulating function-focused sub-populations of platelets are of current research interest. Newly-formed platelets are more haemostatically active and, as function is mediated by expression of surface receptors and proteins, flow cytometry based immunophenotyping approaches are a highly useful tool.

Aims: To couple ex vivo single-platelet immunophenotyping analysis with in vivo temporal labelling to interrogate differential receptor expression of circulating temporally-defined sub-populations.

Methods: Fluorescent anti-CD42c antibodies (Emfret) were injected (i.v) into C57BL/6 mice at various timepoints to temporally-define platelet sub-populations. Blood was diluted and centrifuged to produce platelet-rich plasma, and incubated (20min, 37°C) with PAR-4 amide (AYGKPF, 100µM), thromboxane A2 mimetic (U46619, 30µM) or vehicle in presence of 12 fluorescently-labelled antibodies. Spectral flow cytometry (Cytek Aurora) was used to simultaneously determine levels of 14 physical and surface receptor/protein parameters. Multi-dimensional data were analysed on a global and single platelet level using a combination of NovoExpress, FlowJo and GraphPad Prism software.

Results: Following platelet activation, changes in surface marker expression varied according to agonist stimulation. In general terms, expression of up to 11 parameters (CD9, CD29, CD31, CD36, CD41, CD49b, CD62P, CD63, CD107a, CD29, CD41, JON-A) increased following activation, whilst the expression of up to 3 markers (FSC, SSC and GPVI) decreased. Interestingly, changes in some receptors did not associate with changes in degranulation markers. Consistent with previous reports, newly formed ( < 24 hours) expressed more CD62P and CD107a in response to activation compared to older circulating platelets (>24 hours). Under both basal and activation conditions, newly formed platelets had differential expression of 6 parameters (FSC, CD31, CD9, CD49b, GPVI and H2).

Conclusion(s): Using a non-RNA based approach we can concurrently characterise newly-formed and older platelets across 14 parameters in a low-volume sample. Such an approach will be a powerful tool to detect and track platelet subpopulations across many in vivo disease models.