Enzyme Reaction-Assisted Programmable Transcriptional Switches for Bioactive Molecule Detection.

Bioactive molecules are highly worthwhile to recognize and explore the latent pathogenic mechanism. Conventional methods for bioactive molecule detection, including mass spectrometry and fluorescent probe imaging, are limited due to the complex processing and signal interference. Here, we designed enzyme-reaction-assisted programmable transcriptional switches for the detection of bioactive molecules. The approach is based on the use of programmable enzyme site-specific cleavage-assisted DNA triplex-based conformational switches that, upon responding to bioactive molecules, can trigger the transcription of fluorescent light-up aptamers. Thanks to the programmable nature of the sensing platform, the method can be adapted to different bioactive molecules, and we demonstrated the enzyme-small molecule catalytic reaction combination of myeloperoxidase (MPO)-hydrogen peroxide (H2O2) as a model that transcriptional switches was capable of detecting H2O2 and possessed the specificity and anti-interference ability in vitro. Furthermore, we successfully applied the switches into cells to observe the detection feasibility in vivo, and dynamically monitored changes of H2O2 in cellular oxidative stress levels. Therefore, we attempt to amalgamate the advantages of enzyme reaction with the pluripotency of programmable transcriptional switches, which can take both fields a step further, which may promote the research of biostimuli and the construction of DNA molecular devices.

Metabolic Glycoengineering-Programmed Nondestructive Capture of Circulating Tumor Cells.

Circulating tumor cells (CTCs) are the "seeds" for malignant tumor metastasis, and they serve as an ideal target for minimally invasive tumor diagnosis. Abnormal glycolysis in tumor cells, characterized by glycometabolism disorder, has been reported as a universal phenomenon observed in various types of tumors. This provides a potential powerful tool for universal CTC capture. However, to the best of our knowledge, no metabolic glycoengineering-based CTC capture strategies have been reported. Here, we proposed a nondestructive CTC capture method based on metabolic glycoengineering and a nanotechnology-based proximity effect, allowing for highly specific, sensitive, and universal CTC capture. To achieve this goal, cells are first labeled with DNA tags through metabolic glycoengineering and then captured through a DNA tetrahedra-functionalized dual-tentacle magnetic nanodevice. Due to the difference in metabolic performance, only tumor cells are labeled with more densely packed DNA tags and captured through enhanced intermolecular interaction mediated by the proximity effect. In summary, we have constructed a versatile platform for nondestructive CTC capture, offering a novel perspective for the application of CTC liquid biopsy in tumor diagnosis and treatment.

Biological effect abundance analysis of hemolytic pathogens based on engineered biomimetic sensor.

Conventional pathogen detection strategies based on the molecular structure or chemical characteristics of biomarkers can only provide the "physical abundance" of microorganisms, but cannot reflect the "biological effect abundance" in the true sense. To address this issue, we report an erythrocyte membrane-encapsulated biomimetic sensor cascaded with CRISPR-Cas12a (EMSCC). Taking hemolytic pathogens as the target model, we first constructed an erythrocyte membrane-encapsulated biomimetic sensor (EMS). Only hemolytic pathogens with biological effects can disrupt the erythrocyte membrane (EM), resulting in signal generation. Then the signal was amplified by cascading CRISPR-Cas12a, and more than 6.67 × 104-fold improvement in detection sensitivity compared to traditional erythrocyte hemolysis assay was achieved. Notably, compared with polymerase chain reaction (PCR) or enzyme linked immunosorbent assay (ELISA)-based quantification methods, EMSCC can sensitively respond to the pathogenicity change of pathogens. For the detection of simulated clinical samples based on EMSCC, we obtained an accuracy of 95% in 40 samples, demonstrating its potential clinical value.

A portable and partitioned DNA hydrogel chip for multitarget detection.

A DNA hydrogel, owing to its dual properties of liquid and solid, is considered to be an ideal material for constructing biosensors that can integrate the advantages of both wet chemistry and dry chemistry. Nevertheless, it has struggled to cope with the demands of high-throughput analysis. A partitioned and chip-based DNA hydrogel is a potential avenue to achieve this, but currently remains a formidable challenge. Here, we developed a portable and partitioned DNA hydrogel chip that can be used for multitarget detection. The partitioned and surface-immobilized DNA hydrogel chip was formed by inter-crosslinking amplification by incorporating target-recognizing fluorescent aptamer hairpins into multiple rolling circle amplification products, which can achieve portable and simultaneous detection of multiple targets. This approach expands the application of semi-dry chemistry strategies, which can realize high throughput and point of care testing (POCT) of different targets, improving the development of hydrogel-based bioanalysis and providing new potential solutions for biomedical detection.

Recent advances in tumor biomarker detection by lanthanide upconversion nanoparticles.

Early tumor diagnosis could reliably predict the behavior of tumors and significantly reduce their mortality. Due to the response to early cancerous changes at the molecular or cellular level, tumor biomarkers, including small molecules, proteins, nucleic acids, exosomes, and circulating tumor cells, have been employed as powerful tools for early cancer diagnosis. Therefore, exploring new approaches to detect tumor biomarkers has attracted a great deal of research interest. Lanthanide upconversion nanoparticles (UCNPs) provide numerous opportunities for bioanalytical applications. When excited by low-energy near-infrared light, UCNPs exhibit several unique properties, such as large anti-Stoke shifts, sharp emission lines, long luminescence lifetimes, resistance to photobleaching, and the absence of autofluorescence. Based on these excellent properties, UCNPs have demonstrated great sensitivity and selectivity in detecting tumor biomarkers. In this review, an overview of recent advances in tumor biomarker detection using UCNPs has been presented. The key aspects of this review include detection mechanisms, applications in vitro and in vivo, challenges, and perspectives of UCNP-based tumor biomarker detection.

A peptide-DNA hybrid bio-nanomicelle and its application for detection of caspase-3 activity.

Bio-nanomicelles based on biomaterials such as nucleic acids, peptides, glycans, and lipids have developed rapidly in the field of bioanalysis. Although DNA and peptides have unique advantages, unfortunately, there are few bio-nanomicelles integrating DNA with peptides. Here, we designed a peptide-DNA hybrid bio-nanomicelle for the activity detection of caspase-3. The detection mechanism is based on caspase-3 specific recognition and cleavage of peptide substrates, which owns high sensitivity and selectivity. Under optimal conditions, the detection of caspase-3 activity can be achieved using our designed bio-nanomicelles and the detection limit is 0.72 nM. Furthermore, the proposed method was also successfully applied for the detection of caspase-3 in cell lysate samples after apoptosis-inducing.

Extension of the alkyl chain length to adjust the properties of laccase-mimicking MOFs for phenolic detection and discrimination.

2-Methylimidazole (MIM) is a classic organic ligand that shows excellent thermal stability and chemical robustness and is widely used in ZIFs. Recently, transformations of MOFs have been realized by using metals or ligands. In this study, we propose a new strategy-adjusting MIM by extending the alkyl chain length -to change the properties of related MOFs. Furthermore, we used copper as the metal core to replace zinc to mimic the active sites of laccases (electron transfer between copper and imidazole ring). As a result, the nanostructures transformed from nanoleaves to nanovesicles, which changed the Cu(II)/Cu(I) ratio from 3.7 to 1.7, as well as the lattice constant (decreased the diffraction angle) and enzyme-like activity (inhibition). In addition, we revealed that superoxidase anions were the main factors responsible for its laccase-like activity. We applied it to detect and discriminate phenolics. Laccase-mimicking activity was best at pH 7.0. When compared to protein laccase, the Cu-MeIm nanozyme had a greater Vmax at the same mass concentration. It was used to identify and distinguish phenolics. In the presence of Cu-MeIm nanozymes, the linear range is 0.1-2 mM and the detection limit of 2,4-DCP is 0.034 mM.

Computer-Aided Design of DNA Self-Limited Assembly for Relative Quantification of Membrane Proteins.

Immunofluorescence imaging of cells plays a vital role in biomedical research and clinical diagnosis. However, when it is applied to relative quantification of proteins, it suffers from insufficient fluorescence intensity or partial overexposure, resulting in inaccurate relative quantification. Herein, we report a computer-aided design of DNA self-limited assembly (CAD-SLA) technology and apply it for relative quantification of membrane proteins, a concept proposed for the first time. CAD-SLA can achieve exponential cascade signal amplification in one pot and terminate at any desired level. By conjugating CAD-SLA with immunofluorescence, in situ imaging of cell membrane proteins is achieved with a controllable amplification level. Besides, comprehensive fluorescence intensity information from fluorescent images can be obtained, accurately showing relative quantitative information. Slight protein expression differences previously indistinguishable by immunofluorescence or Western blotting can now be discriminated, making fluorescence imaging-based relative quantification a promising tool for membrane protein analysis. From the perspectives of both DNA self-assembly technology and immunofluorescence technology, this work has solved difficult problems and provided important reference for future development.

Tetrahedron supported click ligation initiated by dual recognition for precise bacterial analysis.

For the 16S rRNA gene of bacterial analysis, the current usage of single recognition probe always causes the false positive result. Meanwhile, it is usually impossible for direct ligation of two free DNA strands modified with click ligation groups in the solution. In our work, A DNA tetrahedron supported click ligation has been elaborately designed; thereby a new method has been further developed for bacterial analysis with dual recognition on two target regions of 16S rRNA gene. Compared with free click ligation, DNA tetrahedron supported click ligation exhibits high reaction rate and ligation efficiency as a result of proximity effect on the supporting interface. The designed DNA tetrahedron can simultaneously bind with two target regions of 16S rRNA gene in bacteria, inducing the proximity of reaction groups and efficient occurrence of click ligation. The established method shows the practical applicability in the serum sample. In a word, inspired by high ligation efficiency on the interface, DNA tetrahedron supported click ligation has been firstly developed and served for bacterial analysis through dual recognition with high specificity, high sensitivity and good performance.

Nanocomposite of Peroxidase-Like Cucurbit[6]uril with Enzyme-Encapsulated ZIF-8 and Application for Colorimetric Biosensing.

In this work, cucurbiturils (CBs), a class of macrocyclic supramolecules, were observed to have an interesting peroxidase-like activity, which is metal-free, substrate-specific, thermophilic, acidophilic, and insensitive to ionic strength. By coating CBs on enzyme-encapsulated zeolitic imidazolate framework-8 (ZIF-8), a composite nanozyme was constructed, which retains the catalytic ability of CBs and enzymes and makes them cascade. On addition of the substrate, i.e., the detection target, a highly efficient cascade catalysis can be launched in all the spatial directions to generate sensitive and visible signals. Convenient detection of glucose and cholesterol as models is thereby achieved. More importantly, we have also successfully constructed a composite nanozyme-based sensor array (6 × 8 wells) and thereby achieved simultaneous colorimetric analysis of multiple samples. The concept and successful practice of the construction of the unique core-shell supramolecule/biomolecule@nanomaterial architecture provide the possibility to fabricate next-generation multifunctional materials and create new applications by integrating their unique functions.

Optimizing hyper-parameters of neural networks with swarm intelligence: A novel framework for credit scoring.

Neural networks are widely used in automatic credit scoring systems with high accuracy and outstanding efficiency. However, in the absence of prior knowledge, it is difficult to determine the set of hyper-parameters, which makes its application limited in practice. This paper presents a novel framework of credit-scoring model based on neural networks trained by the optimal swarm intelligence (SI) algorithm. This framework incorporates three procedures. Step 1, pre-processing, including imputation, normalization, and re-ordering of the samples. Step 2, training, where SI algorithms optimize hyper-parameters of back-propagation artificial neural networks (BP-ANN) with the area under curve (AUC) as the evaluation function. Step 3, test, applying the optimized model in Step 2 to predict new samples. The results show that the framework proposed in this paper searches the hyper-parameter space efficiently and finds the optimal set of hyper parameters with appropriate time complexity, which enhances the fitting and generalization ability of BP-ANN. Compared with existing credit-scoring models, the model in this paper predicts with a higher accuracy. Additionally, the model enjoys a greater robustness, for the difference of performance between training and testing phases.