Person:

Nigrovic, Peter

Microparticles, also called microvesicles, are submicron extracellular vesicles produced by plasma membrane budding and shedding recognized as key actors in numerous physio(patho)logical processes. Since they can be released by virtually any cell lineages and are retrieved in biological fluids, microparticles appear as potent biomarkers. However, the small dimensions of microparticles and soluble factors present in body fluids can considerably impede their quantification. Here, flow cytometry with improved methodology for microparticle resolution was used to detect microparticles of human and mouse species generated from platelets, red blood cells, endothelial cells, apoptotic thymocytes and cells from the male reproductive tract. A family of soluble proteins, the secreted phospholipases A2 (sPLA2), comprises enzymes concomitantly expressed with microparticles in biological fluids and that catalyze the hydrolysis of membrane phospholipids. As sPLA2 can hydrolyze phosphatidylserine, a phospholipid frequently used to assess microparticles, and might even clear microparticles, we further considered the impact of relevant sPLA2 enzymes, sPLA2 group IIA, V and X, on microparticle quantification. We observed that if enriched in fluids, certain sPLA2 enzymes impair the quantification of microparticles depending on the species studied, the source of microparticles and the means of detection employed (surface phosphatidylserine or protein antigen detection). This study provides analytical considerations for appropriate interpretation of microparticle cytofluorometric measurements in biological samples containing sPLA2 enzymes.

Immunoglobulins, antigens and complement can assemble to form immune complexes (IC). ICs can be detrimental as they propagate inflammation in autoimmune diseases. Like ICs, submicron extracellular vesicles termed microparticles (MP) are present in the synovial fluid from patients affected with autoimmune arthritis. We examined MPs in rheumatoid arthritis (RA) using high sensitivity flow cytometry and electron microscopy. We find that the MPs in RA synovial fluid are highly heterogeneous in size. The observed larger MPs were in fact MP-containing ICs (mpICs) and account for the majority of the detectable ICs. These mpICs frequently express the integrin CD41, consistent with platelet origin. Despite expression of the Fc receptor FcγRIIa by platelet-derived MPs, we find that the mpICs form independently of this receptor. Rather, mpICs display autoantigens vimentin and fibrinogen, and recognition of these targets by anti-citrullinated peptide antibodies contributes to the production of mpICs. Functionally, platelet mpICs are highly pro-inflammatory, eliciting leukotriene production by neutrophils. Taken together, our data suggest a unique role for platelet MPs as autoantigen-expressing elements capable of perpetuating formation of inflammatory ICs.

Extracellular vesicles (EV) are small membrane vesicles produced by cells upon activation and apoptosis. EVs are heterogeneous according to their origin, mode of release, membrane composition, organelle and biochemical content, and other factors. Whereas it is apparent that EVs are implicated in intercellular communication, they can also be used as biomarkers. Continuous improvements in pre-analytical parameters and flow cytometry permit more efficient assessment of EVs; however, methods to more objectively distinguish EVs from cells and background, and to interpret multiple single-EV parameters are lacking. We used spanning-tree progression analysis of density-normalized events (SPADE) as a computational approach for the organization of EV subpopulations released by platelets and erythrocytes. SPADE distinguished EVs, and logically organized EVs detected by high-sensitivity flow cytofluorometry based on size estimation, granularity, mitochondrial content, and phosphatidylserine and protein receptor surface expression. Plasma EVs were organized by hierarchy, permitting appreciation of their heterogeneity. Furthermore, SPADE was used to analyze EVs present in the synovial fluid of patients with inflammatory arthritis. Its algorithm efficiently revealed subtypes of arthritic patients based on EV heterogeneity patterns. Our study reveals that computational algorithms are useful for the analysis of high-dimensional single EV data, thereby facilitating comprehension of EV functions and biomarker development.