Revealing the diversity of extracellular vesicles using high-dimensional flow cytometry analyses.
Revealing the diversity of extracellular vesicles using high-dimensional flow cytometry analyses.
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DOI：
10.1038/srep35928
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发表时间：
2016-10-27
影响因子：
4.6
通讯作者：
Boilard E
中科院分区：
文献类型：
--
作者：
Marcoux G;Duchez AC;Cloutier N;Provost P;Nigrovic PA;Boilard E
细胞外小泡(EV)是细胞在激活和凋亡过程中产生的膜小泡。EVS的来源、释放方式、膜成分、细胞器和生化含量以及其他因素都是不同的。虽然EV显然与细胞间的通讯有关，但它们也可以用作生物标记物。分析前参数和流式细胞术的不断改进使EVS的评估更加有效；然而，缺乏更客观地区分EVS与细胞和背景的方法，以及解释多个单一EV参数的方法。我们使用密度归一化事件的生成树级数分析(SPADE)作为组织由血小板和红细胞释放的EV亚群的计算方法。SPADE根据大小估计、粒度、线粒体含量以及磷脂酰丝氨酸和蛋白质受体的表面表达，通过高灵敏度的流式细胞荧光法检测到区分EV和逻辑组织的EV。血浆电动汽车是按等级组织的，允许评价它们的异质性。此外，使用SPADE分析了炎性关节炎患者滑液中存在的EVS。它的算法基于EV异质性模式有效地揭示了关节炎患者的亚型。我们的研究表明，计算算法对于分析高维的单个电动汽车数据是有用的，从而有助于理解电动汽车的功能和生物标志物的开发。
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.
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影响因子：
14.8
作者：
Anchang, Benedict;Hart, Tom D. P.;Plevritis, Sylvia K.
通讯作者：
Plevritis, Sylvia K.
DOI：
10.1126/science.1198704
发表时间：
2011-05-06
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Bendall SC;Simonds EF;Qiu P;Amir el-AD;Krutzik PO;Finck R;Bruggner RV;Melamed R;Trejo A;Ornatsky OI;Balderas RS;Plevritis SK;Sachs K;Pe'er D;Tanner SD;Nolan GP
通讯作者：
Nolan GP
影响因子：
48
作者：
Aghaeepour N;Finak G;FlowCAP Consortium;DREAM Consortium;Hoos H;Mosmann TR;Brinkman R;Gottardo R;Scheuermann RH
通讯作者：
Scheuermann RH
影响因子：
20.3
作者：
Gitz, Eelo;Pollitt, Alice Y.;Watson, Steve P.
通讯作者：
Watson, Steve P.
影响因子：
11.1
作者：
Cloutier, Nathalie;Tan, Sisareuth;Boudreau, Luc H.;Cramb, Catriona;Subbaiah, Roopashree;Lahey, Lauren;Albert, Alexandra;Shnayder, Ruslan;Gobezie, Reuben;Nigrovic, Peter A.;Farndale, Richard W.;Robinson, William H.;Brisson, Alain;Lee, David M.;Boilard, Eric
通讯作者：
Boilard, Eric