An automated DNA computing platform for rapid etiological diagnostics

Implementing complex nucleic acid circuits in living cells

Synthetic nucleic acid-based computing has demonstrated complex computational capabilities in vitro. However, translating these circuits into living cells remains challenging because of instability and cellular interference. We introduce an allosteric strand exchange (ASE) strategy for complex intracellular computing. Leveraging conformational cooperativity to regulate strand exchange, ASE offers a modular platform for designing intracellular circuits with flexible programmability. We engineer a scalable circuit architecture based on ASE that can execute AND and OR logic and scale to an eight-input expression. We demonstrate ASE-based circuits can detect messenger RNAs with high specificity in mammalian cells via AND logic computation. The capacity of ASE-based circuits to accept messenger RNAs as inputs enables integration of endogenous cellular information for efficient multi-input information processing, demonstrated by a multi-input molecular classifier monitoring key cell reprogramming events. Reprogramming ASE-based circuit to interface with CRISPR-Cas9 enables programmable control of Cas9-targeting activity for gene editing, highlighting their potential for advancing intracellular biocomputation.

Circular RNA-Based Molecular Computation Enhances Plasma Biomarker Detection in Biliary Tract Cancer

Molecular diagnosis of biliary tract cancer (BTC) remains a significant clinical challenge due to the lack of sensitive and specific diagnostic tools. Although large panels based on multi-biomarkers have demonstrated potential in enhancing diagnostic accuracy, their clinical application is hindered by complexity and high costs. To overcome these limitations, we have developed a DNA-based molecular computation system that integrates the detection of plasma circular RNAs (circRNAs) with molecular computation, enabling intelligent and rapid diagnosis of BTC. By identifying and validating a specific and small set of circRNA biomarkers that are differentially expressed in BTC patients, we designed a DNA computation framework that directly translates biomarker levels into diagnostic outcomes without the need of external analysis and interpretation. In a validation cohort of 70 individuals, our approach achieved a diagnostic accuracy of 83%, demonstrating its potential as a cost-effective and efficient tool for rapid diagnosis of BTC. This work highlights the potential of molecular computation in enhancing integrated and rapid cancer diagnostics, paving the way for clinical implementation.

Opportunities and Challenges of DNA Materials toward Sustainable Development Goals

Sustainable development represents a significant and pressing challenge confronting the global community at present. A wide variety of macroscopic engineering systems has been developed to promote sustainable development. Recent advancements in DNA materials have showcased their substantial contributions toward achieving sustainable development goals (SDGs). Compared to nonbiological materials, DNA materials possess exceptional properties such as genetic functionality, molecular programmability, recognition capabilities, and biocompatibility. These unique characteristics enable DNA materials to serve as general and versatile substrates beyond their genetic role. Consequently, they can be used to develop DNA-based engineering systems that offer versatile solutions to support sustainable development. In this Perspective, we critically examine the opportunities that DNA-based engineering systems present in contributing to the achievement of the SDGs within various real-world scenarios. We establish direct relationships between DNA-based engineering systems and the SDGs, highlighting their inherent merits in accelerating sustainable development. Furthermore, in order to successfully achieve SDGs, we address the challenges associated with these systems and emphasize the urgent need for developing multifunctional, reliable, biosafe, and intelligent DNA-based engineering systems to overcome these challenges.

Multimodal integration of liquid biopsy and radiology for the noninvasive diagnosis of gallbladder cancer and benign disorders

Gallbladder cancer (GBC) frequently mimics gallbladder benign lesions (GBBLs) in radiological images, leading to preoperative misdiagnoses. To address this challenge, we initiated a prospective, multicenter clinical trial (ChicCTR2100049249) and proposed a multimodal, non-invasive diagnostic model to distinguish GBC from GBBLs. A total of 301 patients diagnosed with gallbladder-occupying lesions (GBOLs) from 11 medical centers across 7 provinces in China were enrolled and divided into a discovery cohort and an independent external validation cohort. An artificial intelligence (AI)-based integrated model, GBCseeker, is created using cell-free DNA (cfDNA) genetic signatures, radiomic features, and clinical information. It achieves high accuracy in distinguishing GBC from GBBL patients (93.33% in the discovery cohort and 87.76% in the external validation cohort), reduces surgeons' diagnostic errors by 56.24%, and reclassifies GBOL patients into three categories to guide surgical options. Overall, our study establishes a tool for the preoperative diagnosis of GBC, facilitating surgical decision-making.

A Multi-Input Molecular Classifier Based on Digital DNA Strand Displacement for Disease Diagnostics

DNA-based molecular computing systems for biomarkers have emerged as powerful tools for intelligent diagnostics. However, with the variety of feature biomarkers expanding, current molecular computing systems suffer from the use of a large number of oligonucleotides and limited encoding capability. Here, the study develops an alternative molecular computing approach termed Digital DNA Strand Displacement (DDSD) which recognizes targets and operates target valence through DNA polymerase-based extension and strand release. DDSD significantly reduced the number of used oligonucleotide species, provided robust molecular classifiers. In clinical blood samples, a 96% accuracy rate is achieved with a DDSD-based binary classifier for distinguishing bacterial and viral infections, a 100% accuracy rate is achieved with a multiclass classifier for identifying pathogen types, surpassing existing classifier systems. Moreover, DDSD can be readily expanded. Cascade DDSD is developed, enabling simultaneous computing of up to 14 valence states with a maximum valence of 25. Multiway junction DDSD is implemented to achieve high-valence computing by compact DNA nanostructures rather than split DNA computing units, reducing the potential leakage. The implementation of DDSD enhances the capability of valence-based intelligent molecular diagnostics and multiplexed biomarker detection.

Regulation of transmembrane current through modulation of biomimetic lipid membrane composition

Ion transport through biological channels is influenced not only by the structural properties of the channels themselves but also by the composition of the phospholipid membrane, which acts as a scaffold for these nanochannels. Drawing inspiration from how lipid membrane composition modulates ion currents, as seen in the activation of the K+ channel in Streptomyces A (KcsA) by anionic lipids, we propose a biomimetic nanochannel system that integrates DNA nanotechnology with two-dimensional graphene oxide (GO) nanosheets. By modifying the length of the multibranched DNA nanowires generated through the hybridization chain reaction (HCR) and varying the concentration of the linker strands that integrate these DNA nanowire structures with the GO membrane, the composition of the membrane can be effectively adjusted, consequently impacting ion transport. This method provides a strategy for developing devices with highly efficient and tunable ion transport, suitable for applications in mass transport, environmental protection, biomimetic channels, and biosensors.

High-Security Data Encryption Enabled by DNA Multi-Strand Solid-Phase Hybridization and Displacement in Inkjet-Printed Microarrays

Multicolor fluorescent encryption systems that respond to specific stimuli have drawn widespread attention to data storage and encryption due to their low cost and facile data access. However, existing encryption systems are limited by encryption materials, restricting their encryption depth. This study uses DNA molecules as encryption materials that offer exceptional specificity and encryption depth within sequences. With inkjet-printed microarrays on a solid-phase interface, a multicolor fluorescent data storage system based on DNA hybridization and strand displacement is developed, achieving an encryption system with high encryption depth and flexibility. DNA strands, modified with different fluorescent labels, are delivered onto solid-phase interfaces containing a DNA self-assembled monolayer (SAM) via inkjet printing, forming multicolor fluorescent data microarrays. Data storage and encryption are achieved through the hybridization of fluorescent DNA strands for data presentation and interference with the DNA SAM at the interface between the solid phase and droplets. Interference DNA strands can be removed by DNA strand displacement for decryption. The encryption depth of this system is determined by the design of the DNA sequences and the combination of multiple DNA strands, showcasing its outstanding encryption ability. Meanwhile, high-throughput inkjet printing accelerates the data writing process, further enhancing the system efficiency. With DNA solid-phase reaction in inkjet-printed microarrays, this system provides a scalable and robust strategy for high-depth and efficient data encryption.

Logical Analysis of Multiple miRNAs with Isothermal Molecular Classifiers Based on LATE-RCA

Logical analysis of multiple-miRNA expression information and immediate output of diagnostic results facilitates early cancer detection. In this work, we constructed an isothermal molecular classifier capable of performing computations on multiple miRNAs and directly providing diagnosis results. First, we developed linear-after-the-exponential rolling circle amplification (LATE-RCA), a nearly linear isothermal amplification that does not destroy the original quantitative information about miRNAs. By designing different numbers of weighted coding sequences on the circular template, we naturally implemented multiplication in the LATE-RCA process. Summation, subtraction, and reporting were then carried out by strand displacement reactions. The entire workflow of the classifier was validated using synthetic gastric cancer and healthy miRNA samples with an accuracy of 100%, demonstrating its robustness and accuracy. Compared with existing molecular classifiers, our approach performs under isothermal conditions, streamlines computational procedures, and simplifies probe design. We believe that this isothermal molecular classifier has promising prospects in personalized precision medicine.

Aptamer-functionalized nucleic acid nanotechnology for biosensing, bioimaging and cancer therapy

Nucleic acids have enabled the fabrication of self-assemblies and dynamic operations. Among different functional nucleic acids, aptamers can specifically bind to a wide range of targets, including proteins, viral antigens, living cells and even tissues, and have thus emerged as molecular recognition tools in molecular medicine. Hence, aptamer-functionalized nucleic acid nanotechnology offers applications of biosensing, bioimaging, and cancer therapy. In this review, after a brief overview of nucleic acid nanotechnology, we focus on the integration of aptamers with nucleic acid nanotechnology, including self-assembly constructions and dynamic molecular manipulations. The emerging applications in molecular medicine are subsequently reviewed with aptamer-based self-assemblies and aptamer-involved dynamic molecular manipulation. For convenience, applications are broadly categorized into biosensing, bioimaging, and cancer therapy. Finally, challenges and potential development of nucleic acid nanotechnology are discussed.

High-Speed Sequential DNA Computing Using a Solid-State DNA Origami Register

DNA computing leverages molecular reactions to achieve diverse information processing functions. Recently developed DNA origami registers, which could be integrated with DNA computing circuits, allow signal transmission between these circuits, enabling DNA circuits to perform complex tasks in a sequential manner, thereby enhancing the programming space and compatibility with various biomolecules of DNA computing. However, these registers support only single-write operations, and the signal transfer involves cumbersome and time-consuming register movements, limiting the speed of sequential computing. Here, we designed a solid-state DNA origami register that compresses output data from a 3D solution to a 2D surface, establishing a rewritable register suitable for solid-state storage. We developed a heterogeneous integration architecture of liquid-state circuits and solid-state registers, reducing the register-mediated signal transfer time between circuits to less than 1 h, thereby achieving fast sequential DNA computing. Furthermore, we designed a trace signal amplifier to read surface-stored signals back into solution. This compact approach not only enhances the speed of sequential DNA computing but also lays the foundation for the visual debugging and automated execution of DNA molecular algorithms.

Advancements in DNA computing: exploring DNA logic systems and their biomedical applications

DNA computing is regarded as one of the most promising candidates for the next generation of molecular computers, utilizing DNA to execute Boolean logic operations. In recent decades, DNA computing has garnered widespread attention due to its powerful programmable and parallel computing capabilities, demonstrating significant potential in intelligent biological analysis. This review summarizes the latest advancements in DNA logic systems and their biomedical applications. Firstly, it introduces recent DNA logic systems based on various materials such as functional DNA sequences, nanomaterials, and three-dimensional DNA nanostructures. The material innovations driving DNA computing have been summarized, highlighting novel molecular reactions and analytical performance metrics like efficiency, sensitivity, and selectivity. Subsequently, it outlines the biomedical applications of DNA computing-based multi-biomarker analysis in cellular imaging, clinical diagnosis, and disease treatment. Additionally, it discusses the existing challenges and future research directions for the development of DNA computing.

Precise pathogen quantification by CRISPR-Cas: a sweet but tough nut to crack

Pathogen detection is increasingly applied in medical diagnosis, food processing and safety, and environmental monitoring. Rapid, sensitive, and accurate pathogen quantification is the most critical prerequisite for assessing protocols and preventing risks. Among various methods evolved, those based on clustered regularly interspaced short palindromic repeats (CRISPR)-associated proteins (Cas) have been developed as important pathogen detection strategies due to their distinct advantages of rapid target recognition, programmability, ultra-specificity, and potential for scalability of point-of-care testing (POCT). However, arguments and concerns on the quantitative capability of CRISPR-based strategies are ongoing. Herein, we systematically overview CRISPR-based pathogen quantification strategies according to the principles, properties, and application scenarios. Notably, we review future challenges and perspectives to address the of precise pathogen quantification by CRISPR-Cas. We hope the insights presented in this review will benefit development of CRISPR-based pathogen detection methods.

Plug-and-play protein biosensors using aptamer-regulated in vitro transcription

Molecular biosensors that accurately measure protein concentrations without external equipment are critical for solving numerous problems in diagnostics and therapeutics. Modularly transducing the binding of protein antibodies, protein switches or aptamers into a useful output remains challenging. Here, we develop a biosensing platform based on aptamer-regulated transcription in which aptamers integrated into transcription templates serve as inputs to molecular circuits that can be programmed to a produce a variety of responses. We modularly design molecular biosensors using this platform by swapping aptamer domains for specific proteins and downstream domains that encode different RNA transcripts. By coupling aptamer-regulated transcription with diverse transduction circuits, we rapidly construct analog protein biosensors and digital protein biosensors with detection ranges that can be tuned over two orders of magnitude and can exceed the binding affinity of the aptamer. Aptamer-regulated transcription is a straightforward and inexpensive approach for constructing programmable protein biosensors that could have diverse applications in research and biotechnology.

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.

Precise control of transmembrane current via regulating bionic lipid membrane composition

The transport of ions through biological ion channels is regulated not only by their structural characteristics but also by the composition of the phospholipid membrane, which serves as a carrier for nanochannels. Inspired by the modulation of ion currents by lipid membrane composition, exemplified by the activation of the K+ channel of Streptomyces A by anionic lipids, we present a biomimetic nanochannel system based on combining DNA nanotechnology with two-dimensional graphene oxide (GO) nanosheets. By designing multibranched DNA nanowires, we assemble programmable DNA scaffold networks (DSNs) on the GO surface to precisely control membrane composition. Modulating the DSN layers from one to five enhances DNA composition, yielding a maximum 12-fold enhancement in ion current, primarily due to charge effects. Incorporating DNAzymes facilitates reversible modulation of membrane composition, enabling cyclic conversion of ion current. This approach offers a pathway for creating devices with highly efficient, tunable ion transport, applicable in diverse fields like mass transport, environmental protection, biomimetic channels, and biosensors.

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.

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.

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 signal transmission strategy driven by gap-regulated exonuclease hydrolysis for hierarchical molecular networks

Exonucleases serve as efficient tools for signal processing and play an important role in biochemical reactions. Here, we identify the mechanism of cooperative exonuclease hydrolysis, offering a method to regulate the cooperative hydrolysis driven by exonucleases through the modulation of the number of bases in gap region. A signal transmission strategy capable of producing amplified orthogonal DNA signal is proposed to resolve the polarity of signals and byproducts, which provides a solution to overcome the signal attenuation. The gap-regulated mechanism combined with DNA strand displacement (DSD) reduces the unpredictable secondary structures, allowing for the coexistence of similar structures in hierarchical molecular networks. For the application of the strategy, a molecular computing model is constructed to solve the maximum weight clique problems (MWCP). This work enhances for our knowledge of these important enzymes and promises application prospects in molecular computing, signal detection, and nanomachines.

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.

Disrupting the infectious disease ecosystem in the digital precision health era innovations and converging emerging technologies

This commentary explores the convergence of precision health and evolving technologies, including the critical role of artificial intelligence (AI) and emerging technologies in infectious diseases (ID) and microbiology. We discuss their disruptive impact on the ID ecosystem and examine the transformative potential of frontier technologies in precision health, public health, and global health when deployed with robust ethical and data governance guardrails in place.

Plug-and-play protein biosensors using aptamer-regulated in vitro transcription

Molecular biosensors that accurately measure protein concentrations without external equipment are critical for solving numerous problems in diagnostics and therapeutics. Modularly transducing the binding of protein antibodies, protein switches or aptamers into a useful output remains challenging. Here, we develop a biosensing platform based on aptamer-regulated transcription in which aptamers integrated into transcription templates serve as inputs to molecular circuits that can be programmed to a produce a variety of responses. We modularly design molecular biosensors using this platform by swapping aptamer domains for specific proteins and downstream domains that encode different RNA transcripts. By coupling aptamer-regulated transcription with diverse transduction circuits, we rapidly construct analog protein biosensors or digital protein biosensors with detection ranges that can be tuned over two orders of magnitude. Aptamer-regulated transcription is a straightforward and inexpensive approach for constructing programmable protein biosensors suitable for diverse research and diagnostic applications.

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.