On-Particle Rolling Circle Amplification-Based Core-Satellite Magnetic Superstructures for MicroRNA Detection.
Bo TianZhen QiuJing MaMarco DonolatoMikkel Fougt HansenPeter SvedlindhMattias StrömbergPublished in: ACS applied materials & interfaces (2018)
Benefiting from the specially tailored properties of the building blocks as well as of the scaffolds, DNA-assembled core-satellite superstructures have gained increasing interest in drug delivery, imaging, and biosensing. The load of satellites plays a vital role in core-satellite superstructures, and it determines the signal intensity in response to a biological/physical stimulation/actuation. Herein, for the first time, we utilize on-particle rolling circle amplification (RCA) to prepare rapidly responsive core-satellite magnetic superstructures with a high load of magnetic nanoparticle (MNP) satellites. Combined with duplex-specific nuclease-assisted target recycling, the proposed magnetic superstructures hold great promise in sensitive and rapid microRNA detection. The long single-stranded DNA produced by RCA serving as the scaffold of the core-satellite superstructure can be hydrolyzed by duplex-specific nuclease in the presence of target microRNA, resulting in a release of MNPs that can be quantified in an optomagnetic sensor. The proposed biosensor has a simple mix-separate-measure strategy. For let-7b detection, the proposed biosensor offers a wide linear detection range of approximately 5 orders of magnitude with a detection sensitivity of 1 fM. Moreover, it has the capability to discriminate single-nucleotide mismatches and to detect let-7b in cell extracts and serum, thus showing considerable potential for clinical applications.
Keyphrases
- label free
- loop mediated isothermal amplification
- drug delivery
- real time pcr
- sensitive detection
- molecularly imprinted
- nucleic acid
- stem cells
- gold nanoparticles
- mesenchymal stem cells
- single cell
- high intensity
- cell therapy
- circulating tumor
- photodynamic therapy
- smoking cessation
- big data
- deep learning
- human health
- transcription factor