DNA-framework-based multidimensional molecular classifiers for cancer diagnosis

A "dual-key-and-lock" DNA nanodevice enables spatially controlled multimodal imaging and combined cancer therapy

DNA-based theragnostic platforms have attracted more and more attention, while their applications are still impeded by nonspecific interference and insufficient therapeutic efficacy. Herein, we fabricate an integrated "dual-key-and-lock" DNA nanodevice (DKL-DND) which is composed of the inner Dox/Hairpin/Aptazyme-Au@Ag@Au probes and the outer metal-organic frameworks loaded with Fuel strand. Once internalized into human breast cancer cells (MCF-7), the DKL-DND is activated by cascaded endogenous stimuli (acidic pH in the lysosome and high expression of ATP in the cytoplasm), leading to spatially controlled optical/magnetic resonance multimodal imaging and gene/chemo/small molecule combined cancer therapy. By engineering pH and ATP-responsive units as cascaded locks on the DKL-DND, the operating status of the nanodevice and accessibility of encapsulated anti-tumour drugs can be precisely regulated in the specified physiological states, avoiding the premature activation and release during assembly and delivery. Both in vitro and in vivo assessments demonstrate that the DKL-DND with excellent stimuli-responsive ability, biocompatibility, stability and accumulation behaviour was capable of simultaneously affording accurate tumour diagnosis and efficient tumour growth inhibition. This integrated DKL-DND exhibits great promise in constructing self-adaptive nanodevices for multimodal imaging-guided combination therapy.

Spatially Localized Entropy-Driven Evolution of Nucleic Acid-Based Constitutional Dynamic Networks for Intracellular Imaging and Spatiotemporal Programmable Gene Therapy

The primer-guided entropy-driven high-throughput evolution of the DNA-based constitutional dynamic network, CDN, is introduced. The entropy gain associated with the process provides a catalytic principle for the amplified emergence of the CDN. The concept is applied to develop a programmable, spatially localized DNA circuit for effective in vitro and in vivo theranostic, gene-regulated treatment of cancer cells. The localized circuit consists of a DNA tetrahedron core modified at its corners with four tethers that include encoded base sequences exhibiting the capacity to emerge and assemble into a [2 × 2] CDN. Two of the tethers are caged by a pair of siRNA subunits, blocking the circuit into a mute, dynamically inactive configuration. In the presence of miRNA-21 as primer, the siRNA subunits are displaced, resulting in amplified release of the siRNAs silencing the HIF-1α mRNA and fast dynamic reconfiguration of the tethers into a CDN. The resulting CDN is, however, engineered to be dynamically reconfigured by miRNA-155 into an equilibrated mixture enriched with a DNAzyme component, catalyzing the cleavage of EGR-1 mRNA. The DNA tetrahedron nanostructure stimulates enhanced permeation into cancer cells. The miRNA-triggered entropy-driven reconfiguration of the spatially localized circuit leads to the programmable, cooperative bis-gene-silencing of HIF-1α and EGR-1 mRNAs, resulting in the effective and selective apoptosis of breast cancer cells and effective inhibition of tumors in tumor bearing mice.

Concept and Development of Algebraic Topological Framework Nucleic Acids

Nucleic acids are considered as promising materials for developing exquisite nanostructures from one to three dimensions. The advances of DNA nanotechnology facilitate ingenious design of DNA nanostructures with diverse shapes and sizes. Especially, the algebraic topological framework nucleic acids (ATFNAs) are functional DNA nanostructures that engineer guest molecules (e. g., nucleic acids, proteins, small molecules, and nanoparticles) stoichiometrically and spatially. The intrinsic precise properties and tailorable functionalities of ATFNAs hold great promise for biological applications, such as cell recognition and immunotherapy. This Perspective highlights the concept and development of precisely assembled ATFNAs, and outlines the new frontiers and opportunities for exploiting the structural advantages of ATFNAs for biological applications.

DNA Framework-Enabled 3D Organization of Antiarrhythmic Drugs for Radiofrequency Catheter Ablation

Preorganizing molecular drugs within a microenvironment is crucial for the development of efficient and controllable therapeutic systems. Here, the use of tetrahedral DNA framework (TDF) is reported to preorganize antiarrhythmic drugs (herein doxorubicin, Dox) in 3D for catheter ablation, a minimally invasive treatment for fast heartbeats, aiming to address potential complications linked to collateral tissue damage and the post-ablation atrial fibrillation (AF) recurrence resulting from incomplete ablation. Dox preorganization within TDF transforms its random distribution into a confined, regular spatial arrangement governed by DNA. This, combined with the high affinity between Dox and DNA, significantly increases local Dox concentration. The exceptional capacity of TDF for cellular internalization leads to a 5.5-fold increase in intracellular Dox amount within cardiomyocytes, effectively promoting cellular apoptosis. In vivo investigations demonstrate that administering TDF-Dox reduces the recurrence rate of electrical conduction after radiofrequency catheter ablation (RFCA) to 37.5%, compared with the 77.8% recurrence rate in the free Dox-treated group. Notably, the employed Dox dosage exhibits negligible adverse effects in vivo. This study presents a promising treatment paradigm that strengthens the efficacy of catheter ablation and opens a new avenue for reconciling the paradox of ablation efficacy and collateral damage.

Engineering Modular DNA Reaction Networks for Signal Processing

Diversified molecular information-processing methods have significant implications for nanoscale manipulation and control, monitoring and disease diagnosis of organisms, and direct intervention in biological activities. However, as an effective approach for implementing multifunctional molecular information processing, DNA reaction networks (DRNs) with numerous functionally specialized molecular structures have challenged them on scale design, leading to increased network complexity, further causing problems such as signal leakage, attenuation, and cross-talk in network reactions. Our study developed a strategy for performing various signal-processing tasks through engineering modular DRNs. This strategy is based on a universal core unit with signal selection capability, and a time-adjustable signal self-resetting module is achieved by combing the core unit and self-resetting unit, which improves the time controllability of modular DRNs. In addition, multi-input and -output signal cross-catalytic and continuously adjustable signal delay modules were realized by combining core and threshold units, providing a flexible, precise method for modular DRNs to process the signal. The strategy simplifies the design of DRNs, helps generate design ideas for large-scale integrated DRNs with multiple functions, and provides prospects in biocomputing, gene regulation, and biosensing.

Chronopotentiometric Nanopore Sensor Based on a Stimulus-Responsive Molecularly Imprinted Polymer for Label-Free Dual-Biomarker Detection

The development of sensors for detection of biomarkers exhibits an exciting potential in diagnosis of diseases. Herein, we propose a novel electrochemical sensing strategy for label-free dual-biomarker detection, which is based on the combination of stimulus-responsive molecularly imprinted polymer (MIP)-modified nanopores and a polymeric membrane chronopotentiometric sensor. The ion fluxes galvanostatically imposed on the sensing membrane surface can be blocked by the recognition reaction between the target biomarker in the sample solution and the stimulus-responsive MIP receptor in the nanopores, thus causing a potential change. By using two external stimuli (i.e., pH and temperature), the recognition abilities of the stimulus-responsive MIP receptor can be effectively modulated so that dual-biomarker label-free chronopotentiometric detection can be achieved. Using alpha fetoprotein (AFP) and prostate-specific antigen (PSA) as model biomarkers, the proposed sensor offers detection limits of 0.17 and 0.42 ng/mL for AFP and PSA, respectively.

Buoyant Metal-Organic Framework Corona-Driven Fast Isolation and Ultrasensitive Profiling of Circulating Extracellular Vesicles

Accurately assaying tumor-derived circulating extracellular vesicles (EVs) is fundamental in noninvasive cancer diagnosis and therapeutic monitoring but limited by challenges in efficient EV isolation and profiling. Here, we report a bioinspired buoyancy-driven metal-organic framework (MOF) corona that leverages on-bubble coordination and dual-encoded surface-enhanced Raman scattering (SERS) nanotags to streamline rapid isolation and ultrasensitive profiling of plasma EVs in a single assay for cancer diagnostics. This integrated bubble-MOF-SERS EV assay (IBMsv) allows barnacle-like high-density adhesion of MOFs on a self-floating bubble surface to enable fast isolation (2 min, near 90% capture efficiency) of tumor EVs via enhanced EV-MOF binding. Also, IBMsv harnesses four-plexed SERS nanotags to profile the captured EV surface protein markers at a single-particle level. Such a sensitive assay allows multiplexed profiling of EVs across five cancer types, revealing heterogeneous EV surface expression patterns. Furthermore, the IBMsv assay enables cancer diagnosis in a pilot clinical cohort (n = 55) with accuracies >95%, improves discrimination between cancer and noncancer patients via an algorithm, and monitors the surgical treatment response from hepatocellular carcinoma patients. This assay provides a fast, sensitive, streamlined, multiplexed, and portable blood test tool to enable cancer diagnosis and response monitoring in clinical settings.

DNA Framework-Based Programmable Atom-Like Nanoparticles for Non-Coding RNA Recognition and Differentiation of Cancer Cells

The cooperative diagnosis of non-coding RNAs (ncRNAs) can accurately reflect the state of cell differentiation and classification, laying the foundation of precision medicine. However, there are still challenges in simultaneous analyses of multiple ncRNAs and the integration of biomarker data for cell typing. In this study, DNA framework-based programmable atom-like nanoparticles (PANs) are designed to develop molecular classifiers for intra-cellular imaging of multiple ncRNAs associated with cell differentiation. The PANs-based molecular classifier facilitates signal amplification through the catalytic hairpin assembly. The interaction between PAN reporters and ncRNAs enables high-fidelity conversion of ncRNAs expression level into binding events, and the assessment of in situ ncRNAs levels via measurement of the fluorescent signal changes of PAN reporters. Compared to non-amplified methods, the detection limits of PANs are reduced by four orders of magnitude. Using human gastric cancer cell lines as a model system, the PANs-based molecular classifier demonstrates its capacity to measure multiple ncRNAs in living cells and assesses the degree of cell differentiation. This approach can serve as a universal strategy for the classification of cancer cells during malignant transformation and tumor progression.

Framework Nucleic Acids Combined with 3D Hybridization Chain Reaction Amplifiers for Monitoring Multiple Human Tear Cytokines

Existing tear sensors are difficult to perform multiplexed assays due to the minute amounts of biomolecules in tears and the tiny volume of tears. Herein, the authors leverage DNA tetrahedral frameworks (DTFs) modified on the wireless portable electrodes to effectively capture 3D hybridization chain reaction (HCR) amplifiers for automatic and sensitive monitoring of multiple cytokines in human tears. The developed sensors allow the sensitive determination of various dry eye syndrome (DES)-associated cytokines in human tears with the limit of detection down to 0.1 pg mL-1, consuming as little as 3 mL of tear fluid. Double-blind testing of clinical DES samples using the developed sensor and commercial ELISA shows no significant difference between them. Compared with single-biomarker diagnosis, the diagnostic accuracy of this sensor based on multiple biomarkers has improved by ≈16%. The developed system offers the potential for tear sensors to enable personalized and accurate diagnosis of various ocular diseases.

A spatially localized DNA linear classifier for cancer diagnosis

Molecular computing is an emerging paradigm that plays an essential role in data storage, bio-computation, and clinical diagnosis with the future trends of more efficient computing scheme, higher modularity with scaled-up circuity and stronger tolerance of corrupted inputs in a complex environment. Towards these goals, we construct a spatially localized, DNA integrated circuits-based classifier (DNA IC-CLA) that can perform neuromorphic architecture-based computation at a molecular level for medical diagnosis. The DNA-based classifier employs a two-dimensional DNA origami as the framework and localized processing modules as the in-frame computing core to execute arithmetic operations (e.g. multiplication, addition, subtraction) for efficient linear classification of complex patterns of miRNA inputs. We demonstrate that the DNA IC-CLA enables accurate cancer diagnosis in a faster (about 3 h) and more effective manner in synthetic and clinical samples compared to those of the traditional freely diffusible DNA circuits. We believe that this all-in-one DNA-based classifier can exhibit more applications in biocomputing in cells and medical diagnostics.

Triblock PolyA-Mediated Protein Biosensor Based on a Size-Matching Proximity Hybridization Analysis

The antibodies in the natural biological world utilize bivalency/multivalency to achieve a higher affinity for antigen capture. However, mimicking this mechanism on the electrochemical sensing interface and enhancing biological affinity through precise spatial arrangement of bivalent aptamer probes still pose a challenge. In this study, we have developed a novel self-assembly layer (SAM) incorporating triblock polyA DNA to enable accurate organization of the aptamer probes on the interface, constructing a "lock-and-key-like" proximity hybridization assay (PHA) biosensor. The polyA fragment acts as an anchoring block with a strong affinity for the gold surface. Importantly, it connects the two DNA probes, facilitating one-to-one spatial proximity and enabling a controllable surface arrangement. By precisely adjusting the length of the polyA fragment, we can tailor the distance between the probes to match the molecular dimensions of the target protein. This design effectively enhances the affinity of the aptamers. Notably, our biosensor demonstrates exceptional specificity and sensitivity in detecting PDGF-BB, as confirmed through successful validation using human serum samples. Overall, our biosensor presents a novel and versatile interface for proximity assays, offering a significantly improved surface arrangement and detection performance.

Simulation-Guided Rational Design of DNA Walker-Based Theranostic Platform

Biomolecule-functionalized nanoparticles represent a type of promising biomaterials in biomedical applications owing to their excellent biocompatibility and versatility. DNA-based reactions on nanoparticles have enabled emerging applications including intelligent biosensors, drug delivery, and biomimetic devices. Among the reactions, strand hybridization is the critical step to control the sensitivity and specificity of biosensing, and the efficiency of drug delivery. However, a comprehensive understanding of DNA hybridization on nanoparticles is still lacking, which may differ from the process in homogeneous solutions. To address this limitation, coarse-grained model-based molecular dynamic simulation is harnessed to disclose the critical factors involved in intermolecular hybridization. Based on simulation guidance, DNA walker-based smart theranostic platform (DWTP) based on "on-particle" hybridization is developed, showing excellent consistency with simulation. DWTP is successfully applied for highly sensitive miRNA 21 detection and tumor-specific miRNA 21 imaging, driven by tumor-endogenous APE 1 enzyme. It enables the precise release of antisense oligonucleotide triggered by tumor-endogenous dual-switch miRNA 21 and APE 1, facilitating effective gene silencing therapy with high biosafety. The simulation of "on-particle" DNA hybridization has improved the corresponding biosensing performance and the release efficiency of therapeutic agents, representing a conceptually new approach for DNA-based device design.

Machine Learning-Assistant Colorimetric Sensor Arrays for Intelligent and Rapid Diagnosis of Urinary Tract Infection

Urinary tract infections (UTIs), which can lead to pyelonephritis, urosepsis, and even death, are among the most prevalent infectious diseases worldwide, with a notable increase in treatment costs due to the emergence of drug-resistant pathogens. Current diagnostic strategies for UTIs, such as urine culture and flow cytometry, require time-consuming protocols and expensive equipment. We present here a machine learning-assisted colorimetric sensor array based on recognition of ligand-functionalized Fe single-atom nanozymes (SANs) for the identification of microorganisms at the order, genus, and species levels. Colorimetric sensor arrays are built from the SAN Fe1-NC functionalized with four types of recognition ligands, generating unique microbial identification fingerprints. By integrating the colorimetric sensor arrays with a trained computational classification model, the platform can identify more than 10 microorganisms in UTI urine samples within 1 h. Diagnostic accuracy of up to 97% was achieved in 60 UTI clinical samples, holding great potential for translation into clinical practice applications.

DNA Framework-Programmed Nanoscale Enzyme Assemblies

Multienzyme assemblies mediated by multivalent interaction play a crucial role in cellular processes. However, the three-dimensional (3D) programming of an enzyme complex with defined enzyme activity in vitro remains unexplored, primarily owing to limitations in precisely controlling the spatial topological configuration. Herein, we introduce a nanoscale 3D enzyme assembly using a tetrahedral DNA framework (TDF), enabling the replication of spatial topological configuration and maintenance of an identical edge-to-edge distance akin to natural enzymes. Our results demonstrate that 3D nanoscale enzyme assemblies in both two-enzyme systems (glucose oxidase (GOx)/horseradish peroxidase (HRP)) and three-enzyme systems (amylglucosidase (AGO)/GOx/HRP) lead to enhanced cascade catalytic activity compared to the low-dimensional structure, resulting in ∼5.9- and ∼7.7-fold enhancements over homogeneous diffusional mixtures of free enzymes, respectively. Furthermore, we demonstrate the enzyme assemblies for the detection of the metabolism biomarkers creatinine and creatine, achieving a low limit of detection, high sensitivity, and broad detection range.

Scalable search of massively pooled nucleic acid samples enabled by a molecular database query language

The surge in nucleic acid analytics requires scalable storage and retrieval systems akin to electronic databases used to organize digital data. Such a system could transform disease diagnosis, ecological preservation, and molecular surveillance of biothreats. Current storage systems use individual containers for nucleic acid samples, requiring single-sample retrieval that falls short compared with digital databases that allow complex and combinatorial data retrieval on aggregated data. Here, we leverage protective microcapsules with combinatorial DNA labeling that enables arbitrary retrieval on pooled biosamples analogous to Structured Query Languages. Ninety-six encapsulated pooled mock SARS-CoV-2 genomic samples barcoded with patient metadata are used to demonstrate queries with simultaneous matches to sample collection date ranges, locations, and patient health statuses, illustrating how such flexible queries can be used to yield immunological or epidemiological insights. The approach applies to any biosample database labeled with orthogonal barcodes, enabling complex post-hoc analysis, for example, to study global biothreat epidemiology.

Electro-Optical Multiclassification Platform for Minimizing Occasional Inaccuracy in Point-of-Care Biomarker Detection

On-site diagnostic tests that accurately identify disease biomarkers lay the foundation for self-healthcare applications. However, these tests routinely rely on single-mode signals and suffer from insufficient accuracy, especially for multiplexed point-of-care tests (POCTs) within a few minutes. Here, this work develops a dual-mode multiclassification diagnostic platform that integrates an electrochemiluminescence sensor and a field-effect transistor sensor in a microfluidic chip. The microfluidic channel guides the testing samples to flow across electro-optical sensor units, which produce dual-mode readouts by detecting infectious biomarkers of tuberculosis (TB), human rhinovirus (HRV), and group B streptococcus (GBS). Then, machine-learning classifiers generate three-dimensional (3D) hyperplanes to diagnose different diseases. Dual-mode readouts derived from distinct mechanisms enhance the anti-interference ability physically, and machine-learning-aided diagnosis in high-dimensional space reduces the occasional inaccuracy mathematically. Clinical validation studies with 501 unprocessed samples indicate that the platform has an accuracy approaching 99%, higher than the 77%-93% accuracy of rapid point-of-care testing technologies at 100% statistical power (>150 clinical tests). Moreover, the diagnosis time is 5 min without a trade-off of accuracy. This work solves the occasional inaccuracy issue of rapid on-site diagnosis, endowing POCT systems with the same accuracy as laboratory tests and holding unique prospects for complicated scenes of personalized healthcare.

Functionalized tetrahedral DNA frameworks for the capture of circulating tumor cells

Identification and characterization of circulating tumor cells (CTCs) from blood samples of patients with cancer can help monitor parameters such as disease stage, disease progression and therapeutic efficiency. However, the sensitivity and specificity of current multivalent approaches used for CTC capture is limited by the lack of control over the ligands' position. In this Protocol Update, we describe DNA-tetrahedral frameworks anchored with aptamers that can be configured with user-defined spatial arrangements and stoichiometries. The modified tetrahedral DNA frameworks, termed 'n-simplexes', can be used as probes to specifically target receptor-ligand interactions on the cell membrane. Here, we describe the synthesis and use of n-simplexes that target the epithelial cell adhesion molecule expressed on the surface of CTCs. The characterization of the n-simplexes includes measuring the binding affinity to the membrane receptors as a result of the spatial arrangement and stoichiometry of the aptamers. We further detail the capture of CTCs from patient blood samples. The procedure for the preparation and characterization of n-simplexes requires 11.5 h, CTC capture from clinical samples and data processing requires ~5 h per six samples and the downstream analysis of captured cells typically requires 5.5 h. The protocol is suitable for users with basic expertise in molecular biology and handling of clinical samples.

Development of Optical Differential Sensing Based on Nanomaterials for Biological Analysis

The discrimination and recognition of biological targets, such as proteins, cells, and bacteria, are of utmost importance in various fields of biological research and production. These include areas like biological medicine, clinical diagnosis, and microbiology analysis. In order to efficiently and cost-effectively identify a specific target from a wide range of possibilities, researchers have developed a technique called differential sensing. Unlike traditional "lock-and-key" sensors that rely on specific interactions between receptors and analytes, differential sensing makes use of cross-reactive receptors. These sensors offer less specificity but can cross-react with a wide range of analytes to produce a large amount of data. Many pattern recognition strategies have been developed and have shown promising results in identifying complex analytes. To create advanced sensor arrays for higher analysis efficiency and larger recognizing range, various nanomaterials have been utilized as sensing probes. These nanomaterials possess distinct molecular affinities, optical/electrical properties, and biological compatibility, and are conveniently functionalized. In this review, our focus is on recently reported optical sensor arrays that utilize nanomaterials to discriminate bioanalytes, including proteins, cells, and bacteria.

Advances in Machine Learning Processing of Big Data from Disease Diagnosis Sensors

Exploring accurate, noninvasive, and inexpensive disease diagnostic sensors is a critical task in the fields of chemistry, biology, and medicine. The complexity of biological systems and the explosive growth of biomarker data have driven machine learning to become a powerful tool for mining and processing big data from disease diagnosis sensors. With the development of bioinformatics and artificial intelligence (AI), machine learning models formed by data mining have been able to guide more sensitive and accurate molecular computing. This review presents an overview of big data collection approaches and fundamental machine learning algorithms and discusses recent advances in machine learning and molecular computational disease diagnostic sensors. More specifically, we highlight existing modular workflows and key opportunities and challenges for machine learning to achieve disease diagnosis through big data mining.

A Programmable DNAzyme for the Sensitive Detection of Nucleic Acids

Nucleic acids in biofluids are emerging biomarkers for the molecular diagnostics of diseases, but their clinical use has been hindered by the lack of sensitive detection assays. Herein, we report the development of a sensitive nucleic acid detection assay named SPOT (sensitive loop-initiated DNAzyme biosensor for nucleic acid detection) by rationally designing a catalytic DNAzyme of endonuclease capability into a unified one-stranded allosteric biosensor. SPOT is activated once a nucleic acid target of a specific sequence binds to its allosteric module to enable continuous cleavage of molecular reporters. SPOT provides a highly robust platform for sensitive, convenient and cost-effective detection of low-abundance nucleic acids. For clinical validation, we demonstrated that SPOT could detect serum miRNAs for the diagnostics of breast cancer, gastric cancer and prostate cancer. Furthermore, SPOT exhibits potent detection performance over SARS-CoV-2 RNA from clinical swabs with high sensitivity and specificity. Finally, SPOT is compatible with point-of-care testing modalities such as lateral flow assays. Hence, we envision that SPOT may serve as a robust assay for the sensitive detection of a variety of nucleic acid targets enabling molecular diagnostics in clinics.

DNA as a universal chemical substrate for computing and data storage

DNA computing and DNA data storage are emerging fields that are unlocking new possibilities in information technology and diagnostics. These approaches use DNA molecules as a computing substrate or a storage medium, offering nanoscale compactness and operation in unconventional media (including aqueous solutions, water-in-oil microemulsions and self-assembled membranized compartments) for applications beyond traditional silicon-based computing systems. To build a functional DNA computer that can process and store molecular information necessitates the continued development of strategies for computing and data storage, as well as bridging the gap between these fields. In this Review, we explore how DNA can be leveraged in the context of DNA computing with a focus on neural networks and compartmentalized DNA circuits. We also discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we provide insights into how DNA computing can be integrated with DNA data storage and explore the use of DNA for near-memory computing for future information technology and health analysis applications.

Programmable Atom-Like Nanoparticle Reporters for High-Precision Urinalysis of In Situ Membrane Proteins

The expression of disease-specific membrane proteins (MPs) is a crucial indicator for evaluating the onset and progression of diseases. Urinalysis of in situ MPs has the potential for point-of-care disease diagnostics, yet remains challenging due to the lack of molecular reporter to transform the expression information of in situ MPs into the measurable urine composition. Herein, a series of tetrahedral DNA frameworks (TDFs) are employed as the cores of programmable atom-like nanoparticles (PANs) to direct the self-assembly of PAN reporters with defined ligand valence and spatial distribution. With the rational spatial organization of ligands, the interaction between PAN reporters and MPs exhibits superior stability on cell-membrane interface under renal tubule-mimic fluid microenvironment, thus enabling high-fidelity conversion of MPs expression level into binding events and reverse assessment of in situ MP levels via measurement of the renal clearance efficiency of PAN reporters. Such PAN reporter-mediated signal transformation mechanism empowers urinalysis of the onset of acute kidney injury at least 6 h earlier than the existing methods with an area under the curve of 100%. This strategy has the potential for urinalysis of a variety of in situ membrane proteins.

Programmable Primer Switching for Regulating Enzymatic DNA Circuits

Developing DNA strand displacement reactions (SDRs) offers crucial technical support for regulating artificial nucleic acid circuits and networks. More recently, enzymatic SDR-based DNA circuits have gained significant attention because of their modular design, high orthogonality signaling, and extremely fast reaction rates. Typical enzymatic SDRs are regulated by relatively long primers (20-30 nucleotides) that hybridize to form stable double-stranded structures, facilitating enzyme-initiated events. Implementing more flexible primer-based enzymatic SDR regulations remains challenging due to the lack of convenient and simple primer control mechanism, which consequently limits the development of enzymatic DNA circuits. In this study, we propose an approach, termed primer switching regulation, that implements programmable and flexible regulations of enzymatic circuits by introducing switchable wires into the enzymatic circuits. We applied this method to generate diverse enzymatic DNA circuits, including cascading, fan-in/fan-out, dual-rail, feed-forward, and feedback functions. Through this method, complex circuit functions can be implemented by just introducing additional switching wires without reconstructing the basic circuit frameworks. The method is experimentally demonstrated to provide flexible and programmable regulations to control enzymatic DNA circuits and has future applications in DNA computing, biosensing, and DNA storage.

Exosome-tuned MOF signal amplifier boosting tumor exosome phenotyping with high-affinity nanostars

The natural phospholipid structure imparts exosomes with not only cargo protection, but rich sites for coordination with metal-organic frameworks (MOFs) to assemble functional nanocomplexes, such as signal amplifiers. Here, we exploit exosomes to tune MOF signal amplifiers (Exo-MOF) for ultrasensitive phenotyping of tumor-derived exosomes (tExo) based on self-driven coordination assembly and high-affinity nanostars. Exo-MOF leverages the specific coordination interaction between exosome and MOF that cages abundant redox molecules to assemble a super-redox signal amplifier. Moreover, the dispersed immuno-magnetic nanostars, which are assembled with antibodies on the surface of Au nanostars-coated magnetic nanoparticles, allow for rapid capturing of target tExo, addressing the limited mass transfer on electrode surface. Both Exo-MOF and high-affinity nanostars orchestrate the ultrahigh sensitivity (1 particle per 100 μL, higher than that no Exo-MOF by at least 10-fold), specificity and speed of the sensor in tExo detection. Such a sensitive strategy allows profiling tExo across seven cancer types, and revealing the distinct exosomal surface expression patterns. Further, the Exo-MOF sensor accurately distinguishes cancer patients from healthy individuals in a clinical cohort, and provides new opportunities for functional materials assembly and precision diagnostics.

Holistic Prediction of AuNP Aggregation in Diverse Aqueous Suspensions Based on Machine Vision and Dark-Field Scattering Imaging

The localized surface-plasmon resonance of the AuNP in aqueous media is extremely sensitive to environmental changes. By measuring the signal of plasmon scattering light, the dark-field microscopic (DFM) imaging technique has been used to monitor the aggregation of AuNPs, which has attracted great attention because of its simplicity, low cost, high sensitivity, and universal applicability. However, it is still challenging to interpret DFM images of AuNP aggregation due to the heterogeneous characteristics of the isolated and discontinuous color distribution. Herein, we introduce machine vision algorithms for the training of DFM images of AuNPs in different saline aqueous media. A visual deep learning framework based on AlexNet is constructed for studying the aggregation patterns of AuNPs in aqueous suspensions, which allows for rapid and accurate identification of the aggregation extent of AuNPs, with a prediction accuracy higher than 0.96. With the aid of machine learning analysis, we further demonstrate the prediction ability of various aggregation phenomena induced by both cation species and the concentration of the external saline solution. Our results suggest the great potential of machine vision frameworks in the accurate recognition of subtle pattern changes in DFM images, which can help researchers build predictive analytics based on DFM imaging data.

High-Performance Plasma Biomarker Panel for Alzheimer's Disease Screening Using a Femtomolar-Level Label-Free Biosensing System

Alzheimer's disease (AD) is the most common cause of dementia in older people. However, diagnosing AD through noncognitive methods, such as invasive cerebrospinal fluid sampling or radioactive positron emission tomography, has limited applications. Herein, the femtomolar levels of AD biomarkers amyloid β 40 (Aβ40), amyloid β 42 (Aβ42), phosphorylated tau 181 (P-tau181), phosphorylated tau 217 (P-tau217), and neurofilament light chain (NfL) were determined in human plasma in multicenter clinical cohorts using an ultrasensitive graphene field-effect transistor sensor. A machine-learning algorithm was also used to assemble these plasma biomarkers and optimize their performance in discriminating individual stages of Alzheimer's dementia progression. The "composite-info" biomarker panel, which combines these biomarkers and clinical information, considerably improved the staging performance in AD progression. It achieved an area under the curve of >0.94 in the receiver operator characteristic (ROC) curve. In addition, the panel demonstrated an advantage in the individual-based stage assessment compared with that of the Mini-Mental State Examination/Montreal Cognitive Assessment and nuclear magnetic resonance imaging. This study provides a composite biomarker panel for the screening and early diagnosis of AD using a rapid detection system.

Construction of an Enzyme Cascade Based on the Accurate Adjacent Arrangement of Coupled Enzymes Using a Triblock PolyA DNA Probe

Intracellular enzyme cascades are essential for various biological processes, and mimicking their functions in artificial systems has attracted significant research attention. However, achieving convenient and efficient spatial organization of enzymes on interfaces remains a critical challenge. In this work, we designed a simple single-DNA scaffold using triblock polyA single-stranded DNA for the arrangement of coupled enzymes. The scaffold was assembled onto a gold electrode through the affinity of polyA-Au, and two enzymes (glucose oxidase and horseradish peroxidase) were captured through hybridization. The molecular distance between the enzymes was regulated by changing the length of the polyA fragment. As a proof of concept, a glucose biosensor was constructed based on the enzyme cascade amplification. The biosensor exhibited excellent detection capability for glucose in human serum samples with a limit of detection of 1.6 μM. Additionally, a trienzyme cascade reaction was successfully activated, demonstrating the potential scalability of our approach for multienzyme reactions. This study provides a promising platform for the development of easy-to-operate, highly efficient, and versatile enzyme cascade systems using DNA scaffolds.

Endogenous mRNA-Powered and Spatial Confinement-Derived DNA Nanomachines for Ultrarapid and Sensitive Imaging of Let-7a

DNA nanostructure-based signal amplifiers offer new tools for imaging intracellular miRNA. However, the inadequate kinetics and susceptibility to enzymatic hydrolysis of these amplifiers, combined with a deficient cofactor concentration within the intracellular environment, significantly undermine their operational efficiency. In this study, we address these challenges by encapsulating a localized target strand displacement assembly (L-SD) and a toehold-exchange endogenous-powered component (R-mRNA) within a framework nucleic acid (FNA) structure─20 bp cubic DNA nanocage (termed RL-cube). This design enables the construction of an endogenous-powered and spatial-confinement DNA nanomachine for ratiometric fluorescence imaging of intracellular miRNA Let-7a. The R-mRNA is designed to be specifically triggered by glyceraldehyde 3-phosphate dehydrogenase (GAPDH), an abundant cellular enzyme, and concurrently releases a component that can recycle the target Let-7a. Meanwhile, L-SD reacts with Let-7a to release a stem-loop beacon, generating a FRET signal. The spatial confinement provided by the framework, combined with the ample intracellular supply of GAPDH, imparts remarkable sensitivity (7.57 pM), selectivity, stability, biocompatibility, and attractive dynamic performance (2240-fold local concentration, approximately four times reaction rate, and a response time of approximately 7 min) to the nanomachine-based biosensor. Consequently, this study introduces a potent sensing approach for detecting nucleic acid biomarkers with significant potential for application in clinical diagnostics and therapeutics.

Mercury-Mediated Epitaxial Accumulation of Au Atoms for Stained Hydrogel-Improved On-Site Mercury Monitoring

Trivalent Au ions are easily reduced to be zerovalent atoms by coexisting reductant reagents, resulting in the subsequent accumulation of Au atoms and formation of plasmonic nanostructures. In the absence of stabilizers or presence of weak stabilizers, aggregative growth of Au nanoparticles (NPs) always occurs, and unregular multidimensional Au materials are consequently constructed. Herein, the addition of nanomole-level mercury ions can efficiently prevent the epitaxial accumulation of Au atoms, and separated Au NPs with mediated morphologies and superior plasmonic characteristics are obtained. Experimental results and theoretical simulation demonstrate the Hg-concentration-reliant formation of plasmonic nanostructures with their mediated sizes and shapes in the presence of weak reductants. Moreover, the sensitive plasmonic responses of reaction systems exhibit selectivity comparable to that of Hg species. As a concept of proof, polymeric carbon dots (CDs) were used as the initial reductant, and the reactions between trivalent Au and CDs were studies. Significantly, Hg atoms prevent the epitaxial accumulation of Au atoms, and plasmonic NPs with decreased sizes were in situ synthesized, corresponding to varied surface plasmonic resonance absorption performance of the CD-induced hybrids. Moreover, with the integration of sensing substrates of CD-doped hydrogels, superior response stabilities, analysis selectivity, and sensitivity of Hg2+ ions were achieved on the basis of the mercury-mediated in situ chemical reactions between trivalent Au ions and reductant CDs. Consequently, a high-performance sensing strategy with the use of Au NP-staining hydrogels (nanostaining hydrogels) was exhibited. In addition to Hg sensing, the nanostaining hydrogels facilitated by doping of emerging materials and advanced chem/biostrategies can be developed as high-performance on-site monitoring routes to various pollutant species.

CRISPR/dCas9-Mediated Specific Molecular Assembly Facilitates Genotyping of Mutant Circulating Tumor DNA

Breakthroughs in circulating tumor DNA (ctDNA) analysis are critical in tumor liquid biopsies but remain a technical challenge due to the double-stranded structure, extremely low abundance, and short half-life of ctDNA. Here, we report an electrochemical CRISPR/dCas9 sensor (E-dCas9) for sensitive and specific detection of ctDNA at a single-nucleotide resolution. The E-dCas9 design harnesses the specific capture and unzipping of target ctDNA by dCas9 to introduce a complementary reporter probe for specific molecular assembly and signal amplification. By efficient homogeneous assembly and interfacial click reaction, the assay demonstrates superior sensitivity (up to 2.86 fM) in detecting single-base mutant ctDNA and a broad dynamic range spanning 6 orders of magnitude. The sensor is also capable of measuring 10 fg/μL of a mutated target in excess of wild-type ones (1 ng/μL), equivalent to probing 0.001% of the mutation relative to the wild type. In addition, our sensor can monitor the dynamic expression of cellular genomic DNA and allows accurate analysis of blood samples from patients with nonsmall cell lung cancer, suggesting the potential of E-dCas9 as a promising tool in ctDNA-based cancer diagnosis.

DNA Framework Programmed Conformational Reconstruction of Antibody Complementary Determining Region

The conformation of complementary determining region (CDR) is crucial in dictating its specificity and affinity for binding with an antigen, making it a focal point in artificial antibody engineering. Although desirable, programmable scaffolds that can regulate the conformation of individual CDRs with nanometer precision are still lacking. Here, we devise a strategy to program the CDR conformation by anchoring both ends of a free CDR loop to specific sites of a DNA framework structure. This method allows us to define the span of a single CDR loop with an ∼2 nm resolution. Using this approach, we create a series of DNA framework based artificial antibodies (DNFbodies) with varied CDR loop spans, leading to different antibody-antigen binding affinities. We find that an optimized single CDR loop (∼2.3 nm span) exhibits ∼3-fold improved affinity relative to natural antibodies, confirming the critical role of the CDR conformation. This study may inspire the rational design of artificial antibodies.

Detection of SARS-CoV-2 RNA with a plasmonic chiral biosensor

The detection of SARS-CoV-2 infection is crucial for effective prevention and surveillance of COVID-19. In this study, we report the development of a novel detection assay named CENSOR that enables sensitive and specific detection of SARS-CoV-2 RNA using a plasmonic chiral biosensor in combination with CRISPR-Cas13a. The chiral biosensor was designed by assembling gold nanorods (AuNR) into three-dimensional plasmonic architectures of controllable chirality on a DNA origami template. This modular assembly mode enhances the flexibility and adaptability of the sensor, thereby improving its universality as a sensing platform. In the presence of SARS-CoV-2 RNA, the CRISPR-Cas13a enzyme triggers collateral cleavage of RNA molecules, resulting in a differential chiral signal readout by the biosensor compared to when there are no RNA targets present. Notably, even subtle variations in the concentration of SARS-CoV-2 RNA can provoke significant changes in chiral signals after preamplification of RNA targets (calculated LOD: 0.133 aM), which establishes the foundation for quantitative detection. Furthermore, CENSOR demonstrated high sensitivity and accuracy in detecting SARS-CoV-2 RNA from clinical samples, suggesting its potential application in clinical settings for viral detection beyond SARS-CoV-2.