posted on 2019-10-30, 15:37authored byYue Zhang, Yaqin Chai, Haijun Wang, Ruo Yuan
Here,
a target-induced three-dimensional DNA network structure
(T-3D Net) produced by catalytic hairpin assembly (CHA) was proposed
as a novel signal amplifier to fabricate an ultrasensitive electrochemiluminescence
(ECL) biosensor for microRNAs detection. Usually, conventional CHA
can produce only one output DNA in each target cycle, while the proposed
strategy could produce multiple output DNA by using DNA-functionalized
magnetic beads (MBs) and gold nanoparticles (AuNPs) to form T-3D Net.
Then, the T-3D Net with high loading capacity could be completely
collapsed by dissolving AuNPs to efficiently convert trace microRNA-21
into a large amount of output DNA. Furthermore, the nanocomposite
containing Ru(bpy)32+ as luminophore and boron
nitride quantum dots (BNQDs) as coreactant provided a strong initial
ECL response owing to the short electron transfer distance between
luminophore and coreactant (signal-on). Next, the DNA duplex probes
labeled with N-(4-aminobutyl)-N-ethylisoluminol (ABEI) and dopamine (DA) (S1-ABEI/S2-DA) were further
immobilized on the nanocomposite to reduce the background signal due
to the double quenching effect of DA for both ABEI and Ru(bpy)32+ (signal-off). In the presence of the output
DNA with enzyme-assisted self-recycling, S2-DA was displaced and detached
from the electrode surface to achieve ECL signal recovery. Simultaneously,
S1-ABEI restored a stable hairpin structure, making ABEI close to
the electrode surface for more effective resonance energy transfer
(RET) between ABEI and Ru(bpy)32+, which greatly
improved the final ECL response (signal-super on). Thus, the ECL biosensor
demonstrated superior performance for ultrasensitive detection of
microRNA-21 with low detection limit (0.33 aM) and was successfully
applied to monitor the expression of microRNA-21 in human cancer cell
lysates. This strategy provided an ultrasensitive way for the detection
of biomolecules and revealed an effective avenue for diseases diagnosis.