Subscribe to RSS
DOI: 10.1160/TH15-10-0799
Counting the platelets: a robust and sensitive quantification method for thrombus formation
Financial support: This study was supported by a grant from the Swedish Research Council, Project No K2015–79X-22644–01–3 and by Linköping University.Publication History
Received:
15 October 2015
Accepted after major revision:
25 January 2016
Publication Date:
27 November 2017 (online)
![](https://www.thieme-connect.de/media/10.1055-s-00035024/201606/lookinside/thumbnails/10-1160-th15-10-0799-1.jpg)
Summary
Flow chambers are common tools used for studying thrombus formation in vitro. However, the use of such devices is not standardised and there is a large diversity among the flow chamber systems currently used, and also in the methods used for quantifying the thrombus development. It was the study objective to evaluate a new method for analysis and quantification of platelet thrombus formation that can facilitate comparison of results between research groups. Whole blood was drawn over a collagen patch in commercial Ibid or in-house constructed PDMS flow chambers. Five percent of the platelets were fluorescently labelled and z-stack time-lapse images were captured during thrombus formation. Images were processed in a Python script in which the number of platelets and their respective x-, yand z-positions were obtained. For comparison with existing methods the platelets were also labelled and quantified using fluorescence intensity and thrombus volume estimations by confocal microscopy. The presented method was found less sensitive to microscope and image adjustments and provides more details on thrombus development dynamics than the methods for measuring fluorescence intensity and thrombus volume estimation. The platelet count method produced comparable results with commercial and PDMS flow chambers, and could also obtain information regarding the stability of each detected platelet in the thrombus. In conclusion, quantification of thrombus formation by platelet count is a sensitive and robust method that enables measurement of platelet accumulation and platelet stability in an absolute scale that could be used for comparisons between research groups.
-
References
- 1 Ruggeri ZM. Platelets in atherothrombosis. Nat Med 2002; 08: 1227-1234.
- 2 Van Kruchten R, Cosemans JMEM, Heemskerk JWM. Measurement of whole blood thrombus formation using parallel-plate flow chambers - a practical guide. Platelets 2012; 23: 229-242.
- 3 Tovar-Lopez FJ, Rosengarten G, Westein E. et al. A microfluidics device to monitor platelet aggregation dynamics in response to strain rate micro-gradients in flowing blood. Lab Chip 2010; 10: 291-302.
- 4 Li R, Diamond SL. Detection of platelet sensitivity to inhibitors of COX-1, P2Y1, and P2Y12 using a whole blood microfluidic flow assay. Thromb Res 2014; 133: 203-210.
- 5 Lee H, Sturgeon S, Jackson SP. et al. The contribution of thrombin-induced platelet activation to thrombus growth is diminished under pathological blood shear conditions. Thromb Haemost 2012; 107: 328-337.
- 6 Roest M, Reininger A, Zwaginga JJ. et al. Flow chamber-based assays to measure thrombus formation in vitro: requirements for standardisation. J Thromb Haemost 2011; 09: 2322-2324.
- 7 Duffy DC, McDonald JC, Schueller OJa. et al. Rapid prototyping of microfluidic systems in poly(dimethylsiloxane). Anal Chem 1998; 70: 4974-4984.
- 8 Maloney SF, Brass LF, Diamond SL. P2Y12 or P2Y1 inhibitors reduce platelet deposition in a microfluidic model of thrombosis while apyrase lacks efficacy under flow conditions. Integr Biol 2010; 02: 183-192.
- 9 Neeves KB, Maloney SF, Fong KP. et al. Microfluidic focal thrombosis model for measuring murine platelet deposition and stability: PAR4 signaling enhances shear-resistance of platelet aggregates. J Thromb Haemost 2008; 06: 2193-2201.
- 10 De Witt SM, Swieringa F, Cavill R. et al. Identification of platelet function defects by multi-parameter assessment of thrombus formation. Nat Commun Nature Publishing Group 2014; 05: 4257.
- 11 Ono A, Westein E, Hsiao S. et al. Identification of a fibrin-independent platelet contractile mechanism regulating primary hemostasis and thrombus growth. Blood 2008; 112: 90-99.
- 12 Westein E, van der Meer AD, Kuijpers MJE. et al. Atherosclerotic geometries exacerbate pathological thrombus formation poststenosis in a von Willebrand factor-dependent manner. Proc Natl Acad Sci USA 2013; 110: 1357-1362.
- 13 Van Der Walt S, Colbert SC, Varoquaux G. The NumPy array: A structure for efficient numerical computation. Comput Sci Eng 2011; 13: 22-30.
- 14 Jones E, Oliphant E, Peterson P. SciPy: Open Source Scientific Tools for Python [Internet]. 2001 Available at: http://www.scipy.org
- 15 McKinney W. Data Structures for Statistical Computing in Python. Proc 9th Python Sci Conf [Internet] 2010; 1697900: 51-6. Available at: http://confer-ence.scipy.org/proceedings/scipy2010/mckinney.html
- 16 Hunter JD. Matplotlib: A 2D graphic environment Comput Sci Eng [Internet] 2007; 09: 90-5. Available at: http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?arnumber=4160265
- 17 Waskom M. Seaborn: statistical data visualisation [Internet]. 2012 Available at: http://stanford.edu/∼mwaskom/software/seaborn/