Dataset Information

Characterization of Extracellular Vesicles from Bronchoalveolar Lavage Fluid and Plasma of Patients with Lung Lesions Using Fluorescence Nanoparticle Tracking Analysis.

ABSTRACT: The current lack of reliable methods for quantifying extracellular vesicles (EVs) isolated from complex biofluids significantly hinders translational applications in EV research. The recently developed fluorescence nanoparticle tracking analysis (FL-NTA) allows for the detection of EV-associated proteins, enabling EV content determination. In this study, we present the first comprehensive phenotyping of bronchopulmonary lavage fluid (BALF)-derived EVs from non-small cell lung cancer (NSCLC) patients using classical EV-characterization methods as well as the FL-NTA method. We found that EV immunolabeling for the specific EV marker combined with the use of the fluorescent mode NTA analysis can provide the concentration, size, distribution, and surface phenotype of EVs in a heterogeneous solution. However, by performing FL-NTA analysis of BALF-derived EVs in comparison to plasma-derived EVs, we reveal the limitations of this method, which is suitable only for relatively pure EV isolates. For more complex fluids such as plasma, this method appears to not be sensitive enough and the measurements can be compromised. Our parallel presentation of NTA-based phenotyping of plasma and BALF EVs emphasizes the great impact of sample composition and purity on FL-NTA analysis that has to be taken into account in the further development of FL-NTA toward the detection of EV-associated cancer biomarkers.

SUBMITTER: Dlugolecka M

PROVIDER: S-EPMC8699990 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundIt is now possible to comprehensively characterize the microbiota of the lungs using culture-independent, sequencing-based assays. Several sample types have been used to investigate the lung microbiota, each presenting specific challenges for preparation and analysis of microbial communities. Bronchoalveolar lavage fluid (BALF) enables the identification of microbiota specific to the lower lung but commonly has low bacterial density, increasing the risk of false-positive signal from contaminating DNA. The objectives of this study were to investigate the extent of contamination across a range of sample densities representative of BALF and identify features of contaminants that facilitate their removal from sequence data and aid in the interpretation of BALF sample 16S sequencing data.ResultsUsing three mock communities across a range of densities ranging from 8E+?02 to 8E+?09 16S copies/ml, we assessed taxonomic accuracy and precision by 16S rRNA gene sequencing and the proportion of reads arising from contaminants. Sequencing accuracy, precision, and the relative abundance of mock community members decreased with sample input density, with a significant drop-off below 8E+?05 16S copies/ml. Contaminant OTUs were commonly inversely correlated with sample input density or not reproduced between technical replicates. Removal of taxa with these features or physical concentration of samples prior to sequencing improved both sequencing accuracy and precision for samples between 8E+?04 and 8E+?06 16S copies/ml. For the lowest densities, below 8E+?03 16S copies/ml BALF, accuracy and precision could not be significantly improved using these approaches. Using clinical BALF samples across a large density range, we observed that OTUs with features of contaminants identified in mock communities were also evident in low-density BALF samples.ConclusionRelative abundance data and community composition generated by 16S sequencing of BALF samples across the range of density commonly observed in this sample type should be interpreted in the context of input sample density and may be improved by simple pre- and post-sequencing steps for densities above 8E+?04 16S copies/ml.

Dataset Information

Characterization of Extracellular Vesicles from Bronchoalveolar Lavage Fluid and Plasma of Patients with Lung Lesions Using Fluorescence Nanoparticle Tracking Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets