Entropy-driven reactions in living cells for assay let-7a microRNA

Precision miRNA profiling: Electrochemiluminescence powered by CRISPR-Cas13a and hybridization chain reaction

The article details a groundbreaking platform for detecting microRNAs (miRNAs), crucial biomolecules involved in gene regulation and linked to various diseases. This innovative platform combines the CRISPR-Cas13a system's precise ability to specifically target and cleave RNA molecules with the amplification capabilities of the hybridization chain reaction (HCR). HCR aids in signal enhancement by creating branched DNA structures. Additionally, the platform employs electrochemiluminescence (ECL) for detection, noted for its high sensitivity and low background noise, making it particularly effective. A key application of this technology is in the detection of miR-17, a biomarker associated with multiple cancer types. It exhibits remarkable detection capabilities, characterized by low detection limits (14.38 aM) and high specificity. Furthermore, the platform's ability to distinguish between similar miRNA sequences and accurately quantify miR-17 in cell lysates underscores its significant potential in clinical and biomedical fields. This combination of precise targeting, signal amplification, and sensitive detection positions the platform as a powerful tool for miRNA analysis in medical diagnostics and research.

Exonuclease III-propelled DNAzyme walker: an electrochemical strategy for microRNA diagnostics

MicroRNA detection is crucial for early infectious disease diagnosis and rapid cancer screening. However, conventional techniques like reverse transcription-quantitative polymerase chain reaction, requiring specialized training and intricate procedures, are less suitable for point-of-care analyses. To address this, we've developed a straightforward amplifier based on an exonuclease III (exo III)-propelled DNAzyme walker for sensitive and selective microRNA detection. This amplifier employs a specially designed hairpin probe with two exposed segments for strand recognition. Once the target microRNA is identified by the hairpin's extended single-strand DNA, exo III initiates its digestion, allowing microRNA regeneration and subsequent hairpin probe digestion cycles. This cyclical process produces a significant amount of DNAzyme, leading to a marked reduction in electrochemical signals. The biosensor exhibits a detection range from 10 fM to 100 pM and achieves a detection limit of 5 fM (3σ criterion). Importantly, by integrating an "And logic gate," our system gains the capacity for simultaneous diagnosis of multiple microRNAs, enhancing its applicability in RNA-based disease diagnostics.

Improving biosensor accuracy and speed using dynamic signal change and theory-guided deep learning

False results and time delay are longstanding challenges in biosensing. While classification models and deep learning may provide new opportunities for improving biosensor performance, such as measurement confidence and speed, it remains a challenge to ensure that predictions are explainable and consistent with domain knowledge. Here, we show that consistency of deep learning classification model predictions with domain knowledge in biosensing can be achieved by cost function supervision and enables rapid and accurate biosensing using the biosensor dynamic response. The impact and utility of the methodology were validated by rapid and accurate quantification of microRNA (let-7a) across the nanomolar (nM) to femtomolar (fM) concentration range using the dynamic response of cantilever biosensors. Data augmentation and cost function supervision based on the consistency of model predictions and experimental observations with the theory of surface-based biosensors improved the F1 score, precision, and recall of a recurrent neural network (RNN) classifier by an average of 13.8%. The theory-guided RNN (TGRNN) classifier enabled quantification of target analyte concentration and false results with an average prediction accuracy, precision, and recall of 98.5% using the initial transient or entire dynamic response, which is indicative of high prediction accuracy and low probability of false-negative and false-positive results. Classification scores were used to establish new relationships among biosensor performance characteristics (e.g., measurement confidence) and design parameters (e.g., inputs and hyperparameters of classification models and data acquisition parameters) that may be used for characterizing biosensor performance.

Reduction of Biosensor False Responses and Time Delay Using Dynamic Response and Theory-Guided Machine Learning

Here, we provide a new methodology for reducing false results and time delay of biosensors, which are barriers to industrial, healthcare, military, and consumer applications. We show that integrating machine learning with domain knowledge in biosensing can complement and improve the biosensor accuracy and speed relative to the performance achieved by traditional regression analysis of a standard curve based on the biosensor steady-state response. The methodology was validated by rapid and accurate quantification of microRNA across the nanomolar to femtomolar range using the dynamic response of cantilever biosensors. Theory-guided feature engineering improved the performance and efficiency of several classification models relative to the performance achieved using traditional feature engineering methods (TSFRESH). In addition to the entire dynamic response, the technique enabled rapid and accurate quantification of the target analyte concentration and false-positive and false-negative results using the initial transient response, thereby reducing the required data acquisition time (i.e., time delay). We show that model explainability can be achieved by combining theory-guided feature engineering and feature importance analysis. The performance of multiple classifiers using both TSFRESH- and theory-based features from the biosensor's initial transient response was similar to that achieved using the entire dynamic response with data augmentation. We also show that the methodology can guide design of experiments for high-performance biosensing applications, specifically, the selection of data acquisition parameters (e.g., time) based on potential application-dependent performance thresholds. This work provides an example of the opportunities for improving biosensor performance, such as reducing biosensor false results and time delay, using explainable machine learning models supervised by domain knowledge in biosensing.

Entropy-driven reactions for controlling CRISPR/Cas12a and constructing an electrochemical biosensor for cardiac biomarkers detection

An electrochemical biosensor is reported for controlling CRISPR/Cas12a activity through the utilization of entropy-driven reactions, alongside the construction of a highly sensitive biosensor for B-type natriuretic peptide (BNP) detection. In the biosensor, entropy-driven reactions are employed to regulate the activity of CRISPR/Cas12a - a gene editing tool - capable of nonspecific cleavage of single-stranded DNA (ssDNA). The biosensor architecture encompasses an electrode that is modified with ssDNA probes designed to hybridize with target BNP aptamers. These aptamers, furnished with labeled ssDNA triggers, facilitate the activation of CRISPR/Cas12a through interaction with its guide RNA. Upon the presence of BNP, it associates with the aptamers, subsequently liberating the triggers that instigate the entropy-driven reactions. As a consequence of these reactions, more stable duplexes emerge between the triggers and guide RNA, thereby activating CRISPR/Cas12a. The activated CRISPR/Cas12a subsequently executes cleavage of ssDNA probes residing on the electrode surface, culminating in the generation of an electrochemical signal directly (the calibration plots of differential pulse voltammetric detection were acquired at a working potential of 0.2 V (vs. ref. electrode)) proportional to the BNP concentration. Validation of the biosensor's performance is undertaken, wherein BNP detection is demonstrated in both buffer and human serum samples. Evident in the findings is the biosensor's discernible sensitivity and specificity for BNP detection, exemplified by a detection limit of 13.53 fM and a lack of interference originating from other cardiac biomarkers, respectively. Furthermore, the biosensor's potential to discriminate between healthy individuals and those afflicted by heart failure, predicated on distinctive BNP levels, is illustrated.

Label-Free, High-Throughput, Sensitive, and Logical Analysis Using Biomimetic Array Based on Stable Luminescent Copper Nanoclusters and Entropy-Driven Nanomachine

The development of an array for high-throughput and logical analysis of biomarkers is significant for disease diagnosis. DNA-templated copper nanoclusters (CuNCs) have a strong potential to serve as a label-free photoluminescence source in array platforms, but their luminescent stability and sensitivity need to be improved. Herein, we report a facile, sensitive, and robust biomimetic array assay by integrating with stable luminescent CuNCs and entropy-driven nanomachine (EDN). In this strategy, the luminescent stability of CuNCs was improved by adding fructose in CuNCs synthesis to offer a reliable label-free signal. Meanwhile, the DNA template for CuNCs synthesis was introduced into EDN with excellent signal amplification ability, in which the reaction triggered by target miRNA would cause the blunt/protruding conformation change of 3'-terminus accompanied by the production or loss of luminescence. In addition, a biomimetic array fabricated by photonic crystals (PCs) physically enhanced the emitted luminescent signal of CuNCs and achieved high-throughput signal readout by a microplate reader. The proposed assay can isothermally detect as low as 4.5 pM of miR-21. Moreover, the logical EDN was constructed to achieve logical analysis of multiple miRNAs by "AND" or "OR" logic gate operation. Therefore, the proposed assay has the advantages of label-free property, high sensitivity, flexible design, and high-throughput analysis, which provides ideas for developing a new generation of facile and smart platforms in the fields of biological analysis and clinical application.

Engineering entropy-driven based multiple signal amplification strategy for visualized assay of miRNA by naked eye

MicroRNAs (miRNAs) are currently recognized as novel biomarkers for cancer early diagnosis, therapy selection, and progression monitoring. Herein, we developed an ultrasensitive and label-free homogeneous colorimetric strategy for miRNA detection based on engineering entropy-driven amplification (EDA) coupled with nicking enzyme-assisted AuNP aggregation. In our design, the target miRNA could specifically trigger the EDA recycling process. One of the EDA products could open the hairpin probe and form a dual strand containing a nicking endonuclease (Nb.BbvCl) cleavage region. After adding nicking endonuclease in the sensing solution, the product DNA fragments could act as two linkers, inducing the aggregation of ssDNA-modified AuNPs. Simultaneously, the liberating complementary strands continued to cyclic hybridization with the hairpin probe. This multiple signal amplification colorimetric strategy showed a wide linear range from 10 fM to 100 pM with a much lower detection limit of 3.13 fM for miRNA let-7a, which also performed well in a complex sample matrix. Most importantly, the naked eye could clearly distinguish the 10 fM color change caused by let-7a to be measured. Moreover, this approach could easily extend to multiple miRNAs with target-specific sequence substitutions. Therefore, this ultrasensitive visual strategy for miRNA demonstrated attractive potentials for promising applications in clinical diagnosis.

Catalytic Amplification of Electrochemical Signal in Homogeneous Solution Using an Entropy-driven DNA Circuit

The electrochemical signal from ferrocene on a DNA probe was successfully modulated in a homogeneous solution by the template-directed formation and dissociation of an inclusion complex with β-cyclodextrin on another probe. The electrochemical response was amplified by combining with a DNA circuit, in which the target DNA served as a catalyst. This system did not require any modification of a complementary DNA with the ferrocene-modified probe on the electrode surface to separate the bound/free probe for the detection of 200 nM target DNA.

Hybridizing clinical translatability with enzyme-free DNA signal amplifiers: recent advances in nucleic acid detection and imaging

Nucleic acids have become viable prognostic and diagnostic biomarkers for a diverse class of diseases, particularly cancer. However, the low femtomolar to attomolar concentration of nucleic acids in human samples require sensors with excellent detection capabilities; many past and current platforms fall short or are economically difficult. Strand-mediated signal amplifiers such as hybridization chain reaction (HCR) and catalytic hairpin assembly (CHA) are superior methods for detecting trace amounts of biomolecules because one target molecule triggers the continuous production of synthetic double-helical DNA. This cascade event is highly discriminatory to the target via sequence specificity, and it can be coupled with fluorescence, electrochemistry, magnetic moment, and electrochemiluminescence for signal reporting. Here, we review recent advances in enhancing the sensing abilities in HCR and CHA for improved live-cell imaging efficiency, lowered limit of detection, and optimized multiplexity. We further outline the potential for clinical translatability of HCR and CHA by summarizing progress in employing these two tools for in vivo imaging, human sample testing, and sensing-treating dualities. We finally discuss their future prospects and suggest clinically-relevant experiments to supplement further related research.

Catalytic formation of luminescent lanthanide complexes using an entropy-driven DNA circuit

Luminescent lanthanide complexes were catalytically formed through an entropy-driven DNA circuit triggered by a target nucleic acid.

A smart fluorescent biosensor for the highly sensitive detection of BRCA1 based on a 3D DNA walker and ESDR cascade amplification

Nucleic acid analysis plays an important role in the diagnosis of diseases. There is a continuous demand to develop rapid and sensitive methods for the specific detection of nucleic acids. Herein, we constructed a highly sensitive and rapid fluorescent biosensor for the detection of BRCA1 by coupling a 3D DNA walker machine with spontaneous entropy-driven strand displacement reactions (ESDRs). In this study, the 3D DNA walker machine was well activated by the target DNA; this resulted in the cyclic utilization of the target DNA and the release of intermediate DNAs. Subsequently, the free intermediate DNAs triggered the circulation process of ESDRs with the help of the assistant probe A, leading to a significant enhancement of the fluorescence intensity. Due to the robust execution of the 3D DNA walker machine and highly efficient amplification capability of ESDRs, the developed biosensing method shows a wide linear range from 0.1 pM to 10 nM with the detection limit as low as 41.44 fM (S/N = 3). Moreover, the constructed biosensor displays superior specificity and has been applied to monitor BRCA1 in complex matrices. Thus, this elaborated cascade amplification biosensing strategy provides a potential platform for the bioassays of nucleic acids and the clinical diagnosis of diseases.