Technical Note

Measurement of Monocyte-Platelet Aggregates by Imaging Flow Cytometry Henry Hui,1,2 Kathryn Fuller,2 Wendy N. Erber,2 Matthew D. Linden1,2*

1

Centre for Microscopy, Characterisation and Analysis, University of Western Australia, Perth, Australia

2

School of Pathology and Laboratory Medicine, University of Western Australia, Perth, Australia

Received 3 August 2014; Revised 6 October 2014; Accepted 15 October 2014 Grant sponsor: the University, State and Commonwealth Governments. Additional Supporting Information may be found in the online version of this article. *Correspondence to: Matthew Linden, Centre for Microscopy, Characterisation and Analysis, M510, The University of Western Australia, 35 Stirling Highway, Crawley, WA 6009, Australia. E-mail: [email protected] This research was supported by institutional funding at The University of Western Australia. It was presented as an abstract to the 36th annual meeting of the Australasian Cytometry Society meeting, Wellington, New Zealand, Dec 2013. Published online 16 December 2014 in Wiley Online Library (wileyonlinelibrary. com) DOI: 10.1002/cyto.a.22587 C 2014 International Society for V

Advancement of Cytometry

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 Abstract Platelets are subcellular blood elements with a well-established role in haemostasis. Upon activation platelets express P-Selectin (CD62P) on the cell membrane and bind to P-Selectin glycoprotein ligand 1 expressing monocytes, influencing them toward a proadhesive and inflammatory phenotype. It is well established that elevated circulating monocyte-platelet aggregates (MPAs) are linked to atherothrombosis in high risk patients. However, whole blood flow cytometry (FCM) has recently shown that circulating MPAs may also occur in the absence of platelet activation, particularly in healthy children. A potential limitation of conventional FCM is the potential for coincident events to resemble monocyte platelet aggregates. Here we report a novel imaging cytometry approach to further characterize monocyte-platelet aggregate formation by P-Selectin dependent and P-Selectin independent mechanisms and distinguish circulating MPAs from coincidental events. Monocytes were identified by expression of the lipopolysachharide receptor (CD14 BV421), while platelets were identified by expression of the glycoprotein Ib (CD42b APC). Differentiation of P-Selectin dependent and P-Selectin independent binding was achieved with AF488 labeled CD62P. Overall analysis of circulating and in vitro generated MPAs by conventional and imaging cytometry methods showed very strong correlation (r2 5 >0.99, P < 0.01). The Bland-Altman bias of 21.72 was not significantly different to zero. However, when measuring only P-Selectin negative MPAs, a lack of correlation (r2 5 0.27, P 5 n.s.) likely reflects better discrimination of coincidence events using imaging cytometry. Our data demonstrate that IFC is more accurate in enumerating MPAs than conventional FCM, which over-estimates the number of MPAs due to the presence of coincident events. VC 2014 International Society for Advancement of Cytometry  Key terms imaging flow cytometry; platelets; monocytes; thrombosis; cell–cell interaction; heterotypic aggregates; coincidental events

INTRODUCTION

PLATELETS are blood elements that control hemostasis and blood clotting, with excessive platelet activation playing a well-defined role in the pathogenesis of arterial thrombosis. For this reason markers of platelet activation are an early and sensitive indicator of acute cardiovascular disease (1), and antiplatelet therapy is a standard of care for those at high risk of atherothrombosis (2). When platelets secrete their granule content in response to injury or disease, they bind to blood monocytes forming circulating monocyte-platelet aggregates (MPAs) (3). MPAs are elevated in cardiovascular disease, and more so in more acute and unstable atherothrombosis (4). Measurement of MPAs therefore provides both a major biomarker for cardiovascular risk (5) and a means of monitoring the efficacy of antiplatelet drugs such as aspirin and thienopyridines (6). However, recent studies have suggested that, through their interaction with monocytes, sub-clinical platelet activation may also contribute to early stage inflammatory processes which underpin atherogenesis (7). The mechanism by which MPAs form has been well characterized, where activated platelets, which have undergone exocytosis express a-granule P-Selectin on the

Technical Note cell surface. Platelet P-Selectin then interacts with P-Selectin glycoprotein ligand-1 (PSGL-1) (8), which is constitutively expressed on monocyte cell membrane. The interaction is therefore dependent on platelet activation and exocytosis. Activated platelets alter the phenotype of monocytes by binding to them, increasing Mac-1 expression and activation, inducing procoagulant activity and promoting inflammation (9), which contributes to atherogenesis and the development of other inflammatory diseases (7). The resultant intracellular signaling causes the monocyte surface expression of tissue factor and activation of Mac-1 (integrin aMb2, CD11b/CD18). The activation-dependent conformational change in monocyte surface Mac-1 results in the binding of coagulation factor Xa (FXa) and/or fibrinogen to Mac-1 and increased adhesive and atherogenic phenotype (9). A growing body of research indicates that the role of platelets in the initiation and propagation of atherogenesis may be through interaction of activated platelets with monocytes (10). Abundant data from animal models support the concept of atherosclerosis as an inflammatory disease, and the potential role for MPAs in atherogenesis (7,11). In hypercholesteremic rabbits and ApoEdeficient mice, intravital microscopy has shown that activated platelets preferentially adhere to the site of atherosclerotic lesions before the lesions are detectible (12,13). Circulating activated platelets exacerbate atheroma formation when introduced to juvenile ApoE knockout mice (14), and treatment of these juvenile animals with antiplatelet drugs such as thienopyridines, or disruption of CD40-CD40L signaling reduces the size and improves the stability of the plaque (15). However, in a recently published study, we demonstrated increased circulating MPAs occur in healthy children in the absence of overt circulating platelet activation or increased numbers of circulating degranulated platelets (11). This suggests a potential role for a novel P-Selectin independent tethering of monocytes and platelets in circulation. Thus there is a need to further investigate monocyte-platelet adhesion in the circulation, the molecules responsible for this and their effect on monocyte-phenotype. Whole blood flow cytometry (FCM) is a well-established technique for measuring circulating MPAs and has been shown to be a sensitive and reliable indicator of platelet activation in the context of acute atherothrombosis. In lysed whole blood, monocyte events are gated by characteristic laser scatter and expression of CD14 (the lipopolysaccharide receptor). Monocyte events that are positive for a platelet specific epitope (such as integrin aIIbb3 or the glycoprotein Ib-IX-V complex) are identified as MPAs, and the percentage of monocytes with one or more adherent platelets is recorded. However, measurement of this heterotypic cellular interaction by FCM is potentially problematic, as the concentration of platelets in blood is orders of magnitude higher than that of monocytes. Nonadherent but coincident monocytes and platelets passing through the point of laser interrogation near each other may be indistinguishable from an MPA event. This is a problem particularly at high differential sheath and sample pressures (higher flow rates), and in certain flow cytometers double positivity attributable only to coincidence may exceed 30% (16,17). 274

Epifluorescent and confocal microscopy has been used to image MPAs (18), but is not capable of providing the highthroughput quantitative data required to measure moderate, but clinically relevant, increases in %MPAs. Locating and analysing sufficient MPAs by microscopy is extremely labor intensive. Furthermore, because P-Selectin positive platelets can bind monocytes even after fixation, attempts to concentrate the samples in order to help improve this have the potential for preanalytical formation of artefact MPAs. For these reasons, epifluorescent microscopy has not been effectively used to discriminate genuine MPAs from coincident events. While previous attempts have been made to circumvent this problem by computer modeling of coincidence (16,17), recent developments in imaging cytometry present an opportunity to circumvent these challenges. Here we establish an assay using imaging FCM (IFC, ImageStreamX, AmnisV, WA), which combines high throughput surface immunophenotyping with morphological and localization data to demonstrate heterotypic cell adhesion and distinguish genuine MPAs from coincident events. R

MATERIAL AND METHODS Participants and Blood Collection Blood from healthy adult volunteers (n 5 8, age 20–46 years) with no history of atherothrombosis, diabetes, or use of medicines known to affect platelet function within the past 10 days was collected following informed consent and institutional ethics approval (University of Western Australia). Blood was collected by antecubital venepuncture into 3.8% sodium citrate vacuum tubes (Greiner bio-one, Austria) containing 1 volume citrate per 9 volumes of blood according to protocols previously established to minimize preanalytical variables in platelet function testing (19). Blood Preparation and Labeling Labeling occurred within 15 min of collection. Blood was incubated with an antibody cocktail containing Brilliant Violet 421 (BV421) conjugated mouse antihuman CD14 (clone M5E2, BioLegend, CA), Alexa Fluor 488 (AF488) conjugated mouse antihuman CD62P (clone AK4, BioLegend), allophycocyanin (APC) conjugated mouse antihuman CD42b (clone H1P1, BioLegend) or appropriate isotypic control at the concentrations outlined in Table 1, as determined by titration. Staining was performed with or without 50 mM of thrombin receptor activating peptide SFLLRN (TRAP, Sigma Aldrich, MO) in HEPES saline buffer (1 mM HEPES, 0.015M sodium chloride, 0.1% BSA, pH 7.3) followed by fixation and red cell lysis with BD FACSLyse (BD Biosciences, CA). After fixation samples were centrifuged at 280g for 5 min and the pellet resuspended in HEPES saline buffer prior to analysis. Flow Cytometry Conventional FCM was performed with a modification of established methods (19,20) using a BD FACSCantoTM II digital FCM analyzer (BD Biosciences) with FACSDiva v6.1 operating software followed by analysis using FlowJo vX (Treestar, OR). BV421 was excited by a 30 mW 405 nm solid state laser Measurement of Monocyte-Platelet Aggregates

Technical Note Table 1. Three color monocyte-platelet aggregate panel for conventional flow cytometry (FACSCanto II) and imaging flow cytometry (Amnis ISX)

FACSCanto II

Amnis ISX

Concentration Excitation k Emission k Concentration Excitation k Emission k

BV421-CD14

AF488-CD62P

APC-CD42B

1.25 mg/ml 405 nm 425–475 nm 2.5 mg/ml 405 nm 420–505 nm

2.5 mg/ml 488 nm 515–545 nm 2.5 mg/ml 488 nm 660–740 nm

1.25 mg/ml 633 nm 650–670 nm 1.25 mg/ml 647 nm 505–560 nm

and emission collected with a 450/50 nm bandpass (BP) filter. AF488 was excited by a 20 mW 488 nm solid state laser and emission collected with a 530/30 nm BP filter. APC was excited by a 17 mW 633 nm helium-neon (HeNe) laser and emission collected with a 660/20 nm BP filter. Threshold triggering was set on forward light scatter and photomultiplier PMT settings were established such that unstained and stained cells were clearly discriminated and fell within the linear dynamic range of each detector. Day to day adjustment of PMT settings was performed using CS&T calibration beads. Samples were analyzed from tubes at low flow rate (10 ml/min) and all monocyte events per sample were recorded over 8 min. Monocytes were identified by sequential gating for characteristic forward and orthogonal scattering of the 488 nm laser, as well as differential expression of CD14-BV421. MPAs were identified as CD42b-APC positive monocyte events. P-Selectin positive and negative MPAs were determined using isotypic control. Digital compensation was performed using single stained antimouse Ig j Comp Beads (BD Biosciences) and repeated for each experiment. Gating strategy was initially determined using fluorescence minus one (FMO) controls and gates were refined for each experiment using IgG1 isotype (clone MOPC-21) and TRAP stimulated controls. Imaging Flow Cytometry IFC was performed on a two-camera ImageStreamx (ISX) with INSPIRE v4.1 acquisition software (Amnis). Excitation lasers used for analysis include 100 mW 405 nm, 40 mW 488 nm, and 40 mW 647 nm. A 2.5 mW 785 nm laser provided a scatter signal and measurement of SpeedBeads for internal calibration. BV421 was excited by the 405 nm laser and emission captured in the range 420–505 nm (Ch7), AF488 was excited by the 488 nm laser and emission captured in the range 505–560 nm (Ch2), APC was excited by the 647 nm laser and emission captured in the wavelength range 660–740 nm (Ch11). All images were captured with the 203 objective and a cell classifier (threshold) applied to the brightfield channel (Ch1) to exclude small particles. Single stained Quantum Simply Cellular anti-mouse compensation standard controls (Bangs Laboratories, IN) were analyzed using identical laser settings in the absence of brightfield and 785 nm laser illumination to calculate a compensation matrix using INSPIRE software. IFC data analysis was performed using IDEAS v6.0 image analysis software (Amnis) using compensated data. BV421CD14 fluorescence intensity (Ch7) was used to interrogate 1,000 in focus, single monocyte events. CD42b associated Cytometry Part A  87A: 273 278, 2015

monocyte events were gated and P-Selectin expression measured. For initial experiments, monocyte-platelet events were determined to be heterotypic aggregates or coincidental events by individual visual inspection. Results of this were compared with automated determination using the Internalization Feature. In brief BV421 CD14 (Ch7) fluorescence was selected to define the cell surface, CD42b APC (Ch11) fluorescence defined the internalizing probe and a dilate mask (plus 1 pixel) was defined on the bright field image to detect platelets bound to the monocyte cell surface. The Internalization Feature then calculated the fluorescence intensity of the CD42b APC signal inside the cell compared to the entire cell. A positive or score close to zero indicated full to partial overlay of the monocyte and platelet signals (aggregation). A negative score indicated no internalization or a coincident event (Fig. 1). The percentage of monocyte with adherent platelets or coincidental associated platelets was determined, as well as the percentage of MPAs that were P-Selectin dependent and independent. Statistics All monocyte-platelet events were recorded as a percentage of all monocytes. All statistical analyses were carried out using Prism v6 (GraphPad Software, CA). Differences in %MPAs between techniques were compared using Pearson product-moment correlation coefficient. Least squares regression lines were fitted to investigate the relationship. Agreement between the two assays was determined by a Bland-Altman plot with limits of agreement of bias 6 1.96 standard deviations of the difference.

RESULTS Our overall goal was to determine if IFC could measure MPAs, and whether this combination of high throughput surface immunophenotyping with morphological and localization data could provide an advantage over FCM by discriminating genuine from coincidental events. A shown in Figure 2, IFC was effective in discerning coincidental events, both by individual visual inspection of the events, and through automated analysis using the IDEAS internalization mask with 1 pixel oversample (Fig. 3). Monocyte-platelet attachment was seen in 2.6% (SD 1.3%) of circulating monocytes by IFC and 4.2% (SD 0.9%) by FCM (P < 0.05). In addition, coincidental platelet localization without attachment to monocytes was seen in 4.8% (SD 1.5%) by IFC (Table 2). 275

Technical Note

Figure 1. Imaging cytometry analysis of monocyte platelet aggregates (MPAs). Monocytes are identified by monoclonal anti CD14 BV421 and platelets are identified by monoclonal anti CD42b APC. Using imaging cytometry, MPAs can be differentiated from coincidental events. AF488 labeled monoclonal anti CD62P is used to distinguish P-Selectin dependent and independent binding.

Following activation with TRAP, monocyte-platelet attachment was found in 79.5% (SD 10.3%) of monocytes by IFC and 73.4% (SD 8.7%) by FCM (P 5 ns). In addition, coincidental platelet localization without attachment was observed in 2.3% (SD 1.7%) of monocytes measured by IFC (Table 2). Overall, the bias between FCM and IFC was not significantly greater than zero (21.72 6 12.57, Fig. 2A) and there

was a very strong linear correlation between the methods (r2 5 0.98, P < 0.05, Fig. 2B). Analysis by IFC found 2.3% (SD 1.4%) of circulating monocytes with attached P-Selectin negative platelets as compared with 4.0% (SD 0.9%) as measured by FCM (P < 0.05) (Table 2). Following activation with TRAP, P-Selectin independent monocyte-platelet attachment was found in 0.6% (SD 0.5%) of monocytes by IFC in contrast with 3.2% (SD

Figure 2. Statistical comparison of monocyte-platelet aggregates (%MPAs) obtained by conventional flow cytometry (FACSCanto II) and imaging flow cytometry (ImageStreamX). The Bland Altman (A) plots each sample’s difference in %MPAs between measurement techniques (%MPAs as measured by FCM–%MPAs as measured by IFC) and %MPAs averaged from the techniques (mean of %MPAs as measured by FCM and %MPAs as measured by IFC). This shows that the bias (or mean difference between %MPAs between techniques) is 21.72, which was not found to be significantly greater than zero. The limits of agreement between the two methods are 21.72 6 12.57 (mean 6 1.96 standard deviations). (B) Plot of %MPAs as measured by FCM compared to IFC with linear regression model and mean 95% confidence interval. The r2 between the two techniques was 0.98 (P < 0.001 for correlation). The equation for the line of best fit was y 5 0.89x 1 2.15. (C) Bland Altman plots each sample’s difference in CD62P Negative MPAs and CD62P Negative MPAs averaged from IFC and FCM. This shows that the bias is 2.04, which was not significantly greater than zero. The limits of agreement between the two methods are 2.04 6 3.95. (D) Plot of % C62P Negative MPAs as measured by FCM as compared to IFC with linear regression model and mean 95% confidence interval. The r2 between the two techniques was 0.2 (P 5 n.s. for correlation).

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Measurement of Monocyte-Platelet Aggregates

Technical Note

Figure 3. Statistical comparison of monocyte-platelet aggregates (%MPAs) obtained by imaging flow cytometry (ImageStreamX) by individual visual inspection of each event and automated analysis using the IDEAS internalization mask with a 1 pixel oversample. The Bland Altman (A) plot shows that the bias (or mean difference between %MPAs between techniques) is 20.23, which was not significantly different than zero. The limits of agreement are 20.23 6 1.69 (mean 6 1.96 standard deviations). (B) Plot of %MPAs as measured by IFC by visual inspection compared to automated internalization masking with linear regression model and mean 95% confidence interval. The r2 between the two techniques was greater than 0.99 (P < 0.001 for correlation). The equation for the line of best fit was y 5 0.99x 1 0.17.

2.5%) of CD42b positive/CD62P negative monocyte events as measured by FCM (P < 0.05) (Table 2). Overall, bias between FCM and IFC determination of P-Selectin independent MPAs was not significantly greater than zero (2.04 6 3.95, Fig. 2C) but there was no significant correlation between the methods (r2 5 0.02, P 5 n.s., Fig. 2D). For IFC, visual determination of MPAs and coincident events had no significant bias (20.23 6 1.69, Fig. 3A) and correlated very strongly with automated analysis using the internalization feature of IDEAS (r2 > 0.99, P < 0.01, Fig. 3B).

DISCUSSION Here we detail a novel assay utilizing IFC to investigate monocyte-platelet aggregation in human blood. This technology, through the use of use of an automated internalization mask, is ideal for the analysis of these interactions, due to the ability to distinguish monocytes with nearby unattached platelets (coincident events) from true MPAs. Like FCM, IFC allows for analysis of a large number of cells in suspension. While differences in gating likely contributed to some of the difference in the determination of MPAs between FCM and IFC, the ability of IFC to automatically exclude coincident

(unattached platelet) events resulted in significantly lower determinations of circulating MPAs than by conventional FCM. This effect was entirely due to over-estimation of PSelectin negative circulating MPAs by FCM. Despite this difference there was strong correlation and minimal bias in the percentage of circulating and in vitro TRAP stimulated MPAs between the two methods. Over-estimation of P-Selectin negative MPAs by conventional FCM when compared with IFC was also apparent following in vitro activation of platelets with 50 mM of the PAR1 platelet thrombin receptor agonist TRAP, though this did not significantly affect the overall determination of MPAs, as the majority of these were CD62P positive. These results therefore demonstrate that greater accuracy in enumerating MPAs is obtained using IFC. Conventional FCM over-estimates the number of MPAs because of the presence of coincident events. Some sample centrifugation (280g for 5 min) was used in our imaging FCM method in order to increase the sample concentration for analysis and therefore increase the number of monocyte events collected over time. Because P-Selectin positive platelets can bind monocytes even after fixation, this opens the potential for preanalytical formation of artefact

Table 2. Comparison of populations as measured by conventional flow cytometry (FCM) and imaging flow cytometry (IFC)

Circulating

TRAP Stimulated

Coincident All MPAs CD62P Neg MPAs CD62P Pos MPAs Coincident All MPAs CD62P Neg MPAs CD62P Pos MPAs

FCM MEAN (S.D.)

IFC MEAN (S.D.)

N/A 4.2% (0.9%) 4.0% (0.9%) 0.2% (0.2%) N/A 73.4% (8.7%) 3.2% (2.5%) 70.2% (10.0%)

4.8% (1.5%) 2.6% (1.3%)a 2.3% (1.4%)a 0.3% (0.6%) 2.3% (1.7%) 79.5% (10.3%) 0.6% (0.5%)a 78.9% (9.9%)

Monocyte events with nearby unattached CD42b positive platelets were identified as coincident events by IFC, while all CD42b positive monocyte events were considered monocyte-platelet aggregates (MPAs) by FCM. a Indicates P < 0.05 between FCM and IFC.

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Technical Note MPAs. However, no significant bias was introduced by sample concentration in our study. This research confirms previous observations that a subpopulation (2.6% in this study) of circulating monocytes have a platelet attached (4,5,20). Using innovative IFC, we show that the vast majority of these are independent of P-Selectin, as described above; this population is over-estimated by conventional FCM. We are the first to distinguish P-Selectin positive and P-Selectin negative monocyte-platelet events, and show a clear advantage of IFC over conventional FCM in distinguishing these from coincidental events. The ability to distinguish P-Selectin negative MPAs from coincidental events is important, as studies have shown that platelet adhesion can impart a proatherogenic monocyte phenotype (7), but little is known about the role of P-Selectin in this process. Thus IFC represents an important tool in further studies of the mechanisms and significance of different monocyte-platelet interactions in the circulation. We highlight the potential for coincidental events by conventional FCM and illustrate the use of IFC to overcome this. We confirm previous estimations of coincidence events exceeding 30% (16,17). Indeed our data suggests the issue of coincidence may be even worse than previously estimated, with up to 65% of monocyte platelet associated monocyte events directly measured by IFC due to coincidence. However, because the depth of field of the Imagestream X was 120 mm, platelet signals that were completely overlaying monocytes occurred (Supporting Information Fig. 3, panel v). These had a high internalization score and were therefore included in MPA gate by the Internalization Feature calculation. While we were unable to determine if the platelets were tethered to monocytes, there was no clear rationale for designating these as coincidence events and therefore these were included as MPAs for statistical calculations. It is therefore possible that some untethered platelets were included as monocyte-platelet events with IFC. Future developments in 3D imaging FCM, such as light-sheet cytometry, may therefore further improve the ability to differentiate tethered and untethered monocyteplatelet events. The impact of coincidental events is greatest when measuring circulating (i.e., unstimulated) MPAs, and it is necessary to recognise the impact of these for the proper interpretation of results. While FCM still has a valuable role to play in the measurement of MPAs, and particularly where an agonist such as TRAP is used, our data demonstrate that IFC is a more accurate tool for the analysis of monocyte-platelet interactions than conventional FCM because it overcomes the

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over-estimation the of P-Selectin negative MPAs due to the presence of coincident events.

ACKNOWLEDGMENTS The authors acknowledge the facilities, and the scientific and technical assistance of the Australian Microscopy and Microanalysis Research Facility at the Centre for Microscopy, Characterisation and Analysis, The University of Western Australia.

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Measurement of Monocyte-Platelet Aggregates

Measurement of monocyte-platelet aggregates by imaging flow cytometry.

Platelets are subcellular blood elements with a well-established role in haemostasis. Upon activation platelets express P-Selectin (CD62P) on the cell...
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