Ultrafast Detection of Exosomal RNAs via Cationic Lipoplex Nanoparticles in a Micromixer Biochip for Cancer Diagnosis.
ACS Appl Nano Mater
; 4(3): 2806-2819, 2021 Mar 26.
Article
in En
| MEDLINE
| ID: mdl-34849458
Exosomes are cell-derived, nanosized extracellular vesicles for intercellular communication. Exosomal RNAs have been shown as one type of promising cancer liquid biopsy biomarkers. Conventional methods to characterize exosomal RNAs such as quantitative reverse transcription polymerase chain reaction (qRT-PCR) are limited by low sensitivity, large sample consumption, time-consuming process, and high cost. Many technologies have been developed to overcome these challenges; however, many hours are still required to complete the assays, especially when exosome lysis and RNA extraction are required. We have developed a microfluidic cationic lipoplex nanoparticles (mCLN) assay that utilizes a micromixer biochip to allow for the effective capture of exosomes by cationic lipoplex nanoparticles and thus enables ultrafast and sensitive exosomal RNA detection for cancer diagnosis. The sensing performance and diagnostic performance of the mCLN assay were investigated using non-small cell lung cancer (NSCLC) as the disease model and exosomal microRNA-21 and TTF-1 mRNA as the biomarkers. The limits of detection of the mCLN assay were 2.06 × 109 and 3.71 × 109 exosomes/mL for microRNA-21 and TTF-1 mRNA, respectively, indicating that the mCLN assay may require as low as 1 µL of serum for exosomal RNA detection. The mCLN assay successfully distinguished NSCLC from normal controls by detecting significantly higher microRNA-21 and TTF-1 mRNA levels in exosomes from both NSCLC patient serum samples and A549 NSCLC cells than those from normal controls and BEAS-2B normal bronchial epithelial cells. Compared with conventional qRT-PCR assay, the mCLN assay showed a higher diagnostic accuracy in lung cancer, required less sample volume (30 vs 100 µL), and consumed much less time (10 min vs 4 h).
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Diagnostic_studies
Language:
En
Journal:
ACS Appl Nano Mater
Year:
2021
Type:
Article
Affiliation country:
United States