Cancer diagnosis with DNA molecular computation

SemiSynBio: A new era for neuromorphic computing

Neuromorphic computing has the potential to achieve the requirements of the next-generation artificial intelligence (AI) systems, due to its advantages of adaptive learning and parallel computing. Meanwhile, biocomputing has seen ongoing development with the rise of synthetic biology, becoming the driving force for new generation semiconductor synthetic biology (SemiSynBio) technologies. DNA-based biomolecules could potentially perform the functions of Boolean operators as logic gates and be used to construct artificial neural networks (ANNs), providing the possibility of executing neuromorphic computing at the molecular level. Herein, we briefly outline the principles of neuromorphic computing, describe the advances in DNA computing with a focus on synthetic neuromorphic computing, and summarize the major challenges and prospects for synthetic neuromorphic computing. We believe that constructing such synthetic neuromorphic circuits will be an important step toward realizing neuromorphic computing, which would be of widespread use in biocomputing, DNA storage, information security, and national defense.

Endogenous Enzyme-Driven Amplified DNA Nanocage Probe for Selective and Sensitive Imaging of Mature MicroRNAs in Living Cancer Cells

Selective and sensitive imaging of intracellular mature microRNAs (miRNAs) is of great importance for biological process study and medical diagnostics. However, this goal remains challenging because of the interference of precursor miRNAs (pre-miRNAs) and the low abundance of mature miRNAs. Herein, we develop an endogenous enzyme-driven amplified DNA nanocage probe (Acage) for the selective and sensitive imaging of mature miRNAs in living cells. The Acage consists of a microRNA-responsive probe, an endogenous enzyme-driven fuel strand, and a DNA nanocage framework with an inner cavity. Benefiting from the size selectivity of DNA nanocage, smaller mature miRNAs rather than larger pre-miRNAs are allowed to enter the cavity of DNA nanocage for molecular recognition; thus, Acage can significantly reduce the signal interference of pre-miRNAs. Moreover, with the driving force of an endogenous enzyme apurinic/apyrimidinic endonuclease 1 (APE1) for efficient signal amplification, Acage enables sensitive intracellular miRNA imaging without an additional external intervention. With these features, Acage was successfully applied for intracellular imaging of mature miRNAs during drug treatment. We believe that this strategy provides a promising pathway for better understanding the functions of mature microRNAs in biological processes and medical diagnostics.

A Methylation-Gated DNAzyme Circuit for Spatially Controlled Imaging of MicroRNA in Cells and Animals

Epigenetic modification plays an indispensable role in regulating routine molecular signaling pathways, yet it is rarely used to modulate molecular self-assembly networks. Herein, we constructed a bioorthogonal demethylase-stimulated DNA circuitry (DSC) system for high-fidelity imaging of microRNA (miRNA) in live cells and mice by eliminating undesired off-site signal leakage. The simple and robust DSC system is composed of a primary cell-specific circuitry regulation (CR) module and an ultimate signal-transducing amplifier (SA) module. After the modularly designed DSC system was delivered into target live cells, the DNAzyme of the CR module was site-specifically activated by endogenous demethylase to produce fuel strands for the subsequent miRNA-targeting SA module. Through the on-site and multiply guaranteed molecular recognitions, the lucid yet efficient DSC system realized the reliably amplified in vivo miRNA sensing and enabled the in-depth exploration of the demethylase-involved signal pathway with miRNA in live cells. Our bioorthogonally on-site-activated DSC system represents a universal and versatile biomolecular sensing platform via various demethylase regulations and shows more prospects for more different personalized theragnostics.

Coupling Exponential to Linear Amplification for Endpoint Quantitative Analysis

Exponential DNA amplification techniques are fundamental in ultrasensitive molecular diagnostics. These systems offer a wide dynamic range, but the quantification requires real-time monitoring of the amplification reaction. Linear amplification schemes, despite their limited sensitivity, can achieve quantitative measurement from a single end-point readout, suitable for low-cost, point-of-care, or massive testing. Reconciling the sensitivity of exponential amplification with the simplicity of end-point readout would thus break through a major design dilemma and open a route to a new generation of massively scalable quantitative bioassays. Here a hybrid nucleic acid-based circuit design is introduced to compute a logarithmic function, therefore providing a wide dynamic range based on a single end-point measurement. CELIA (Coupling Exponential amplification reaction to LInear Amplification) exploits a versatile biochemical circuit architecture to couple a tunable linear amplification stage - optionally embedding an inverter function - downstream of an exponential module in a one-pot format. Applied to the detection of microRNAs, CELIA provides a limit of detection in the femtomolar range and a dynamic range of six decades. This isothermal approach bypasses thermocyclers without compromising sensitivity, thereby opening the way to applications in various diagnostic assays, and providing a simplified, cost-efficient, and high throughput solution for quantitative nucleic acid analysis.

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.

Fluorophore and nanozyme-functionalized DNA walking: A dual-mode DNA logic biocomputing platform for microRNA sensing in clinical samples

Inspired by the programmability and modifiability of nucleic acids, point-of-care (POC) diagnostics for nucleic acid target detection is evolving to become more diversified and intelligent. In this study, we introduce a fluorescent and photothermal dual-mode logic biosensing platform that integrates catalytic hairpin assembly (CHA), toehold-mediated stand displacement reaction (SDR) and a DNA walking machine. Dual identification and signal reporting modules are incorporated into DNA circuits, orchestrated by an AND Boolean logic gate operator and magnetic beads (MBs). In the presence of bispecific microRNAs (miRNAs), the AND logic gate activates, driving the DNA walking machine, and facilitating the collection of hairpin DNA stands modified with FAM fluorescent group and CeO2@Au nanoparticles. The CeO2@Au nanoparticles, served as a nanozyme, can oxidize TMB into oxidation TMB (TMBox), enabling a near-infrared (NIR) laser-driven photothermal effect following the magnetic separation of MBs. This versatile platform was employed to differentiate between plasma samples from breast cancer patients, lung cancer patients, and healthy donors. The thermometer-readout transducers, derived from the CeO2@Au@DNA complexes, provided reliable results, further corroborated by fluorescence assays, enhancing the confidence in the diagnostics compared to singular detection method. The dual-mode logic biosensor can be easily customized to various nucleic acid biomarkers and other POC signal readout modalities by adjusting recognition sequences and modification strategies, heralding a promising future in the development of intelligent, flexible diagnostics for POC testing.

Modified Unit-Mediated Strand Displacement Reactions for Direct Detection of Single Nucleotide Variants in Active Double-Stranded DNA

Accurate identification of single nucleotide variants (SNVs) in key driver genes holds a significant value for disease diagnosis and treatment. Fluorescent probes exhibit tremendous potential in specific, high-resolution, and rapid detection of SNVs. However, additional steps are required in most post-PCR assays to convert double-stranded DNA (dsDNA) products into single-stranded DNA (ssDNA), enabling them to possess hybridization activity to trigger subsequent reactions. This process not only prolongs the complexity of the experiment but also introduces the risk of losing target information. In this study, we proposed two strategies for enriching active double-stranded DNA, involving PCR based on obstructive groups and cleavable units. Building upon this, we explored the impact of modified units on the strand displacement reaction (SDR) and assessed their discriminatory efficacy for mutations. The results showed that detection of low variant allele frequencies (VAF) as low as 0.1% can be achieved. The proposed strategy allowed orthogonal identification of 45 clinical colorectal cancer tissue samples with 100% specificity, and the results were generally consistent with sequencing results. Compared to existing methods for enriching active targets, our approach offers a more diverse set of enrichment strategies, characterized by the advantage of being simple and fast and preserving original information to the maximum extent. The objective of this study is to offer an effective solution for the swift and facile acquisition of active double-stranded DNA. We anticipate that our work will facilitate the practical applications of SDR based on dsDNA.

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-based molecular classifiers for the profiling of gene expression signatures

Although gene expression signatures offer tremendous potential in diseases diagnostic and prognostic, but massive gene expression signatures caused challenges for experimental detection and computational analysis in clinical setting. Here, we introduce a universal DNA-based molecular classifier for profiling gene expression signatures and generating immediate diagnostic outcomes. The molecular classifier begins with feature transformation, a modular and programmable strategy was used to capture relative relationships of low-concentration RNAs and convert them to general coding inputs. Then, competitive inhibition of the DNA catalytic reaction enables strict weight assignment for different inputs according to their importance, followed by summation, annihilation and reporting to accurately implement the mathematical model of the classifier. We validated the entire workflow by utilizing miRNA expression levels for the diagnosis of hepatocellular carcinoma (HCC) in clinical samples with an accuracy 85.7%. The results demonstrate the molecular classifier provides a universal solution to explore the correlation between gene expression patterns and disease diagnostics, monitoring, and prognosis, and supports personalized healthcare in primary care.

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.

Predicting DNA toehold-mediated strand displacement rate constants using a DNA-BERT transformer deep learning model

Dynamic DNA nanotechnology is driving exciting developments in molecular computing, cargo delivery, sensing and detection. Combining this innovative area of research with the progress made in machine learning will aid in the design of sophisticated DNA machinery. Herein, we present a novel framework based on a transformer architecture and a deep learning model which can predict the rate constant of toehold-mediated strand displacement, the underlying process in dynamic DNA nanotechnology. Initially, a dataset of 4450 DNA sequences and corresponding rate constants were generated in-silico using KinDA. Subsequently, a 1D convolution neural network was trained using specific local features and DNA-BERT sequence embedding to produce predicted rate constants. As a result, the newly trained deep learning model predicted toehold-mediated strand displacement rate constants with a root mean square error of 0.76, during testing. These findings demonstrate that DNA-BERT can improve prediction accuracy, negating the need for extensive computational simulations or experimentation. Finally, the impact of various local features during model training is discussed, and a detailed comparison between the One-hot encoder and DNA-BERT sequences representation methods is presented.

A DNA circuit that records molecular events

Characterizing the relative onset time, strength, and duration of molecular signals is critical for understanding the operation of signal transduction and genetic regulatory networks. However, detecting multiple such molecules as they are produced and then quickly consumed is challenging. A MER can encode information about transient molecular events as stable DNA sequences and are amenable to downstream sequencing or other analysis. Here, we report the development of a de novo molecular event recorder that processes information using a strand displacement reaction network and encodes the information using the primer exchange reaction, which can be decoded and quantified by DNA sequencing. The event recorder was able to classify the order at which different molecular signals appeared in time with 88% accuracy, the concentrations with 100% accuracy, and the duration with 75% accuracy. This simultaneous and highly programmable multiparameter recording could enable the large-scale deciphering of molecular events such as within dynamic reaction environments, living cells, or tissues.

A simultaneous strategy with multiple-signal amplification and self-calibration for ultrasensitive assay of miRNA-21 based on 3D MNPs-IL-rGO-AuNPs

MicroRNA-21 (miRNA-21) is a significant biomarker for the development and progression of diverse cancers but is present in relatively low concentrations. Detecting such low-abundance molecules accurately can be challenging, especially in early-stage cancers where the concentration may be even lower. Herein, a self-calibration biosensing platform based on 3D novel MNPs-IL-rGO-AuNPs nanocomposites was successfully established for the ultrasensitive detection of miRNA-21. Duplex-specific nuclease (DSN) was introduced to recognize perfectly matched duplexes and trigger target recycling, enhancing the specificity and sensitivity of the biosensor. DSN-assisted target recycling, in conjunction with magnetic separation enrichment and high-performance MNPs-IL-rGO-AuNPs, collectively formed a multiple-signal amplification strategy. The obtained biosensor could output dual signals in both electrochemical and fluorescent modes, enabling self-correcting detection to enhance the accuracy. The obtained dual-mode biosensor prepared exhibited a wide detection range from 5 fM to 100 nM with a remarkably low LOD of 1.601 fM. It accomplished the sensitive evaluation of miRNA-21 in total RNA extracted from various human cancer cell lines and normal cell lines. Additionally, the greatly satisfactory outcomes in the analysis of human serum samples suggested that the proposed biosensor was a powerful screening candidate in early clinical diagnosis of cancer.

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.

Towards the development of a DNA automaton: modular RNA-cleaving deoxyribozyme logic gates regulated by miRNAs

Advancements in DNA computation have unlocked molecular-scale information processing possibilities, utilizing the intrinsic properties of DNA for complex logical operations with transformative applications in biomedicine. DNA computation shows promise in molecular diagnostics, enabling precise and sensitive detection of genetic mutations and disease biomarkers. Moreover, it holds potential for targeted gene regulation, facilitating personalized therapeutic interventions with enhanced efficacy and reduced side effects. Herein, we have developed six DNAzyme-based logic gates able to process YES, AND, and NOT Boolean logic. The novelty of this work lies in their additional functionalization with a common DNA scaffold for increased cooperativity in input recognition. Moreover, we explored hierarchical input binding to multi-input logic gates, which helped gate optimization. Additionally, we developed a new design of an allosteric hairpin switch used to implement NOT logic. All DNA logic gates achieved the desired true-to-false output signal when detecting a panel of miRNAs, known for their important role in malignancy regulation. This is the first example of DNAzyme-based logic gates having all input-recognizing elements integrated in a single DNA nanostructure, which provides new opportunities for building DNA automatons for diagnosis and therapy of human diseases.

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.

Engineering Logic DNA Nanoprobes on Live Cell Membranes for Simultaneously Monitoring Extracellular pH and Precise Drug Delivery

It remains a challenge to use a single probe to simultaneously detect extracellular pH fluctuations and specifically recognize cancer cells for precise drug delivery. Here, we engineered a tetrahedral framework nucleic acid-based logic nanoprobe (isgc8-tFNA) on live cell membranes for simultaneously monitoring extracellular pH and targeted drug delivery. Isgc8-tFNA was anchored stably on the cell surface through three cholesterol molecules inserting into the bilayer of the cell membrane. Once responding to the acidic tumor microenvironment, isgc8-tFNA formed an i-motif structure, leading to turn-on FRET signals for monitoring changes of extracellular pH. The nanoprobe exhibited a narrow pH-response window and excellent reversibility. Moreover, the nanoprobe could execute logic identification on the cell surface for precise drug delivery. Only if both in the acidic microenvironment and aptamer-targeting marker are present on the cell surface, the sgc8-ASO-chimera strand, carrying an antisense oligonucleotide drug, was released from the nanoprobe and entered into targeted cancer cells for gene silence. Additionally, the in situ drug release facilitated the uptake of drugs mediated by the interaction between sgc8 aptamer and membrane proteins, resulting in enhanced inhibition of cancer cell migration and proliferation. This logic nanoprobe will provide inspiration for designing smart devices for diagnosis of pH-related diseases and targeted drug delivery.

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.

Ultra-Fast Single-Nucleotide-Variation Detection Enabled by Argonaute-Mediated Transistor Platform

"Test-and-go" single-nucleotide variation (SNV) detection within several minutes remains challenging, especially in low-abundance samples, since existing methods face a trade-off between sensitivity and testing speed. Sensitive detection usually relies on complex and time-consuming nucleic acid amplification or sequencing. Here, a graphene field-effect transistor (GFET) platform mediated by Argonaute protein that enables rapid, sensitive, and specific SNV detection is developed. The Argonaute protein provides a nanoscale binding channel to preorganize the DNA probe, accelerating target binding and rapidly recognizing SNVs with single-nucleotide resolution in unamplified tumor-associated microRNA, circulating tumor DNA, virus RNA, and reverse transcribed cDNA when a mismatch occurs in the seed region. An integrated microchip simultaneously detects multiple SNVs in agreement with sequencing results within 5 min, achieving the fastest SNV detection in a "test-and-go" manner without the requirement of nucleic acid extraction, reverse transcription, and amplification.

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.

Programmable Biomolecule-Mediated Processors

Programmable biomolecule-mediated computing is a new computing paradigm as compared to contemporary electronic computing. It employs nucleic acids and analogous biomolecular structures as information-storing and -processing substrates to tackle computational problems. It is of great significance to investigate the various issues of programmable biomolecule-mediated processors that are capable of automatically processing, storing, and displaying information. This Perspective provides several conceptual designs of programmable biomolecule-mediated processors and provides some insights into potential future research directions for programmable biomolecule-mediated processors.

Toward Minute-Level DNA Computing: An Ultrafast, Cost-Effective, and Universal System for Lighting Up Various Concurrent DNA Logic Nanodevices (CDLNs) and Concatenated Circuits

DNA logic nanodevices are powerful tools for both molecular computing tasks and smart bioanalytical applications. Nevertheless, the hour-level operation time and high cost caused by the frequent redesign/reconstruction of gates, tedious strand-displacement reaction, and expensive labeled probes (or tool enzymes) in previous works are ineluctable drawbacks. Herein, we report an ultrafast and cost-effective system for engineering concurrent DNA logic nanodevices (CDLNs) by combining polythymine CuNCs with SYBR Green I (SG I) as universal dual-output producers. Particularly, benefiting from the concomitant minute-level quick response of both unlabeled illuminators and the exquisite strand-displacement-free design, all CDLNs including contrary logic pairs (YES∧NOT, OR∧NOR, and Even∧Odd number classifier), noncontrary ones (IDE∧IMP, OR∧NAND), and concatenated circuits are implemented in just 10 min via a "one-stone-two-birds" method, resulting in only 1/12 the operation time and 1/4 the cost needed in previous works, respectively. Moreover, all of them share the same threshold value, and the dual output can be easily visualized by the naked eye under a portable UV lamp, indicating the universality and practicality of this system. Furthermore, by exploiting the "positive/negative cross-verification" advantages of concurrent contrary logic, the smart in vitro analysis of the polyadenine strand and its polymerase is realized, providing novel molecular tools for the early diagnosis of cancer-related diseases.

Switchable Fluorescent Light-Up Aptamers Based on Riboswitch Architectures

Fluorescent light-up RNA aptamers (FLAPs) such as Spinach or Mango can bind small fluorogens and activate their fluorescence. Here, we adopt a switching mechanism otherwise found in riboswitches and use it to engineer switchable FLAPs that can be activated or repressed by trigger oligonucleotides or small metabolites. The fluorophore binding pocket of the FLAPs comprises guanine (G) quadruplexes, whose critical nucleotides can be sequestered by corresponding anti-FLAP sequences, leading to an inactive conformation and thus preventing association with the fluorophore. We modified the FLAPs with designed toehold hairpins that carry either an anti-FLAP or an anti-anti-FLAP sequence within the loop region. The addition of an input RNA molecule triggers a toehold-mediated strand invasion process that refolds the FLAP into an active or inactive configuration. Several of our designs display close-to-zero leak signals and correspondingly high ON/OFF fluorescence ratios. We also modified purine aptamers to sequester a partial anti-FLAP or an anti-anti-FLAP sequence to control the formation of the fluorogen-binding conformation, resulting in FLAPs whose fluorescence is activated or deactivated in the presence of guanine or adenine. We demonstrate that switching modules can be easily combined to generate FLAPs whose fluorescence depends on several inputs with different types of input logic.

DNA-based programmable gate arrays for general-purpose DNA computing

The past decades have witnessed the evolution of electronic and photonic integrated circuits, from application specific to programmable1,2. Although liquid-phase DNA circuitry holds the potential for massive parallelism in the encoding and execution of algorithms3,4, the development of general-purpose DNA integrated circuits (DICs) has yet to be explored. Here we demonstrate a DIC system by integration of multilayer DNA-based programmable gate arrays (DPGAs). We find that the use of generic single-stranded oligonucleotides as a uniform transmission signal can reliably integrate large-scale DICs with minimal leakage and high fidelity for general-purpose computing. Reconfiguration of a single DPGA with 24 addressable dual-rail gates can be programmed with wiring instructions to implement over 100 billion distinct circuits. Furthermore, to control the intrinsically random collision of molecules, we designed DNA origami registers to provide the directionality for asynchronous execution of cascaded DPGAs. We exemplify this by a quadratic equation-solving DIC assembled with three layers of cascade DPGAs comprising 30 logic gates with around 500 DNA strands. We further show that integration of a DPGA with an analog-to-digital converter can classify disease-related microRNAs. The ability to integrate large-scale DPGA networks without apparent signal attenuation marks a key step towards general-purpose DNA computing.

Unraveling the Possibilities: Recent Progress in DNA Biosensing

Due to the advantages of its numerous modification sites, predictable structure, high thermal stability, and excellent biocompatibility, DNA is the ideal choice as a key component of biosensors. DNA biosensors offer significant advantages over existing bioanalytical techniques, addressing limitations in sensitivity, selectivity, and limit of detection. Consequently, they have attracted significant attention from researchers worldwide. Here, we exemplify four foundational categories of functional nucleic acids: aptamers, DNAzymes, i-motifs, and G-quadruplexes, from the perspective of the structure-driven functionality in constructing DNA biosensors. Furthermore, we provide a concise overview of the design and detection mechanisms employed in these DNA biosensors. Noteworthy advantages of DNA as a sensor component, including its programmable structure, reaction predictility, exceptional specificity, excellent sensitivity, and thermal stability, are highlighted. These characteristics contribute to the efficacy and reliability of DNA biosensors. Despite their great potential, challenges remain for the successful application of DNA biosensors, spanning storage and detection conditions, as well as associated costs. To overcome these limitations, we propose potential strategies that can be implemented to solve these issues. By offering these insights, we aim to inspire subsequent researchers in related fields.

Implementing Logic Gates by DNA Crystal Engineering

DNA self-assembly computation is attractive for its potential to perform massively parallel information processing at the molecular level while at the same time maintaining its natural biocompatibility. It has been extensively studied at the individual molecule level, but not as much as ensembles in 3D. Here, the feasibility of implementing logic gates, the basic computation operations, in large ensembles: macroscopic, engineered 3D DNA crystals is demonstrated. The building blocks are the recently developed DNA double crossover-like (DXL) motifs. They can associate with each other via sticky-end cohesion. Common logic gates are realized by encoding the inputs within the sticky ends of the motifs. The outputs are demonstrated through the formation of macroscopic crystals that can be easily observed. This study points to a new direction of construction of complex 3D crystal architectures and DNA-based biosensors with easy readouts.

Design and Simulation of a Multilayer Chemical Neural Network That Learns via Backpropagation

The design and implementation of adaptive chemical reaction networks, capable of adjusting their behavior over time in response to experience, is a key goal for the fields of molecular computing and DNA nanotechnology. Mainstream machine learning research offers powerful tools for implementing learning behavior that could one day be realized in a wet chemistry system. Here we develop an abstract chemical reaction network model that implements the backpropagation learning algorithm for a feedforward neural network whose nodes employ the nonlinear "leaky rectified linear unit" transfer function. Our network directly implements the mathematics behind this well-studied learning algorithm, and we demonstrate its capabilities by training the system to learn a linearly inseparable decision surface, specifically, the XOR logic function. We show that this simulation quantitatively follows the definition of the underlying algorithm. To implement this system, we also report ProBioSim, a simulator that enables arbitrary training protocols for simulated chemical reaction networks to be straightforwardly defined using constructs from the host programming language. This work thus provides new insight into the capabilities of learning chemical reaction networks and also develops new computational tools to simulate their behavior, which could be applied in the design and implementations of adaptive artificial life.

A molecular assessment of the practical potential of DNA-based computation

The immense information density of DNA and its potential for massively parallelized computations, paired with rapidly expanding data production and storage needs, have fueled a renewed interest in DNA-based computation. Since the construction of the first DNA computing systems in the 1990s, the field has grown to encompass a diverse array of configurations. Simple enzymatic and hybridization reactions to solve small combinatorial problems transitioned to synthetic circuits mimicking gene regulatory networks and DNA-only logic circuits based on strand displacement cascades. These have formed the foundations of neural networks and diagnostic tools that aim to bring molecular computation to practical scales and applications. Considering these great leaps in system complexity as well as in the tools and technologies enabling them, a reassessment of the potential of such DNA computing systems is warranted.

DNA-framework-based multidimensional molecular classifiers for cancer diagnosis

A molecular classification of diseases that accurately reflects clinical behaviour lays the foundation of precision medicine. The development of in silico classifiers coupled with molecular implementation based on DNA reactions marks a key advance in more powerful molecular classification, but it nevertheless remains a challenge to process multiple molecular datatypes. Here we introduce a DNA-encoded molecular classifier that can physically implement the computational classification of multidimensional molecular clinical data. To produce unified electrochemical sensing signals across heterogeneous molecular binding events, we exploit DNA-framework-based programmable atom-like nanoparticles with n valence to develop valence-encoded signal reporters that enable linearity in translating virtually any biomolecular binding events to signal gains. Multidimensional molecular information in computational classification is thus precisely assigned weights for bioanalysis. We demonstrate the implementation of a molecular classifier based on programmable atom-like nanoparticles to perform biomarker panel screening and analyse a panel of six biomarkers across three-dimensional datatypes for a near-deterministic molecular taxonomy of prostate cancer patients.

Enthalpy and entropy synergistic regulation-based programmable DNA motifs for biosensing and information encryption

Deoxyribonucleic acid (DNA) provides a collection of intelligent tools for the development of information cryptography and biosensors. However, most conventional DNA regulation strategies rely solely on enthalpy regulation, which suffers from unpredictable stimuli-responsive performance and unsatisfactory accuracy due to relatively large energy fluctuations. Here, we report an enthalpy and entropy synergistic regulation-based pH-responsive A⁺/C DNA motif for programmable biosensing and information encryption. In the DNA motif, the variation in loop length alters entropic contribution, and the number of A⁺/C bases regulates enthalpy, which is verified through thermodynamic characterizations and analyses. On the basis of this straightforward strategy, the performances, such as pK_a, of the DNA motif can be precisely and predictably tuned. The DNA motifs are finally successfully applied for glucose biosensing and crypto-steganography systems, highlighting their potential in the field of biosensing and information encryption.

NIR-II Imaging and Sandwiched Plasmonic Biosensor for Ultrasensitive Intraoperative Definition of Tumor-Invaded Lymph Nodes

Radical lymphadenectomy remains the cornerstone of preventing tumor metastasis through the lymphatic system. Current surgical resection of lymph nodes (LNs) based on fluorescence-guided surgery (FGS) suffers from low sensitivity/selectivity with only qualitative information, hampering accurate intraoperative decision-making. Herein, we develop a modularized theranostic system including NIR-II FGS and a sandwiched plasmonic chip (SPC). Intraoperative NIR-II FGS and detection of tumor-positive lymph nodes were performed on the gastric tumor to determine the feasibility of the modularized theranostic system in defining LN metastasis. Under the NIR-II imaging window, the orthotopic tumor and sentinel lymph nodes (SLNs) were successfully excised without ambient light interference in the operating room. Importantly, the SPC biosensor achieved 100% sensitivity and 100% specificity for tumor markers and realized rapid and high-throughput intraoperative SLN detection. We propose the synergetic design of combining the NIR-II FGS and suitable biosensor will substantially improve the efficiency of cancer diagnosis and therapy follow-up.

A survey on molecular-scale learning systems with relevance to DNA computing

DNA computing has emerged as a promising alternative to achieve programmable behaviors in chemistry by repurposing the nucleic acid molecules into chemical hardware upon which synthetic chemical programs can be executed. These chemical programs are capable of simulating diverse behaviors, including boolean logic computation, oscillations, and nanorobotics. Chemical environments such as the cell are marked by uncertainty and are prone to random fluctuations. For this reason, potential DNA-based molecular devices that aim to be deployed into such environments should be capable of adapting to the stochasticity inherent in them. In keeping with this goal, a new subfield has emerged within DNA computing, focusing on developing approaches that embed learning and inference into chemical reaction systems. If realized in biochemical contexts, such molecular machines can engender novel applications in fields such as biotechnology, synthetic biology, and medicine. Therefore, it would be beneficial to review how different ideas were conceived, how the progress has been so far, and what the emerging ideas are in this nascent field of 'molecular-scale learning'.

Design Approaches to Expand the Toolkit for Building Cotranscriptionally Encoded RNA Strand Displacement Circuits

Cotranscriptionally encoded RNA strand displacement (ctRSD) circuits are an emerging tool for programmable molecular computation, with potential applications spanning in vitro diagnostics to continuous computation inside living cells. In ctRSD circuits, RNA strand displacement components are continuously produced together via transcription. These RNA components can be rationally programmed through base pairing interactions to execute logic and signaling cascades. However, the small number of ctRSD components characterized to date limits circuit size and capabilities. Here, we characterize over 200 ctRSD gate sequences, exploring different input, output, and toehold sequences and changes to other design parameters, including domain lengths, ribozyme sequences, and the order in which gate strands are transcribed. This characterization provides a library of sequence domains for engineering ctRSD components, i.e., a toolkit, enabling circuits with up to 4-fold more inputs than previously possible. We also identify specific failure modes and systematically develop design approaches that reduce the likelihood of failure across different gate sequences. Lastly, we show the ctRSD gate design is robust to changes in transcriptional encoding, opening a broad design space for applications in more complex environments. Together, these results deliver an expanded toolkit and design approaches for building ctRSD circuits that will dramatically extend capabilities and potential applications.

Toward Rapid and Accurate Molecular Diagnostics at Home

The global outbreaks of infectious diseases have significantly driven an imperative demand for rapid and accurate molecular diagnostics. Nucleic acid amplification tests (NAATs) feature high sensitivity and high specificity; however, the labor-intensive sample preparation and nucleic acid amplification steps remain challenging in order to carry out rapid and precision molecular diagnostics at home. This review discusses the advances and challenges of automatic solutions of sample preparation integrated with on-chip nucleic acid amplification for effective and accurate molecular diagnostics at home. The sample preparation methods of whole blood, urine, saliva/nasal swab, and stool on chip are examined. Then, the repurposable integrated sample preparation on a chip using various biological samples is investigated. Finally, the on-chip NAATs that can be integrated with automated sample preparation are evaluated. The user-friendly approaches with combined sample preparation and NAATs can be the game changers for next-generation rapid and precision home diagnostics.

Unlocking Genetic Profiles with a Programmable DNA-Powered Decoding Circuit

Human genetic architecture provides remarkable insights into disease risk prediction and personalized medication. Advances in genomics have boosted the fine-mapping of disease-associated genetic variants across human genome. In healthcare practice, interpreting intricate genetic profiles into actionable medical decisions can improve health outcomes but remains challenging. Here an intelligent genetic decoder is engineered with programmable DNA computation to automate clinical analyses and interpretations. The DNA-based decoder recognizes multiplex genetic information by one-pot ligase-dependent reactions and interprets implicit genetic profiles into explicit decision reports. It is shown that the DNA decoder implements intended computation on genetic profiles and outputs a corresponding answer within hours. Effectiveness in 30 human genomic samples is validated and it is shown that it achieves desirable performance on the interpretation of CYP2C19 genetic profiles into drug responses, with accuracy equivalent to that of Sanger sequencing. Circuit modules of the DNA decoder can also be readily reprogrammed to interpret another pharmacogenetics genes, provide drug dosing recommendations, and implement reliable molecular calculation of polygenic risk score (PRS) and PRS-informed cancer risk assessment. The DNA-powered intelligent decoder provides a general solution to the translation of complex genetic profiles into actionable healthcare decisions and will facilitate personalized healthcare in primary care.

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

FRET cascade miRNA addition probe from non-crosstalk DNA photonic wire assisted with clustering algorithm for early diagnosis of acute myocardial infarction

Early and accurate diagnosis of acute myocardial infarction (AMI) can significantly reduce patient mortality. A variety of miRNAs are found to dysregulate in AMI patients, but the up- or down-regulation of a specific miRNA may not be evident in the early stage, making it difficult to achieve accurate diagnosis. Here, proposing the design that DNA photonic wire (PW) with no spectral crosstalk would make an excellent template for miRNA conjoint analysis, we report the construction of a miRNA addition probe for the additive analysis of two up-regulated miRNAs (miR-133a and miR-208a) for early diagnosis of AMI in clinical serum samples. A three-dye non-crosstalk DNA PW is built to form the two-step fluorescence resonance energy transfer (FRET) cascade system, in which three paths can blocking the FRET cascade for separate or additive analysis of the two miRNAs. K-Means clustering algorithm is further utilized to classify the output signals of the miRNA addition probe, achieving a 100% accurate diagnosis of early AMI in both the training (n = 40) and validation (n = 19) cohorts of clinical serum samples.

Controllable DNA nanodevices regulated by logic gates for multi-stimulus recognition

DNA biosensors have attracted considerable attention due to their great potential in environmental monitoring and medical diagnosis. Despite the great achievements, the single function and uncontrollability of the sensors restrict their further application. Therefore, it is necessary to construct controllable nanodevices with both sensing and responding capabilities to external stimuli. Herein, we develop a strategy to engineer structure-switching biosensors which can respond to external stimuli while preserving the sensing capability. The engineered nanodevice consists of an actuation module and a sensing module. Initially, the sensing module is disabled by a blocker strand which acts as an allosteric switch. Once the stimuli-responsive actuation module displaces the blocker DNA, the sensing module is activated. Based on the strategy, the engineered nanodevice could recognize both the target and external stimuli. As a demonstration of this strategy, a controllable Hg²⁺ sensor was designed, in which a 'YES', 'AND', and 'OR' logic gate is employed as the actuation module respectively to facilitate recognition of oligonucleotide inputs. The modular nature of the proposed strategy makes it easily generalizable to other structure-switching sensors. As a demonstration of this, we successfully apply it to the ATP sensor. The proposed strategy has potential in the fields of programmable biosensing, disease diagnosis, DNA computing, and intelligent nanodevices.

DNA Droplets: Intelligent, Dynamic Fluid

Breathtaking advances in DNA nanotechnology have established DNA as a promising biomaterial for the fabrication of programmable higher-order nano/microstructures. In the context of developing artificial cells and tissues, DNA droplets have emerged as a powerful platform for creating intelligent, dynamic cell-like machinery. DNA droplets are a microscale membrane-free coacervate of DNA formed through phase separation. This new type of DNA system couples dynamic fluid-like property with long-established DNA programmability. This hybrid nature offers an advantageous route to facile and robust control over the structures, functions, and behaviors of DNA droplets. This review begins by describing programmable DNA condensation, commenting on the physical properties and fabrication strategies of DNA hydrogels and droplets. By presenting an overview of the development pathways leading to DNA droplets, it is shown that DNA technology has evolved from static, rigid systems to soft, dynamic systems. Next, the basic characteristics of DNA droplets are described as intelligent, dynamic fluid by showcasing the latest examples highlighting their distinctive features related to sequence-specific interactions and programmable mechanical properties. Finally, this review discusses the potential and challenges of numerical modeling able to connect a robust link between individual sequences and macroscopic mechanical properties of DNA droplets.

Molecular Computation for Molecular Classification

DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

Programmable DNA biocomputing circuits for rapid and intelligent screening of SARS-CoV-2 variants

The frequent emergence of SARS-CoV-2 variants increased viral transmissibility and reduced protection afforded by vaccines. The rapid, multichannel, and intelligent screening of variants is critical to minimizing community transmissions. DNA molecular logic gates have attracted wide attention in recent years due to the powerful information processing capabilities and molecular data biocomputing functions. In this work, some molecular switches (MSs) were connected with each other to implement arbitrary binary functions by emulating the threshold switching of MOS transistors and the decision tree model. Using specific sequences of different SARS-CoV-2 variants as inputs, the MSs net was used to build several molecular biocomputing circuits, including NOT, AND, OR, INHIBIT, XOR, half adder, half subtractor, full adder, and full subtractor. Four fluorophores (FAM, Cy3, ROX, and Cy5) were employed in the logic systems to realize the multichannel monitoring of the logic operation results. The logic response is fast and can be finished with 10 min, which facilitates the rapid wide-population screening for SARS-CoV-2 variants. Importantly, the logic results can be directly observed by the naked eye under a portable UV lamp, thus providing a simple and intelligent method to enable high-frequency point-of-care diagnostics, particularly in low-resource communities.

Heteromultivalent scaffolds fabricated by biomimetic co-assembly of DNA-RNA building blocks for the multi-analysis of miRNAs

Heteromultivalent scaffolds with different repeated monomers have great potential in biomedicine, but convenient construction strategies for integrating various functional modules to achieve multiple biological functions are still lacking. Here, taking advantage of the heteromultivalent effect of dendritic nucleic acids and the specific biochemical properties of microRNAs (miRNAs), we assembled novel heteromultivalent nucleic acid scaffolds by biomimetic co-assembly of DNA-RNA building blocks. In our approach, two miRNAs were used to initiate and maintain dendritic structures in an interdependent manner; so, the heteromultivalent nanostructure can only form in the presence of both miRNAs. The proposed nanostructure can be used for one-step analysis of two miRNAs in an AND logic format. Taking miR-18b-5p and miR-342-3p which are associated with Alzheimer's disease as an example, a FRET sensing system was fabricated for the simultaneous analysis of two miRNAs within one hour at picomolar concentration. Further studies show that the designed device may have the potential to distinguish between AD patients and the healthy population by analysis of two miRNAs in CSF (cerebrospinal fluid) samples, suggesting its possible applicability in clinics.

DNA-based customized functional modules for signal transformation

Information on the temporal and spatial scale of cellular molecules in biological systems is crucial for estimating life processes and may be conducive to an improved understanding of disease progression. This intracellular and extracellular information is often difficult to obtain at the same time due to the limitations of accessibility and sensing throughput. DNA is an excellent material for in vivo and in vitro applications, and can be used to build functional modules that can transform bio-information (input) into ATCG sequence information (output). Due to their small volume and highly amenable programming, DNA-based functional modules provide an opportunity to monitor a range of information, from transient molecular events to dynamic biological processes. Over the past two decades, with the advent of customized strategies, a series of functional modules based on DNA networks have been designed to gather different information about molecules, including the identity, concentration, order, duration, location, and potential interactions; the action of these modules are based on the principle of kinetics or thermodynamics. This paper summarizes the available DNA-based functional modules that can be used for biomolecular signal sensing and transformation, reviews the available designs and applications of these modules, and assesses current challenges and prospects.

A DNA Finite-State Machine Based on the Programmable Allosteric Strategy of DNAzyme

Living organisms can produce corresponding functions by responding to external and internal stimuli, and this irritability plays a pivotal role in nature. Inspired by such natural temporal responses, the development and design of nanodevices with the ability to process time-related information could facilitate the development of molecular information processing systems. Here, we proposed a DNA finite-state machine that can dynamically respond to sequential stimuli signals. To build this state machine, a programmable allosteric strategy of DNAzyme was developed. This strategy performs the programmable control of DNAzyme conformation using a reconfigurable DNA hairpin. Based on this strategy, we first implemented a finite-state machine with two states. Through the modular design of the strategy, we further realized the finite-state machine with five states. The DNA finite-state machine endows molecular information systems with the ability of reversible logic control and order detection, which can be extended to more complex DNA computing and nanomachines to promote the development of dynamic nanotechnology.

MnO2@Au nanostructures supported colorimetric biosensing with duplex-specific nuclease-assisted DNA structural transition

Manganese dioxide (MnO₂) nanosheets are regarded as a new class of two-dimensional nanomaterials with several attractive properties with enormous progress in biomedical fields. Gold nanoparticles (AuNPs) are also important biocompatible nanomaterials with unusual optical properties. Hetero-nanostructure of MnO₂ and AuNPs with the medium of DNA is an interesting topic. In this work, the protection of the hetero-nanostructure from salt-induced aggregation is systematically investigated including the effects of sequence length, reagents concentrations, incubation time and temperature. The MnO₂@Au nanostructures are thus applied for the analysis of miRNA. Duplex-specific nuclease (DSN) catalyzed digestion, hybridization chain reaction (HCR) and catalytic hairpin assembly (CHA) are utilized for signal amplification. By finally analyzing the optical responses of the nanocomponents, highly sensitive analysis of target miRNA can be achieved. Excellent analytical performances are attributed to the unique features of MnO₂@Au nanostructures and signal amplification designs. They are promising basis for the construction of novel biosensors for clinical applications.

Multi-parameter Inputted Logic-Gating on Aptamer-Encoded Extracellular Vesicles for Colorectal Cancer Diagnosis

Extracellular vesicles (EVs) have emerged as a potential biomarker in liquid biopsy. However, cancer heterogeneity poses significant challenge to precise molecular diagnosis based on single-parameter input. Hence, strategies for analyzing multiple inputs with molecular computing were developed with the aim of improving diagnostic accuracy in liquid biopsy. In the present study, based on the surface of aptamer-encoded EVs, three toe-hold extended DNA aptamers served as specific inputs to perform AND-logic-gating to distinguish between healthy and cancerous EVs. In addition, this strategy has been successfully employed to analyze circulating EVs in clinical samples from colorectal cancer patients and healthy donors. The developed method has a promising future in the analysis of multiplex EV membrane proteins and the identification of early cancer.

A novel cascade signal amplification strategy integrating CRISPR/Cas13a and branched hybridization chain reaction for ultra-sensitive and specific SERS detection of disease-related nucleic acids

The molecular diagnosis of disease by high-sensitively and specifically detecting extremely trace amounts of nucleic acid biomarkers in biological samples is still a great challenge, and the powerful sensing strategy has become an urgent need for basic researches and clinical applications. Herein, a novel one-pot cascade signal amplification strategy (Cas13a-bHCR) integrating CRISPR/Cas13a system (Cas13a) and branched hybridization chain reaction (bHCR) was proposed for ultra-highly sensitive and specific SERS assay of disease-related nucleic acids on SERS-active silver nanorods sensing chips. The Cas13a-bHCR based SERS assay of gastric cancer-related miRNA-106a (miR-106a) can be achieved within 60 min and output significantly enhanced SERS signal due to the multiple signal amplification, which possesses a good linear calibration curve from 10 aM to 1 nM with the limit of detection (LOD) low to 8.55 aM for detecting gastric cancer-related miR-106a in human serum. The Cas13a-bHCR based SERS sensing also shows good specificity, uniformity, repeatability and reliability, and has good practicability for detection of miR-106a in clinical samples, which can provide a potential powerful tool for SERS detection of disease-related nucleic acids and promise brighter prospects in the field of clinical diagnosis of early disease.