Recent Progress on Single-Molecule Detection Technologies for Food Safety

Nanopore-assisted ELISA for ultrasensitive, portable, and on-site detection of ricin

Effective detection technologies in food safety with the merits of portable and on-site detection potential are always in pressing demand. Herein, we developed a nanopore-assisted Enzyme-linked immunosorbent assay (NELISA) platform, which innovatively introduced hairpin DNA (HP DNA) probes as reaction substrates. This innovation of substrates effectively avoided the inherent limitations of colorimetric signals (i.e., low sensitivity and inaccurate results) and greatly improved the sensitivity and accuracy of NELISA platform. The alkaline phosphatase (ALP)-modified detection antibody (ALP-Ab2) can specifically bind to ricin and hydrolyze the phosphate groups modified on the HP DNA probes. Nanopore recordings demonstrated that two states of probes produced highly distinguishable nanopore events, enabling the qualitative and quantitative detection of ricin. This NELISA platform fully combined the specificity of ELISA with the ultra-sensitivity, and unique single-molecule fingerprint recognition of nanopore, showing a great on-site detection potential. This method achieved the ultrasensitive and reliable detection of ricin down to 2.46 fg/mL, which enhanced the detection sensitivity by at least 106-fold compared to traditional ELISA. Furthermore, the proposed method was capable of accurately detecting ricin in real food samples with satisfactory recoveries.

Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms

The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition particularly for early alarm. In this study, the whole-cell biosensor array was constructed and employed for rapid recognition of wheat contamination by combining with machine learning algorithms. Seven key VOCs were explored through univariate coupling to multivariate analysis of orthogonal partial least squares-discrimination analysis (OPLS-DA) models. The promoters of dnaK, katG, oxyR, soxS obtained from the stress-responsive of key VOCs were fused to the bacterial operon and fabricated on the whole-cell biosensor. The constructed whole-cell biosensor array was consisted with four kinds of sensors and 18 sensor unit. The bioluminescent intensity combined with linear machine learning algorithm of partial least squares discriminant analysis (PLS-DA) and non-linear algorithms of back propagating artificial neural network (BP-ANN) and least square support vector machine (LS-SVM) were employed to establish discrimination models for mold contamination especially for early warning. The Monte-Carlo strategy was performed to generate thirty subsets for modeling to give more reliable results. As a result, the whole-cell biosensor combined with non-linear algorithm of LS-SVM was practicable for detecting mold identification for wheat early-warning with the accuracy of 97.24%. Additionally, this study provides practical and effective methods not only for wheat quality guarantee and supervision but also for other foodstuffs.

Deep learning-assisted single-atom detection of copper ions by combining click chemistry and fast scan voltammetry

Cell ion channels, cell proliferation and metastasis, and many other life activities are inseparable from the regulation of trace or even single copper ion (Cu+ and/or Cu2+). In this work, an electrochemical sensor for sensitive quantitative detection of 0.4-4 amol L-1 copper ions is developed by adopting: (1) copper ions catalyzing the click-chemistry reaction to capture numerous signal units; (2) special adsorption assembly method of signal units to ensure signal generation efficiency; and (3) fast scan voltammetry at 400 V s-1 to enhance signal intensity. And then, the single-atom detection of copper ions is realized by constructing a multi-layer deep convolutional neural network model FSVNet to extract hidden features and signal information of fast scan voltammograms for 0.2 amol L-1 of copper ions. Here, we show a multiple signal amplification strategy based on functionalized nanomaterials and fast scan voltammetry, together with a deep learning method, which realizes the sensitive detection and even single-atom detection of copper ions.

A nanopore-based label-free CRISPR/Cas12a system for portable and ultrasensitive detection of zearalenone

BACKGROUND

Food safety has become a serious global concern. Therefore, there is a need for effective detection technologies in this field. Currently, the development of effective on-site detection techniques is extremely important for food safety. However, the traditional on-site detection methods currently lack effective signal amplification. Herein, the aim of this study was to construct a nanopore-based label-free CRISPR/Cas12a system for the detection of Zearalenone (ZEN). The method is expected to be highly sensitive for portable detection of ZEN in food.

RESULTS

The proposed strategy was mainly involved three steps, including the displacement of the target DNA, the triggering of the cleavage of hairpin DNA probes (probes 1) by the trans-cleavage of CRISPR/Cas12a, and the generation of a measurable nanopore current signal. The probes 1 and DNA after the cleavage of probes 1 (probes 2) produce different characteristic nanopore signals as they pass through the nanopore. The established method achieved a low limit of detection (LOD) of 6.52 fM for ZEN and a wide liner range under optimized conditions. Furthermore, the practical applicability of this method was verified in real maize samples and showed good recoveries (90.68-101.98 %) and low relative standard deviations (RSD) (9.21-9.72 %). Therefore, this method is a promising option for rapid and ultrasensitive detection of ZEN.

SIGNIFICANCE AND NOVELTY

The study presented a portable nanopore-based CRISPR/Cas12a signal amplification detection system for the detection of ZEN in food, which had a low LOD and the advantages of rapid, portability, and on-site detection potential. In conclusion, the method presented a promising prospect and universal platform for the detection of ZEN and other mycotoxins, offering a novel insight into on-site food safety detection.

Nanopore-Based Biomimetic ssDNA Reporters for Multiplexed Detection of Meat Adulteration

Meat adulteration detection is crucial for ensuring food safety, protecting consumer rights, and maintaining market integrity. However, the current methods face challenges in achieving multiplexed detection through efficient signal conversion and output. This study introduces a nanopore-based approach for the simultaneous detection of multiple meat adulteration. Leveraging the interaction between DADA dipeptide and vancomycin, we designed a series of biomimetic ssDNA reporters with DADA-Van tags for multiplexed signal output. These ssDNA reporters can generate highly distinctive current blockage signals, which can be discriminated simultaneously by the current waveform. Combined with PCR-based strand displacement, ssDNA reporters can be released from magnetic beads in the presence of the target gene and are analyzed using α-hemolysin (α-HL) nanopores for multiplexed signal output. The signal frequency for each target gene has a linear relationship with the specific concentration range (for duck: 1 × 10-3 ∼ 10 nmol/L, for pork: 1 × 10-2 ∼ 10 nmol/L, and for chicken: 1 × 10-3 ∼ 10 nmol/L). The limits of detection are as follows: 0.418 pmol/L for duck, 4.473 pmol/L for pork, and 0.531 pmol/L for chicken. This method effectively enables the simultaneous detection of adulterated chicken, pork, and duck meat in lamb samples and holds potential for broad applications in ensuring the authenticity and safety of meat products.

Melitoxin Inhibits Proliferation, Metastasis, and Invasion of Glioma U251 Cells by Down-regulating F2RL1

As the principal active component of bee venom, melittin has an anti-cancer effect in different cancers. This study was aimed to investigate the effect of melittin in glioma and explore whether F2RL1 is closely involved in glioblastoma cells proliferation. TCGA and GES databases were used to evaluate the role of F2RL1 in gliomas. The U251 cells were divided into a control lentivirus + PBS group (NC-PBS), F2RL1 intervention lentivirus + PBS group (KD-PBS), control lentivirus + melittin group (NC-melittin), and F2RL1 intervention lentivirus + melittin group (KD-melittin). Cell proliferation was detected by MTT and EDU staining assays. The apoptosis rate was assessed by flow cytometry. Expressions of genes related to apoptosis, cycle arrest, migration, and invasion were detected by qRT-PCR. Cellular LDH concentrations were detected by ELISA. The subcutaneous tumor volume of nude mice was analyzed by xenograft method. F2RL1 was significantly overexpressed in glioma tissues and were reduced in the melittin-treated group compared to the blank group. F2RL1 knockdown and melittin alone or in combination increased the proportion of cells in the G1-phase, and the combination was more pronounced. The KD-melittin group showed a decrease in the number of viable cells at 24, 48, 72, and 96 h compared to the NC-PBS group. The number of cell migration and invasion was decreased in the KD-melittin group compared to the other groups. Moreover, the genes related to cell cycle arrest and apoptosis were significantly changed in the KD-melittin group. At weeks 4, 5, and 6, the tumor volume in the KD-melittin group was smaller than that in the KD-PBS group and NC-melittin group. Interference with the target gene F2RL1 inhibited the proliferation of glioma U251 cells, and melittin treatment inhibited the proliferation of glioma U251 cells. Melittin inhibited the proliferation of glioma U251 cells by suppressing the expression of target gene F2RL1.

Research Progress of Biosensor Based on Organic Photoelectrochemical Transistor

In order to solve the food safety problem better, it is very important to develop a rapid and sensitive technology for detecting food contamination residues. Organic photoelectrochemical transistor (OPECT) biosensor rely on the photovoltage generated by a semiconductor upon excitation by light to regulate the conductivity of the polymer channels and realize biosensor analysis under zero gate bias. This technology integrates the excellent characteristics of photoelectrochemical (PEC) bioanalysis and the high sensitivity and inherent amplification ability of organic electrochemical transistor (OECT). Based on this, OPECT biosensor detection has been proven to be superior to traditional biosensor detection methods. In this review, we summarize the research status of OPECT biosensor in disease markers and food residue analysis, the basic principle, classification, and biosensing mechanism of OPECT biosensor analysis are briefly introduced, and the recent applications of biosensor analysis are discussed according to the signal strategy. We mainly introduced the OPECT biosensor analysis methods applied in different fields, including the detection of disease markers and food hazard residues such as prostate-specific antigen, heart-type fatty acid binding protein, T-2 toxin detection in milk samples, fat mass and objectivity related protein, ciprofloxacin in milk. The OPECT biosensor provides considerable development potential for the construction of safety analysis and detection platforms in many fields, such as agriculture and food, and hopes to provide some reference for the future development of biosensing analysis methods with higher selectivity, faster analysis speed and higher sensitivity.

High-Performance enrichment and sensitive analysis of bisphenol and its analogues in water and milk using a novel Ni-Based cationic Metal-Organic framework

A novel cationic metal-organic framework (iMOF-Ni) was designed and synthesized by a solvothermal method. It was fabricated as a solid-phase extraction (SPE) cartridge and exhibited high adsorption performance for Bisphenols (BPs). The theoretical simulation demonstrated that the adsorption mechanism between iMOF-Ni and BPs was attributed to cation-π bonding, π-π interaction, and electrostatic interactions. Under optimized SPE, a method for analyzing BPs was established by combining high-performance liquid chromatography-diode array detection (HPLC-DAD). The developed method has good linearity (R2 ≥ 0.994), low detection limits (0.07-0.16 ng/mL), and good reproducibility (1.72-6.35 %, n = 6). The applicability of the method was further evaluated by analyzing water and milk samples. Recoveries of four BPs in spiked samples were from 72.2 % to 96.6 %.

Eco-Friendly Spiking Approach Based on Microfluidics for Preparation of Matrix Reference Materials

Medicated bath is the most common spiking method used in the development of matrix reference materials for aquatic products; however, the environmental issues caused by the treatment of waste liquid after medicated bath cannot be ignored. We proposed an environmentally friendly spiking method based on microfluidics, which significantly improved the drug utilization rate without the need for subsequent drug residue treatment. Finely processed minced fish samples were fully mixed with quinolone drugs, and minced fish gel microspheres were prepared by microfluidic technology, utilizing the gel's water-locking function to enhance the drug-loading capacity. The results showed that this method can significantly increase the drug-loading capacity of the matrix (2.33-4.03 times) compared with the traditional spiking methods. In addition, the matrix reference material prepared by this method has good stability, and the drug concentration was adjustable and controllable.

Recent Advances in and Applications of Electrochemical Sensors Based on Covalent Organic Frameworks for Food Safety Analysis

The international community has been paying close attention to the issue of food safety as a matter of public health. The presence of a wide range of contaminants in food poses a significant threat to human health, making it vital to develop detection methods for monitoring these chemical contaminants. Electrochemical sensors using emerging materials have been widely employed to detect food-derived contaminants. Covalent organic frameworks (COFs) have the potential for extensive applications due to their unique structure, high surface area, and tunable pore sizes. The review summarizes and explores recent advances in electrochemical sensors modified with COFs for detecting pesticides, antibiotics, heavy metal ions, and other food contaminants. Furthermore, future challenges and possible solutions will be discussed regarding food safety analysis using COFs.

Machine Learning-Based Analytical Systems: Food Forensics

Despite a large amount of money being spent on both food analyses and control measures, various food-borne illnesses associated with pathogens, toxins, pesticides, adulterants, colorants, and other contaminants pose a serious threat to human health, and thus food safety draws considerable attention in the modern pace of the world. The presence of various biogenic amines in processed food have been frequently considered as the primary quality parameter in order to check food freshness and spoilage of protein-rich food. Various conventional detection methods for detecting hazardous analytes including microscopy, nucleic acid, and immunoassay-based techniques have been employed; however, recently, array-based sensing strategies are becoming popular for the development of a highly accurate and precise analytical method. Array-based sensing is majorly facilitated by the advancements in multivariate analytical techniques as well as machine learning-based approaches. These techniques allow one to solve the typical problem associated with the interpretation of the complex response patterns generated in array-based strategies. Consequently, the machine learning-based neural networks enable the fast, robust, and accurate detection of analytes using sensor arrays. Thus, for commercial applications, most of the focus has shifted toward the development of analytical methods based on electrical and chemical sensor arrays. Therefore, herein, we briefly highlight and review the recently reported array-based sensor systems supported by machine learning and multivariate analytics to monitor food safety and quality in the field of food forensics.

CRISPR-Cas systems mediated biosensing and applications in food safety detection

Food safety, closely related to economic development of food industry and public health, has become a global concern and gained increasing attention worldwide. Effective detection technology is of great importance to guarantee food safety. Although several classical detection methods have been developed, they have some limitations in portability, selectivity, and sensitivity. The emerging CRISPR-Cas systems, uniquely integrating target recognition specificity, signal transduction, and efficient signal amplification abilities, possess superior specificity and sensitivity, showing huge potential to address aforementioned challenges and develop next-generation techniques for food safety detection. In this review, we focus on recent progress of CRISPR-Cas mediated biosensing and their applications in food safety monitoring. The properties and principles of commonly used CRISPR-Cas systems are highlighted. Notably, the frequently coupled nucleic acid amplification strategies to enhance their selectivity and sensitivity, especially isothermal amplification methods, as well as various signal output modes are also systematically summarized. Meanwhile, the application of CRISPR-Cas systems-based biosensors in food safety detection including foodborne virus, foodborne bacteria, food fraud, genetically modified organisms (GMOs), toxins, heavy metal ions, antibiotic residues, and pesticide residues is comprehensively described. Furthermore, the current challenges and future prospects in this field are tentatively discussed.

Integrating Ordered Two-Dimensional Covalent Organic Frameworks to Solid-State Nanofluidic Channels for Ultrafast and Sensitive Detection of Mercury

Grafting specific recognition moieties onto solid-state nanofluidic channels is a promising way for selective and sensitive sensing of analytes. However, the time-consuming interaction between recognition moieties and analytes is the main hindrance to the application of nanofluidic channel-based sensors in rapid detection. Here, we show the integration of ordered two-dimensional covalent organic frameworks (2D COFs) to solid-state nanofluidic channels to achieve rapid, selective, and sensitive detection of contaminants. As a proof of concept, a thiourea-linked 2D COF (JNU-3) as the recognition unit is covalently bonded on the stable artificial anodic aluminum oxide nanochannels (AAO) to fabricate a JNU-3@AAO-based nanofluidic sensor. The rapid and selective interaction of Hg(II) with the highly ordered channels of JNU-3 allows the JNU-3@AAO-based nanofluidic sensor to realize ultrafast and precise determination of Hg(II) (90 s) with a low limit of detection (3.28 fg mL^-1), wide linear range (0.01-100 pg mL^-1), and good precision (relative standard deviation of 3.8% for 11 replicate determination of 10 pg mL^-1). The developed method was successfully applied to the determination of mercury in a certified reference material A072301c (rice powder), real water, and rice samples with recoveries of 90.4-99.8%. This work reveals the great potential of 2D COFs-modified solid-state nanofluidic channels as a sensor for the rapid and precise detection of contaminants in complicated samples.

An aptamer-assisted biological nanopore biosensor for ultra-sensitive detection of ochratoxin A with a portable single-molecule measuring instrument

Biological nanopore-based single-molecule detection technology has shown ultrahigh sensitivity to various target analyte. But the detection scope of interesting targets is limited due to the lack of effective signal conversion strategies. In addition, conventional nanopore detection instruments are cumbersome, resulting nanopore detection can only be performed in laboratory. Herein, a customizable nanopore current amplifier is constructed to lower the cost and increase the portability of the nanopore instrument, and then an immobilized aptamer-based signal conversion strategy is proposed for α-hemolysin (α-HL) nanopore to detect small molecules (ochratoxin A, OTA). The presence of OTA in sample would trigger the release of probe single-strand DNA (ssDNA) from magnetic beads, which could subsequently cause current blockage in nanopore. The results show that the signal frequency of probe ssDNA has a linear relationship with the OTA concentration in the range of 2 × 10¹～2 × 10³ pmol/L. Compared to other methods, our sensing system has achieved an ultra-sensitive detection of OTA with the detection limit as low as 1.697 pmol/L. This strategy could broaden the scope of nanopore detection and have the potential for rapid and in-situ detection of other food contaminants in the future.