Label-free colorimetric protein assay and logic gates design based on the self-assembly of hemin-graphene hybrid nanosheet.

Here we report a label-free colorimetric method for protein assay based on the intrinsic peroxidase-like catalytic activity of DNA-hemin-graphene (DNA-GH) composite. By using aptamers as protein recognition elements, protein-mediated aggregation of the DNA-GH composite leads to the decrease or increase of the colorimetric signal depending on the sandwich or competitive design strategy. Thrombin and PDGF-BB were chosen as model analytes and the detection limits (LOD) by this method were estimated to be 0.5 nM and 5 nM, respectively. Compared to traditional ELISA method for protein detection, this method possesses the advantages of high sensitivity, simplicity, and low cost. In addition, by designing different DNA-modified hemin-graphene (GH) constructs, using proteins as inputs, the "OR" and "INHIBIT" logic gates were built. This procedure does not require chemical modification on the aptamer probes or analytes and circumvents the limitation associated with the number of target binding sites. Given the attractive analytical characteristics and distinct advantages of DNA-GH composite, the universal approach can be widely applied for the detection of diverse proteins and for the design of versatile logic gates.

A highly sensitive immunosensor for calmodulin assay based on enhanced biocatalyzed precipitation adopting a dual-layered enzyme strategy.

Calmodulin (CaM) is a ubiquitous protein in eukaryotic cells, and it plays an important role in cancer progression. In this paper, a highly sensitive immunosensor adopting a dual-layered enzyme strategy was proposed for electrochemical detection of CaM. This immunosensor was constructed by introducing honeycomb-like mesoporous carbon (HMPC) as a sensor platform to sequentially immobilize antibody (Ab1), CaM and a multi-functionalized label. The label (HRP-PAupc-Ab1) was synthesized by covalently binding Ab1 and horseradish peroxidase (HRP) to poly(acrylic acid)-functionalized Au popcorn (PAupc) nanoparticles. A novel dual-layered enzyme strategy was employed by incubating HRP-secondary antibody (HRP-Ab2) onto the label surface and the enhanced biocatalyzed precipitation was therefore induced. This immunosensor exhibited satisfactory analytical performances for CaM detection with a linear response ranging from 5.0 pg mL(-1) to 100 ng mL(-1) and a detection limit of 1.5 pg mL(-1). The immunosensor has also been successfully applied to the CaM analysis in two cancer cells (HepG2 and MCF-7) with high sensitivity, which has shown great potency for cancer study.

Fluorescence assay for glycan expression on living cancer cells based on competitive strategy coupled with dual-functionalized nanobiocomposites.

Cell surface glycans are a class of sophisticated biomolecules related to cancer development and progression, and their analysis is of great significance for early cancer diagnosis and treatment. In this paper, we proposed a fluorescence assay to evaluate glycan expression on living cancer cells based on a competitive strategy coupled with dual-functionalized nanobiocomposites. The competitive assay was conducted between living cancer cells and thiomannosyl derivatives using concanavalin A (Con A)-modified electrode as the interaction platform. To impart fluorescence signaling ability to competitive derivatives, quantum dots (QDs) were anchored on BSA-protected Au nanoparticles, and thiomannosyl derivatives were further immobilized on the nanoparticle surface through Au-S binding. Due to the spacing between QDs and Au nanoparticles by BSA, the {QDs-Au-BSA-mannose} nanobiocomposites maintained the fluorescence of QDs and showed binding ability with the Con A-modified electrode. Au nanorods (AuNRs)-modified electrode was used as an effective substrate to immobilize Con A. This assay was successfully applied to the analysis of two cancer cells lines (A549 and QGY-7701). The method is simple and shows promise for the study of glycan expression on living cancer cells.