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. 2023 Sep 20;7(7):102205.
doi: 10.1016/j.rpth.2023.102205. eCollection 2023 Oct.

Preanalytical conditions for multiparameter platelet flow cytometry

Matthew S Hindle  1   2 , Lih T Cheah  1 , Daisie M Yates  1 , Khalid M Naseem  1
Affiliations

Preanalytical conditions for multiparameter platelet flow cytometry

Matthew S Hindle et al. Res Pract Thromb Haemost. .

Abstract

Background: Flow cytometry is an important technique for understanding multiple aspects of blood platelet biology. Despite the widespread use of the platform for assessing platelet function, the optimization and careful consideration of preanalytical conditions, sample processing techniques, and data analysis strategies should be regularly assessed. When set up and designed with optimal conditions, it can ensure the acquisition of robust and reproducible flow cytometry data. However, these parameters are rarely described despite their importance.

Objectives: We aimed to characterize the effects of several preanalytical variables on the analysis of blood platelets by multiparameter fluorescent flow cytometry.

Methods: We assessed anticoagulant choice, sample material, sample processing, and storage times on 4 distinct and commonly used markers of platelet activation, including fibrinogen binding, expression of CD62P and CD42b, and phosphatidylserine exposure.

Results: The use of suboptimal conditions led to increases in basal platelet activity and reduced sensitivities to stimulation; however, the use of optimal conditions protected the platelets from artifactual stimulation and preserved basal activity and sensitivity to activation.

Conclusion: The optimal preanalytical conditions identified here for the measurement of platelet phenotype by flow cytometry suggest a framework for future development of multiparameter platelet assays for high-quality data sets and advanced analysis.

Keywords: anticoagulants; blood platelets; cytofluorometry; flow; flow cytometry; platelet activation; platelet function tests.

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Figures

Figure 1
Figure 1
Effects of different anticoagulants on platelet activation. Whole blood drawn into sodium citrate, sodium heparin, and potassium EDTA vacutainers were compared for multiparameter platelet activation at basal or stimulated with SFLLRN (20 μM) alone and with CRP-XL (10 μg/mL). (A) Fibrinogen binding, (B) CD62P expression, (C) PS exposure, (D) CD42b expression, (i) indicates MFI data and (ii) percentage positive data (n = 3 ± SD). MFI, mean/median fluoresence intensities.
Figure 2
Figure 2
The impact of sample processing time on platelet activity. Multiparameter platelet activation was assayed in whole blood drawn into sodium citrate at 0 hours (freshly drawn), 1.5 and 4.5 hours postdraw at basal or stimulated with SFLLRN (20 μM) alone and with CRP-XL (10 μg/mL). (A) Fibrinogen binding, (B) CD62P expression, (C) PS exposure, (D) CD42b expression, (i) indicates MFI data and (ii) percentage positive data (n = 4 ± SD). MFI, mean/median fluoresence intensities.
Figure 3
Figure 3
The sample material impacts assay sensitivity. Whole blood was compared with platelet rich plasma and washed platelets for multiparameter platelet activation, at basal or stimulated with SFLLRN (20 μM) alone and with CRP-XL (10 μg/mL). Stimulated EDTA and IgG controls are included for reference to assay performance. (A) Fibrinogen binding, (B) CD62P expression, (C) PS exposure, (D) CD42b expression, (i) indicates MFI data and (ii) percentage positive data (n = 3 ± SD). MFI, mean/median fluoresence intensities.
Figure 4
Figure 4
Effect of sample storage on assay performance. Samples fixed and ran immediately compared with 1.5 and 4.5 hours at 4 °C dark storage were compared for multiparameter platelet activation, at basal or stimulated with SFLLRN (20 μM) with CRP-XL (10 μg/mL). (A) Fibrinogen binding, (B) CD62P expression, (C) PS exposure, (D) CD42b expression, (i) indicates MFI data and (ii) percentage positive data (n = 4 ± SD). MFI, mean/median fluoresence intensities.
Figure 5
Figure 5
opt-FIt-SNE and Phenograph analysis of platelet subpopulations under optimal preanalytical conditions. 30,000 platelet events from either basal samples or samples treated with SFLLRN (20 μM) and CRP-XL (10 μg/mL) were concatenated and analyzed by opt-FIt-SNE and Phenograph then visualized by ClusterExplorer. (A) tSNE map false colored for Phenograph clusters described in (B). (C) tSNE map false heat-colored (hot-cold/high-low) for indicated marker expression. (D) Heat map (hot-cold/high-low expression) for each cluster identified by Phenograph and relative marker expression within each subpopulation. (E) Stimulated sample only tSNE map highlighted with clusters positive for each marker. tSNE, t-stochastic neighborhood embedded.
Supplementary Figure 1
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Supplementary Figure 2
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Supplementary Table 1
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Supplementary Table 2
Supplementary Table 2
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