scholarly journals Opportunities and Pitfalls of Fluorescent Labeling Methodologies for Extracellular Vesicle Profiling on High-Resolution Single-Particle Platforms

2021 ◽  
Vol 22 (19) ◽  
pp. 10510
Author(s):  
Diogo Fortunato ◽  
Danilo Mladenović ◽  
Mattia Criscuoli ◽  
Francesca Loria ◽  
Kadi-Liis Veiman ◽  
...  

The relevance of extracellular vesicles (EVs) has grown exponentially, together with innovative basic research branches that feed medical and bioengineering applications. Such attraction has been fostered by the biological roles of EVs, as they carry biomolecules from any cell type to trigger systemic paracrine signaling or to dispose metabolism products. To fulfill their roles, EVs are transported through circulating biofluids, which can be exploited for the administration of therapeutic nanostructures or collected to intercept relevant EV-contained biomarkers. Despite their potential, EVs are ubiquitous and considerably heterogeneous. Therefore, it is fundamental to profile and identify subpopulations of interest. In this study, we optimized EV-labeling protocols on two different high-resolution single-particle platforms, the NanoFCM NanoAnalyzer (nFCM) and Particle Metrix ZetaView Fluorescence Nanoparticle Tracking Analyzer (F-NTA). In addition to the information obtained by particles’ scattered light, purified and non-purified EVs from different cell sources were fluorescently stained with combinations of specific dyes and antibodies to facilitate their identification and characterization. Despite the validity and compatibility of EV-labeling strategies, they should be optimized for each platform. Since EVs can be easily confounded with similar-sized nanoparticles, it is imperative to control instrument settings and the specificity of staining protocols in order to conduct a rigorous and informative analysis.

Structure ◽  
2017 ◽  
Vol 25 (4) ◽  
pp. 663-670.e3 ◽  
Author(s):  
Xiangsong Feng ◽  
Ziao Fu ◽  
Sandip Kaledhonkar ◽  
Yuan Jia ◽  
Binita Shah ◽  
...  

2010 ◽  
Vol 171 (2) ◽  
pp. 244
Author(s):  
Scott M. Stagg ◽  
Gabriel C. Lander ◽  
Joel Quispe ◽  
Neil R. Voss ◽  
Anchi Cheng ◽  
...  

Micron ◽  
2008 ◽  
Vol 39 (7) ◽  
pp. 934-943 ◽  
Author(s):  
Sacha De Carlo ◽  
Nicolas Boisset ◽  
Andreas Hoenger

Author(s):  
Donal M. McSweeney ◽  
Sean M. McSweeney ◽  
Qun Liu

AbstractHigh-resolution single-particle cryo-EM data analysis relies on accurate particle picking. To facilitate the particle picking process, we have developed a self-supervised workflow. Our workflow includes an iterative strategy to use the 2D class average to improve training particles and a progressively improved convolutional neural network (CNN) for particle picking. To automate the selection of particles, we define a threshold (%/Res) using the ratio of percentage class distribution and resolution as a cutoff. Our workflow has been tested using six publicly available data sets with different particle sizes and shapes, and is able to automatically pick particles with minimal user input. The picked particles support high-resolution reconstructions at 3.0 Å or better. Our workflow offers a way toward automated single-particle Cryo-EM data analysis at the stage of particle picking. The workflow may be used in conjunction with commonly used single-particle analysis packages such as Relion, cryoSPARC, cisTEM, SPHIRE, and EMAN2.


2020 ◽  
Vol 209 (2) ◽  
pp. 107437 ◽  
Author(s):  
Feng Wang ◽  
Zanlin Yu ◽  
Miguel Betegon ◽  
Melody G. Campbell ◽  
Tural Aksel ◽  
...  

2018 ◽  
Vol 114 (3) ◽  
pp. 11a
Author(s):  
Eugene Palovcak ◽  
David Bulkley ◽  
Shawn Zheng ◽  
Feng Wang ◽  
David Agard ◽  
...  

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