Comparative morphology analysis of live blood platelets using scanning ion conductance and robotic dark-field microscopy.

Many conventional microscopy techniques for investigating platelet morphology such as electron or fluorescence microscopy require highly invasive treatment of the platelets such as fixation, drying and metal coating or staining. Here, we present two unique but entirely different microscopy techniques for direct morphology analysis of live, unstained platelets: scanning ion conductance microscopy (SICM) and robotic dark-field microscopy (RDM). We demonstrate that both techniques allow for a quantitative evaluation of the morphological features of live adherent platelets. We show that their morphology can be quantified by both techniques using the same geometric parameters and therefore can be directly compared. By imaging the same identical platelets subsequently with SICM and RDM, we found that area, perimeter and circularity of the platelets are directly correlated between SICM and dark-field microscopy (DM), while the fractal dimension (FD) differed between the two microscopy techniques. We show that SICM and RDM are both valuable tools for the ex vivo investigation of the morphology of live platelets, which might contribute to new insights into the physiological and pathophysiological role of platelet spreading.

Fractal and Euclidean descriptors of platelet shape.

Platelet shape change is a dynamic membrane surface process that exhibits remarkable morphological heterogeneity. Once the outline of an irregular shape is identified and segmented from a digital image, several mathematical descriptors can be applied to numerical characterize the irregularity of the shapes surface. 13072 platelet outlines (PLO) were segmented automatically from 1928 microscopic images using a newly developed algorithm for the software product Matlab R2012b. The fractal dimension (FD), circularity, eccentricity, area and perimeter of each PLO were determined. 972 PLO were randomly assigned for computer-assisted manual measurement of platelet diameter as well as number, width and length of filopodia per platelet. FD can be used as a surrogate parameter for determining the roughness of the PLO and circularity can be used as a surrogate to estimate the number and length of filopodia. The relationship between FD and perimeter of the PLO reveals the existence of distinct groups of platelets with significant structural differences which may be caused by platelet activation. This new method allows for the standardized continuous numerical classification of platelet shape and its dynamic change, which is useful for the analysis of altered platelet activity (e.g. inflammatory diseases, contact activation, drug testing).

A New Method for Measuring the Dynamic Shape Change of Platelets.

BACKGROUND: Platelet shape change is a dynamic process that has been classified in different types. Exact documentation of platelet structure needs an improved method of measuring platelet shape. METHODS: 10 µl of platelet-rich plasma (PRP) from anticoagulated whole blood (3.2% buffered sodium citrate 0.105 mol/l) was put onto a glass slide covered with a cover slip. By use a of dark field light microscope connected with a CMOS-Camera a photographic snap-shot was taken after 5 and 30 min. Diameter of platelets and length of filopodia were measured with a self-developed plugin for ImageJ software. Statistic calculation was performed with Excel WinSTAT Microsoft software. RESULTS: We showed a swelling of the granulomer from 2.06 ± 0.56 µm to 2.33 ± 0.59 µm (p < 0.05), a reduction of pseudopodia (2.10 ± 0.94 vs. 1.78 ± 1.04 µm; p < 0.05) in conjunction with an increase of hyalomer diameter from 3.29 ± 0.83 to 3.50 ± 0.85 µm (p < 0.05), and an increase of pseudopodia length from 2.68 ± 1.45 µm to 3.67 ± 1.79 µm (p < 0.005) in conjunction with an increase of hyalomer diameter from 6.58 ± 1.91 µm to 7.94 ± 1.87 µm (p < 0.05). CONCLUSION: We revealed and documented a dynamic change of platelet size and filopodia structure in PRP. This method allows an exact analysis of platelet size and surface structures.

Multivariate Platelet Analysis Differentiates Between Patients with Alzheimer's Disease and Healthy Controls at First Clinical Diagnosis.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) is challenging, and easily accessible biomarkers are an unmet need. Blood platelets frequently serve as peripheral model for studying AD pathogenesis and might represent a reasonable biomarker source. OBJECTIVE: In the present study, we investigated the potential to differentiate AD patients from healthy controls (HC) based on blood count, platelet morphology, and function as well as molecular markers at the time of first clinical diagnosis. METHODS: Blood samples from 40 AD patients and 29 age-matched HC were included for determination of 78 parameter by blood counting, platelet morphometry, aggregometry, flow cytometry (CD62P, CD63, activated fibrinogen receptor), protein quantification of nicotinic acetylcholine receptor α7 (nAChRα7) and caveolin-1 (CAV-1), and miRNA quantification (miR-26b, miR-199a, miR-335). Group comparison between patients and controls was performed in univariate and multivariate statistical analyses. RESULTS: AD patients showed significantly lower aggregation response to ADP and arachidonic acid and significantly decreased CD62P and CD63 surface expression induced by ADP and U46619 compared to HC. Relative nAChRα7 and CAV-1 expression was significantly higher AD platelets than in HC. Multivariate analysis of 63 parameter revealed significant differences between AD patients and healthy controls. The best performing feature model revealed a sensitivity of 96.6%, a specificity of 80.0%, and a positive predictive value of 89.3%. No grouping could be achieved by using single parameter groups. CONCLUSION: Significant differences between platelet characteristics from AD patients and HC at the time of first clinical diagnosis were observed. The best performing parameter can be used as a blood-based biomarker for AD diagnosis in a multivariate model in addition to the standardized mental tests.