A well-designed fluorescence-based analysis of extracellular vesicles (EV) can provide insights into the size, morphology, and biological function of EVs, which can be used in medical applications. Fluorescent nanoparticle tracking analysis with appropriate controls can provide reliable data for size and concentration measurements, while nanoscale flow cytometry is the most appropriate tool for characterizing molecular cargoes. Label selection is a crucial element in all fluorescence methods. The most comprehensive data can be obtained if several labeling approaches for a given marker are used, as they would provide complementary information about EV populations and interactions with the cells. In all EV-related experiments, the influence of lipoproteins and protein corona on the results should be considered. By reviewing and considering all the factors affecting EV labeling methods used in fluorescence-based techniques, we can assert that the data will provide as accurate as possible information about true EV biology and offer precise, clinically applicable information for future EV-based diagnostic or therapeutic applications.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11445045PMC
http://dx.doi.org/10.3389/fbioe.2024.1479516DOI Listing

Publication Analysis

Top Keywords

fluorescence-based techniques
8
factors consider
4
consider choosing
4
choosing labeling
4
labeling method
4
method fluorescence-based
4
techniques well-designed
4
well-designed fluorescence-based
4
fluorescence-based analysis
4
analysis extracellular
4

Similar Publications

Coating synthetic nanoparticles (NPs) with lipid membranes is a promising approach to enhance the performance of nanomaterials in various biological applications, including therapeutic delivery to target organs. Current methods for achieving this coating often rely on bulk approaches which can result in low efficiency and poor reproducibility. Continuous processes coupled with quality control represent an attractive strategy to manufacture products with consistent attributes and high yields.

View Article and Find Full Text PDF

Background: Increased ribosome biogenesis is required for tumor growth. In this study, we investigated the function and underlying molecular mechanism of ribosome biogenesis factor (RBIS) in the progression of non-small cell lung cancer (NSCLC).

Methods: In our study, we conducted a comprehensive analysis to identify key genes implicated in ribosome biogenesis by leveraging a Gene Set Enrichment Analysis (GSEA) dataset.

View Article and Find Full Text PDF

Aims: Acute hypoglycaemia promotes pro-inflammatory cytokine production, increasing the risk for cardiovascular events in diabetes. AMP-activated protein kinase (AMPK) is regulated by and influences the production of pro-inflammatory cytokines. We sought to examine the mechanistic role of AMPK in low glucose-induced changes in the pro-inflammatory cytokine macrophage migration inhibitory factor (MIF), which is elevated in people with diabetes.

View Article and Find Full Text PDF

Stabilization of mitochondria-associated endoplasmic reticulum membranes regulates Aβ generation in a three-dimensional neural model of Alzheimer's disease.

Alzheimers Dement

December 2024

Genetics and Aging Research Unit, MassGeneral Institute for Neurodegenerative Disease, Henry and Allison McCance Center for Brain Health, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA.

Introduction: We previously demonstrated that regulating mitochondria-associated endoplasmic reticulum (ER) membranes (MAMs) affects axonal Aβ generation in a well-characterized three-dimensional (3D) neural Alzheimer's disease (AD) model. MAMs vary in thickness and length, impacting their functions. Here, we examined the effect of MAM thickness on Aβ in our 3D neural model of AD.

View Article and Find Full Text PDF

Multiplexed tissue imaging (MTI) technologies enable high-dimensional spatial analysis of tumor microenvironments but face challenges with technical variability in staining intensities. Existing normalization methods, including z-score, ComBat, and MxNorm, often fail to account for the heterogeneous, right-skewed expression patterns of MTI data, compromising signal alignment and downstream analyses. We present UniFORM, a non-parametric, Python-based pipeline for normalizing both feature- and pixel-level MTI data.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!