Analyzing Platelet Subpopulations by Multi-color Flow Cytometry

Multi-color flow cytometry for platelets can identify new platelet subtypes within classical platelet subpopulations, such as resting, aggregatory, procoagulant, and apoptotic platelets. This allows comparison of different subpopulation patterns induced by various platelet agonists. Here, a procedure for establishing a multi-color flow cytometry panel is described in detail.

Platelets, or thrombocytes, are small anucleated blood cells that play a crucial role in hemostasis and thrombosis. Defects in platelet functions can cause bleeding or thrombotic events in patients with cardiovascular diseases. Therefore, it is important to characterize platelets phenotypically to be able to assign platelet subpopulations to platelet function. Stimulation of human platelets with platelet agonists and activators induces morphological and physiological changes in platelets, accompanied by changes in the surface receptor population. This leads to functionally diverse platelet subpopulations. Classically defined platelet subpopulations are resting, aggregatory, procoagulant, and apoptotic platelets. To characterize the effect of agonists on platelet subtypes, we established an assay using multi-color flow cytometry consisting of 10 different antibodies and dyes (anti-CD62P, anti-CD63, anti-CD61, anti-CD41, anti-CD42b, anti-Integrin_αIIbβ3 (clone: PAC-1), anti-CXCR4, anti-ACKR3, Annexin V, Zombie NIR). Isolated human platelets were incubated with platelet agonists and stained with specific fluorophore-conjugated antibodies and dyes. Afterwards, they were measured by flow cytometry. This allows us to define agonist-specific subtypes within the classic four subpopulations. In conclusion, the combination of activation markers (anti-CD62P, anti-CD63, anti-Integrin_αIIbβ3 (clone: PAC-1)), inflammatory markers (anti-CXCR4, anti-ACKR3) and apoptotic markers (Annexin V, Zombie NIR) that compose the 10-color flow cytometry panel described in this manuscript opens up the possibility to define further platelet subtypes that could be linked to specific platelet function. This method can be applied in basic research on platelet function and physiology, as well as in defining new platelet subtypes in disease models and patient studies.

Thrombocytes, also called platelets, are small, nucleus-free blood cells essential for hemostasis and the development of thrombosis^1,2. They can be activated by various platelet agonists. Classical, physiological platelet agonists are adenosine diphosphate (ADP), which binds to the P2Y12 and P2Y1 receptors, thrombin that activates platelets through the PAR1 and PAR4 receptors, and collagen that interacts with glycoprotein VI. Collagen can be substituted in in vitro experiments by the collagen-related peptide (CRP-XL)^3,4. Recently, hemin has been shown to induce platelet activation by the interaction of C-type lectin-like receptor (CLEC-2) and glycoprotein VI (GPVI), establishing hemin as a new endogenous platelet activator/agonist^5,6. Several pathological situations can lead to hemolysis, resulting in the liberation of free ferric iron-containing hemin. All these platelet agonists alter the surface receptor expression on platelets. The alteration of the surface receptors and plasma membrane depends on the strength and concentration of the platelet agonist^7,8,9. Therefore, it is important to phenotypically characterize platelets to be able to assign platelet subpopulations to platelet function.

High-dimensional methods such as flow cytometry have revealed that platelets form subpopulations with distinct functions. The subpopulations can be classified by different surface markers. In the literature, it is well known that two distinct platelet subpopulations are present: the procoagulant, which is characterized by phosphatidylserine externalization, and the aggregatory phenotype that activates integrin α_IIIbβ_3. In addition to the classical activation markers, fluorescence dyes such as Glutathione-S-Aryloxide (GSAO) that detects mitochondrial function and oxidative stress can be used for further categorization, e.g., procoagulant platelets by their metabolic state¹⁰. This allows a distinction to be made between ballooning and mitochondrial permeability transition pore formation (MPTP) procoagulant platelet phenotype¹¹.

Heemskerk et al. have shown by analyzing phosphatidylserine (PS) exposure and integrin α_IIbβ₃ activity that different platelet populations can control distinct coagulation steps and that strong activators such as thrombin and collagen are required for phosphatidylserine exposure¹². Further, activating the platelets by GPVI with collagen or the specific GPVI agonist convulxin is primarily responsible for the formation of adherent procoagulant platelets. Likewise, arachidonic acid, which leads to platelet activation by producing thromboxane A2 by cyclooxygenase, induces PS exposure¹³. However, arachidonic acid is, in comparison to thrombin or collagen, a weak platelet activator¹⁴. Van Velzen et al. also evaluated platelet surface antigens (CD42a/b, CD36, CD41, CD61) and activation markers (PAC-1, CD63, CD62P) in two separate measurements¹⁵. Additionally, van Velzen et al. were able to analyze blood from thrombasthenia patients and compared the subpopulations between healthy and diseased patients. Hindle et al. measured the influence of prostacyclin on platelet subpopulations after thrombin and CRP-XL treatment with two individual panels consisting of four markers each (PAC-1, Annexin V, CD62P, CD42b, or CDCD154, CD62P, CD63, CD42b)¹⁶. Recently, multi-color flow cytometry has been used to define resting (CD42b⁺, PAC1^-, CD62P^-, Annexin V^-), aggregatory (CD42b⁺, PAC1⁺, CD62P⁺, Annexin V^-) procoagulant (CD42b⁺, PAC1^-, CD62P⁺, Annexin V⁺) and apoptotic (Cd42b^-, PAC1^-, CD62P^-, Annexin V⁺) platelet subpopulations^7,17. Laspa et al. extended/refined the multi-color flow cytometry approach by the addition of antibodies against the chemokine receptors CXCR4 and ACKR3¹⁸. Based on the available literature, this is the first 10-color flow cytometer panel for isolated human platelets that allows the detection of 10 different antigens on an individual platelet (single-cell level) within a population of isolated human platelets in one tube. Hence, it is possible to simultaneously measure activation, adhesion, and aggregation potential, chemokine receptors, and apoptotic potential of a single cell to further distinguish the existing subpopulation. Thus, it complements Johnson et al., who defined the four main subpopulations with four markers (resting, procoagulant, aggregatory, apoptotic)¹⁷. So, establishing a multi-color flow cytometry assay enables the comparison of different platelet agonists in regard to their activation pattern and the formation of platelet subtypes within classical platelet subpopulations.

The method can be used for basic research regarding the analysis of platelet function and physiology. Moreover, the established multi-color flow cytometry assay for human platelets can be performed with platelets gained within patient studies, e.g., researching cardiovascular diseases and thrombotic events, to decode the platelet subpopulations induced by these diseases and the effects of treatments on these induced phenotypes. For example, Mueller et al. have shown that the platelet subpopulations can differ in aortic stenosis patients¹⁹. The gained knowledge might offer new openings for pharmaceutical interventions.

Blood collection and handling for research purposes were approved by the Ethics Committee at the Medical Faculty of the Eberhard Karls University and the University Hospital of Tübingen (ethics vote 238/2018B02), and the study methodologies conformed to the standards set by the Declaration of Helsinki. Healthy donors gave written informed consent for blood collection. The baseline characteristics for healthy donors are listed in Table 1. Figure 1 gives an overview of the flow cytometry protocol.

1. Isolation of human platelets from whole blood

NOTE: ISTH guidelines were followed in terms of sample collection, tourniquets, and the choice of anticoagulant in order to ensure a standardized manner to isolate platelets²⁰.

1. Buffer preparation
   1. Acid-citrate-dextrose (ACD) anticoagulant buffer: Dissolve 12.50 g Na₃-Citrat (85 mM C₆H₅Na₃O₇ x 2 H₂O), 6.82 g citrate acid (71 mM) and 10.00 g glucose (111 mM) in 475 mL of aqua destillata. Adjust the pH with 1 M NaOH to 4.69. Fill up to 500 mL with aqua destillata. Then, filter sterilize the buffer and store at 4 °C until use.
      NOTE: All solutions are filter sterilized with a vacuum-driven filter system, which uses a 0.22 µm pore size fast flow polyether sulfone membrane.
   2. Tyrode's buffer (10x): Weigh 80.00 g NaCl, 10.15 g NaHCO_3, and 1.95 g KCl and add aqua destillata to 1 L. Filter sterilize the buffer and store at 4 °C until use.
   3. Tyrode's-HEPES buffer (1x; 137 mM NaCl, 2.8 mM KCl, 12 mM NaHCO₃, 5 mM glucose, 10 mM HEPES): Mix 10 mL of 10x Tyrode's buffer and 90 mL of aqua destillata. Dissolve 0.1 g glucose and 0.1 g bovine serum albumin (BSA). Adjust the pH with 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid (HEPES) to 7.4. Set 10 mL of Tyrode's buffer to pH 7.4 and adjust the pH of the residual 90 mL of Tyrode's to 7.4 with 1 N HCl to pH 6.5.
      NOTE: BSA could scavenge some chemicals, such as hemin. In this case, it's important to prepare the 1x Tyrode's buffer without BSA. All buffers must be allowed to warm up to room temperature or 37 °C before use.
2. Blood sample collection
   1. Prepare for every donor one 10 mL syringe with 2 mL of ACD anticoagulant and let it adjust to room temperature or 37 °C using an incubator. Keep the dilution factor of 1:5 (ACD: blood).
      NOTE: Use smaller or larger syringes to draw blood samples. Cold ACD buffer could pre-activate the platelets and impair your results.
   2. Use a butterfly needle (21G) to slowly collect the donor blood in the pre-warmed ACD syringes. Open the tourniquet hose before filling the syringe to avoid pre-activation.
      NOTE: Syringes with a larger gauge needle can minimize the shear-induced activation of platelets.
   3. Slowly transfer the ACD blood into a 15 mL reaction tube. Use the blood immediately to avoid pre-activation of the platelets.
3. Platelet isolation
   1. Centrifuge the ACD blood for 20 min at 209 x g (room temperature, RT) without brakes. Prepare 25 mL of Tyrode's-HEPES buffer pH 6.5 in a 50 mL reaction tube.
   2. Gently transfer the resulting platelet-rich plasma (PRP, upper layer) into the 50 mL reaction tube containing Tyrode's-HEPES buffer, pH 6.5.
   3. Leave approximately 1 mL of PRP above the buffy coat and erythrocytes to avoid contamination with other blood cells.
   4. Centrifuge the suspension at 430 x g for 10 min at RT to pellet the platelets. Carefully resuspend the resulting pellet in 200-300 µL of Tyrode's buffer, pH 7.4.
   5. Use a cell counter to determine the platelet count. Adjust the platelet count with Tyrode's-HEPES-buffer pH 7.4 to 200 x 10³ platelets/µL. See step 3 for further processing of platelet staining.

2. Selection and titration of antibodies for flow cytometry

1. Selection of antibodies and dyes
   1. Select the antibodies and dyes with suitable fluorophores for the flow cytometer²¹. Note the filter configuration recommended by the flow cytometer manufacturer. An example of platelet markers and fluorophore configuration is given in Table 2 and Figure 2 for the following cytometer: BD LSR Fortessa with Laser configuration: violet (402 nm), blue (488 nm), yellow/green (561 nm), red (640 nm).
      NOTE: Use brighter fluorochromes for lower-expressed antigens. For example, the bright fluorochromes BV412 and BV650 were used for the weakly expressed chemokine receptors (ACKR3 and CXCR4)^22,23,24.
2. Titration of antibodies and dyes
   1. Titrate the antibodies to find the right working concentration. For titration, use isolated human platelets from step 1 and a concentration series of the antibody.
      NOTE: The working concentrations given by the manufacturer are generally a good starting point for the concentration series.
      1. If working with antigens that only get expressed under treatment, activate the platelets prior to staining to be sure that negative and positive cells can be detected as well. For example, PAC-1 binds only to activated integrin α_IIbβ₃. Therefore, strongly activate the platelets to get a bright, positive signal, e.g., with 10 µg/mL of collagen-related peptide (CRP; see Figure 3).
3. Titration of samples
   1. Isolate the human platelets as in step 1 and adjust the platelet count to 1 x 10⁶ platelets per sample.
      1. For receptors that need no activation prior to staining: CD61, CD42b, CD41, CXCR7, CXCR4 use one unstained and a stained sample per antibody. For receptors/markers that need to be activated with, for example, 10 µg/mL of CRP-XL prior to staining: PAC1, CD63, CD62P, Annexin V, Zombie NIR, use one activated and one non-activated sample^15,25,26.
   2. Activate the platelets (1 x 10⁶ platelets per sample) if necessary for 30 min with 10 µg/mL CRP-XL. Then, incubate the isolated platelets (1 x 10⁶ platelets per sample) for 30 min with different concentrations of the fluorochrome-conjugated antibody or dye and add 300 µL of 1x Annexin Binding Buffer to dilute the sample (examples for concentrations are given in Table 3).
   3. Measure 10,000 events for every sample.
   4. Calculate the stain index that shows the optimal separation of the positive and negative populations for every antibody, as shown in Supplementary Figure 1.
   5. Plot the stain index for each concentration of antibody to find the optimal concentration.

3. Staining for flow cytometry

NOTE: See Table 4 for a staining example with four different platelet activators (CRP-XL, ADP, thrombin, hemin).

1. Hemin needs to be prepared freshly. Dissolve the powder in 1.4 M NaOH to a 3 M solution and heat it to 96 °C for 5 min at 450 rpm. Then, dilute it with distilled H₂O to a 3 mM stock solution.
2. After adjustment of the platelet count (per sample: 1 x 10⁶ platelets), activate the platelets in a tube with the platelet activators for 30 min at RT as given in Table 4.
3. Add 43.3 µL of antibody cocktail to the samples and incubate the platelets for 30 min at RT in the dark.
4. After incubation, dilute the samples with 300 µL of 1x Annexin Binding Buffer (10 mM HEPES/NaOH, pH 7.4, 140 mM NaCl, 2.5 mM CaCl₂) so that the Annexin V protein can bind to the phosphatidylserine on the outer side of the cell membrane. Measure the samples immediately.
   NOTE: Measure the samples immediately after preparation. As the samples are not fixed, delaying sample preparation and measurement can increase the variability of the results. Fixation impairs Annexin V binding.

4. Compensation setup and fluorescence minus one control (FMO) measurement

NOTE: Before recording data, run a compensation setup. A flow cytometer automatically calculates compensation settings; refer to the usual manuals of the flow cytometer for the particular compensation procedure. Nevertheless, use compensation controls either with cells or with beads.

1. Compensation setup
   1. For compensation with beads, prepare 150 µL of phosphate-buffered saline (PBS) for each fluorophore with one drop of beads and 1 µL of the fluorescence-conjugated antibody.
   2. Monitor the species' reactivity of the beads. Use anti-mouse IgGκ and the negative control of polystyrene microparticles. To compensate for Annexin V, label PE/Cy7 beads with antibody PE/Cy7 anti-human CD62L that conjugates the same fluorophore as Annexin V.
   3. Use amine-reactive compensation beads for the amine-reactive dye. Use two drops of positive and one drop of negative beads in 150 µL of PBS with 1 µL of the dye. Check that the amine-reactive compensation beads react with the dye and show a positive signal. Check that the negative beads do not react with the amine-reactive dye and present a negative signal.
      NOTE: For Zombie NIR APC-Cy7, use negative and positive amine reactive beads.
   4. Incubate the compensation samples for 30 min at room temperature in the dark.
   5. Run every compensation tube separately and gate on the singlet bead population based on FSC and SSC. The bead population appears as one small concentrated population. Draw a gate around this population.
   6. Display the histograms for every compensation tube and adjust the compensation values until the positive peaks of the different antibodies and dyes do not overlap. Then, measure every single compensation tube and let the spillover of the fluorophores be calculated as per the manufacturer's SOP.
   7. Proceed with acquiring the actual staining experiment with the isolated human platelets (see step 3 and Table 4). Adjust the SSC/FSC voltage before measurement to find the platelet population. Since CD61 is ubiquitously expressed on platelets, check with this fluorescence threshold if every size of the platelet can be detected.
   8. Then, measure 20,000 events per sample.
2. FMO measurement
   1. Prepare for every marker an FMO control as per Table 5. In order to be able to determine the overlaps of all markers, prepare both unstimulated and stimulated samples.

5. Data analysis

1. Analysis with a flow cytometry data analysis software (Figure 4)
   1. Drag and drop the FSC files into the software. Define with the FMO control the positive population for each marker.
      1. Determine FMOs by plotting each FMO control against each marker in a dot plot. This makes the spill over into other channels visible. Use the FMO gate with the highest fluorescence spillover as the boundary to the positive population for the treated 10-color samples.
2. Analyzing with a cloud-based flow cytometry analysis (Figure 5)
   1. Open the software and create a new dataset. Drag and drop the FCS files acquired with the flow cytometer.
   2. Start the analysis by creating a workflow. The compensation matrix is automatically created from the raw data files.
   3. Run a UMAP analysis with the following settings: Neighbors: 15, Minimum Distance: 0.4; Components: 2; Metric: Euclidean; Learning Rate: 1; Epochs: 200; Embedding Initialization: spectral. Select all files and all 10 markers (features).
   4. Add Phenopraph analysis as a new task and run the analysis with the following settings: K Nearest Neighbors: 20; Distance Metric: Euclidean; Louvain Runs: 1; Number of Results: 1.
   5. Add a gating task to indicate the generated clusters. After running the analysis, it is possible to present the data in different blots, which can be exported.

There are two options for analyzing the raw data collected by a multi-color flow cytometry panel. One option is to analyze it manually with the analysis software (Figure 3 and Figure 4). Therefore, the raw data needs to be dragged and dropped into the software window. First, the cells need to be found by adjusting the SSC/FSC scale in the scatterplot display mode to show the platelet population as a distinct point cloud. Then, the positive cells for each marker can be defined using the untreated and treated samples by bringing the display into histogram mode and setting the threshold between the untreated pique and the treatment pique or using the scatterplot display and defining a gate determined by the FMO.

A clear shift in the platelet population can be seen in the dot plots (Figure 4A). Hemin forms, in comparison to the other activators, more platelets with low FSC. About 40%-60% of the treated platelets with ADP, thrombin, and CRP-XL are CD42b positive (Figure 4B). Only hemin significantly decreases the CD42 b-positive platelets (*p < 0.05; Figure 4B). All platelet agonists have no effect on CD41 (integrin αIIb) and CD61 (integrin βIII) surface expression (Figure 4C,D). However, activation of the integrin α_IIbβ₃ measured with a conformation-dependent PAC-1 antibody is dependent on the platelet agonist. The addition of 2.5 µM ADP induces a weaker activation of the fibrinogen receptor (*p < 0.05; Figure 4E) than the other platelet agonists (**p < 0.01; Figure 4E). Further, platelet α- and lysosomal degranulation in response to 2.5 µM ADP is weaker than the 0.1 U/mL thrombin-, 1 µg/mL CRP-XL-, and 25 µM hemin-dependent degranulation (Figure 4F,G). Only hemin significantly increases the CXCR4 and CXCR7 surface expression (*p < 0.05; Figure 4H,I). In addition, the phosphatidylserine exposure is also enhanced under hemin treatment (*p <0.05; Figure 4J), resulting in an enhancement of Annexin V-positive cells (Figure 4J). A 0.1 U/mL thrombin and 1 µg/mL CRP-XL also show a tendency for increased Annexin V signal (Figure 4J). Necrotic cells can be identified, among others, by the amine-reactive dye. Only 0.1 U/mL thrombin and no other platelet agonist increases the amine-reactive dye signal (Figure 4K).

The second option for analyzing high-dimensional data is to use software that includes various machine learning tools (for example, OMIQ; Figure 5). To further define the effect of platelet agonists on the formation of platelet subpopulations, unsupervised data analysis by applying uniform manifold approximation and projection (UMAP) dimension reduction was performed to group phenotypically similar events, followed by unsupervised clustering analysis using PhenoGraph. PhenoGraph analysis resolved 27 clusters (Figure 5A). The subpopulation pattern is altered by platelet agonists (Figure 5A). The addition of 2.5 µM ADP slightly shifts the pattern. The addition of 0.1 U/mL thrombin and 1 µg/mL CRP-XL induces significant changes to the pattern, and 25 µM hemin completely alters the subpopulation pattern in comparison to the untreated samples (Figure 5A). Hence, multi-color flow cytometry allows for the definition of distinct platelet clusters/subpopulations associated with distinct functions. Analysis of the surface receptor expression in these clusters can allocate the clusters to platelet subpopulations and functions. Cluster pg-08 and pg-13 determine the resting platelet subpopulation (CD42b⁺, PAC1^-, CD62P^-, Annexin V^-, CXCR4^-, CXCR7^-, CD63^-, CD61⁺, CD41⁺, amine-reactive dye^-; Figure 5B). The stronger the platelet agonist, the fewer resting platelets are present (resting:  2.5 µM ADP > 1 µg/mL CRP-XL > 0.1 U/mL thrombin > 25 µM hemin; Figure 5B). Cluster pg-17 and pg-20 determine the aggregatory subpopulation (CD42b⁺, PAC1⁺, CD62P⁺, Annexin V^-, CXCR4^-, CXCR7^-/+, CD63⁺, CD61⁺, CD41⁺, amine-reactive dye^-; Figure 5B). The stronger the platelet agonist, the more aggregatory the platelets become. Platelets treated with 25 µM hemin do not show a high amount of aggregatory platelets (Figure 5B). Cluster pg-09 determines the procoagulant subpopulation (CD42b⁺, PAC1^-, CD62P⁺, Annexin V⁺, CXCR4^-, CXCR7^-, CD63^-, CD61⁺, CD41⁺, amine-reactive dye^-; Figure 5B). Platelet activation with 0.1 U/mL thrombin and 1 µg/mL CRP-XL induces procoagulant platelets (Figure 5B). The addition of 25 µM hemin also favors the formation of procoagulant platelets (Figure 5C). Interestingly, the hemin-induced procoagulant platelet subpopulation expresses high levels of CXCR4 and ACKR3. These ACKR3/CXCR3 specific clusters (pg-03 and pg-23) only occur under hemin treatment and can be associated with procoagulant/inflammatory platelet function (CD42b^+/-, PAC1^-, CD62P⁺, Annexin V⁺, CXCR4⁺, CXCR7⁺, CD63⁺, CD61⁺, CD41⁺, amine-reactive dye^-; Figure 5C).

Figure 1: Overview of flow cytometry staining protocol. 1) Blood collection from healthy donors. 2) Isolation of human platelets from whole blood. 3) Platelet activation and staining, including selection and trituration of antibodies and dyes. 4) Analyzing the samples by flow cytometry. 5) Data analysis with OMIQ and creation of subpopulations. Created with Biorender. Please click here to view a larger version of this figure.

Figure 2: Fluorescence spectra overview with light source (laser wavelength), excitation, and emission curve of the fluorophores and dyes. (A) Spectra from violet laser: 402 nm; fluorochromes: BV421, BV605, BV650. (B) Spectra from blue laser: 488 nm; fluorochromes: FITC, PerCP-Cy5.5. (C) Spectra from yellow/green laser: 560 nm; fluorochromes: PE, PE-Cy5, PE-Cy7. (D) Spectra from red laser: 640 nm; fluorochromes: AF700, amine reactive dye. Dotted lines present excitation plots. Abbreviations: BV = Brilliant Violet, FITC = Fluorescein isothiocyanate, PerCP-Cy = Peridinin chlorophyll protein-Cyanine, PE = Phycoerythrin, PE-Cy = Phycoerythrin-Cyanine, AF = Alexa Fluor, APC-Cy = Allophycocyanin-Cyanine. Created with a Fluorescence SpectraViewer. Please click here to view a larger version of this figure.

Figure 3: Histograms of human-washed platelets stained. (A) The final concentration of PAC-1 FITC is 1:10. (B) The final concentration of CD62P PEis 1:20. (C) The final concentration of CD63 PE-Cy5 is 1:25. (D) The final concentration of Annexin V PE-Cy7 is 1:20. (E) The final concentration of Zombie NIR (amine-reactive dye) is 1:250. (A-E) The left figure shows untreated; middle: treated for 30 min with 10 µg/mL with CRP-XL; right: overlay. (F) CD61 BV605. The left figure shows unstained; the middle has a concentration of 1:80; the right has the overlay. (G) CD41 AF700. The left figure shows a concentration of 1:10; the middle a concentration of 1:80; the right is the overlay. (H) CD42b PerCP-Cy5.5. The left figure shows the concentration of 1:20; the middle is a concentration of 1:40; and the right is the overlay. (I) CXCR4 BV650. The left figure shows a concentration of 1:25; the middle shows a concentration of 1:45; the right shows the overlay. (J) CXCR7 BV421. The left figure shows a concentration of 1:10; the middle: a concentration of 1:25; the right: overlay. Please click here to view a larger version of this figure.

Figure 4: Effect of platelet agonists on platelet surface receptors. (A) Dot plot example of each treatment, which includes 2.5 µM ADP, 0.1 U/mL thrombin, 1 µg/mL CRP-XL, and 25 µM hemin. (B-K) Flow cytometry measurements. The positive cells, which were defined using FMOs, are shown on the left. The MFI of all events are shown on the right (B) CD42b, (C) CD41, (D) CD61, (E) PAC-1, (F) CD62P, (G) CD63, (H) CXCR4, (I) CXCR7, (J) Annexin V, (K) amine-reactive dye induced by 2.5 µM ADP, 0.1 U/mL thrombin, 1 µg/mL CRP-XL and 25 µM hemin for 1 h at RT. The plot shows Mean ± SD; n = 3; significance test used: RM one-way ANOVA, *p < 0.05; **p < 0.01; ***p < 0 001. Please click here to view a larger version of this figure.

Figure 5: Platelet phenotype and formation of agonist-induced platelet subpopulations. (A) Platelet subpopulation was determined by the PhenoGraph algorithm for unsupervised clustering (pg-01 to pg-27) of human washed platelets; n = 3. Plots represent an overlay of all platelets per treatment: untreated, 2.5 µM ADP, 0.1 U/mL thrombin, 1 mg/mL CRP-XL, 25 µM hemin. (B) Example of some clusters for each treatment associated with the function pg-08: resting: CD42b⁺, PAC1^-, CD62P^-, Annexin V^-, CXCR4^-, CXCR7^-, CD63^-, CD61⁺, CD41⁺, amine-reactive dye^- and pg-13: resting: CD42b⁺, PAC1^-, CD62P^-, Annexin V^-, CXCR4^-, CXCR7^-, CD63^-, CD61⁺, CD41⁺, amine-reactive dye^-; pg-09: procoagulant: CD42b⁺, PAC1^-, CD62P⁺, Annexin V⁺, CXCR4^-, CXCR7^-, CD63^-, CD61⁺, CD41⁺, amine-reactive dye^-; pg-17: aggregatory: CD42b^-, PAC1⁺, CD62P⁺, Annexin V^-, CXCR4^-, CXCR7^-, CD63⁺, CD61⁺, CD41⁺, amine-reactive dye^-; and pg-20: aggregatory: CD42b⁺, PAC1⁺, CD62P⁺, Annexin V^-/+, CXCR4^-, CXCR7^-/+, CD63⁺, CD61⁺, CD41⁺, amine-reactive dye^-. (C) Hemin-induced platelet procoagulant/inflammatory clusters pg-03: CD42b^+/-, PAC1^-, CD62P⁺, Annexin V⁺, CXCR4⁺, CXCR7⁺, CD63⁺, CD61⁺, CD41⁺, amine-reactive dye^-; and pg-23: CD42b^+/-, PAC1^-, CD62P⁺, Annexin V⁺⁺, CXCR4⁺⁺, CXCR7⁺⁺, CD63⁺, CD61⁺⁺, CD41⁺⁺, amine-reactive dye^-. Please click here to view a larger version of this figure.

Table 1: Baseline characteristics of healthy donors. The body-mass index is the weight in kg divided by the square of the height in meters. Please click here to download this Table.

Table 2: Example of a 10-color multi-panel with platelet surface receptors and antibodies with conjugated fluorophores. Abbreviations: ACKR3 = atypical chemokine receptor 7, CXCR7 =C-X-C chemokine receptor 7, CXCR4 = C-X-C chemokine receptor 4, CD = Cluster of differentiation, BV = Brilliant Violet, FITC = Fluorescein isothiocyanate, PerCP-Cy = Peridinin chlorophyll protein-Cyanine, PE = Phycoerythrin, PE-Cy = Phycoerythrin-Cyanine, AF = Alexa Fluor, APC-Cy = Allophycocyanin-Cyanine. Please click here to download this Table.

Table 3: Titration of the antibodies and dyes. Concentrations of antibodies and dyes used for titration and final concentration (in bold) were chosen using the stain index. Please click here to download this Table.

Table 4: Example of staining human-washed platelets with different platelet activators. 1 µg/mL CRP-XL: collagen-related peptide, 2.5 µM ADP: Adenosine diphosphate, 0.1 U/mL thrombin and 25 µM hemin. Please click here to download this Table.

Table 5: Stimulated and unstimulated FMO controls. Please click here to download this Table.

Supplementary Figure 1: Antibody titration. Plotting the stain index for each dilution of antibody or dye. Stain index (Δ): (Δ) = (MFI pos-MFI neg)/(2x SD) where MFI pos = mean fluorescence intensity of the positive population; MFI neg = mean fluorescence intensity of the negative population; SD = standard deviation of the negative population. Please click here to download this File.

Multi-color flow cytometry allows to define distinct platelet clusters/subpopulations associated with discrete functions^12,17,18. Decisive for the results of the subpopulations and the receptor surface expression is the quality of the isolated human platelets. Therefore, one key step in the presented protocol is the preparation of the platelets. They need to be gently isolated to avoid activation prior to sample treatment with different agonists. Even when the method is implemented to compare different diseases, it is important to slowly withdraw blood from patients to avoid platelet activation that is not caused by the disease and would impact the results.

The biggest challenge in establishing a 10-color flow cytometry panel is finding the right fluorescence-labeled antibody for each surface receptor that can be measured with the available flow cytometer^21,27. The selection is further limited by the spectral overlap between fluorescence signals of the various fluorochromes and hence requires careful compensation. Further, verification with positive, negative, and FMO controls of the antibodies and dyes is important to avoid signals that are not caused by a biological effect and would impact the results. Especially in a 10-color panel, the controls help to set accurate gates and precisely identify the negative and positive populations. In addition, it is mandatory to titrate the antibodies to find the optimal final concentration for each antibody and dye within the assay. Even though there are some critical steps to consider, the described protocol of a 10-color flow cytometer panel for isolated human platelets allows the detection of 10 different antigens on isolated human platelets in a single tube at the single-cell level. This provides a wealth of information about the surface expression of receptors and enables high-parameter analysis.

Analyzing the high-dimensional data with software that includes machine learning tools, like dimension reduction (UMAP), accelerates the evaluation of flow cytometry measurements with 10 different colors and dyes and enables the unbiased classification of platelet subpopulations. Therefore, presenting the data in UMAP blots allows us to quickly recognize the increase in activation and induced changes in the subpopulation pattern with different platelet agonists (Figure 5A). Furthermore, a 10-color panel makes it possible to define agonistically specific subtypes within the classical platelet subpopulations as resting (CD42b+, PAC-1-, AnnexinV-), aggregatory (CD42b-, PAC-1+, CD62P-, AnnexinV+), and procoagulant (CD42b+, PAC-1^-, CD62P⁺, AnnexinV⁺) ^7,17,18. It is well known that upon strong activation, resting platelet populations transform into procoagulant and aggregatory subtypes^8,10. However, the ratio of the different platelet subpopulations formed is specific to agonist and concentration (Figure 5B,C). For example, high hemin concentrations (25 µM) induce a high amount of procoagulant platelets, but the aggregatory subpopulation decreases (Figure 5B,C)^7,28. Thus, the advantage of analyzing a 10-color panel with OMIQ is the identification of subpopulations beyond the classical subpopulations, resting, aggregatory, and procoagulant platelet subtypes. It is possible to classify the hemin-induced procoagulant platelet subpopulation in more detail. The data in this manuscript demonstrate that about 40% of procoagulant platelets express high levels of CXCR4 and CXCR7 (Figure 5C) and introduce this platelet subtype as an inflammatory subpopulation (CD42b⁺, CXCR4⁺, CXCR7⁺, AnnexinV⁺; Figure 5C)¹⁸.

Manually analyzing the platelet subpopulations of a 10-color panel with analysis software requires more time. However, it is recommended that each marker be evaluated regarding fluorescence, and the negative and positive platelets be determined (Figure 4). These results can be referred to and compared to those automatically generated from OMIQ (Figure 5). So, manual and automatic analysis can complement each other.

In summary, a 10-color flow cytometry platelet panel combines markers that can provide information about the adhesion potential  (anti-CD42b, anti-CD41, anti-CD61, integrin α_IIbβ₃ (clone: PAC-1)), the aggregation potential (anti-CD41, anti-CD61, anti-α_IIbβ₃ (clone: PAC-1)) and the degranulation of α- and lysosomal granules (anti-CD62P, anti-CD63) of platelets. Additionally, apoptotic markers (Annexin V, amine-reactive dye) and inflammatory markers (anti-CXCR4, anti-ACKR3) raise possibilities to define further platelet subtypes that could be linked to specific functions.

The authors have nothing to disclose.

The project was supported by the German Research Foundation (DFG) - Project number 335549539 - GRK 2381.