CLEC-2 expression is maintained on activated platelets and on platelet microparticles

The C-type lectin-like receptor CLEC-2 mediates platelet activation through a hem-immunoreceptor tyrosine-based activation motif (hemITAM). CLEC-2 initiates a Src- and Syk-dependent signaling cascade that is closely related to that of the 2 platelet ITAM receptors: glycoprotein (GP)VI and FcγRIIa. Activation of either of the ITAM receptors induces shedding of GPVI and proteolysis of the ITAM domain in FcγRIIa. In the present study, we generated monoclonal antibodies against human CLEC-2 and used these to measure CLEC-2 expression on resting and stimulated platelets and on other hematopoietic cells. We show that CLEC-2 is restricted to platelets with an average copy number of ∼2000 per cell and that activation of CLEC-2 induces proteolytic cleavage of GPVI and FcγRIIa but not of itself. We further show that CLEC-2 and GPVI are expressed on CD41+ microparticles in megakaryocyte cultures and in platelet-rich plasma, which are predominantly derived from megakaryocytes in healthy donors, whereas microparticles derived from activated platelets only express CLEC-2. Patients with rheumatoid arthritis, an inflammatory disease associated with increased microparticle production, had raised plasma levels of microparticles that expressed CLEC-2 but not GPVI. Thus, CLEC-2, unlike platelet ITAM receptors, is not regulated by proteolysis and can be used to monitor platelet-derived microparticles.

Rapid, automated detection of stem canker symptoms in woody perennials using artificial neural network analysis

Background Pseudomonas syringae can cause stem necrosis and canker in a wide range of woody species including cherry, plum, peach, horse chestnut and ash. The detection and quantification of lesion progression over time in woody tissues is a key trait for breeders to select upon for resistance. Results In this study a general, rapid and reliable approach to lesion quantification using image recognition and an artificial neural network model was developed. This was applied to screen both the virulence of a range of P. syringae pathovars and the resistance of a set of cherry and plum accessions to bacterial canker. The method developed was more objective than scoring by eye and allowed the detection of putatively resistant plant material for further study. Conclusions Automated image analysis will facilitate rapid screening of material for resistance to bacterial and other phytopathogens, allowing more efficient selection and quantification of resistance responses.