Accurate discrimination of single nucleotide polymorphisms (SNPs) remains a fundamental challenge in nucleic acid biosensing due to the minimal thermodynamic differences between perfectly matched and single-base mismatched sequences. This intrinsic limitation often compromises recognition fidelity at the initial hybridization stage, regardless of subsequent signal amplification strategies. Herein, we propose a electrochemical biosensing strategy based on a competitive loop-recognition dual-hairpin probe for highly specific SNP identification. This paradigm utilizes a wild-type (WT) sequestering probe to form an inhibiting complex (HW/WT) that actively suppresses background interference, while a target-specific probe binds with the SNP to form a triggering complex (HS/SNP) to initiate signal transduction. By positioning the recognition domains within the loop regions, the formation of the triggering complex is subjected to pronounced structural confinement, thereby significantly amplifying the thermodynamic penalty associated with single-base mismatches. On the basis of this enhanced competitive recognition fidelity, a dual-ratiometric electrochemical biosensor with high robustness and specificity was constructed for reliable SNP detection. Furthermore, benefiting from the exceptional single-base resolution of the inhibiting-triggering mechanism, SNP and WT sequences were directly utilized as molecular logic inputs to implement Exclusive OR (XOR), AND, and OR logic operations. This work demonstrates how structural regulation and competitive molecular sorting, prior to signal amplification, can convert subtle genetic variations into reliable analytical and computational signals, providing a versatile biointerface for precise nucleic acid analysis and intelligent biocomputing.

Abstract Fluorescence imaging, serving as the primary imaging modality in modern life science research, faces a fundamental challenge in achieving high‐sensitivity imaging: optimizing the signal‐to‐noise ratio (SNR) under dynamic and complex experimental conditions. Due to autofluorescence, shot noise, and tissue scattering, this SNR deficiency disrupts subcellular morphometry, restricts recording reliability, and ultimately propagates artifacts in subsequent analysis. This review evaluates data‐driven deep learning denoising methods that overcome conventional limitations through effective feature extraction and nonlinear modeling. Focusing on fluorescence imaging acquisition under photon‐limited conditions, we delineate cutting‐edge architectures, including supervised learning, unsupervised learning, zero‐shot learning, and hybrid approaches. By producing higher‐fidelity image data, these denoising methods enhance the reliability of live‐cell imaging and the accuracy of neural mechanism analysis. This advancement provides a stronger foundation for elucidating dynamic biological processes and accelerating precision medicine.

Single-nucleotide polymorphism (SNP) genotyping is crucial for genetic research and precision medicine, yet reliable discrimination of single-base variants in complex genomic backgrounds remains analytically challenging. Although CRISPR/Cas12a-based biosensing offers high sequence specificity, its intrinsic mismatch tolerance often leads to nonspecific activation by wild-type sequences, thereby compromising SNP fidelity. Herein, we report a highly sensitive electrochemical sensing platform based on a programmable DNA dumbbell (Dum) probe that functions as a conformational energy-barrier regulator of CRISPR/Cas12a activation. The closed-loop dumbbell architecture sterically shields the crRNA-activating sequence, establishing a high activation threshold that suppresses nonspecific Cas12a triggering. Only precise SNP hybridization induces a thermodynamically favorable conformational rearrangement, releasing the mediator and transitioning the CRISPR system from an inactive to an active state. To further enhance analytical sensitivity and reliability, nucleic acid-functionalized FeCo nanozymes were incorporated as catalytic signal transducers, enabling a self-validating dual-mode signal electrochemical readout through intrinsic metal redox and H2O2 electrocatalysis. The resulting platform achieved reliably discriminates mutation abundances down to 0.1%. Validation using soybean genomic DNA samples demonstrates the robustness and practical applicability of the proposed strategy. This work establishes a conformational energy-barrier-regulated CRISPR activation paradigm, providing a generalizable analytical framework for high-fidelity SNP genotyping in molecular breeding and clinical diagnostics.

Single nucleotide polymorphisms (SNPs) are important genetic variations closely associated with various diseases and agronomic traits. However, existing SNP detection methods often suffer from dependence on enzymes, and limited adaptability to different sequence lengths, which restrict their practical applications. Herein, we report an enzyme-free, resettable recognition probe based dual-mode electrochemical/UV-vis SNP detection strategy. Specifically, the mutation site is embedded within the stem of a hairpin structure and anchored onto inert polystyrene microspheres to construct a toehold-mediated identifying unit (Rec@PSM), enabling single-base resolution. Target SNP binding triggers strand displacement, subsequently initiate a hybridization chain reaction (HCR). The resulting G-quadruplex (G4) structures bind methylene blue (MB), significantly amplifying the electrochemical signal, while the decrease of free MB in the supernatant produces a concentration-dependent reduction in UV-vis absorbance, providing complementary cross-validation. Upon alkaline treatment, Rec@PSM units can be reset multiple times and release bound sequences, allowing multi-cycle detection without enzyme assistance. The method achieves sensitive detection with a detection of limit of 16.2 aM, and exhibits high selectivity, stability, and reproducibility for both short oligonucleotides and long DNA while effectively reducing detection costs. This novel SNP sensing platform combining resettable capability, and dual-mode design, offering broad application potential in early disease diagnosis, crop breeding, and precision medicine.

Accurate identification of single nucleotide polymorphisms (SNPs) holds significant importance for crop genetic improvement and precision breeding. However, traditional detection methods suffer from high costs, operational complexity, and heavy reliance on specialized equipment, limiting their large-scale application in breeding practices. Here, we present an electrochemical biosensing platform based on conformational change design, specifically tailored for sensitive and selective SNP analysis, achieved through the spatial reconfiguration of conformation-switchable hairpin probes. Upon hybridization with target sequences, the hairpin structure undergoes spatial remodeling to modulate its distance from the electrode surface, enabling signal transduction. This design leverages precise spatial control and structural dynamics to achieve high signal specificity, allowing single-base mismatch discrimination with exceptional reproducibility and sensitivity. The operationally simple biosensing platform demonstrates high specificity in distinguishing single-nucleotide variations. By applying this system to identify SNPs associated with key phenotypic traits related to leaf morphology in soybean genotypes, we validate its potential for genotype screening and molecular breeding applications.

Accurate detection of low-abundance single nucleotide polymorphisms (SNPs) against a large excess of homologous wild-type sequences requires both selective molecular recognition and effective transduction of small sequence differences into measurable signals. Here, we report a spatially confined electrochemical strategy that couples sequence-selective recognition with size-dependent mass-transport gating. DNA-hybridization-driven self-assembly of gold nanoparticles (AuNPs) forms a three-dimensional self-assembled electrode (3D-SAE) with a DNA-defined interparticle architecture. Competitive probes (SP/WP) convert single-base recognition into distinct molecular-size states: the SNP-associated pathway preferentially triggers a hybridization chain reaction (HCR), generating bulky AuNP-anchored HCR/methylene blue complexes (Au@HCR/MB) with reduced electrochemical accessibility through the porous 3D-SAE, whereas the wild-type pathway does not trigger HCR and maintains a high-current response from more readily accessible MB-containing species. Thus, sequence recognition is translated into a molecular-size difference and subsequently into an electrochemical signal through differential mass transport. Under buffer conditions, the platform achieved a statistically estimated detection limit of ∼0.47 fM and a quantitative range of 1 fM-100 pM. It discriminated a 0.1% mutant abundance in a fragmented genomic-DNA background. The downstream signal-transduction chemistry is enzyme-free and isothermal. This work establishes a mechanistical recognition-size-conversion-mass-transport-gating architecture for electrochemical nucleic acid analysis.

Light-field imaging has wide applications in various domains, including microscale life science imaging, mesoscale neuroimaging, and macroscale fluid dynamics imaging. The development of deep learning-based reconstruction methods has greatly facilitated high-resolution light-field image processing, however, current deep learning-based light-field reconstruction methods have predominantly concentrated on the microscale. Considering the multiscale imaging capacity of light-field technique, a network that can work over variant scales of light-field image reconstruction will significantly benefit the development of volumetric imaging. Unfortunately, to our knowledge, no one has reported a universal high-resolution light-field image reconstruction algorithm that is compatible with microscale, mesoscale, and macroscale. To fill this gap, we present a real-time and universal network (RTU-Net) to reconstruct high-resolution light-field images at any scale. RTU-Net, as the first network that works over multiscale light-field image reconstruction, employs an adaptive loss function based on generative adversarial theory and consequently exhibits strong generalization capability. We comprehensively assessed the performance of RTU-Net through the reconstruction of multiscale light-field images, including microscale tubulin and mitochondrion dataset, mesoscale synthetic mouse neuro dataset, and macroscale light-field particle imaging velocimetry dataset. The results indicated that RTU-Net has achieved real-time and high-resolution light-field image reconstruction for volume sizes ranging from 300 μm × 300 μm × 12 μm to 25 mm × 25 mm × 25 mm, and demonstrated higher resolution when compared with recently reported light-field reconstruction networks. The high-resolution, strong robustness, high efficiency, and especially the general applicability of RTU-Net will significantly deepen our insight into high-resolution and volumetric imaging.

Single-nucleotide variations (SNVs) represent vital clinical and biological information in the onset and progression of many cancers, but lacking of cost-effective, high-sensitive and reliable SNVs detection method. In this study, we propose a programmable electrochemical biosensing strategy initiated simultaneously from multi-directions by enzyme-free amplifying circuit for high-sensitivity SNVs detection. Through elaborate design, we utilized the power of conventional enzyme-free catalytic reaction to activate a multidirectional initiation self-assembly process, enabling multiple amplification. This innovative cascade strategy significantly improved the amplification performance and detection sensitivity. Subsequently, KRAS gene of cancer cells was used as proof-of concept model for SNVs recognition to demonstrate the capability. With the help of cascade design, the single-base differences between SNV sequence and wild-type sequence (WT) could be differentiated and amplified effectively. Consequently, abundant Y-shaped DNA structure efficiently was induced by DNA variant to generate on the electrode surface, facilitating the incorporation of methylene blue (MB) redox indicator. Therefore, a "signal-on" electrochemical biosensing platform was constructed. Our enzyme-free biosensor achieved a low detection limit of 36 aM and a broader linear range spanning from 100 aM to 1 nM under optimal experimental conditions. The capability of proposed cascaded DNA network to detect DNA variants in complex cancer cells and serum samples indicated the potential applicability in real sample analysis.

Single-nucleotide polymorphism (SNP) detection plays a critical role in early disease screening, personalized medicine, and crop genetic improvement. In recent years, DNAzymes have attracted widespread attention in molecular recognition and catalytic diagnostics because of the sequence programmability and strong strand-cleavage activity. However, current DNAzyme-based systems still face significant challenges, such as limited sequence selectivity and high nonspecific reactivity, which constrain their broader application in high-precision genotyping, particularly in SNP discrimination. To overcome these limitations, we rationally redesigned the catalytic core of the DNAzyme to construct a competitive molecular switch governed by an "activation-silencing" mechanism, thereby addressing the bottleneck of single-base specificity in DNAzyme systems. This protein-enzyme-free strategy for SNP recognition also breaks the strict stoichiometric paradigm of conventional enzyme-free strand displacement reactions, resulting in a significant enhancement of target selectivity. To tackle the common issue where improved selectivity often compromises detection sensitivity, we innovatively introduced a solid-liquid phase cross-reaction mechanism and developed a cascade system based on electrochemical biosensing to improve analytical sensitivity. Our strategy enables sensitive detection of SNPs with a detection limit as low as 11.3 aM, representing a marked improvement over sensors with conventionally vertical amplification (370 aM). Furthermore, it demonstrates high consistency in genotyping representative soybean variants. Beyond theoretical model for improved single-base recognition and signal transduction, this work provides an innovative, enzyme-free, and scalable platform for SNP sensing and signal regulation, offering new concepts for precision genotyping and molecular diagnostics.

DNA walkers, as structurally and functionally programmable signal amplification tools, exhibit great potential for application in the field of biosensing. Traditional DNA walkers often rely on enzymes for operation, posing compatibility challenges, while the handful of existing enzyme-free DNA walkers demonstrate limited performance. To address this, we innovatively developed an efficient enzyme-free 3D DNA walker with dual capture and dual output capabilities. Coupled with ligase chain reaction (LCR), this system facilitates highly sensitive and specific detection of single nucleotide polymorphisms (SNPs). Specifically, LCR precisely identifies single-base mutations, effectively transmitting biological information. The 3D DNA walker system is based on entropy-driven circuit cycling reaction technology. In this system, LCR products serve as the driving strands for the DNA walker, independently binding to track strands and walking legs immobilized on gold nanoparticles, forming a unique dual signal capture mechanism. Each track strand carries two signal chains, significantly enhancing signal amplification efficiency. Benefiting from this novel enzyme-free 3D DNA walker strategy, our biosensing system exhibits exceptional sensitivity to mutant targets (MT), detecting MT at concentrations as low as 30.3 aM and distinguishing heterozygous samples with a 0.01% mutation frequency. Furthermore, this system has been successfully applied to genotyping and mutation abundance assessment of genomes from fresh soybean leaves, demonstrating its vast potential for practical applications. In summary, this research pioneers a novel enzyme-free 3D DNA walker with dual capture and dual output capabilities, and develops an ultrasensitive genotyping tool. This provides strong technical support for the advancement of genetic research.

In recent years, entropy-driven circuit (EDC) dynamic DNA networks have garnered significant attention in nucleic acid detection owing to their simplicity, efficiency, and flexible design. Nevertheless, conventional EDC reactions face a constraint in achieving optimal signal amplification due to a solitary and feeble driving force. To overcome this limitation, we innovatively devised a gold nanoparticle (AuNP) dispersion-enhanced EDC (Au-EDC) approach, pioneering a novel colorimetric signal amplification and output system. The system was harmoniously integrated with the ligase chain reaction (LCR) for precise single nucleotide polymorphism (SNP) genotyping. Specifically, LCR was selectively executed solely on the positive strand of the mutant target (MT), facilitating precise point-to-strand information transduction. Subsequently, the LCR product triggered the Au-EDC cycling reaction, causing the DNA-AuNPs network to progressively disintegrate and release a pronounced colorimetric signal. This strategic design ingeniously harnessed the entropy increase that occurs as AuNPs undergo a transition from aggregated to dispersed states, offering a supplemental impetus for the EDC cycle. The integrated LCR-Au-EDC system excelled in detecting MT at concentrations as low as 320 fM and differentiating pooled samples with mutation frequencies as low as 0.1%. Moreover, the system accurately performed SNP genotyping on the real genomes derived from soybean leaves. Consequently, this study not only develops a colorimetric signal amplification and output sensing system based on EDC reactions but also provides a cost-effective and efficient SNP genotyping tool.

Entropy driver circuits (EDCs) are favored in the field of nucleic acid detection due to their fastness, simplicity, and high compatibility. However, traditional EDC reactions have weak driving forces and single signal outputs, leading to unsatisfactory signal amplification effects. Here, we rationally utilized the DNA loading function and light quenching properties of gold nanoparticles (AuNPs) to develop a dual-signal enrichment enhanced EDC strategy, which was harmoniously integrated with the ligase chain reaction (LCR) for specific detection of single nucleotide polymorphisms. First, LCR transduces the single-base mutation information within mutant targets (MT) into single-stranded DNA information, simultaneously achieving preliminary amplification of the biological signal. Subsequently, the enhanced EDC cycling reaction is activated by the LCR products, accompanied by the detachment of a large number of FAM-modified probes from the surface of AuNPs to output fluorescent signals. The enhanced EDC strategy effectively addresses the issue of inadequate signal amplification in traditional EDC methods. Ultimately, the integrated system with dual-signal amplification capability is proven to accurately detect MT at concentrations as low as 30.3 fM. In addition, 0.01 % of MT in the heterozygous sample pool can be specifically identified by the detection system. Additionally, we verified the practical application potential of the sensing system using real soybean genome samples. Therefore, this study not only proposes an effective EDC enhancement strategy and develops an efficient and practical SNP detection tool, but also provides an important reference idea for the rational construction of the detection platform based on EDC reaction.

The subtle free energy differences resulting from single nucleotide mutations pose a challenge for the specificity of nearly all DNA hybridization probes in identifying single nucleotide polymorphisms (SNPs). The narrow detection window between mutant target (MT) and wild-type target (WT) concentrations that produce the same level of detection signals limits the widespread application of current SNP detection technologies. In this paper, we introduce an efficient method for converting single-base information using a rationally designed ratio-signal DNA competitive converter (RDCC). This converter significantly expands the detection window for single-base mutations in nucleic acid sequences by enabling a user-defined conversion of quantitative relationships between detection signals and target concentrations. Both computer simulations and experimental validations have confirmed the effectiveness of RDCC in converting single-base information and expanding the detection window. By balancing both MT and WT signals, RDCC excels in identifying heterozygous samples with low mutation abundances. Additionally, RDCC has been proven to be harmoniously compatible with commonly used nucleic acid amplification techniques, such as PCR. Furthermore, we have demonstrated the practical application value of RDCC through genotyping tests on genomic samples from soybean leaves. Therefore, this study not only develops a powerful tool for SNP detection but also provides a paradigm for the design of specific nucleic acid probes.

The subtle free energy difference introduced by a single nucleotide mutation results in poor specificity of almost all DNA hybridization probe-based single nucleotide polymorphism (SNP) detection techniques. The development of SNP biosensing strategies with both specificity and sensitivity is a hot and difficult issue in the current field. In this study, we creatively constructed a competitive toehold-mediated strand displacement sensing platform (CTMSD) based on the traditional TMSD reaction, which increased the energy barrier through the intrinsic competition mechanism and expanded the detection window of SNPs. Furthermore, based on the characteristics of the CTMSD platform, the dual-signal detection mode was introduced to change the function model of the detection curve through reporting internal reference ratio signal. The new detection curve model not only compensated for sensitivity, significantly enhanced the discrimination factor, but also greatly expanded the detection window with infinite robustness factor over the detection range. The expansion of the detection window and the improvement of specificity of CTMSD for SNP recognition based on the ratiometric signal output model were verified by computer simulations and experiments. In addition, as a deformation of the strand displacement reaction, the CTMSD was readily adaptable to commonly used signal amplification techniques, such as catalytic hairpin assembly (CHA). Through the CTMSD-CHA performance analysis and real testing of cell genomic samples, the practical application value of CTMSD with the ratiometric signal output model was confirmed. This study provides an important reference for the design and improvement of SNP biosensors and even for all nucleic acid biosensors.

The analysis of single nucleotide polymorphism (SNP) is an effective approach for evaluating tumor initiation and progression. However, the high homology between wild-type and mutant sequences poses challenges in distinguishing between them. Additionally, many cancer-related genes harbor numerous distinct mutations, leading to a lack of efficient and practical methods for analyzing SNPs markers. In this study, we report an electrochemical method for the specific detection of SNPs using competitive bridge probes (CBPs). Through rational design, the CBPs can detect multiple mutant alleles from the same region of the gene. All SNPs-induced probe recognition and bridge-hybridization introduce nanozyme sensitizing factors into the electrochemical sensing system, which catalyze the decomposition of hydrogen peroxide, generating a significant electrochemical signal. As a proof of concept, the proposed biosensor has demonstrated to quantitatively detect various target KRAS variants. Given the high sensitivity, accessibility and versatility of the detection results, we believe the electrochemical biosensing platform we designed holds potential for future applications in cancer clinical diagnosis.

The accuracy of single-nucleotide polymorphism (SNP) detection in long sequences is fundamentally constrained by the minuscule thermodynamic differences arising from single-base mismatches, wherein the overall probe binding energy frequently masks single-base-discrimination signals. Here, we develop a plug-and-play competitive hairpin conversion module (CHCM) based on strand-competitive hybridization and toehold-mediated strand displacement. CHCM incorporates two structured hairpin probes (H1 and H2) that specifically recognize wild-type and mutant alleles, respectively. Through thermodynamic competition triggered by single-base variations, the system precisely modulates probe-target binding stability, directing the formation of differential DNA assemblies that convert single-nucleotide information into detectable sequence signals. Critically, CHCM discriminates heterozygous samples with 0.1% mutation abundance in long-sequence backgrounds using only conventional fluorescent probes without enzymatic assistance. Furthermore, CHCM exhibits exceptional technical compatibility: it seamlessly integrates with upstream PCR amplification or downstream catalytic hairpin assembly (CHA) signal amplification while sustaining robust performance. Successful SNP genotyping in soybean leaf genomic DNA validates its practical utility. This work delivers a high-resolution, cost-effective solution for SNP detection in complex matrices and establishes a scalable modular framework for dynamic nucleic acid probe design.

As a key pathway for understanding behavior, cognition, and emotion, neural decoding and encoding provide effective tools to bridge the gap between neural mechanisms and imaging recordings, especially at single-cell resolution. While neural decoding aims to establish an interpretable theory of how complex biological behaviors are represented in neural activities, neural encoding focuses on manipulating behaviors through the stimulation of specific neurons. We thoroughly analyze the application of fluorescence imaging techniques, particularly two-photon fluorescence imaging, in decoding neural activities, showcasing the theoretical analysis and technological advancements from imaging recording to behavioral manipulation. For decoding models, we compared linear and nonlinear methods, including independent component analysis, random forests, and support vector machines, highlighting their capabilities to reveal the intricate mapping between neural activity and behavior. By employing synthetic stimuli via optogenetics, fundamental principles of neural encoding are further explored. We elucidate various encoding types based on different stimulus paradigms—quantity encoding, spatial encoding, temporal encoding, and frequency encoding—enhancing our understanding of how the brain represents and processes information. We believe that fluorescence imaging-based neural decoding and encoding techniques have deepened our understanding of the brain, and hold great potential in paving the way for future neuroscience research and clinical applications.

The subtle difference of the single nucleotide variation makes it difficult for DNA hybridization probes to specifically sense single nucleotide polymorphisms (SNPs). It is a consensus in related fields to design effective methods to enable the sensitive identification of single-base mutations in long sequences by enzyme-free hybridization probes. Here, we developed a novel competitive strategy that converting single nucleotide variation locus on a long sequence (27 bases) into a short sequence (4-5 bases), then inducing the catalytic hairpin assembly to synchronously achieve the single-base recognition and signal amplification. The design overcomes the insensitivity of DNA hybridization probes to single nucleotide variations and enables enzyme-free detection of ultralow-abundance SNPs. First, we rationally design the DNA probe to modulate competitive hybridization between mutant-type (MT) gene and wild-type (WT) gene. The competitive hybridization successfully converted the single nucleotide differences between the MT and WT genes into three-stranded hybridization intermediates with different toehold tags (4-5 bases). Second, we used the catalytic hairpin assembly (CHA) reaction to identify the metastable triple-strand intermediates into red and green fluorescence changes by circularly unfolding quenched fluorescent hairpin. The biosensor not only provided high selectivity for MT, WT, and interference genes individually but also effectively identified the presence of MT genes in mixed solutions of MT, WT, and interference genes. Notably, this strategy enabled the successful differentiation of heterozygous samples with mutant abundances as low as 0.001 %, demonstrating accurate genotyping of human cancer cell-derived genomic samples and highlighting its potential for developing SNP detection biosensors applicable to agriculture, medicine, and environmental monitoring.

Compared to conventional nucleic acid detection methods, label-free single nucleotide polymorphism (SNP) detection presents challenging due to the necessity of discerning single base mismatches, especially in the field of enzyme-free detection. In this study, we introduce a novel bulged-type DNA duplex probe designed to significantly amplify single-base differences. This probe is integrated with programmable DNA-based nanostructures to develop a sensitive, label-free biosensor for nonenzymatic SNP detection. The duplex probe with one bulge could selectively identify wild-typed DNA (WT) and mutant-type DNA (MT) based on a competitive strand displacement reaction mechanism. The hyperbranched HCR (HHCR) by incorporating of hairpin DNA into the DNA tetrahedron and surface-tethering on the portable screen printing electrode (SPCE) significantly favor the formation of negatively charged DNA nanostructure. We harnessed strong repulsion of DNA nanostructure towards the electroactive Fe(CN)₆³⁻/⁴⁻ in combination with electrochemical technique to create a label-free biosensor. This simple, enzyme-free and label-free biosensor could detect MT with a detection limit of 56 aM, even in multiple sequence backgrounds. The study served as the proof-of-concept for the integration of enzyme-free competitive mechanism and label-free strategy, which can be extended as a powerful tool to various fields.

Single-nucleotide polymorphism (SNP) is widely used in the study of disease-related genes and in the genetic study of animal and plant strains. Therefore, SNP detection is crucial for biomedical diagnosis and treatment as well as for molecular design breeding of animals and plants. In this regard, this article describes a novel technique for detecting SNP using flap endonuclease 1 (FEN 1) as a specific recognition element and catalytic hairpin assembly (CHA) cascade reaction as a signal amplification strategy. The mutant target (MT) was hybridized with a biotin-modified upstream probe and hairpin-type downstream probe (DP) to form a specific three-base overlapping structure. Then, FEN 1 was employed for three-base overlapping structure-specific recognition, namely, the precise SNP site identification and the 5 ' flap of DP dissociation. After dissociation, the hybridized probes were magnetically separated by a streptavidin-biotin complex. Especially, the ability to establish such a hairpin-type DP provided a powerful tool that could be used to hide the cut sequence (CS) and avoid false-positive signals. The cleaved CS initiated the CHA reaction and allowed superior fluorescence signal generation. Owing to the high specificity of FEN 1 for single base recognition, only the MT could be distinguished from the wild-type target and mismatched DNA. Owing to the dual signal amplification, as low as 0.36 fM MT and 1% mutation abundance from the mixtures could be detected, respectively. Furthermore, it could accurately identify SNPs from human cancer cells, as well as soybean leaf genome extracts. This strategy paves the way for the development of more precise and sensitive tools for diagnosing early onset diseases as well as molecular design breeding tools.