Programmable Engineering of DNA-AuNP Encoders Integrated Multimodal Coupled Analysis for Precision Discrimination of Multiple Metal Ions

Stimuli-triggered Self-Assembly of Gold Nanoparticles: Recent Advances in Fabrication and Biomedical Applications

Gold nanoparticles have been widely used in engineering, material chemistry, and biomedical applications owing to their ease of synthesis and functionalization, localized surface plasmon resonance (LSPR), great chemical stability, excellent biocompatibility, tunable optical and electronic property. In recent years, the decoration and modification of gold nanoparticles with small molecules, ligands, surfactants, peptides, DNA/RNA, and proteins have been systematically studied. In this review, we summarize the recent approaches on stimuli-triggered self-assembly of gold nanoparticles and introduce the breakthrough of gold nanoparticles in disease diagnosis and treatment. Finally, we discuss the current challenge and future prospective of stimuli-responsive gold nanoparticles for biomedical applications.

Enhanced "Electronic Tongue" for Dental Bacterial Discrimination and Elimination Based on a DNA-Encoded Nanozyme Sensor Array

Bacterial infections are the second leading cause of death around the world, especially those caused by delayed treatment and misdiagnosis. Therefore, rapid discrimination and effective elimination of multiple bacteria are of great importance for improving the survival rate in clinic. Herein, a novel colorimetric sensor array for bacterial discrimination and elimination is constructed using programmable DNA-encoded iron oxide nanoparticles (IONPs) as sensing elements. Utilizing differential interactions of bacteria on DNA-encoded IONPs, 11 kinds of dental bacteria and 6 kinds of proteins have been successfully identified by linear discriminant analysis (LDA). Moreover, the developed sensing system also performs well in the quantitative determination of individual bacteria and identification of bacterial mixtures. More importantly, the practicability of this sensing strategy is further verified by precise differentiation of blind and artificial saliva samples. Furthermore, the sensor array is used for efficiently killing multiple bacteria, demonstrating great potential in clinical prophylaxis and therapy.

DNA-functionalized metal or metal-containing nanoparticles for biological applications

The conjugation of DNA molecules with metal or metal-containing nanoparticles (M/MC NPs) has resulted in a number of new hybrid materials, enabling a diverse range of novel biological applications in nanomaterial assembly, biosensor development, and drug/gene delivery. In such materials, the molecular recognition, gene therapeutic, and structure-directing functions of DNA molecules are coupled with M/MC NPs. In turn, the M/MC NPs have optical, catalytic, pore structure, or photodynamic/photothermal properties, which are beneficial for sensing, theranostic, and drug loading applications. This review focuses on the different DNA functionalization protocols available for M/MC NPs, including gold NPs, upconversion NPs, metal-organic frameworks, metal oxide NPs and quantum dots. The biological applications of DNA-functionalized M/MC NPs in the treatment or diagnosis of cancers are discussed in detail.

An explainable machine-learning approach for revealing the complex synthesis path-property relationships of nanomaterials

Machine learning (ML) models have recently shown important advantages in predicting nanomaterial properties, which avoids many trial-and-error explorations. However, complex variables that control the formation of nanomaterials exhibiting the desired properties still need to be better understood owing to the low interpretability of ML models and the lack of detailed mechanism information on nanomaterial properties. In this study, we developed a methodology for accurately predicting multiple synthesis parameter-property relationships of nanomaterials to improve the interpretability of the nanomaterial property mechanism. As a proof-of-concept, we designed glutathione-gold nanoclusters (GSH-AuNCs) exhibiting an appropriate fluorescence quantum yield (QY). First, we conducted 189 experiments and synthesized different GSH-AuNCs by varying the thiol-to-metal molar ratio and reaction temperature and time in reasonable ranges. The fluorescence QY of GSH-AuNCs could be systematically and independently programmed using different experimental parameters. We used limited GSH-AuNC synthesis parameter data to train an extreme gradient boosting regressor model. Moreover, we improved the interpretability of the ML model by combining individual conditional expectation, double-variable partial dependence, and feature interaction network analyses. The interpretability analyses established the relationship between multiple synthesis parameters and fluorescence QYs of GSH-AuNCs. The results represent an essential step towards revealing the complex fluorescence mechanism of thiolated AuNCs. Finally, we constructed a synthesis phase diagram exceeding 6.0 × 104 prediction variables for accurately predicting the fluorescence QY of GSH-AuNCs. A multidimensional synthesis phase diagram was obtained for the fluorescence QY of GSH-AuNCs by searching the synthesis parameter space in the trained ML model. Our methodology is a general and powerful complementary strategy for application in material informatics.

Metabolic Footprinting-Based DNA-AuNP Encoders for Extracellular Metabolic Response Profiling

Metabolic footprinting as a convenient and non-invasive cell metabolomics strategy relies on monitoring the whole extracellular metabolic process. It covers nutrient consumption and metabolite secretion of in vitro cell culture, which is hindered by low universality owing to pre-treatment of the cell medium and special equipment. Here, we report the design and a variety of applicability, for quantifying extracellular metabolism, of fluorescently labeled single-stranded DNA (ssDNA)-AuNP encoders, whose multi-modal signal response is triggered by extracellular metabolites. We constructed metabolic response profiling of cells by detecting extracellular metabolites in different tumor cells and drug-induced extracellular metabolites. We further assessed the extracellular metabolism differences using a machine learning algorithm. This metabolic response profiling based on the DNA-AuNP encoder strategy is a powerful complement to metabolic footprinting, which significantly applies potential non-invasive identification of tumor cell heterogeneity.

Programming the dynamic range of nanobiosensors with engineering poly-adenine-mediated spherical nucleic acid

Spherical nucleic acid (SNA) conjugates consisting of gold cores functionalized with a densely packed DNA shells are of great significance in the field of medical detection and intracellular imaging. Especially, poly adenine (polyA)-mediated SNAs can improve the controllability and reproducibility of DNA assembly on the nanointerface, showing the tunable hybridization ability. However, due to the physics of single-site binding, the biosensor based on SNA usually exhibits a dynamic range spanning a fixed 81-fold change in target concentration, which limits its application in disease monitoring. To address this problem, we report a tri-block DNA-based approach to assemble SNA for nucleic acid detection based on structure-switching mechanism with programmable dynamic range. The tri-block DNA is a FAM-labeled stem-loop structure, which contains three blocks: polyA block as an anchoring block for tunable surface density, stem block with different GC base pair content for varying the structure stability, and the fixed loop block for target recognition. We find that varying the polyA block, the reaction temperature, and the GC base pair, SNA shows different target binding affinity and detection limit but with normally 81-fold dynamic range. We can extend the dynamic range to 1000-fold by using the combination of two SNAs with different affinity, and narrow the dynamic range to 5-fold by sequestration mechanism. Furthermore, the tunable SNA enables sensitive detection of mRNA in cells. Given its tunable dynamic range, such nanobiosensor based on SNA offers new possibility for various biomedical and clinical applications.

Point-and-shoot Strategy based on Enzyme-assisted DNA "Paper-Cutting" to Construct Arbitrary Planar DNA Nanostructures

DNA self-assembly provides a "bottom-up" route to fabricating complex shapes on the nanometer scale. However, each structure needs to be designed separately and carried out by professionally trained technicians, which seriously restricts its development and application. Herein, a point-and-shoot strategy based on enzyme-assisted DNA "paper-cutting" to construct planar DNA nanostructures using the same DNA origami as the template is reported. Precisely modeling the shapes with high precision in the strategy based on each staple strand of the desired shape structure hybridizes with its nearest neighbor fragments from the long scaffold strand. As a result, some planar DNA nanostructures by one-pot annealing the long scaffold strand and selected staple strands is constructed. The point-and-shoot strategy of avoiding DNA origami staple strands' re-designing based on different shapes breaks through the shape complexity limitation of the planar DNA nanostructures and enhances the simplicity of design and operation. Overall, the strategy's simple operability and great generality enable it to act as a candidate tool for manufacturing DNA nanostructures.

A DNA walker triggered isothermal amplification method based on freezing construction of AuNP probes and its application in ricin detection

DNA molecular machines are widely used in the fields of biosensors and biological detection. Among them, DNA walkers have attracted much attention due to their simple design and controllability. Herein, we attempt to develop a DNA walker triggered exponential amplification method and explore its application. The AuNP probes in the DNA walker are constructed by a freezing technology, instead of the time-consuming and complex synthesis process of the traditional method. Meanwhile, after the "recognition-cleavage-relative motion" cycle of this DNA walker reaction, the exponential amplification reaction is initiated, and leads to the fluorescence recovery of the molecular beacon. Taking ricin as a target, this new method shows a limit of detection of 2.25 pM by selecting aptamers with strong binding affinity, and exhibits a wide detection range, satisfactory specificity, and excellent stability in practical application. Therefore, our method provides a universal sensing platform and has great prospects in the fields of biosensors, food safety detection, and clinical diagnostics.

DNA-functionalized gold nanoparticles: Modification, characterization, and biomedical applications

With the development of technologies based on gold nanoparticles (AuNPs), bare AuNPs cannot meet the increasing requirements of biomedical applications. Modifications with different functional ligands are usually needed. DNA is not only the main genetic material, but also a good biological material, which has excellent biocompatibility, facile design, and accurate identification. DNA is a perfect ligand candidate for AuNPs, which can make up for the shortcoming of bare AuNPs. DNA-modified AuNPs (DNA-AuNPs) have exciting features and bright prospects in many fields, which have been intensively investigated in the past decade. In this review, we summarize the various approaches for the immobilization of DNA strands on the surface of AuNPs. Representative studies for biomedical applications based on DNA-AuNPs are also discussed. Finally, we present the challenges and future directions.

2D nanomaterial sensing array using machine learning for differential profiling of pathogenic microbial taxonomic identification

An integrated custom cross-response sensing array has been developed combining the algorithm module’s visible machine learning approach for rapid and accurate pathogenic microbial taxonomic identification. The diversified cross-response sensing array consists of two-dimensional nanomaterial (2D-n) with fluorescently labeled single-stranded DNA (ssDNA) as sensing elements to extract a set of differential response profiles for each pathogenic microorganism. By altering the 2D-n and different ssDNA with different sequences, we can form multiple sensing elements. While interacting with microorganisms, the competition between ssDNA and 2D-n leads to the release of ssDNA from 2D-n. The signals are generated from binding force driven by the exfoliation of either ssDNA or 2D-n from the microorganisms. Thus, the signal is distinguished from different ssDNA and 2D-n combinations, differentiating the extracted information and visualizing the recognition process. Fluorescent signals collected from each sensing element at the wavelength around 520 nm are applied to generate a fingerprint. As a proof of concept, we demonstrate that a six-sensing array enables rapid and accurate pathogenic microbial taxonomic identification, including the drug-resistant microorganisms, under a data size of n = 288. We precisely identify microbial with an overall accuracy of 97.9%, which overcomes the big data dependence for identifying recurrent patterns in conventional methods. For each microorganism, the detection concentration is 10⁵ ~ 10⁸ CFU/mL for Escherichia coli, 10² ~ 10⁷ CFU/mL for E. coli-β, 10³ ~ 10⁸ CFU/mL for Staphylococcus aureus, 10³ ~ 10⁷ CFU/mL for MRSA, 10² ~ 10⁸ CFU/mL for Pseudomonas aeruginosa, 10³ ~ 10⁸ CFU/mL for Enterococcus faecalis, 10² ~ 10⁸ CFU/mL for Klebsiella pneumoniae, and 10³ ~ 10⁸ CFU/mL for Candida albicans. Combining the visible machine learning approach, this sensing array provides strategies for precision pathogenic microbial taxonomic identification.

Metal-Nanoparticle-Supported Nanozyme-Based Colorimetric Sensor Array for Precise Identification of Proteins and Oral Bacteria

Convenient, precise, and high-throughput discrimination of multiple bioanalytes is of great significance for an early diagnosis of diseases. Array-based pattern recognition has proven to be a powerful tool to detect diverse analytes, but developing sensing elements featuring favorable surface diversity still remains a challenge. In this work, we presented a simple and facile method to prepare programmable metal-nanoparticle (NP)-supported nanozymes (MNNs) as artificial receptors for the accurate identification of multiple proteins and oral bacteria. The in situ reduction of metal NPs on hierarchical MoS₂ on polypyrrole (PPy), which generated differential nonspecific interactions with bioanalytes, was envisaged as the encoder to break through the limited supply of the receptor's quantity. As a proof of concept, three metal NPs, i.e., Au, Ag, and Pd NPs, were taken as examples to deposit on PPy@MoS₂ as colorimetric probes to construct a cross-reactive sensor array. Based on the principal component analysis (PCA), the proposed MNN sensor array could well discriminate 11 proteins with unique fingerprint-like patterns at a concentration of 250 nM and was sufficiently sensitive to determine individual proteins with a detection limit down to the nanomolar level. Remarkably, two highly similar hemoglobins from different species (hemoglobin and bovine hemoglobin) have been precisely identified. Additionally, five oral bacteria were also well separated from each other without cross-classification at the level of 10⁷ CFU mL^-1. Furthermore, the sensor array allowed effective discrimination of complex protein mixtures either at different molar ratios or with minor varying components. Most importantly, the blind samples, proteins in human serums, proteins in simulated body fluid environment, the heat-denatured proteins, and even clinical cancer samples all could be well distinguished by the sensor array, demonstrating the real-world applications in clinical diagnosis.

A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds

Rapid and accurate identification of the mechanisms of action (MoAs) of antibacterial compounds remains a challenge for the development of antibacterial compounds. Computational inference methods for determining the MoAs of antibacterial compounds have been developed in recent years. In particular, approaches combining machine learning technology enable precisely recognizing the MoA of antibacterial compounds. However, these methods heavily rely on the big data resulting from multiplexed experiments. As such, these approaches tend to produce minimal throughput and are not comprehensive enough to be adapted to widespread industrial applications. Here, we present a machine learning approach based on a customized array sensor for directly identifying the MoAs of antibacterial compounds. The array sensor consists of different two-dimensional nanomaterial fluorescence quenchers with different fluorescence-labeled single-stranded DNAs (ssDNAs). By mapping the subtle difference of the physicochemical properties on the bacterial surface treated with different antibacterial compound stimuli, the array sensor ensures visualizing the recognition process. Moreover, the customized array sensor produces a high volume of the MoA database, overcoming the dependence on big data. We further use the array sensor to build a chemical-response unique "fingerprint" database of MoAs. By combining a neural network-based genetic algorithm (NNGA), we rapidly discriminate the MoAs of four antibiotics with an overall accuracy of 100%. Furthermore, a new screening antibacterial peptide has been discovered and evaluated by our approach for determining the MoA with high accuracy proven by other techniques.

Aptamer/AuNPs encoders endow precise identification and discrimination of lipoprotein subclasses

Lipoproteins are composed of lipid and apolipoproteins in conjunction with noncovalent bonds. Different lipoprotein categories, particularly Low-Density Lipoprotein (LDL), High-Density Lipoprotein (HDL) and Very Low-Density Lipoprotein (VLDL) disagree in roles for the occurrence and development of cardiovascular disease, and their exact discrimination are critically required. Herein, a multiplexed sensor platform combined with an encoder system is introduced for accurate analysis of multiple lipoproteins in complex matrix. Three encoders, i.e., bare AuNPs, AuNPs-anti-LDL aptamer (AuNPs-apt) and AuNPs-non-aptamer DNA (AuNPs-n), facilitate precise discrimination for lipoprotein subclasses at a fairly low level of 0.490 nM. The binding of single-stranded DNA (ssDNA) with AuNPs prevents them from gathering in a relatively higher level of salt. In targets stimuli, the weaker binding between ssDNA and AuNPs is destroyed to certain degrees depending on the differential affinities among DNA, AuNPs, and multifarious proteins. It results in distinct aggregation states of encoders to cause diverse ultraviolet absorption, which may be statistically characterized to achieve highly facile and precise identification for lipoprotein subclasses. Remarkably, LDL at 0.05-37.5 μg/mL could be identified by the encoder system. 11 typical proteins including three lipoprotein subclasses in human serum were also precisely discriminated. Furthermore, the accurate identification of lipoprotein subclasses with different molar ratios from real clinical serum samples were obtained.

Self-assembly of DNA Nanogels with Endogenous MicroRNA Toehold Self-regulating Switches for Targeted Gene Regulation Therapy

Herein, a smart nanohydrogel with endogenous microRNA-21 toehold is developed to encapsulate gemcitabine-loaded mesoporous silica nanoparticles for targeted pancreatic cancer therapy. This toehold mediated strand displacement method can simultaneously achieve...

Digital Microfluidic Thermal Control Chip-Based Multichannel Immunosensor for Noninvasively Detecting Acute Myocardial Infarction

Rapid and automated detection of acute myocardial infarction (AMI) at its developing stage is very important due to its high mortality rate. To quantitatively diagnose AMI, Myo, CK-MB, and cTnI are chosen as three biomarkers, which are usually detected through an immunosorbent assay, such as the enzyme-linked immunosorbent assay. However, the approach poses many drawbacks, such as long detection time, the cumbersome process, the need for professionals, and the difficulty of realizing automatic operation. Here, a multichannel digital microfluidic (DMF) thermal control chip integrated with a sandwich-based immunoassay strategy is proposed for the automated, rapid, and sensitive detection of AMI biomarkers. A miniaturized temperature control module is integrated on the back of the DMF chip, meeting the temperature requirement for the immunoassay. With this DMF thermal control chip, sample and reagent consumption are reduced to several microliters, significantly alleviating reagent consumption and sample dependence, and the automated and multichannel detection of biomarkers can be achieved. In this work, the simultaneously noninvasive detection of the human serum sample containing the three biomarkers of AMI is also achieved within 30 min, which improves the diagnostic accuracy of AMI. Due to the features of automation and miniaturization, the multichannel immunosensor can be used in community hospitals to increase the speed of diagnosis of patients with various acute diseases.