Dephosphorylation Switch DNAzyme-RCA Circuit: A Robust Strategy for the Homogeneous and Reliable Detection of FTO Demethylase.

Fat mass and obesity-associated protein (FTO) plays a crucial role in regulating the dynamic modification of N6-methyladenosine (m6A) in eukaryotic mRNA. Sensitive detection of the FTO level and efficient evaluation of the FTO demethylase activity are of great importance to early cancer diagnosis and anticancer drug discovery, which are currently challenged by limited sensitivity/precision and low throughput. Herein, a robust strategy based on the dephosphorylation switch DNAzyme-rolling circle amplification (RCA) circuit, termed DSD-RCA, is developed for highly sensitive detection of FTO and inhibitor screening. Initially, the catalytic activity of DNAzyme is silenced by engineering with an m6A modification in its catalytic core. Only in the presence of target FTO can the methyl group on DNAzyme be eliminated, resulting in the activation of the catalytic activity of DNAzyme and thus cleaving the hairpin substrate to release numerous primers. Different from the conventional methods that use the downstream cleavage primer with the original 3'-hydroxyl end directly as the RCA primer with the problem of high background signal, which should be compensated by additional separation and wash steps in heterogeneous format, our DSD-RCA assay uses the upstream cleavage primer with a 2',3'-cyclic phosphate terminus at the 3'-end serving as an intrinsically blocked 3' end. Only after a dephosphorylation reaction mediated by T4 polynucleotide kinase can the upstream cleavage primers with a resultant 3'-hydroxyl end be extended by RCA. With the high signal-to-noise ratio and homogeneous property, the proposed platform can sensitively detect FTO with a limit of detection of 31.4 pM, and the relative standard deviations (RSDs %) ranging from 0.8 to 2.0% were much lower than the heterogeneous methods. The DSD-RCA method was applied for analyzing FTO in cytoplasmic lysates from different cell lines and tissues of breast cancer patients and further used for screening FTO inhibitors without the need for separation or cleaning, providing an opportunity for achieving high throughput and demonstrating the potential applications of this strategy in disease diagnostics, drug discovery, and biological applications.

On-Site-Activated Transmembrane Logic DNA Nanodevice Enables Highly Specific Imaging of Cancer Cells by Targeting Tumor-Related Nucleolin and Intracellular MicroRNA.

The ability to specifically image cancer cells is essential for cancer diagnosis; however, this ability is limited by the false positive associated with single-biomarker sensors and off-site activation of "always active" nucleic acid probes. Herein, we propose an on-site, activatable, transmembrane logic DNA (TLD) nanodevice that enables dual-biomarker sensing of tumor-related nucleolin and intracellular microRNA for highly specific cancer cell imaging. The TLD nanodevice is constructed by assembling a tetrahedral DNA nanostructure containing a linker (L)-blocker (B)-DNAzyme (D)-substrate (S) unit. AS-apt, a DNA strand containing an elongated segment and the AS1411 aptamer, is pre-anchored to nucleolin protein, which is specifically expressed on the membrane of cancer cells. Initially, the TLD nanodevice is firmly sealed by the blocker containing an AS-apt recognition zone, which prevents off-site activation. When the nanodevice encounters a target cancer cell, AS-apt (input 1) binds to the blocker and unlocks the sensing ability of the nanodevice for miR-21 (input 2). The TLD nanodevice achieves dual-biomarker sensing from the cell membrane to the cytoplasm, thereby ensuring cancer cell-specific imaging. This TLD nanodevice represents a promising strategy for the highly reliable analysis of intracellular biomarkers and a promising platform for cancer diagnosis and related biomedical applications.

DNAzyme-Amplified Cascade Catalytic Hairpin Assembly Nanosystem for Sensitive MicroRNA Imaging in Living Cells.

Sensitive imaging of microRNAs (miRNAs) in living cells is significant for accurate cancer clinical diagnosis and prognosis research studies, but it is challenged by inefficient intracellular delivery, instability of nucleic acid probes, and limited amplification efficiency. Herein, we engineered a DNAzyme-amplified cascade catalytic hairpin assembly (CHA)-based nanosystem (DCC) that overcomes these challenges and improves the imaging sensitivity. This enzyme-free amplification nanosystem is based on the sequential activation of DNAzyme amplification and CHA. MnO2 nanosheets were used as nanocarriers for the delivery of nucleic acid probes, which can resist the degradation by nucleases and supply Mn2+ for the DNAzyme reaction. After entering into living cells, the MnO2 nanosheets can be decomposed by intracellular glutathione (GSH) and release the loaded nucleic acid probes. In the presence of target miRNA, the locking strand (L) was hybridized with target miRNA, and the DNAzyme was released, which then cleaved the substrate hairpin (H1). This cleavage reaction resulted in the formation of a trigger sequence (TS) that can activate CHA and recover the fluorescence readout. Meanwhile, the DNAzyme was released from the cleaved H1 and bound to other H1 for new rounds of DNAzyme-based amplification. The TS was also released from CHA and involved in the new cycle of CHA. By this DCC nanosystem, low-abundance target miRNA can activate many DNAzyme and generate numerous TS for CHA, resulting in sensitive and selective analysis of miRNAs with a limit of detection of 5.4 pM, which is 18-fold lower than that of the traditional CHA system. This stable, sensitive, and selective nanosystem holds great potential for miRNA analysis, clinical diagnosis, and other related biomedical applications.

NanoSuit-Assisted Liquid-Cell Scanning Electron Microscopy Enables Dynamic Gold Nanoparticle Monitoring for the Aggregation and Transmembrane Processes in Living Cells.

Dynamic observation of the behaviors of nanomaterials in the cellular environment is of great significance in mechanistic investigations on nanomaterial-based diagnostics and therapeutics. Realizing label-free observations with nanometer resolution is necessary but still has major challenges. Herein, we propose a NanoSuit-assisted liquid-cell scanning electron microscopy (NanoSuit-LCSEM) method that enables imaging of the behaviors of nanoparticles in living cells. Taking A549 cells and gold nanoparticles (AuNPs) as a cell-nanoparticle interaction model, the NanoSuit-LCSEM method showed a significantly improved resolution to 10 nm, which is high enough to distinguish single and two adjacent 30 nm AuNPs in cells. The continuous observation time for living cells is extended to 30 min, and the trajectories and velocities for the transmembrane movement of AuNP aggregates are obtained. This study provides a new approach for dynamic observation of nanomaterials in intact living cells and will greatly benefit the interdisciplinary research of nanomaterials, nanomedicine, and nanotechnology.

A Knowledge-Graph-Based Multimodal Deep Learning Framework for Identifying Drug-Drug Interactions.

The identification of drug-drug interactions (DDIs) plays a crucial role in various areas of drug development. In this study, a deep learning framework (KGCN_NFM) is presented to recognize DDIs using coupling knowledge graph convolutional networks (KGCNs) with neural factorization machines (NFMs). A KGCN is used to learn the embedding representation containing high-order structural information and semantic information in the knowledge graph (KG). The embedding and the Morgan molecular fingerprint of drugs are then used as input of NFMs to predict DDIs. The performance and effectiveness of the current method have been evaluated and confirmed based on the two real-world datasets with different sizes, and the results demonstrate that KGCN_NFM outperforms the state-of-the-art algorithms. Moreover, the identified interactions between topotecan and dantron by KGCN_NFM were validated through MTT assays, apoptosis experiments, cell cycle analysis, and molecular docking. Our study shows that the combination therapy of the two drugs exerts a synergistic anticancer effect, which provides an effective treatment strategy against lung carcinoma. These results reveal that KGCN_NFM is a valuable tool for integrating heterogeneous information to identify potential DDIs.

Isolation of Structurally Heterogeneous TCR-CD3 Extracellular Vesicle Subpopulations Using Caliper Strategy.

Cells in different states can release diverse types of extracellular vesicles (EVs) that participate in intracellular communication or pathological processes. The identification and isolation of EV subpopulations are significant to explore their physiological functions and clinical value. In this study, structurally heterogeneous T-cell receptor (TCR)-CD3 EVs were proposed and verified for the first time using a caliper strategy. Two CD3-targeting aptamers were designed in the shape of a caliper with an optimized probe distance and were assembled on gold nanoparticles (Au-Caliper) to distinguish TCR-CD3 monomeric and dimeric EVs (m/dCD3 EVs) in skin-transplanted mouse plasma. Phenotyping and sequencing analysis revealed clear heterogeneity in the isolated m/dCD3 EVs, providing the potential for mCD3 EVs as a candidate biomarker of acute cellular rejection (ACR) and holding great prospects for distinguishing EV subpopulations based on protein oligomerization states.

Highly Efficient Isolation and Sensitive Detection of Small Extracellular Vesicles Using a Paper-Based Device.

Small extracellular vesicles (sEVs) play important roles in mediating intercellular communication and regulating biological processes. Facile sEV isolation is the essential and preliminary issue for their function investigation and downstream biomedical applications, while the traditional methods are challenged by tedious procedures, low purity, low yield, and potential damage. In this work, we developed an sEV isolation paper-based device (sEV-IsoPD) based on a three-dimensional (3D) paper chip, which is composed of a porous membrane for size exclusion and a metal-organic framework (MOF)/antibody-modified paper for immunoaffinity capture. In combination with a peristaltic pump-driven flow system, the sEV-IsoPD can efficiently isolate EV from cell culture medium and serum. Compared with the ultracentrifugation method, sEV-IsoPD exhibited a 5.1 times higher yield (1.7 × 109 mL-1), 1.6 times higher purity (1.6 × 1011 mg-1), and 7.5 times higher recovery (77.3%) with only 8.3% of the time (30 min) and 1.0% of the instrument cost ($710). Moreover, sEV concentration can be visually detected in a quantitative manner with this paper-based device with a linear range from 3.0 × 106 to 3.0 × 1010 mL-1 and a detection limit of 2.2 × 106 mL-1. The sEV-IsoPD provides an efficient and practical approach for the rapid isolation and visible detection of sEVs, which are promising for the preparation of sEVs and diagnosis of disease.

Light-Controlled Recruitable Hybridization Chain Reaction on Exosome Vehicles for Highly Sensitive MicroRNA Imaging in Living Cells.

Sensitive imaging of intracellular microRNA (miRNA) in living cells is of great significance. Isothermal hybridization chain reaction (HCR)-based methods, although have been widely used to monitor intracellular low-abundance miRNA, are still subjected to the challenges of limited signal amplification efficiency and compromised imaging resolution. In this work, we design a light-controlled recruitable HCR (LCR-HCR) strategy that enables us to well overcome these limitations. Exosomes as delivery and recruitment vehicles are modified with three cholesterol-modified hairpins (H1, H2, and H3), in which H1 is for anchoring target miRNA and H2 and H3 with photocleavable linkers (PC-linkers) are designed for spatiotemporal HCR. By controllably releasing probes with high local concentrations to efficiently trigger HCR and further recruiting the generated double-stranded DNA (dsDNA) polymers instead of dispersion in the cytoplasm, the LCR-HCR method can significantly improve the imaging contrast by confining all of the reactants on exosome vehicles. For a proof-of-concept demonstration, the miR-21 was analyzed by LCR-HCR with a limit of detection (LOD) down to 3.3 pM (corresponding to 165 amol per 50 µL) in vitro and four times higher response than traditional HCR in vivo. In general, the LCR-HCR method provides a powerful tool for sensitive miRNA imaging in living cells and cancer diagnosis.

Dual-Locked DNAzyme Platform for In Vitro and In Vivo Discrimination of Cancer Cells.

Imaging of tumor-associated microRNAs (miRNAs) can provide abundant information for cancer diagnosis, whereas the occurrence of trace amounts of miRNAs in normal cells inevitably causes an undesired false-positive signal in the discrimination of cancer cells during miRNA imaging. In this study, we propose a dual-locked (D-locked) platform consisting of the enzyme/miRNA-D-locked DNAzyme sensor and the honeycomb MnO2 nanosponge (hMNS) nanocarrier for highly specific cancer cell imaging. For a proof-of-concept demonstration, apurinic/apyrimidinic endonuclease 1 (APE1) and miR-21 were chosen as key models. The hMNS nanocarrier can efficiently release the D-locked DNAzyme sensor in living cells due to the decomposition of hMNS by glutathione, which can also supply Mn2+ for DNAzyme cleavage. Ascribing to the smart design of the D-locked DNAzyme sensor, the fluorescence signal can only be generated by the synergistic response of APE1 and miR-21 that are overexpressed in cancer cells. Compared with the miRNA single-locked DNAzyme sensor and the small-molecule (ATP)/miRNA D-locked DNAzyme sensor, the proposed enzyme (APE1)/miRNA D-locked DNAzyme sensor exhibited 2.6-fold and 2.4-fold higher discrimination ratio (Fcancer/Fnormal) for cancer cell discrimination, respectively. Owing to the superior performance, the D-locked strategy can selectively generate a fluorescence signal in cancer cells, facilitating accurate discrimination of cancer both in vitro and in vivo. Furthermore, this D-locked platform is easily adaptable toward other target molecules by redesigning the DNA sequences. The outstanding performance and expansibility of this D-locked platform holds promising prospects for cancer diagnosis and related biomedical applications.

Long-term continuous monitoring of microRNA in living cells using modified gold nanoprobe.

Long-term and continuous monitoring of the microRNA (miRNA) expression in living cells is essential in biomedical research, but it is currently limited by fast consumption and easy digestion of probes in the intracellular environment. Herein, we report polydopamine-modified gold nanoparticles (AuNPs@PDA) as protective and efficient nanocarriers for DNA hairpin probes (hpDNA), achieving long-term monitoring (48 h) of the miRNA (let-7a) levels in living cells after drug treatments. This method enabled excellent sensitivity and high selectivity toward let-7a with a limit of detection of 0.51 nM (n = 3) and a linear range from 1 to 100 nM. More importantly, AuNPs@PDA can not only efficiently improve the loading of hpDNA on each nanoparticle, but also effectively protect hpDNA from hydrolysis in the cell microenvironment, finally realizing the continuous monitoring of let-7a in living cells for 48 h. This simple method would be of great significance for drug screening and precision medicine.

Identification of MiRNA-Disease Associations Based on Information of Multi-Module and Meta-Path.

Cumulative research reveals that microRNAs (miRNAs) are involved in many critical biological processes including cell proliferation, differentiation and apoptosis. It is of great significance to figure out the associations between miRNAs and human diseases that are the basis for finding biomarkers for diagnosis and targets for treatment. To overcome the time-consuming and labor-intensive problems faced by traditional experiments, a computational method was developed to identify potential associations between miRNAs and diseases based on the graph attention network (GAT) with different meta-path mode and support vector (SVM). Firstly, we constructed a multi-module heterogeneous network based on the meta-path and learned the latent features of different modules by GAT. Secondly, we found the average of the latent features with weight to obtain a final node representation. Finally, we characterized miRNA-disease-association pairs with the node representation and trained an SVM to recognize potential associations. Based on the five-fold cross-validation and benchmark datasets, the proposed method achieved an area under the precision-recall curve (AUPR) of 0.9379 and an area under the receiver-operating characteristic curve (AUC) of 0.9472. The results demonstrate that our method has an outstanding practical application performance and can provide a reference for the discovery of new biomarkers and therapeutic targets.

Identification of Potential Parkinson's Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network.

Parkinson's disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson's disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision-recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.

Performance improvement for a 2D convolutional neural network by using SSC encoding on protein-protein interaction tasks.

BACKGROUND: The interactions of proteins are determined by their sequences and affect the regulation of the cell cycle, signal transduction and metabolism, which is of extraordinary significance to modern proteomics research. Despite advances in experimental technology, it is still expensive, laborious, and time-consuming to determine protein-protein interactions (PPIs), and there is a strong demand for effective bioinformatics approaches to identify potential PPIs. Considering the large amount of PPI data, a high-performance processor can be utilized to enhance the capability of the deep learning method and directly predict protein sequences. RESULTS: We propose the Sequence-Statistics-Content protein sequence encoding format (SSC) based on information extraction from the original sequence for further performance improvement of the convolutional neural network. The original protein sequences are encoded in the three-channel format by introducing statistical information (the second channel) and bigram encoding information (the third channel), which can increase the unique sequence features to enhance the performance of the deep learning model. On predicting protein-protein interaction tasks, the results using the 2D convolutional neural network (2D CNN) with the SSC encoding method are better than those of the 1D CNN with one hot encoding. The independent validation of new interactions from the HIPPIE database (version 2.1 published on July 18, 2017) and the validation of directly predicted results by applying a molecular docking tool indicate the effectiveness of the proposed protein encoding improvement in the CNN model. CONCLUSION: The proposed protein sequence encoding method is efficient at improving the capability of the CNN model on protein sequence-related tasks and may also be effective at enhancing the capability of other machine learning or deep learning methods. Prediction accuracy and molecular docking validation showed considerable improvement compared to the existing hot encoding method, indicating that the SSC encoding method may be useful for analyzing protein sequence-related tasks. The source code of the proposed methods is freely available for academic research at https://github.com/wangy496/SSC-format/ .

Highly Sensitive Fluorescence Detection of Global 5-Hydroxymethylcytosine from Nanogram Input with Strongly Emitting Copper Nanotags.

Quantitative analysis of 5-hydroxymethylcytosine (5hmC) has remarkable clinical significance to early cancer diagnosis; however, it is limited by the requirement in current assays for large amounts of starting material and expensive instruments requring expertise. Herein, we present a highly sensitive fluorescence method, termed hmC-TACN, for global 5hmC quantification from several nanogram inputs based on terminal deoxynucleotide transferase (TdT)-assisted formation of fluorescent copper (Cu) nanotags. In this method, 5hmC is labeled with click tags by T4 phage ß-glucosyltransferase (ß-GT) and cross-linked with a random DNA primer via click chemistry. TdT initiates the template-free extension along the primer at the modified 5hmC site and then generates a long polythymine (T) tail, which can template the production of strongly emitting Cu nanoparticles (CuNPs). Consequently, an intensely fluorescent tag containing numerous CuNPs can be labeled onto the 5hmC site, providing the sensitive quantification of 5hmC with a limit of detection (LOD) as low as 0.021% of total nucleotides (S/N = 3). With only a 5 ng input (∼1000 cells) of genomic DNA, global 5hmC levels were accurately determined in mouse tissues, human cell lines (including normal and cancer cells of breast, lung, and liver), and urines of a bladder cancer patient and healthy control. Moreover, as few as 100 cells can also be distinguished between normal and cancer cells. The hmC-TACN method has great promise of being cost effective and easily mastered, with low-input clinical utility, and even for the microzone analysis of tumor models.

Isothermal Self-Primer EXPonential Amplification Reaction (SPEXPAR) for Highly Sensitive Detection of Single-Stranded Nucleic Acids and Proteins.

Development of versatile sensing methods for sensitive and specific detection of clinically relevant nucleic acids and proteins is of great value for disease monitoring and diagnosis. In this work, we propose a novel isothermal Self-primer EXPonential Amplification Reaction (SPEXPAR) strategy based on a rationally engineered structure-switchable Metastable Hairpin template (MH-template). The MH-template initially keeps inactive with its self-primer overhanging a part of target recognition region to inhibit polymerization. The present targets can specifically compel the MH-template to transform into an "activate" conformation that primes a target-recyclable EXPAR. The method is simple and sensitive, can accurately and facilely detect long-chain single-stranded nucleic acids or proteins without the need of exogenous primer probes, and has a high amplification efficiency theoretically more than 2n. For a proof-of-concept demonstration, the SPEXPAR method was used to sensitively detect the characteristic sequence of the typical swine fever virus (CSFV) RNA and thrombin, as nucleic acid and protein models, with limits of detection down to 43 aM and 39 fM, respectively, and even the CSFV RNA in attenuated vaccine samples and thrombin in diluted serum samples. The SPEXPAR method may serve as a powerful technique for the biological research of single-stranded nucleic acids and proteins.

Pearl Necklacelike Strategy Enables Quantification of Global 5-Hydroxymethylcytosine and 5-Formylcytosine by Inductively Coupled Plasma-Atomic Emission Spectrometry.

5-Hydroxymethylcytosine (5hmC) and 5-formylcytosine (5fC) are key intermediates of active DNA demethylation, for which the global detection methods are still restricted by high cost and long operation time. Here, we demonstrate a pearl necklacelike strategy to accurately quantify global 5hmC and 5fC in genomic DNA. In this method, the metal-organic framework (MOF), [Cu3(BTC)2] (denoted as HKUST-1, H3BTC = 1,3,5-benzenetricarboxylic acid), with a diameter of ∼30 nm that contains ∼15 000 copper ions (Cu2+) as the "super label" was in situ grown in the carboxylated 5hmC and 5fC loci of genomic DNA via the coordination between Cu2+ and the carboxyl group. After the acid digestion of MOF, the concentration of Cu2+, which has a quantitative relationship with the 5hmC/5fC content, was measured by inductively coupled plasma-atomic emission spectroscopy (ICP-AES). The metal element enrichment during MOF growth has amplified the signal by 4 orders of magnitude, realizing sensitive and accurate quantification of global 5hmC and 5fC in different tissues with a detection limit of 0.031% and 0.019‰ in DNA, respectively. The bisulfite- and mass spectrometry-free strategy is easily performed in almost all research and medical laboratories and would provide potential capability to quantify other candidate modifications in nucleotides.

Novel Potential Small Molecule-MiRNA-Cancer Associations Prediction Model Based on Fingerprint, Sequence, and Clinical Symptoms.

As an important biomarker in organisms, miRNA is closely related to various small molecules and diseases. Research on small molecule-miRNA-cancer associations is helpful for the development of cancer treatment drugs and the discovery of pathogenesis. It is very urgent to develop theoretical methods for identifying potential small molecular-miRNA-cancer associations, because experimental approaches are usually time-consuming, laborious, and expensive. To overcome this problem, we developed a new computational method, in which features derived from structure, sequence, and symptoms were utilized to characterize small molecule, miRNA, and cancer, respectively. A feature vector was construct to characterize small molecule-miRNA-cancer association by concatenating these features, and a random forest algorithm was utilized to construct a model for recognizing potential association. Based on the 5-fold cross-validation and benchmark data set, the model achieved an accuracy of 93.20 ± 0.52%, a precision of 93.22 ± 0.51%, a recall of 93.20 ± 0.53%, and an F1-measure of 93.20 ± 0.52%. The areas under the receiver operating characteristic curve and precision recall curve were 0.9873 and 0.9870. The real prediction ability and application performance of the developed method have also been further evaluated and verified through an independent data set test and case study. Some potential small molecules and miRNAs related to cancer have been identified and are worthy of further experimental research. It is anticipated that our model could be regarded as a useful high-throughput virtual screening tool for drug research and development. All source codes can be downloaded from https://github.com/LeeKamlong/Multi-class-SMMCA.

Polymerization retardation isothermal amplification (PRIA): a strategy enables sensitively quantify genome-wide 5-methylcytosine oxides rapidly on handy instruments with nanoscale sample input.

The current methods for quantifying genome-wide 5-methylcytosine (5mC) oxides are still scarce, mostly restricted with two limitations: assay sensitivity is seriously compromised with cost, assay time and sample input; epigenetic information is irreproducible during polymerase chain reaction (PCR) amplification without bisulfite pretreatment. Here, we propose a novel Polymerization Retardation Isothermal Amplification (PRIA) strategy to directly amplify the minute differences between epigenetic bases and others by arranging DNA polymerase to repetitively pass large electron-withdrawing groups tagged 5mC-oxides. We demonstrate that low abundant 5-hydroxymethylcytosine (5hmC), 5-formylcytosine (5fC) and 5-carboxycytosine (5caC) in genomic DNA can be accurately quantified within 10 h with 100 ng sample input on a laboratory real-time quantitative PCR instrument, and even multiple samples can be analyzed simultaneously in microplates. The global levels of 5hmC and 5fC in mouse and human brain tissues, rat hippocampal neuronal tissue, mouse kidney tissue and mouse embryonic stem cells were quantified and the observations not only confirm the widespread presence of 5hmC and 5fC but also indicate their significant variation in different tissues and cells. The strategy is easily performed in almost all research and medical laboratories, and would provide the potential capability to other candidate modifications in nucleotides.

Metastable Dumbbell Probe-Based Hybridization Chain Reaction for Sensitive and Accurate Imaging of Intracellular-Specific MicroRNAs In Situ in Living Cells.

Sensitive and accurate imaging of intracellular-specific microRNAs (miRNAs) in situ in living cells is seriously challenged by the susceptibility of nucleic acid probes and the low dynamics of the hybridization reaction in cellular environments. Herein, we engineer a set of new metastable dumbbell probes (M xDPs) to overcome these key limitations by concurrently boosting transfection, antidigestibility, assembly dynamics, and nanostructural uniformity. The M xDPs can maintain their stability up to 16 h in living cells and produce uniform and dense DNA nanostructures rapidly (<2 h) and specifically from a hybridization chain reaction (HCR). A sharp signal from the cascade accumulation of fluorescence resonance energy transfer (FRET) further minimizes the effect of system fluctuations. The M xDPs-based HCR (M xDPHCR) method showed identical performance in the analysis of miR-27a in cell lysate and buffer condition and obtained a limit of detection down to 3.2 pM (corresponding to 160 amol per 50 µL), which is 44-fold lower than on conventional hairpin probes. The M xDPHCR method clearly distinguished normal cells from tumor cells and provided more accurate quantitative information on the intracellular-specific miRNAs. The strategy would offer a powerful tool for visualizing and localizing desired nucleic acids in living cells.

Assembly of a miRNA-modified QCM sensor for miRNA recognition through response patterns.

In this paper, a miRNA-based quartz crystal microbalance (QCM) biosensor was fabricated and used to the rapid and effective sensing of miRNA. The specific hybridization between probe miRNA and different selected miRNAs (miR-27a, miR-27b, and Let-7a) cause a different interaction mode, thus display different frequency change and response patterns in the QCM sensor, which were used to detect miR-27a and miR-27b. The selective sensing of miR-27a in mixed miRNA solution was also achieved. This miRNA-based QCM biosensor has the advantages of real-time, label-free, and short cycle detection.