Cellular nucleus image-based smarter microscope system for single cell analysis.

Cell imaging technology is undoubtedly a powerful tool for studying single-cell heterogeneity due to its non-invasive and visual advantages. It covers microscope hardware, software, and image analysis techniques, which are hindered by low throughput owing to abundant hands-on time and expertise. Herein, a cellular nucleus image-based smarter microscope system for single-cell analysis is reported to achieve high-throughput analysis and high-content detection of cells. By combining the hardware of an automatic fluorescence microscope and multi-object recognition/acquisition software, we have achieved more advanced process automation with the assistance of Robotic Process Automation (RPA), which realizes a high-throughput collection of single-cell images. Automated acquisition of single-cell images has benefits beyond ease and throughout and can lead to uniform standard and higher quality images. We further constructed a single-cell image database-based convolutional neural network (Efficient Convolutional Neural Network, E-CNN) exceeding 20618 single-cell nucleus images. Computational analysis of large and complex data sets enhances the content and efficiency of single-cell analysis with the assistance of Artificial Intelligence (AI), which breaks through the super-resolution microscope's hardware limitation, such as specialized light sources with specific wavelengths, advanced optical components, and high-performance graphics cards. Our system can identify single-cell nucleus images that cannot be artificially distinguished with an accuracy of 95.3%. Overall, we build an ordinary microscope into a high-throughput analysis and high-content smarter microscope system, making it a candidate tool for Imaging cytology.

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.

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.

Microglial exosomal miR-466i-5p induces brain injury via promoting hippocampal neuron apoptosis in heatstroke.

Background: Brain injury is the main cause of poor prognosis in heatstroke (HS) patients due to heat-stress-induced neuronal apoptosis. However, as a new cross-talk way among cells, whether microglial exosomal-microRNAs (miRNAs) are involved in HS-induced neuron apoptosis has not been elucidated. Methods: We established a heatstroke mouse model and a heat-stressed neuronal cellular model on HT22 cell line. Then, we detected neuron apoptosis by histopathology and flow cytometry. The microglial exosomes are isolated by standard differential ultracentrifugation and characterized. Recipient neurons are treated with the control and HS exosomes, whereas in vivo, the exosomes were injected into the mice tail vein. The internalization of HS microglial exosomes by neurons was tracked. Apoptosis of HT22 was evaluated by flow cytometry and Western blot in vitro, TUNEL assay, and immunohistochemistry in vivo. We screened miR-466i-5p as the mostly upregulated microRNAs in HS exosomes by high-throughput sequencing and further conducted gene ontology (GO) pathway analysis. The effect and mechanism of HS exosomal miR-466i-5p on the induction of neuron apoptosis are demonstrated by nasal delivery of miR-466i-5p antagomir in vivo and transfecting miR-466i-5p mimics to HT22 in vitro. Results: HS induced an increase in neurons apoptosis. Microglial exosomes are identified and taken up by neurons, which induced HT22 apoptosis in vivo and vitro. HS significantly changed the miRNA profiles of microglial exosomes based on high-throughput sequencing. We selected miR-466i-5p as a target, and upregulated miR-466i-5p induced neurons apoptosis in vivo and vitro experiments. The effects are exerted by targeting Bcl-2, activating caspase-3 to induce neurons apoptosis. Conclusions: We demonstrate the effect of microglial exosomal miR-466i-5p on neurons apoptosis and reveal potentially Bcl-2/caspase-3 pathway in heatstroke.

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-ß, 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. • A molecular response differential profiling (MRDP) was established based on custom cross-response sensor array for rapid and accurate recognition and phenotyping common pathogenic microorganism. • Differential response profiling of pathogenic microorganism is derived from the competitive response capacity of 6 sensing elements of the sensor array. Each of these sensing elements' performance has competitive reaction with the microorganism. • MRDP was applied to LDA algorithm and resulted in the classification of 8 microorganisms.

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 specific drug release and miRNA-21 silencing, resulting in the up-regulation of the expression of tumor suppressor genes PTEN and PDCD4.

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

Correction for 'A machine learning approach-based array sensor for rapidly predicting the mechanisms of action of antibacterial compounds' by Zhijun Li et al., Nanoscale, 2022, 14, 3087-3096, DOI: 10.1039/D1NR07452K.

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.

Delivery of therapeutic oligonucleotides in nanoscale.

Therapeutic oligonucleotides (TOs) represent one of the most promising drug candidates in the targeted cancer treatment due to their high specificity and capability of modulating cellular pathways that are not readily druggable. However, efficiently delivering of TOs to cancer cellular targets is still the biggest challenge in promoting their clinical translations. Emerging as a significant drug delivery vector, nanoparticles (NPs) can not only protect TOs from nuclease degradation and enhance their tumor accumulation, but also can improve the cell uptake efficiency of TOs as well as the following endosomal escape to increase the therapeutic index. Furthermore, targeted and on-demand drug release of TOs can also be approached to minimize the risk of toxicity towards normal tissues using stimuli-responsive NPs. In the past decades, remarkable progresses have been made on the TOs delivery based on various NPs with specific purposes. In this review, we will first give a brief introduction on the basis of TOs as well as the action mechanisms of several typical TOs, and then describe the obstacles that prevent the clinical translation of TOs, followed by a comprehensive overview of the recent progresses on TOs delivery based on several various types of nanocarriers containing lipid-based nanoparticles, polymeric nanoparticles, gold nanoparticles, porous nanoparticles, DNA/RNA nanoassembly, extracellular vesicles, and imaging-guided drug delivery nanoparticles.

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.

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

A cross-responsive strategy (CRS) based on gold nanoparticles (AuNPs) through attaching various recognition receptors on the surface of AuNPs for identifying multiple analytes is presented, and the detection throughput and overall identification accuracy are improved. However, the CRS's recognition receptor cannot get comprehensive information from the target analytes limited in number and type, which determines the overall identification accuracy. Therefore, the practicability of the CRS runs into a bottleneck. Herein, we report a programmable DNA-AuNP encoder combined with a multimodal coupled analysis algorithm for high-throughput detection and accurate analysis of multiple metal ions. The programmable DNA-AuNP encoder breaks through the limitation of the recognition receptor's quantity. Furthermore, the multimodal signals from target metal ion-induced DNA-AuNP aggregation are related to and observed in the ultraviolet absorbance spectrum, surface potential, and particle diameter. The multimodal coupled analysis algorithm can reflect comprehensive information on the target analyte more completely. Finally, this study provides a highly generic tool for the cross-responsive strategy.

Photothermal-responsive nanosized hybrid polymersome as versatile therapeutics codelivery nanovehicle for effective tumor suppression.

Effective cancer therapies often demand delivery of combinations of drugs to inhibit multidrug resistance through synergism, and the development of multifunctional nanovehicles with enhanced drug loading and delivery efficiency for combination therapy is currently a major challenge in nanotechnology. However, such combinations are more challenging to administer than single drugs and can require multipronged approaches to delivery. In addition to being stable and biodegradable, vehicles for such therapies must be compatible with both hydrophobic and hydrophilic drugs, and release drugs at sustained therapeutic levels. Here, we report synthesis of porous silicon nanoparticles conjugated with gold nanorods [composite nanoparticles (cNPs)] and encapsulate them within a hybrid polymersome using double-emulsion templates on a microfluidic chip to create a versatile nanovehicle. This nanovehicle has high loading capacities for both hydrophobic and hydrophilic drugs, and improves drug delivery efficiency by accumulating at the tumor after i.v. injection in mice. Importantly, a triple-drug combination suppresses breast tumors by 94% and 87% at total dosages of 5 and 2.5 mg/kg, respectively, through synergy. Moreover, the cNPs retain their photothermal properties, which can be used to significantly inhibit multidrug resistance upon near-infrared laser irradiation. Overall, this work shows that our nanovehicle has great potential as a drug codelivery nanoplatform for effective combination therapy that is adaptable to other cancer types and to molecular targets associated with disease progression.

Ultrasensitive Detection of Metal Ions with DNA Nanostructure.

In spite of its greatly scientific and technological importance, developing rapid, low cost and sensitive microarray sensors for onsite monitoring heavy metal contamination remains challenging. Here we develop a DNA nanostructured microarray (DNM) with a tubular three-dimensional sensing surface and an ordered nanotopography for rapid and sensitive multiplex detection of heavy metal ions. In our design, DNA tetrahedral-structured probes (TSPs) are used to engineer the sensing interface with spatially resolved and density-tunable sensing spots, improving the micro-confined molecular recognition. Meanwhile, a bubble-mediated shuttle reaction inside the DNM-functionalized microchannel improves the target-capturing efficiency. Thus, the sensitive and selective detection of multiple heavy metal ions (i.e., Hg2+, Ag+, and Pb2+) with this novel DNM biosensor can be achieved within 5 min. Moreover, the detection limit is down to 10, 10, and 20 nM for Hg2+, Ag+, and Pb2+, respectively. Therefore, the DNM biosensor capable of simultaneously detecting multiple heavy metal ions with sensitivity and selectivity shows great potential to be point-of-test devices.

Programming Chemical Reaction Networks Using Intramolecular Conformational Motions of DNA.

The programmable regulation of chemical reaction networks (CRNs) represents a major challenge toward the development of complex molecular devices performing sophisticated motions and functions. Nevertheless, regulation of artificial CRNs is generally energy- and time-intensive as compared to natural regulation. Inspired by allosteric regulation in biological CRNs, we herein develop an intramolecular conformational motion strategy (InCMS) for programmable regulation of DNA CRNs. We design a DNA switch as the regulatory element to program the distance between the toehold and branch migration domain. The presence of multiple conformational transitions leads to wide-range kinetic regulation spanning over 4 orders of magnitude. Furthermore, the process of energy-cost-free strand exchange accompanied by conformational change discriminates single base mismatches. Our strategy thus provides a simple yet effective approach for dynamic programming of complex CRNs.

MoS2 Nanoprobe for MicroRNA Quantification Based on Duplex-Specific Nuclease Signal Amplification.

MicroRNAs (miRNAs) play significant regulatory roles in physiologic and pathologic processes and are considered as important biomarkers for disease diagnostics and therapeutics. Simple, fast, sensitive, and selective detection of miRNAs, however, is challenged by their short length, low abundance, susceptibility to degradation, and homogenous sequence. Here, we report a novel design of nanoprobes for highly sensitive and selective detection of miRNAs based on MoS2-loaded molecular beacons (MBs) and duplex-specific nuclease (DSN)-mediated signal amplification (DSNMSA). We show that MoS2 nanosheets not only exhibit high affinity toward MBs but also act as an efficient quencher for absorbed MBs. The strong fluorescence-quenching ability of MoS2 in combination with cyclic DSNMSA contributes to the superior sensitivity of our method, with a limit of detection 4 orders of magnitude lower than that of traditional hybridization methods. Moreover, the nanoprobes also show high selectivity for discriminating homogenous miRNA sequences with one-base differences because of the discrimination ability of MBs and DSN. Furthermore, we demonstrate that the MoS2-loaded MB nanoprobes can be utilized for multiplexed detection of miRNAs. Given its high sensitivity and specificity, as well as the multiplexed function; this novel method as an effective tool shows a great promise for simultaneous quantitative analysis of multiple miRNAs in biomedical research and clinical diagnosis.

Programmable and Multifunctional DNA-Based Materials for Biomedical Applications.

DNA encodes the genetic information; recently, it has also become a key player in material science. Given the specific Watson-Crick base-pairing interactions between only four types of nucleotides, well-designed DNA self-assembly can be programmable and predictable. Stem-loops, sticky ends, Holliday junctions, DNA tiles, and lattices are typical motifs for forming DNA-based structures. The oligonucleotides experience thermal annealing in a near-neutral buffer containing a divalent cation (usually Mg2+ ) to produce a variety of DNA nanostructures. These structures not only show beautiful landscape, but can also be endowed with multifaceted functionalities. This Review begins with the fundamental characterization and evolutionary trajectory of DNA-based artificial structures, but concentrates on their biomedical applications. The coverage spans from controlled drug delivery to high therapeutic profile and accurate diagnosis. A variety of DNA-based materials, including aptamers, hydrogels, origamis, and tetrahedrons, are widely utilized in different biomedical fields. In addition, to achieve better performance and functionality, material hybridization is widely witnessed, and DNA nanostructure modification is also discussed. Although there are impressive advances and high expectations, the development of DNA-based structures/technologies is still hindered by several commonly recognized challenges, such as nuclease instability, lack of pharmacokinetics data, and relatively high synthesis cost.

Self-Assembly of Enzyme-Like Nanofibrous G-Molecular Hydrogel for Printed Flexible Electrochemical Sensors.

Conducting hydrogels provide great potential for creating designer shape-morphing architectures for biomedical applications owing to their unique solid-liquid interface and ease of processability. Here, a novel nanofibrous hydrogel with significant enzyme-like activity that can be used as "ink" to print flexible electrochemical devices is developed. The nanofibrous hydrogel is self-assembled from guanosine (G) and KB(OH)4 with simultaneous incorporation of hemin into the G-quartet scaffold, giving rise to significant enzyme-like activity. The rapid switching between the sol and gel states responsive to shear stress enables free-form fabrication of different patterns. Furthermore, the replication of the G-quartet wires into a conductive matrix by in situ catalytic deposition of polyaniline on nanofibers is demonstrated, which can be directly printed into a flexible electrochemical electrode. By loading glucose oxidase into this novel hydrogel, a flexible glucose biosensor is developed. This study sheds new light on developing artificial enzymes with new functionalities and on fabrication of flexible bioelectronics.

Logic Catalytic Interconversion of G-Molecular Hydrogel.

By incorporating hemin into G-quadruplex (G4) during cation-templated self-assembly between guanosine and KB(OH)4, we have constructed an artificial enzyme hydrogel (AEH)-based system for the highly sensitive and selective detection of Pb2+. The sensing strategy is based on a Pb2+-induced decrease in AEH activity. Because of the higher efficiency of Pb2+ for stabilizing G4 compared with K+, the Pb2+ ions substitute K+ and trigger hemin release from G4, thus giving rise to a conformational interconversion accompanied by the loss of enzyme activity. The Pb2+-induced catalytic interconversion endows the AEH-based system with high sensitivity and selectivity for detecting Pb2+. As a result, the AEH-based system shows an excellent response for Pb2+ in the range from 1 pM to 50 nM with a limit of detection of ∼0.32 pM, which is much lower than that of the previously reported G4-DNAzyme. We also demonstrate that this AEH-based system exhibits high selectivity toward Pb2+ over other metal ions. Furthermore, two two-input INHIBIT logic gates have been constructed via switching of the catalytic interconversion induced by K+ and Pb2+ or K+ and pH. Given its versatility, this AEH-based system provides a novel platform for sensing and biomolecular computation.

Framework Nucleic Acid-Mediated Pull-Down MicroRNA Detection with Hybridization Chain Reaction Amplification.

Gastric cancer remains a disease of high mortality worldwide due to its poor prognosis. Previous studies have shown that microRNAs (miRNAs) are effective biomarkers for early diagnosis of gastric cancer. To realize sensitive detection of related miRNAs for improved early diagnosis, classification, and survival prognosis of gastric cancer, herein we developed a framework nucleic acid (FNA)-mediated microarray for quantitative analysis of multiple miRNAs. By rationally designing FNA with different sizes, we systematically modulated the surface density and lateral interactions of DNA probes, which provides an effective means for programmable tailoring of the hybridization efficiency and kinetics of the biosensing interface. We found that the hybridization efficiency was increased along with the size of the FNA and was optimum for FNA-17. In combination with the hybridization chain reaction amplification strategy, this established FNA microarray can serve as an ultrasensitive and selective analytical platform for simultaneous multiplexed detection of miRNA (e.g., FNA-miR-652, FNA-miR-627, and FNA-miR-629) biomarkers in gastric cancer.