Advancing Nano-Flow Cytometry: High-Precision Sorting and Analysis of Extracellular Vesicles
Castrosin, I.; Costa, V.; Pinckney, B.; Ghiran, I.; Brennan, K.; Delgado, F.; Reyes-Perez, C.; Blanco, A.; Tigges, J.; Mc Gee, M.
Show abstract
Extracellular Vesicles (EVs) are small membrane-bound particles secreted by cells that play key roles in intercellular communication, gene regulation and modulation of cell function. They are involved in both physiological and pathological processes and, due to their ability to transport biomolecules across biological barriers, have emerged as promising tools for use as drug delivery vehicles and biomarkers with diagnostic and prognostic applications. Various methodologies are currently employed for the isolation, characterization, and analysis of EVs, including Ultracentrifugation (UC), Transmission Electron Microscopy (TEM), Nanoparticle Tracking Analysis (NTA), and Flow Cytometry. Flow Cytometry has emerged as a powerful technique capable of providing a multiparametric analysis of individual EVs. Recent advancements have led to the development of cytometers with higher sensitivity and increased limit of detection, enabling the detection and sorting of nanoscale particles--a technique known as Nano-Flow Cytometry. In this study, we show the optimization of small particle sorting, termed nanoFACS, via the CytoFLEX SRT. This method enables sorting based on size or fluorescence, enhancing reproducibility and broadening the potential for application in biological and clinical assays. Furthermore, we demonstrate the utility of nanoFACS in isolating nanoparticles from complex biofluids and in detecting miRNA using molecular beacons (MBs) highlighting its potential in both basic research and translational applications.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Interferometric nanoparticle tracking analysis enables label-free discrimination of extracellular vesicles from large lipoproteins 97%
- Asymmetric depth-filtration - a versatile and scalable approach for isolation and purification of extracellular vesicles 96%
- Physical association of low density lipoprotein particles and extracellular vesicles unveiled by single particle analysis 96%
Similar papers in this journal
- Multiplexed electrokinetic sensor for detection and therapy monitoring of extracellular vesicles from liquid biopsies of non-small-cell lung cancer patients 94%
- A Mem-dELISA platform for dual color and ultrasensitive digital detection of colocalized proteins on extracellular vesicles 94%
- Exploring Breast Cancer-Related Biochemical Changes in Circulating Extracellular Vesicles Using Raman Spectroscopy 92%
Similar papers in this journal
- Digital Profiling of Tumor Extracellular Vesicle-associated RNAs Directly from Unprocessed Blood Plasma 94%
- Massively parallel encapsulation of single cells with structured microparticles and secretion-based flow sorting 94%
- Single extracellular vesicle imaging and computational analysis identifiesinherent architectural heterogeneity 93%
Similar papers in this journal
- Advanced Extracellular Vesicle Isolation: A Hybrid Electrokinetic-Tangential Flow Filtration Approach for Improved Yield, Purity, and Scalability 95%
- High-Efficiency Capture and Proteomic Analysis of Plasma-Derived Extracellular Vesicles through Affinity Purification 95%
- Toward Community Surveillance: Detecting Intact SARS-CoV-2 Using Exogeneous Oligonucleotide Labels 93%
Similar papers in this journal
- Rapid Assessment of Biomarkers on Single Extracellular Vesicles Using 'Catch and Display' on Ultrathin Nanoporous Silicon Nitride Membranes 97%
- Multiparametric Profiling of Single Nanoscale Extracellular Vesicles by Combined Atomic Force and Fluorescence Microscopy: Correlation and Heterogeneity in Their Molecular and Biophysical Features 95%
- Membrane fusion-based drug delivery liposomes transiently modify the material properties of synthetic and biological membranes 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.