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Review

Advances in Platelet Function Testing—Light Transmission Aggregometry and Beyond

by
Jessica Le Blanc
1,2,
François Mullier
3,
Caroline Vayne
4,5 and
Marie Lordkipanidzé
1,2,*
1
Montreal Heart Institute Research Center, Montréal, QC H1T 1C8, Canada
2
Faculty of Pharmacy, Université de Montréal, Montréal, QC H3C 3J7, Canada
3
Université catholique de Louvain, CHU UCL Namur, Namur Thrombosis and Hemostasis Center (NTHC), Hematology Laboratory, 5530 Yvoir, Belgium
4
Department of Hemostasis, University Hospital of Tours, 37044 Tours, France
5
EA 7501 GICC, University of Tours, 37000 Tours, France
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2020, 9(8), 2636; https://doi.org/10.3390/jcm9082636
Submission received: 1 July 2020 / Revised: 7 August 2020 / Accepted: 10 August 2020 / Published: 13 August 2020

Abstract

:
Platelet function testing is essential for the diagnosis of hemostasis disorders. While there are many methods used to test platelet function for research purposes, standardization is often lacking, limiting their use in clinical practice. Light transmission aggregometry has been the gold standard for over 60 years, with inherent challenges of working with live dynamic cells in specialized laboratories with independent protocols. In recent years, standardization efforts have brought forward fully automated systems that could lead to more widespread use. Additionally, new technical approaches appear promising for the future of specialized hematology laboratories. This review presents developments in platelet function testing for clinical applications.

1. Introduction

Over the last century, platelet function testing has undergone several transformations. From the development of the Duke–Ivy bleeding time [1], to the invention of the light transmission aggregometer by Gus Born [2], to the newer high throughput advances in platelet function testing [3,4], capturing platelets in their natural ability to form aggregates in response to vascular injury remains a challenge. This article presents recent developments in platelet function testing, and while it is difficult to predict which approaches will translate into widespread clinical hematology laboratory use, aspects of standardization, automation, and point-of-care devices will be specifically highlighted.

2. International Guidelines for Light Transmission Aggregometry Standardization

The invention of the now gold-standard light transmission aggregometer has rapidly revolutionized platelet function testing. The seminal papers by Gus Born in the 1960s describe this invention in the simplest terms [2,5,6,7], and while the technology has seen improvements in bench footprint and user-friendliness over the years, the underlying methodology has remained largely unchanged. The standard in the identification and diagnosis of primary hemostatic defects, light transmission aggregometry (LTA) continues to be time-, labour- and blood sample-intensive, rendering its use limited to specialized hematology laboratories. Even so, international surveys have regularly highlighted a lack of standardization in laboratory practices, making the results difficult to extrapolate to other centers [8,9,10]. Indeed, the Platelet Physiology Scientific and Standardization Committee of the International Society on Thrombosis and Haemostasis has conducted the largest global survey on LTA practices [8], including 359 laboratories from 48 countries. In this survey, the methodology of blood collection, processing and analysis were observed in each center. The results of their report clearly demonstrate the need for methodological standardization among the different centers worldwide. In order to overcome this issue, the International Society on Thrombosis and Haemostasis has published an expert consensus for LTA standardization. This guidance includes several statements on pre-clinical variables to be considered and recommendation for blood collection, preparation of the platelet-rich plasma sample and the choice of platelet agonists for testing [10]. While this publication provides recommendations about technical procedures, Hayward et al. have developed a consensus guide on how to interpret LTA results [9]. Finally, the British Committee for Standards in Haematology has also published a guide about clinical investigation of heritable platelet function disorders [11]. Altogether, these three guidelines bring important standardization of the methodology and the interpretation of light transmission aggregometry testing, but in the absence of international standard reagents, or a quality assessment program for LTA, standardization between laboratories remains a challenge. Table 1 illustrates the differences in the recommended concentrations of activators and ristocetin for LTA in international guidelines [9,10,11,12,13,14].

3. Light Transmission Aggregometry Revisited

Light transmission aggregometry remains the gold standard platelet function test for clinical diagnosis of platelet function disorders. This technique determines platelet aggregation percentage in platelet-rich plasma by measuring the increase in light transmission in response to the addition of a platelet agonist to the platelet suspension. Recently, two developments have simplified the experimental procedure and are worth noting. First, the automation of light transmission platelet aggregation using dedicated software on routine laboratory instruments (such as the Sysmex CS-2x00 series, Norderstedt, Germany) introduces the possibility of running light transmission aggregometry without dedicated experienced personnel. The automated assay allows the user to select the agonists and their concentrations to be tested, in accordance with institutional, national, or international guidelines. The instrument then carries out automated light transmission aggregometry. In instances where it has been compared head-to-head with traditional light transmission aggregometry (Table 2), the Sysmex CS-2000i has shown repeatability, reproducibility and agreement with traditional aggregometers [15,16,17,18]. While the technology is certainly advantageous in terms of labour requirement, it still calls for an appreciable volume of platelet-rich plasma (140 µL per test) albeit less than traditional aggregometry (200–500 µL per test) [16]. Notwithstanding, the automated acquisition allows for concentration-response curves to be generated more readily than with traditional aggregometry. While the gain in personnel time is undeniable, the time to run the assays has remained sensibly the same, and the cost of reagents and consumables is higher than that of traditional aggregometry. Interpretation continues to rely on expert examination of aggregation tracings from a patient, in comparison with tracings from a healthy control, by an experienced hematologist or clinical pharmacologist. However, it is foreseeable that the automated assay could be run in non-specialist centers, with the results sent to a tertiary center for interpretation. The standardization of the automated assay could also alleviate some of the issues around reproducibility among laboratories worldwide.
The second development worth mentioning is that of high-throughput 96- to 384-well based platelet function assays, which allow a much broader overview of platelet function in significantly less time [3,4,19]. The plate-based assays offer the advantage of reduced platelet-rich plasma volume (50–100 µL per test for 96-well plates, 10 µL per test for 384-well plates) and time, as all assays are carried out simultaneously. Platelet function can be measured either kinetically or as an endpoint (e.g., after five minutes) and the results are converted into percentage of aggregation in a similar fashion to traditional light transmission aggregometry-based results. The large number of simultaneous aggregations that can be run on a plate make concentration–response curves to numerous agonists easy to generate, although these can be hard to interpret [20,21]. These assays remain restricted to research laboratories. Direct comparison of the 96-well plate assay with traditional light transmission aggregometry has revealed that the assays behave slightly differently in their sensitivity to agonists despite a similar methodological framework [22,23]. While these plate-based assays could be used as a preliminary screening assay outside of specialized centers, they should not be regarded as a replacement for traditional aggregometry.

4. Evaluation of Granule Defects

There is evidence that dense granule secretion defects can be misdiagnosed if relying solely on platelet aggregometry [25,26]. A modified version of light transmission aggregometry, the lumi-aggregometer, provides information on platelet secretion in parallel with platelet aggregation measures [25]. In this method, the ATP secreted by platelets is quantified using a luciferin/luciferase assay, while aggregation data is collected as in classical LTA. The combined analysis of platelet aggregation and secretion function by lumi-aggregometry enhances the detection of platelet disorders affecting dense granule release [27].
Several instruments are available to measure lumi-aggregometry (Chronolog series). However, there are few reports available in the literature on the validation and the performance of lumi-aggregometry. Lumi-aggregometry is affected by several variables including: concentration of luciferin/luciferase, concentration of agonists, volume of PPP and PRP, concentration of ATP standard, duration of incubation, duration of measurement and adjustment of platelet count of the PRP. As for LTA, non-parametric analyses are the preferred method to establish reference intervals for lumi-aggregometry [9,28,29]. Lumi-aggregometry estimates of platelet dense granule ATP release have a considerably higher CV (around 20–30%) than LTA. A recent report on 150 unique subjects who had multiple ATP release tests has shown that normal findings with all tested agonists were often confirmed by the second test, but impaired release with multiple agonists was confirmed in only some subjects. Inconsistent findings were thus common. The finding of impaired ATP release with two or more agonists on both tests was not associated with an increased likelihood of a definite bleeding disorder [30]. The variability in platelet dense granule ATP release findings amongst patients assessed for diagnostic purposes suggests that the test has limited value for diagnosing platelet disorders. However, these results should be confirmed by other groups before drawing firm conclusions.
In addition, caution should be used in patients with Quebec Platelet Disorder when assessing ATP secretion with the use of the Chronolume® commercial reagent (containing 0.2 mg luciferin, 22,000 units d-luciferase plus magnesium sulphate, human serum albumin, stabilizers and buffer). Indeed, Hayward et al. found that addition of Chronolume® consistently induced a secondary wave of aggregation in response to epinephrine in platelets obtained from patients with Quebec Platelet Disorder, whereas assessment of aggregation without Chronolume® showed the expected absence of a secondary wave consistent with the pathology [31]. This finding however appeared to be restricted to patients with Quebec platelet disorder, as other investigators have reported no adverse effect of Chronolume® addition in investigating platelet disorders [32,33]. These findings come from relatively small cohorts, and there remains an unmet need for systematic evaluation and standardization of methodologies for clinical laboratories using lumi-aggregometry.
A limitation of platelet lumi-aggregometry is that it does not distinguish between dense granule deficiency and primary secretion defects that may also rely on defects in signaling pathways. Therefore, assessment of the endogenous content of dense granules alongside lumi-aggregometry is important [11]. For that purpose, several strategies have been proposed, such as the measurement of platelet serotonin or nucleotides levels, using liquid chromatography, mass spectroscopy, immunoassays or flow cytometry. However, whole-mount transmission electron microscopy (TEM), which allows the direct quantification of platelet dense granules due to their calcium content, remains the gold standard and has been the subject of recent standardization efforts [34]. Despite being less accessible than LTA or ATP release assays in routine testing, this technology appears to be more sensitive and reproducible for detecting dense granule deficiencies associated with a bleeding tendency [35]. Beyond simply counting the number of dense granules per platelet, the measurement of their diameter could also be of importance in TEM as nearly 30% of bleeding patients with a normal number of dense granules may have smaller granules, leading to a reduced storage pool volume [36]. The study of platelet ultrastructure in TEM, although more complex to implement, is also very useful for the characterization of various platelet defects associated with the cytoskeleton and granule abnormalities, such as gray platelet syndrome, Paris-Trousseau syndrome, storage pool diseases, MYH9-related thrombocytopenia, or Wiskott–Aldrich syndrome, but is reserved to highly specialized laboratories [37].
Also of importance, while lumi-aggregometry assesses dense granule secretion, it fails to address secretion from α-granules, lacking a chromo-genic component [38]. In addition to TEM methods that allow assessment of α-granule number and morphology, immunofluorescence assays on blood smears are emerging as promising tools to characterize platelet α-granules [39]. Fluorescence microscopy on blood smears has been proposed to facilitate the diagnosis of several inherited platelet disorders associated with changes in platelet proteins, such as Glanzmann thrombasthenia, Bernard Soulier disease, and delta storage pool deficiencies, or macrothrombocytopenia associated with filamin A, GFI1B and β1-tubuline anomalies, for example [40]. Based on the preparation of standard peripheral blood smears followed by immunofluorescence labeling of various platelet components, this approach may be of interest in non-specialized centers worldwide (that may ship blood smears by regular mail to a specialized center), and particularly in pediatric population since it requires very low volumes of blood (<100 µL).
Genetic screening has also become an integral part of evaluating a patient presenting with inherited bleeding and platelet disorders [41,42], and has identified a number of important transcription factors involved in granule biogenesis and maturation that lead to bleeding disorders. Although informative, genetic screening cannot be taken in isolation, as the phenotype or functional readout of genetic findings is hard to predict. Characterization of α-granule contents by simple (ELISA) or multiple (e.g., Luminex® or bead-based flow cytometry) immunological assays, can offer insights into the ability of platelets to secrete key vasoactive peptides in response to activation [27]. A frequently-used marker of α-granule fusion with the plasma membrane, flow cytometric assessment of P-selectin (CD62p) levels on platelets before and after activation with platelet agonists, may also indicate defects in α-granule biology. Although there is growing international expertise in assessing platelet α-granules, there are so far no universally accepted standardized assays that have been recommended in clinical practice [27].

5. Multiple Electrode Aggregometry

The impedance aggregometer has been described for the first time in 1980 [43]. In this technique, platelet aggregation is assessed by the change of electrical impedance in whole blood or in platelet-rich plasma, between two electrodes. Following agonist stimulation, platelets aggregate to the electrodes, impairing the conduction of electrical current between them. The development of a semi-automated system (Multiplate®, Roche Diagnostics) has allowed wide uptake of these instruments in hematology laboratories, especially for P2Y12 inhibitor monitoring [44]. Multiplate® may be used to assess risk of bleeding or thrombosis during prolonged dual antiplatelet therapy and to shorten the time window to surgery following P2Y12 inhibitor discontinuation [45,46,47]. There is evidence, albeit limited, to support the use of impedance aggregometry for the diagnosis of severe platelet function disorders [48,49,50]. However, multiple electrode aggregometry was shown to be inferior to LTA for the detection and discrimination of mild platelet function disorders [49,51,52,53], since it provides no information about platelet shape change and the reversibility of aggregation. As such, its use is not recommended as a screening test for the diagnosis of bleeding disorders. Finally, several preanalytical and analytical variables affect the results provided by the instrument, including time-interval since blood drawing and analysis, type of anticoagulant, and platelet count [54,55].

6. Detection of Platelet Activation Markers by Flow Cytometry

Flow cytometry is another popular technique for platelet phenotyping. In contrast to aggregometry methods that study dynamic platelet aggregation, flow cytometry can shed insight on the platelet activation status, through analysis of the expression of activation markers. Two studies have compared light transmission aggregometry, with the measure of the expression of P-selectin and activated GPIIbIIIa on the platelet surface by flow cytometry in the detection of inherited platelet disorders [56,57]. The results highlight that flow cytometry has the advantage of requiring a smaller volume of blood and to not require platelet-rich plasma preparation. With a negative predictive value of 87%, flow cytometry analysis has the potential to be used as a screening test to perform before light transmission aggregometry. Thus, both studies have concluded that this technique provides complementary information for platelet function defects, even though further validation and standardization tests are required before use in diagnostic laboratories. In this respect, the setting of an appropriate threshold when studying platelet activation, the use of mean/median fluorescence intensity levels vs. % of positive platelets for a certain biomarker, the nature and concentration of agonists used to induce platelet activation, the use of fixatives (either before or after platelet staining) can all influence interpretation of flow cytometry-based platelet assessments and require standardization [58].
Another potential useful flow cytometry assay is the mepacrine assay which permits an evaluation of the incorporation and secretion capacities of platelets. The mepacrine captured by the δ-granules is then secreted upon stimulation of the cells with various agonists. Platelet fluorescence can thus be quantified by flow cytometry before and after stimulation. Mepacrine assays may be used to exclude platelet dense granule deficiency [59,60]. However, the mepacrine assay is also affected by a lack of standardization. Some of the variables include mepacrine concentration, temperature of mepacrine incubation, concentration and type of agonist used to stimulate platelets and mode of result expression. Alternatively, δ-granules secretion may be assessed by measuring the expression of CD63, a protein naturally present in the membrane of lysosomes and δ-granules, and which is translocated on the platelet surface upon platelet activation [61]. As well as lumi-aggregometry, this approach does not differentiate between storage pool deficiency and primary secretion defects, and its combination with mepacrine has been proposed to better characterize dense granules disorders [60]. Finally, flow cytometry is also used to detect the expression of phosphatidylserine on activated platelets in the case of a suspected diagnosis of Scott syndrome [62].

7. Microfluidics and Microscopy

Understanding platelet function as it occurs within a vessel requires elements of flow to be taken into account. Several point-of-care assays have been developed over the years that incorporate an element of shear, including the PFA-100/200®, Impact®—the cone and platelet analyzer, and Placor® PRT. Of these, only the PFA-200® remains clinically available, despite being fairly insensitive for the detection of mild platelet function defects [11,63]. In research laboratories, parallel-plate flow chambers have been in use since the 1970s and have allowed multiple discoveries to be made regarding the behavior of platelets under physiological and pathological flow [4]. It is only recently that microfluidic devices have been developed for clinical laboratory use. The principle of the assay is simple; it requires blood to be flowed over a surface coated with a thrombogenic substrate (usually collagen) and the assessment of platelet deposition and thrombus growth by microscopy. The Total Thrombus-formation Analysis System (T-TAS 01®) is one such example in clinical laboratory use. It is a flow-microchip chamber with thrombogenic surfaces that easily generates images for two-dimensional analysis of area covered by thrombi, in an imitation of a vessel wall injury [64,65,66,67,68,69]. Similar to the closure time reported by the PFA-200®, the T-TAS 01® instrument reports the flow pressure waveform as the platelet plug obstructs blood flow through the microchip. Its dual-monitoring system adds real-time video imaging (Figure 1), which allows visual assessment of the thrombus formed under variable blood flow conditions. It has been successfully used for the diagnosis and characterization of von Willebrand disease [65], as a screening test for platelet storage pool disease [67,70], for monitoring of antiplatelet therapy [66,68,70], and for the prediction of periprocedural bleeding in patients undergoing coronary artery bypass surgery [71].
Several developments in the world of microfluidic devices are underway. A multi-modal approach with different coating proteins in addition to collagen appears promising to investigating platelet function under flow in clinical settings, including severe combined immune deficiency, Glanzmann thrombasthenia, Hermansky–Pudlak syndrome, MYH9-related disease, or grey platelet syndrome [72]. Microfluidic devices that incorporate endothelial cells could be useful in assessing platelet function in hematologic diseases such as sickle cell disease and hemolytic uremic syndrome [73]. These advances are yet to achieve sufficient standardization to reach the clinical laboratory, but they could dramatically change the way we assess platelet function in bleeding or thrombotic disorders.

8. Platelet Function Testing in Thrombocytopenia

It is not rare that low platelet counts hamper assessment of platelet function by traditional assays, as most platelet function tests are not reliable when platelet counts fall below the normal range [74]. Yet, previous research has shown that bleeding risk is not directly correlated with platelet count. For example, clinically significant bleeding occurred on 25% of days when platelet counts reached ≤ 5 × 109/L, 17% of days when platelet counts varied between 6 and 80 × 109/L, 13% of days when platelet counts ranged from 81 to 100 × 109/L, and 8% of days with platelet counts ≥ 100 × 109/L, in a study of patients with hematological or oncological disorders [75]. This suggests that other qualitative factors may contribute to the risk of bleeding in addition to low platelet counts in patients with thrombocytopenia.
Options for assessment of platelet function in patients with low and very low platelet counts rely on flow cytometric assays of platelet activation markers. In recent years, several flow cytometry approaches have been successfully used to assess platelet function in patients with severe chronic immune thrombocytopenia, showing that impaired platelet function is associated with bleeding, independent of platelet count [76,77,78]. However, it should also be noted that simply decreasing the platelet count induces an impaired platelet function phenotype as measured by flow cytometry, due to the loss of paracrine amplification of platelet responses by ADP release [79]. This indicates that flow cytometric assessment of platelet function is affected by platelet count, admittedly to a lesser extent than other traditional platelet function assays, and highlights the importance of deriving platelet-count-adjusted reference ranges.
Among the available platelet function assays, flow cytometry remains the superior approach to measure platelet function in thrombocytopenia, and several developments are underway to improve on the technology. One such example is the use of multiplex flow cytometry to assess the signaling pathways involved in platelet responses to multiple agonists [80]. Whether these high-throughput flow cytometry approaches will allow for the better characterization of platelet function in thrombocytopenia remains to be established. However, the technology is ready for implementation in larger cohorts, and possibly in patients with bleeding disorders.

9. Reference Ranges & Interpretation

An important part of a platelet function defect diagnosis remains establishing normal reference ranges for each test. Thus, the North American consensus guideline for medical laboratories performing LTA recommend to locally establish the normal range of the maximal aggregation percentage for each concentration of the activators, with a minimum of 40 healthy control volunteers [9]. While this recommendation compensates for technique variation between different medical centers, it is also noteworthy that several characteristics of the patient himself influence the normal reference range of platelet function testing results. Indeed, it is known that platelet count, structure and activity vary during aging [81,82], and that platelet function slightly differs between genders [82,83,84,85] and among different ethnic groups [85,86]. Other authors have also highlighted significant differences in aggregation curves in platelets of neonates and pregnant women compared to those of adults in the general population [87]. In an attempt to determine normal reference values in platelet function testing with flow cytometry, a study has found that the inter-individual variation is approximately 23% for P-selectin expression and 37% for αIIbβ3 activation [82], confirming the importance for the clinician to consider the characteristics of the patient for the interpretation of platelet function tests results. Finally, intra-individual day-to-day variation in platelet function are also observed, justifying the necessity to repeat at least once a test with an abnormal result to confirm the diagnosis.

10. Genetic Screening of Patients with Inherited Platelet Disorders

Genetic analyses are increasingly used in patients with bleeding disorders if there are strong clinical and biological arguments in favour of an inherited platelet function disorder [42]. The gene-target approach, which consists of studying a given gene, has long been used to confirm the diagnosis of various platelet function disorders associated with a typical biological and/or clinical phenotype, such a Glanzmann thrombasthenia or syndromic disorders for example. However, inherited platelet disorders are very heterogeneous, with more than 50 currently known genes [88], and in most cases, a single candidate gene cannot be found despite sometimes extensive family histories. In recent decades, next generation sequencing (NGS) has revolutionized the landscape of molecular diagnostics by increasing throughput, providing an unbiased genetic screen, and identifying rare variants not always accessible with other technologies [41]. Allowing much faster identification of known genetic defects, but also discovery of new defects, NGS is promising for overcoming diagnostic wandering [88]. However, it still suffers from limitations, such as lack of accessibility, high cost, and sometimes difficulties in assigning pathogenicity to novel identified variants, in addition to ethical debates around its use [41,89]. Notwithstanding, future laboratory assessment of individuals with inherited platelet function disorders will certainly rely on a mix of clinical, genetic, morphological, and functional investigations, altogether harnessing a more exhaustive platelet characterization.

11. Conclusions

There are still many challenges to accurately capturing in vivo platelet function with in vitro assays. Future assays will have to find a way to assess platelet interaction with the vessel wall, a likely contributor to certain bleeding phenotypes, independent of platelet aggregation profiles. The development of new platelet function assays is a high-risk endeavor, and not surprisingly, many assays that are promising in research settings do not make it into the clinical laboratory. Nevertheless, improvements in automation, standardization, and usability are likely to render platelet function testing available outside of specialized hematology laboratories in the next few years.

Author Contributions

Conceptualization, J.L.B., F.M., and M.L.; writing—original draft preparation, J.L.B., F.M., and M.L.; writing—review and editing, J.L.B., F.M., C.V., and M.L. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding. The APC was funded by MDPI. M.L. is a Fonds de Recherche du Québec en Santé (FRQS) Junior 1 Research Scholar (award #33048).

Acknowledgments

We are grateful to Thomas Lecompte for thoughtful suggestions and insights on this manuscript.

Conflicts of Interest

J.L.B. declares no conflict of interest. F.M. reports institutional fees from Stago, Werfen, Nodia, Sysmex and Bayer. He also reports speaker fees from Boehringer Ingelheim, Bayer Healthcare, Bristol-Myers Squibb-Pfizer, Werfen, Aspen, Sysmex and Stago, all outside the submitted work. C.V. reports a cooperation contract between Stago and the University of Tours and non-financial support from Shire, Sobi, Roche, CSL Behring, Takeda, all outside the present work. M.L. has received speaker honoraria from Bayer, has served on advisory boards for Servier, has participated in industry-funded trials from Idorsia, and has received in-kind and financial support for investigator-initiated grants from Leo Pharma, Roche Diagnostics, Aggredyne, and Fujimori Kogyo. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. Duke, W.W. The relation of blood platelets to hemorrhagic disease. Description of a method for determining the bleeding time and the coagulation time and report of three cases of hemorrahagic disease relieved by blood transfusion. JAMA 1910, 55, 1185–1192. [Google Scholar] [CrossRef]
  2. Born, G.V. Aggregation of blood platelets by adenosine diphosphate and its reversal. Nature 1962, 194, 927–929. [Google Scholar] [CrossRef] [PubMed]
  3. Chan, M.V.; Armstrong, P.C.; Warner, T.D. 96-well plate-based aggregometry. Platelets 2018, 29, 650–655. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Brouns, S.L.N.; van Geffen, J.P.; Heemskerk, J.W.M. High-throughput measurement of human platelet aggregation under flow: Application in hemostasis and beyond. Platelets 2018. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Clayton, S.; Born, G.V.; Cross, M.J. Inhibition of the Aggregation of Blood Platelets by Nucleosides. Nature 1963, 200, 138–139. [Google Scholar] [CrossRef]
  6. Born, G.V.; Hume, M. Effects of the numbers and sizes of platelet aggregates on the optical density of plasma. Nature 1967, 215, 1027–1029. [Google Scholar] [CrossRef]
  7. Born, G.V.; Cross, M.J. The Aggregation of Blood Platelets. J. Physiol. 1963, 168, 178–195. [Google Scholar] [CrossRef]
  8. Cattaneo, M.; Hayward, C.P.; Moffat, K.A.; Pugliano, M.T.; Liu, Y.; Michelson, A.D. Results of a worldwide survey on the assessment of platelet function by light transmission aggregometry: A report from the platelet physiology subcommittee of the SSC of the ISTH. J. Thromb. Haemost. 2009, 7, 1029. [Google Scholar] [CrossRef]
  9. Hayward, C.P.; Moffat, K.A.; Raby, A.; Israels, S.; Plumhoff, E.; Flynn, G.; Zehnder, J.L. Development of North American consensus guidelines for medical laboratories that perform and interpret platelet function testing using light transmission aggregometry. Am. J. Clin. Pathol. 2010, 134, 955–963. [Google Scholar] [CrossRef] [Green Version]
  10. Cattaneo, M.; Cerletti, C.; Harrison, P.; Hayward, C.P.; Kenny, D.; Nugent, D.; Nurden, P.; Rao, A.K.; Schmaier, A.H.; Watson, S.P.; et al. Recommendations for the Standardization of Light Transmission Aggregometry: A Consensus of the Working Party from the Platelet Physiology Subcommittee of SSC/ISTH. J. Thromb. Haemost. 2013, 11, 1183–1189. [Google Scholar] [CrossRef]
  11. Harrison, P.; Mackie, I.; Mumford, A.; Briggs, C.; Liesner, R.; Winter, M.; Machin, S. Guidelines for the laboratory investigation of heritable disorders of platelet function. Br. J. Haematol. 2011, 155, 30–44. [Google Scholar] [CrossRef]
  12. Christie, D. Platelet Function Testing by Aggregometry; Approved Guideline. Clin. Lab. Stand. Inst. 2008, 58, 17–19. [Google Scholar]
  13. Alessi, M.C.; Payrastre, B.; Sié, P. Intérêt et limites des tests d’agrégation pour le diagnostic des anomalies fonctionnelles plaquettaires constitutionnelles. Hématologie 2017, 23, 298–311. [Google Scholar] [CrossRef]
  14. Alessi, M.C.; Sié, P.; Payrastre, B. Strengths and Weaknesses of Light Transmission Aggregometry in Diagnosing Hereditary Platelet Function Disorders. J. Clin. Med. 2020, 9, 763. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Ling, L.Q.; Liao, J.; Niu, Q.; Wang, X.; Jia, J.; Zuo, C.H.; Jiang, H.; Zhou, J. Evaluation of an automated light transmission aggregometry. Platelets 2017, 28, 712–719. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Lawrie, A.S.; Kobayashi, K.; Lane, P.J.; Mackie, I.J.; Machin, S.J. The automation of routine light transmission platelet aggregation. Int J. Lab. Hematol. 2014, 36, 431–438. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Frere, C.; Kobayashi, K.; Dunois, C.; Amiral, J.; Morange, P.E.; Alessi, M.C. Assessment of platelet function on the routine coagulation analyzer Sysmex CS-2000i. Platelets 2018, 29, 95–97. [Google Scholar] [CrossRef]
  18. Bret, V.E.; Pougault, B.; Guy, A.; Castet, S.; Huguenin, Y.; Pillois, X.; James, C.; Fiore, M. Assessment of light transmission aggregometry on the routine coagulation analyzer Sysmex CS-2500 using CE-marked agonists from Hyphen Biomed. Platelets 2019, 30, 540–542. [Google Scholar] [CrossRef]
  19. Martins Lima, A.; Bragina, M.E.; Burri, O.; Bortoli Chapalay, J.; Costa-Fraga, F.P.; Chambon, M.; Fraga-Silva, R.A.; Stergiopulos, N. An optimized and validated 384-well plate assay to test platelet function in a high-throughput screening format. Platelets 2018. [Google Scholar] [CrossRef]
  20. Chan, M.V.; Armstrong, P.C.; Papalia, F.; Kirkby, N.S.; Warner, T.D. Optical multichannel (optimul) platelet aggregometry in 96-well plates as an additional method of platelet reactivity testing. Platelets 2011, 22, 485–494. [Google Scholar] [CrossRef]
  21. Chan, M.V.; Warner, T.D. Standardised optical multichannel (optimul) platelet aggregometry using high-speed shaking and fixed time point readings. Platelets 2012, 23, 404–408. [Google Scholar] [CrossRef] [PubMed]
  22. Lordkipanidzé, M.; Lowe, G.C.; Kirkby, N.S.; Chan, M.V.; Lundberg, M.H.; Morgan, N.V.; Bem, D.; Nisar, S.P.; Leo, V.C.; Jones, M.L.; et al. Characterization of multiple platelet activation pathways in patients with bleeding as a high-throughput screening option: Use of 96-well Optimul assay. Blood 2014, 123, e11–e22. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Chan, M.V.; Leadbeater, P.D.; Watson, S.P.; Warner, T.D. Not all light transmission aggregation assays are created equal: Qualitative differences between light transmission and 96-well plate aggregometry. Platelets 2018, 29, 686–689. [Google Scholar] [CrossRef] [PubMed]
  24. Stratmann, J.; Karmal, L.; Zwinge, B.; Miesbach, W. Platelet Aggregation Testing on a Routine Coagulation Analyzer: A Method Comparison Study. Clin. Appl. Thromb. Hemost. 2019, 25. [Google Scholar] [CrossRef] [Green Version]
  25. Pai, M.; Wang, G.; Moffat, K.A.; Liu, Y.; Seecharan, J.; Webert, K.; Heddle, N.; Hayward, C. Diagnostic usefulness of a lumi-aggregometer adenosine triphosphate release assay for the assessment of platelet function disorders. Am. J. Clin. Pathol. 2011, 136, 350–358. [Google Scholar] [CrossRef] [Green Version]
  26. Nieuwenhuis, H.K.; Akkerman, J.W.; Sixma, J.J. Patients with a prolonged bleeding time and normal aggregation tests may have storage pool deficiency: Studies on one hundred six patients. Blood 1987, 70, 620–623. [Google Scholar] [CrossRef] [Green Version]
  27. Mumford, A.D.; Frelinger, A.L., 3rd; Gachet, C.; Gresele, P.; Noris, P.; Harrison, P.; Mezzano, D. A review of platelet secretion assays for the diagnosis of inherited platelet secretion disorders. Thromb. Haemost. 2015, 114, 14–25. [Google Scholar] [CrossRef]
  28. Hayward, C.P.; Pai, M.; Liu, Y.; Moffat, K.A.; Seecharan, J.; Webert, K.E.; Cook, R.J.; Heddle, N.M. Diagnostic utility of light transmission platelet aggregometry: Results from a prospective study of individuals referred for bleeding disorder assessments. J. Thromb. Haemost. 2009, 7, 676–684. [Google Scholar] [CrossRef]
  29. Hayward, C.P.; Moffat, K.A.; Pai, M.; Liu, Y.; Seecharan, J.; McKay, H.; Webert, K.E.; Cook, R.J.; Heddle, N.M. An evaluation of methods for determining reference intervals for light transmission platelet aggregation tests on samples with normal or reduced platelet counts. Thromb. Haemost. 2008, 100, 134–145. [Google Scholar]
  30. Badin, M.S.; Graf, L.; Iyer, J.K.; Moffat, K.A.; Seecharan, J.L.; Hayward, C.P.M. Variability in platelet dense granule adenosine triphosphate release findings amongst patients tested multiple times as part of an assessment for a bleeding disorder. Int. J. Lab. Hematol. 2016, 38, 648–657. [Google Scholar] [CrossRef]
  31. Hayward, C.P.; Moffat, K.A.; Castilloux, J.F.; Liu, Y.; Seecharan, J.; Tasneem, S.; Carlino, S.; Cormier, A.; Rivard, G.E. Simultaneous measurement of adenosine triphosphate release and aggregation potentiates human platelet aggregation responses for some subjects, including persons with Quebec platelet disorder. Thromb. Haemost. 2012, 107, 726–734. [Google Scholar] [CrossRef]
  32. Callan, M.B.; Shofer, F.S.; Wojenski, C.; Giger, U. Chrono-lume and magnesium potentiate aggregation of canine but not human platelets in citrated platelet-rich plasma. Thromb. Haemost. 1998, 80, 176–180. [Google Scholar] [PubMed]
  33. Lordkipanidzé, M.; Lowe, G.C.; Watson, S.P. Simultaneous measurement of ATP release and LTA does not potentiate platelet aggregation to epinephrine. Thromb. Haemost. 2013, 110, 199–201. [Google Scholar] [CrossRef] [Green Version]
  34. Chen, D.; Uhl, C.B.; Bryant, S.C.; Krumwiede, M.; Barness, R.L.; Olson, M.C.; Gossman, S.C.; Erdogan Damgard, S.; Gamb, S.I.; Cummins, L.A.; et al. Diagnostic laboratory standardization and validation of platelet transmission electron microscopy. Platelets 2018, 29, 574–582. [Google Scholar] [CrossRef] [PubMed]
  35. Brunet, J.G.; Iyer, J.K.; Badin, M.S.; Graf, L.; Moffat, K.A.; Timleck, M.; Spitzer, E.; Hayward, C.P.M. Electron microscopy examination of platelet whole mount preparations to quantitate platelet dense granule numbers: Implications for diagnosing suspected platelet function disorders due to dense granule deficiency. In.t J. Lab. Hematol. 2018, 40, 400–407. [Google Scholar] [CrossRef] [PubMed]
  36. Gunning, W.T., 3rd; Raghavan, M.; Calomeni, E.P.; Turner, J.N.; Roysam, B.; Roysam, S.; Smith, M.R.; Kouides, P.A.; Lachant, N.A. A Morphometric Analysis of Platelet Dense Granules of Patients with Unexplained Bleeding: A New Entity of Delta-Microgranular Storage Pool Deficiency. J. Clin. Med. 2020, 9, 1734. [Google Scholar] [CrossRef] [PubMed]
  37. Clauser, S.; Cramer-Bordé, E. Role of platelet electron microscopy in the diagnosis of platelet disorders. Semin. Thromb. Hemost. 2009, 35, 213–223. [Google Scholar] [CrossRef] [PubMed]
  38. Karampini, E.; Bierings, R.; Voorberg, J. Orchestration of Primary Hemostasis by Platelet and Endothelial Lysosome-Related Organelles. Arter. Thromb. Vasc. Biol. 2020, 40, 1441–1453. [Google Scholar] [CrossRef]
  39. Zaninetti, C.; Greinacher, A. Diagnosis of Inherited Platelet Disorders on a Blood Smear. J. Clin. Med. 2020, 9, 539. [Google Scholar] [CrossRef] [Green Version]
  40. Greinacher, A.; Pecci, A.; Kunishima, S.; Althaus, K.; Nurden, P.; Balduini, C.L.; Bakchoul, T. Diagnosis of inherited platelet disorders on a blood smear: A tool to facilitate worldwide diagnosis of platelet disorders. J. Thromb. Haemost. 2017, 15, 1511–1521. [Google Scholar] [CrossRef] [Green Version]
  41. Ver Donck, F.; Downes, K.; Freson, K. Strengths and limitations of high-throughput sequencing for the diagnosis of inherited bleeding and platelet disorders. J. Thromb Haemost 2020, 18, 1839–1845. [Google Scholar] [CrossRef] [PubMed]
  42. Gresele, P. Diagnosis of inherited platelet function disorders: Guidance from the SSC of the ISTH. J. Thromb. Haemost. 2015, 13, 314–322. [Google Scholar] [CrossRef] [PubMed]
  43. Cardinal, D.C.; Flower, R.J. The electronic aggregometer: A novel device for assessing platelet behavior in blood. J. Pharm. Methods 1980, 3, 135–158. [Google Scholar] [CrossRef]
  44. Aradi, D.; Storey, R.F.; Komócsi, A.; Trenk, D.; Gulba, D.; Kiss, R.G.; Husted, S.; Bonello, L.; Sibbing, D.; Collet, J.P.; et al. Expert position paper on the role of platelet function testing in patients undergoing percutaneous coronary intervention. Eur. Heart J. 2014, 35, 209–215. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Hardy, M.; Dupuis, C.; Dincq, A.S.; Jacqmin, H.; Lecompte, T.; Mullier, F.; Lessire, S. Reduction of Preoperative Waiting Time before Urgent Surgery for Patients on P2Y(12) Inhibitors Using Multiple Electrode Aggregometry: A Retrospective Study. J. Clin. Med. 2020, 9, 424. [Google Scholar] [CrossRef] [PubMed]
  46. Frelinger, A.L., 3rd; Gachet, C.; Mumford, A.D.; Noris, P.; Mezzano, D.; Harrison, P.; Gresele, P. Laboratory monitoring of P2Y(12) inhibitors: Communication from the SSC of the ISTH. J. Thromb. Haemost. 2018, 16, 2341–2346. [Google Scholar] [CrossRef] [Green Version]
  47. Mahla, E.; Tantry, U.S.; Prüller, F.; Gurbel, P.A. Is There a Role for Preoperative Platelet Function Testing in Patients Undergoing Cardiac Surgery During Antiplatelet Therapy? Circulation 2018, 138, 2145–2159. [Google Scholar] [CrossRef]
  48. Awidi, A.; Maqablah, A.; Dweik, M.; Bsoul, N.; Abu-Khader, A. Comparison of platelet aggregation using light transmission and multiple electrode aggregometry in Glanzmann thrombasthenia. Platelets 2009, 20, 297–301. [Google Scholar] [CrossRef]
  49. Moenen, F.; Vries, M.J.A.; Nelemans, P.J.; van Rooy, K.J.M.; Vranken, J.; Verhezen, P.W.M.; Wetzels, R.J.H.; Ten Cate, H.; Schouten, H.C.; Beckers, E.A.M.; et al. Screening for platelet function disorders with Multiplate and platelet function analyzer. Platelets 2019, 30, 81–87. [Google Scholar] [CrossRef]
  50. Albanyan, A.; Al-Musa, A.; AlNounou, R.; Al Zahrani, H.; Nasr, R.; AlJefri, A.; Saleh, M.; Malik, A.; Masmali, H.; Owaidah, T. Diagnosis of Glanzmann thrombasthenia by whole blood impedance analyzer (MEA) vs. light transmission aggregometry. Int J. Lab. Hematol. 2015, 37, 503–508. [Google Scholar] [CrossRef]
  51. Al Ghaithi, R.; Drake, S.; Watson, S.P.; Morgan, N.V.; Harrison, P. Comparison of multiple electrode aggregometry with lumi-aggregometry for the diagnosis of patients with mild bleeding disorders. J. Thromb. Haemost. 2017, 15, 2045–2052. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Haas, T.; Cushing, M.M.; Varga, S.; Gilloz, S.; Schmugge, M. Usefulness of multiple electrode aggregometry as a screening tool for bleeding disorders in a pediatric hospital. Platelets 2019, 30, 498–505. [Google Scholar] [CrossRef] [PubMed]
  53. Sun, P.; McMillan-Ward, E.; Mian, R.; Israels, S.J. Comparison of light transmission aggregometry and multiple electrode aggregometry for the evaluation of patients with mucocutaneous bleeding. Int J. Lab. Hematol. 2019, 41, 133–140. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Rubak, P.; Skipper, M.T.; Larsen, O.H.; Hvas, A.M. Continuous exploration of parameters derived from multiple electrode platelet aggregometry is warranted. Thromb. Res. 2018, 164, 45–47. [Google Scholar] [CrossRef]
  55. Hardy, M.; Lessire, S.; Kasikci, S.; Baudar, J.; Guldenpfennig, M.; Collard, A.; Dogné, J.M.; Chatelain, B.; Jacqmin, H.; Lecompte, T.; et al. Effects of Time-Interval since Blood Draw and of Anticoagulation on Platelet Testing (Count, Indices and Impedance Aggregometry): A Systematic Study with Blood from Healthy Volunteers. J. Clin. Med. 2020, 9, 2515. [Google Scholar] [CrossRef]
  56. Navred, K.; Martin, M.; Ekdahl, L.; Zetterberg, E.; Andersson, N.G.; Strandberg, K.; Norstrom, E. A simplified flow cytometric method for detection of inherited platelet disorders-A comparison to the gold standard light transmission aggregometry. PLoS ONE 2019, 14, e0211130. [Google Scholar] [CrossRef] [Green Version]
  57. van Asten, I.; Schutgens, R.E.G.; Baaij, M.; Zandstra, J.; Roest, M.; Pasterkamp, G.; Huisman, A.; Korporaal, S.J.A.; Urbanus, R.T. Validation of flow cytometric analysis of platelet function in patients with a suspected platelet function defect. J. Thromb. Haemost. 2018, 16, 689–698. [Google Scholar] [CrossRef] [Green Version]
  58. Spurgeon, B.E.J.; Naseem, K.M. Platelet Flow Cytometry: Instrument Setup, Controls, and Panel Performance. Cytom. B Clin. Cytom. 2020, 98, 19–27. [Google Scholar] [CrossRef]
  59. van Asten, I.; Blaauwgeers, M.; Granneman, L.; Heijnen, H.F.G.; Kruip, M.; Beckers, E.A.M.; Coppens, M.; Eikenboom, J.; Tamminga, R.Y.J.; Pasterkamp, G.; et al. Flow cytometric mepacrine fluorescence can be used for the exclusion of platelet dense granule deficiency. J. Thromb. Haemost. 2020, 18, 706–713. [Google Scholar] [CrossRef] [Green Version]
  60. Cai, H.; Mullier, F.; Frotscher, B.; Briquel, M.E.; Toussaint, M.; Massin, F.; Lecompte, T.; Latger-Cannard, V. Usefulness of Flow Cytometric Mepacrine Uptake/Release Combined with CD63 Assay in Diagnosis of Patients with Suspected Platelet Dense Granule Disorder. Semin Thromb. Hemost. 2016, 42, 282–291. [Google Scholar] [CrossRef]
  61. Dovlatova, N.; Lordkipanidzé, M.; Lowe, G.C.; Dawood, B.; May, J.; Heptinstall, S.; Watson, S.P.; Fox, S.C. Evaluation of a whole blood remote platelet function test for the diagnosis of mild bleeding disorders. J. Thromb Haemost 2014, 12, 660–665. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  62. Halliez, M.; Fouassier, M.; Robillard, N.; Ternisien, C.; Sigaud, M.; Trossaert, M.; Bene, M.C. Detection of phosphatidyl serine on activated platelets’ surface by flow cytometry in whole blood: A simpler test for the diagnosis of Scott syndrome. Br. J. Haematol. 2015, 171, 290–292. [Google Scholar] [CrossRef] [PubMed]
  63. Hayward, C.P.; Harrison, P.; Cattaneo, M.; Ortel, T.L.; Rao, A.K. Platelet function analyzer (PFA)-100 closure time in the evaluation of platelet disorders and platelet function. J. Thromb. Haemost. 2006, 4, 312–319. [Google Scholar] [CrossRef] [PubMed]
  64. Miike, T.; Sakamoto, Y.; Sakurai, R.; Ohta, M.; Goto, A.; Imahase, H.; Yahata, M.; Umeka, M.; Koami, H.; Yamada, K.C.; et al. Effects of hyperbaric exposure on thrombus formation. Undersea Hyperb. Med. 2016, 43, 233–238. [Google Scholar] [PubMed]
  65. Daidone, V.; Barbon, G.; Cattini, M.G.; Pontara, E.; Romualdi, C.; Di Pasquale, I.; Hosokawa, K.; Casonato, A. Usefulness of the Total Thrombus-Formation Analysis System (T-TAS) in the diagnosis and characterization of von Willebrand disease. Haemophilia 2016, 22, 949–956. [Google Scholar] [CrossRef]
  66. Arima, Y.; Kaikita, K.; Ishii, M.; Ito, M.; Sueta, D.; Oimatsu, Y.; Sakamoto, K.; Tsujita, K.; Kojima, S.; Nakagawa, K.; et al. Assessment of platelet-derived thrombogenicity with the total thrombus-formation analysis system in coronary artery disease patients receiving antiplatelet therapy. J. Thromb. Haemost. 2016, 14, 850–859. [Google Scholar] [CrossRef]
  67. Minami, H.; Nogami, K.; Ogiwara, K.; Furukawa, S.; Hosokawa, K.; Shima, M. Use of a microchip flow-chamber system as a screening test for platelet storage pool disease. Int J. Hematol. 2015, 102, 157–162. [Google Scholar] [CrossRef]
  68. Hosokawa, K.; Ohnishi, T.; Miura, N.; Sameshima, H.; Koide, T.; Tanaka, K.A.; Maruyama, I. Antithrombotic effects of PAR1 and PAR4 antagonists evaluated under flow and static conditions. Thromb. Res. 2014, 133, 66–72. [Google Scholar] [CrossRef]
  69. Yamaguchi, Y.; Moriki, T.; Igari, A.; Matsubara, Y.; Ohnishi, T.; Hosokawa, K.; Murata, M. Studies of a microchip flow-chamber system to characterize whole blood thrombogenicity in healthy individuals. Thromb. Res. 2013, 132, 263–270. [Google Scholar] [CrossRef]
  70. Al Ghaithi, R.; Mori, J.; Nagy, Z.; Maclachlan, A.; Hardy, L.; Philippou, H.; Hethershaw, E.; Morgan, N.V.; Senis, Y.A.; Harrison, P. Evaluation of the Total Thrombus-Formation System (T-TAS): Application to human and mouse blood analysis. Platelets 2019, 30, 893–900. [Google Scholar] [CrossRef]
  71. Oimatsu, Y.; Kaikita, K.; Ishii, M.; Mitsuse, T.; Ito, M.; Arima, Y.; Sueta, D.; Takahashi, A.; Iwashita, S.; Yamamoto, E.; et al. Total Thrombus-formation Analysis System Predicts Periprocedural Bleeding Events in Patients With Coronary Artery Disease Undergoing Percutaneous Coronary Intervention. J. Am. Heart Assoc. 2017, 6. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  72. de Witt, S.M.; Swieringa, F.; Cavill, R.; Lamers, M.M.; van Kruchten, R.; Mastenbroek, T.; Baaten, C.; Coort, S.; Pugh, N.; Schulz, A.; et al. Identification of platelet function defects by multi-parameter assessment of thrombus formation. Nat. Commun. 2014, 5, 4257. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  73. Tsai, M.; Kita, A.; Leach, J.; Rounsevell, R.; Huang, J.N.; Moake, J.; Ware, R.E.; Fletcher, D.A.; Lam, W.A. In vitro modeling of the microvascular occlusion and thrombosis that occur in hematologic diseases using microfluidic technology. J Clin. Invest. 2012, 122, 408–418. [Google Scholar] [CrossRef] [Green Version]
  74. Lordkipanidze, M. Platelet Function Tests. Semin. Thromb. Hemost. 2016, 42, 258–267. [Google Scholar] [CrossRef] [PubMed]
  75. Slichter, S.J.; Kaufman, R.M.; Assmann, S.F.; McCullough, J.; Triulzi, D.J.; Strauss, R.G.; Gernsheimer, T.B.; Ness, P.M.; Brecher, M.E.; Josephson, C.D.; et al. Dose of prophylactic platelet transfusions and prevention of hemorrhage. N. Engl. J. Med. 2010, 362, 600–613. [Google Scholar] [CrossRef] [Green Version]
  76. De Cuyper, I.M.; Meinders, M.; van de Vijver, E.; de Korte, D.; Porcelijn, L.; de Haas, M.; Eble, J.A.; Seeger, K.; Rutella, S.; Pagliara, D.; et al. A novel flow cytometry-based platelet aggregation assay. Blood 2013, 121, e70–e80. [Google Scholar] [CrossRef] [Green Version]
  77. van Bladel, E.R.; Laarhoven, A.G.; van der Heijden, L.B.; Heitink-Polle, K.M.; Porcelijn, L.; van der Schoot, C.E.; de Haas, M.; Roest, M.; Vidarsson, G.; de Groot, P.G.; et al. Functional platelet defects in children with severe chronic ITP as tested with 2 novel assays applicable for low platelet counts. Blood 2014, 123, 1556–1563. [Google Scholar] [CrossRef] [Green Version]
  78. Frelinger, A.L., 3rd; Grace, R.F.; Gerrits, A.J.; Berny-Lang, M.A.; Brown, T.; Carmichael, S.L.; Neufeld, E.J.; Michelson, A.D. Platelet function tests, independent of platelet count, are associated with bleeding severity in ITP. Blood 2015. [Google Scholar] [CrossRef] [Green Version]
  79. Boknäs, N.; Macwan, A.S.; Södergren, A.L.; Ramström, S. Platelet function testing at low platelet counts: When can you trust your analysis? Res. Pr. Thromb. Haemost. 2019, 3, 285–290. [Google Scholar] [CrossRef] [Green Version]
  80. Spurgeon, B.E.J.; Naseem, K.M. High-Throughput Signaling Profiling in Blood Platelets by Multiplexed Phosphoflow Cytometry. Methods Mol. Biol. 2018, 1812, 95–111. [Google Scholar] [CrossRef]
  81. Le Blanc, J.; Lordkipanidzé, M. Platelet Function in Aging. Front. Cardiovasc. Med. 2019, 6, 109. [Google Scholar] [CrossRef] [PubMed]
  82. Huskens, D.; Sang, Y.; Konings, J.; van der Vorm, L.; de Laat, B.; Kelchtermans, H.; Roest, M. Standardization and reference ranges for whole blood platelet function measurements using a flow cytometric platelet activation test. PLoS ONE 2018, 13, e0192079. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  83. Otahbachi, M.; Simoni, J.; Simoni, G.; Moeller, J.F.; Cevik, C.; Meyerrose, G.E.; Roongsritong, C. Gender differences in platelet aggregation in healthy individuals. J. Thromb. Thrombolysis 2010, 30, 184–191. [Google Scholar] [CrossRef] [PubMed]
  84. Cowman, J.; Dunne, E.; Oglesby, I.; Byrne, B.; Ralph, A.; Voisin, B.; Müllers, S.; Ricco, A.J.; Kenny, D. Age-related changes in platelet function are more profound in women than in men. Sci. Rep. 2015, 5, 12235. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  85. Miller, C.H.; Rice, A.S.; Garrett, K.; Stein, S.F. Gender, race and diet affect platelet function tests in normal subjects, contributing to a high rate of abnormal results. Br. J. Haematol. 2014, 165, 842–853. [Google Scholar] [CrossRef] [Green Version]
  86. Edelstein, L.C.; Simon, L.M.; Montoya, R.T.; Holinstat, M.; Chen, E.S.; Bergeron, A.; Kong, X.; Nagalla, S.; Mohandas, N.; Cohen, D.E.; et al. Racial differences in human platelet PAR4 reactivity reflect expression of PCTP and miR-376c. Nat. Med. 2013, 19, 1609–1616. [Google Scholar] [CrossRef] [Green Version]
  87. Gader, A.M.; Bahakim, H.; Jabbar, F.A.; Lambourne, A.L.; Gaafar, T.H.; Edrees, Y.B. Dose-response aggregometry in maternal/neonatal platelets. Thromb. Haemost. 1988, 60, 314–318. [Google Scholar] [CrossRef]
  88. Mumford, A.; Westbury, S. Genetic Techniques Used in the Diagnosis of Inherited Platelet Disorders. Semin. Thromb. Hemost. 2019, 45. [Google Scholar] [CrossRef]
  89. Lentaigne, C.; Freson, K.; Laffan, M.A.; Turro, E.; Ouwehand, W.H. Inherited platelet disorders: Toward DNA-based diagnosis. Blood 2016, 127, 2814–2823. [Google Scholar] [CrossRef] [Green Version]
Figure 1. Principle of the T-TAS® system for simplified evaluation of platelet plug formation in clinical laboratories. Reproduced with permission from Fujimori Kogyo, Co. Ltd, Tokyo, Japan.
Figure 1. Principle of the T-TAS® system for simplified evaluation of platelet plug formation in clinical laboratories. Reproduced with permission from Fujimori Kogyo, Co. Ltd, Tokyo, Japan.
Jcm 09 02636 g001
Table 1. Recommended concentrations of activators and ristocetin for LTA in international guidelines.
Table 1. Recommended concentrations of activators and ristocetin for LTA in international guidelines.
Final ConcentrationChristie et al., 2008 [12]Hayward et al., 2010 [9]Harrison et al., 2011 [11]Cattaneo et al., 2013 [10]Alessi et al., 2017 [13]Alessi et al., 2020 [14]
ADP2 µM XXXXX
5 µMX X
10 µM X XX
100 µM XX
Collagen1 µg/mL X
2 µg/mL X XXX
10 µg/mL XX
25 µg/mL X
Type: Type I fibrillaryType IHormNot mentionedNot mentioned
Epinephrine5 µMXXXXXX
10 µM X X
25 µM XX
TRAPs10 µM XXXX
50 µM XX
Type: PAR-1 (SFLLRN) 10–100 µM and PAR-4 (AYPGKF) 100–500 µM TRAPsPAR1 (-AP)TRAP-6TRAP-6
Arachidonic Acid1 mMX (0.5–1.6)X (0.5–1.6)X (0.5–1.0)XXX
Thromboxane A2 analog U66191 µMXXXXXX
2 µMX
3μM XX
5 µM X
10 µM XX
RistocetinLow dose≤0.60.5–0.60.5–0.7
High dose0.8–1.51.2–1.51.2–1.51.2 *1.2 *1.2 *
Collagen-related peptide (CRP)- Convulxin1 µg/mL Concentration not provided
2 µg/mL X (0.01-1)
Gamma-thrombin50–200 ng/mL X
Ca-ionophore A231871.25–10 µmol/L X
Phorbol 12-myristate 13-acetate (PMA)30 nmol/L X Concentration not provided
  • Jcm 09 02636 i001 Screening
  • Jcm 09 02636 i002 To diagnose complete P2Y12 deficiencies
  • Jcm 09 02636 i003 Can allow the TP receptor to be distinguished from TXA2 synthesis deficiencies
  • Jcm 09 02636 i004 If arachidonic acid aggregation is abnormal
  • Jcm 09 02636 i005 If γ thrombin is abnormal
  • Jcm 09 02636 i006 If abnormalities in the thrombin receptors, Receptors targets: Calcium mobilization and procoagulant function
  • Jcm 09 02636 i007 To check the correct functioning of the PKC pathway
  • Jcm 09 02636 i008 GPVI specific activator
  • Jcm 09 02636 i009 To diagnose CalDAG-GEFI deficiency
  • Jcm 09 02636 i010 Thrombin receptors but without clotting
* If normal, then 0.5–0.7 mg/mL; if absent then 2 mg/mL
Table 2. Summary of studies comparing the automated light transmission aggregometers to traditional devices.
Table 2. Summary of studies comparing the automated light transmission aggregometers to traditional devices.
StudyAssessed InstrumentReference InstrumentSamplesAgonistsResultsComments on Assessed Instrument
LQ. Ling et al. [15]Sysmex
CS-2100i
Chrono-log Model 700 (Chrono-log Corporation, Havertown, PA, USA)Pooled PRP from healthy subjects (n=8-10)ADP 5 µM, AA 500 µg/mL, Col 2.5 µg/mL, Epi 5.4 µM, Risto 1.5 mg/mL (Hyphen Biomed)Strong correlation between both instruments (Pearson’s r: 0.69 to 0.88)
Good repeatability of CS-2100i (MA CV < 10%)
Inhibitory effect of PPP on aggregation induced by ADP, AA, Col, Epi
Inhibitory effect of PS on ristocetin-induced aggregation
Short turnaround time
Low PC requirement in PRP: 80 × 109/L
AS. Lawrie et al. [16] Sysmex
CS-2100i
AggRAM aggregometer (Helena Laboratories, Beaumont, TX, USA) PRP from healthy subjects or patients on NSAID (n = 14) or clopidogrel (n = 2)ADP 0.5–10 µM, AA 0.12–1.0 mM
Col 0.5–10 μg/mL, Epi 0.5–10 µM, Risto 0.75–1.25 mg/mL (Hyphen Biomed)
Comparable dose-responses with each of the agonists with both instruments
Comparable aggregation traces with samples from subjects under NSAID or clopidogrel
Similar aggregation imprecision (MA and slope CV to ADP: 3–12%)
Influence of cuvette stirrer speed on the reaction sensitivity: optimum speed of 800 rpm
No clinically significant changes in aggregation response for PC ranging from 150–480 × 109/L in PRP, but poor sensitivity in case of PC <100 × 109/L
C. Frère et al. [17]Sysmex
CS-2100i
APACT-4004 aggregometer (LABiTec, Ahrensburg, Germany)PRP from patients suspected from PFD (n = 46) or with ACS (n = 62) receiving dual antiplatelet therapyADP 2.5–10 μM, AA 0.5 mg/mL, Col 3.3 μg/mL, Epi 10 μM, Risto1.25 mg/mL (Hyphen Biomed)Significant correlations between both instruments (Pearson’s r: 0.38 to 0.98)
Similar aggregation profiles with both systems in patients with bleedings (including 1 GT patient)
Strong inter-agreement rates to detect low responders to thienopyridines or aspirin (weighted kappa> 0.70)
Good intra-serial imprecision of CS-2000i (MA CV < 5% for each agonist)
Cuvette stirrer speed: 800 rpm
VE. Bret et al. [18]Sysmex CS-2500APACT-4004 aggregometer
(LABiTec, Ahrensburg, Germany)
PRP from patients with suspected PFD, vWD or antiplatelet therapy (n = 49)ADP 0.5–10 μM, AA 1 mM, Col 2 μg/mL, Risto 0.625 and 1.2 mg/mL (Hyphen Biomed)Significant correlation between the two aggregometers (Passing and Bablok’s r: 0.48 to 0.90)
More variable response using low concentrations of ADP (≤5 μM) with Sysmex CS-2500
Discrepancies with the low dose of ristocetin: excessive paradoxical agglutination with the Sysmex CS-2500
Good intra-serial imprecision of CS-2500 (MA CV to ADP: 1.5%)
Cuvette stirrer speed: 600 rpm
J. Stratmann et al. [24]Sysmex
CS-2100i
APACT-4004 aggregometer
(LABiTec, Ahrensburg, Germany)
PRP from healthy subjects (n = 61) and from patients with known bleeding disorder (n = 20) or antiplatelet therapy (n = 42)ADP 5 μM, AA 1 mM, Risto 1 mg/mL), Col 2 μg/mL, Epi 5 μM (Hyphen Biomed)Significant MA correlation between both instruments with all subgroups and agonists tested (Pearson’s r ≥ 0.85)
Weak or no correlation between both instruments in regard to lag time (Pearson’s r < 0.20)
Systematic bias to lower measurements below a threshold of 50% MA with the CS-2100i
Successful identification of patients with known bleeding disorder or antiplatelet therapy using the CS-2100i
Non-adjusted PRP
Reading period of 600 s
AA: arachidonic acid, ACS: acute coronary syndrome, ADP: adenosine diphosphate, Col: collagen, CV: coefficient of variation, Epi: epinephrine, GT: Glanzmann thrombasthenia, MA: maximum aggregation, NSAID: nonsteroidal anti-inflammatory drugs (i.e., aspirin or ibuprofen), PC: platelet count, PFD: platelet function disorder, PPP: platelet poor plasma, PRP: platelet rich plasma, PS: physiological saline, Risto: ristocetin, vWD: von Willebrand Disease.

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MDPI and ACS Style

Le Blanc, J.; Mullier, F.; Vayne, C.; Lordkipanidzé, M. Advances in Platelet Function Testing—Light Transmission Aggregometry and Beyond. J. Clin. Med. 2020, 9, 2636. https://doi.org/10.3390/jcm9082636

AMA Style

Le Blanc J, Mullier F, Vayne C, Lordkipanidzé M. Advances in Platelet Function Testing—Light Transmission Aggregometry and Beyond. Journal of Clinical Medicine. 2020; 9(8):2636. https://doi.org/10.3390/jcm9082636

Chicago/Turabian Style

Le Blanc, Jessica, François Mullier, Caroline Vayne, and Marie Lordkipanidzé. 2020. "Advances in Platelet Function Testing—Light Transmission Aggregometry and Beyond" Journal of Clinical Medicine 9, no. 8: 2636. https://doi.org/10.3390/jcm9082636

APA Style

Le Blanc, J., Mullier, F., Vayne, C., & Lordkipanidzé, M. (2020). Advances in Platelet Function Testing—Light Transmission Aggregometry and Beyond. Journal of Clinical Medicine, 9(8), 2636. https://doi.org/10.3390/jcm9082636

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