The development of real-time digital PCR technology using an improved data classification method

Loop-mediated isothermal amplification linked a nanoparticles-based biosensor for detecting Epstein-Barr virus

Epstein-Barr virus (EBV) is a ubiquitous gamma herpesvirus that maintains a lifelong latent association with B lymphocytes. Here, a rapid and reliable diagnosis platform for detecting EBV infection, employing loop-mediated isothermal amplification (LAMP) combined with a gold nanoparticles-based lateral flow biosensors (AuNPs-LFB) (termed LAMP Amplification Mediated AuNPs-LFB Detection, LAMAD), was developed in the current study. A set of specific LAMP primers targeting the Epstein-Barr nuclear antigen (EBNA) leader protein (EBNA-LP) gene was designed and synthesized. Subsequently, these templates extracted from various pathogens and whole blood samples were used to optimize and evaluate the EBV-LAMAD assay. As a result, the limit of detection (LoD) of the EBV-LAMAD assay was 45 copies/reaction. The EBV-LAMAD assay can detect all representative EBV pathogens used in the study, and of note, no cross-reactions were observed with other non-EBV organisms. Moreover, the whole workflow of the EBV-LAMAD assay can be completed within 70 min, including rapid EBV template preparation, EBV-LAMP amplification, and AuNPs-LFB-mediated detection. Taken together, the EBV-LAMAD assay targeting the EBNA-LP gene is a rapid, simplified, sensitive, reliable, and easy-to-use detection protocol that can be used as a competitive potential diagnostic/screening tool for EBV infection in clinical settings, especially in basic laboratories in resource-limited regions. KEY POINTS: • A novel, simplified, and easy-to-use AuNPs-LFB biosensor was designed and prepared. • LAMP combined with an AuNPs-LFB targeting the novel EBNA-LP gene was established. • EBV-LAMAD is a rapid, sensitive, and reliable detection protocol for EBV infection.

Experimental investigation of confinement effect in single molecule amplification via real-time digital PCR on a multivolume droplet array SlipChip

BACKGROUND

Digital polymerase chain reaction (digital PCR) is an important quantitative nucleic acid analysis method in both life science research and clinical diagnostics. One important hypothesis is that by physically constraining a single nucleic acid molecule in a small volume, the relative concentration can be increased therefore further improving the analysis performance, and this is commonly defined as the confinement effect in digital PCR. However, experimental investigation of this confinement effect can be challenging since it requires a microfluidic device that can generate partitions of different volumes and an instrument that can monitor the kinetics of amplification. (96).

RESULTS

Here, we developed a real-time digital PCR system with a multivolume droplet array SlipChip (Muda-SlipChip) that can generate droplet of 125 nL, 25 nL, 5 nL, and 1 nL by a simple "load-slip" operation. In the digital region, by reducing the volume, the relative concentration is increased, the amplification kinetic can be accelerated, and the time to reach the fluorescence threshold, or Cq value, can be reduced. When the copy number per well is much higher than one, the relative concentration is independent of the partition volume, thus the amplification kinetics are similar in different volume partitions. This system is not limited to studying the kinetics of digital nucleic acid amplification, it can also extend the dynamic range of the digital nucleic acid analysis by additional three orders of magnitude by combining a digital and an analog quantification algorithm. (140).

SIGNIFICANCE

In this study, we experimentally investigated for the first time the confinement effect in the community of digital PCR via a new real-time digital PCR system with a multivolume droplet array SlipChip (Muda-SlipChip). And a wider dynamic range of quantification methods compared to conventional digital PCR was validated by this system. This system provides emerging opportunities for life science research and clinical diagnostics. (63).

Photothermal-Based Multiplex Nested Digital PCR System for Rapid Detection of Foodborne Pathogens

The rapid and sensitive detection of foodborne pathogens is crucial for ensuring food safety. Among virus testing methods, polymerase chain reaction (PCR) has served as the gold-standard technique in most food safety regulation organizations. However, to enhance the speed and efficiency of PCR, novel approaches are continually being explored. In this work, leveraging the photothermal effects and high thermal conductivity of gold nanoparticles, we have significantly improved the heating and cooling rates of thermal cycles, enabling ultra-fast PCR detection. Specifically, we present a pre-degassing multiplex digital PCR chip integrated with gold nanoparticles. We further developed a portable system with a light source for photothermal heating cycling, along with an optoelectronic sensor to analyze PCR amplification products after rapid thermal cycling. As proof of concept, the proposed chip and portable device was applied for the on-site detection of several types of foodborne pathogens, including Escherichia coli, Listeria monocytogenes, Staphylococcus aureus, and Salmonella. The whole system could distinguish those pathogens within 20 min, showing good potential for the rapid detection of multiple types of foodborne pathogens.

Self-correction of cycle threshold values by a normal distribution-based process to improve accuracy of quantification in real-time digital PCR

The digital polymerase chain reaction (dPCR) is a new and developing nucleic acid detection technology with high sensitivity that can realize the absolute quantitative analysis of samples. In order to improve the accuracy of quantitative results, real-time digital PCR emphasizes the kinetic information during amplification to identify prominent abnormal data. However, it is challenging to use a unified standard to accurately classify the amplification curve of each well as negative and positive, due to the interference caused by various factors in the experiment. In this work, a normal distribution-based cycle threshold value self-correcting model (NCSM) was established, which focused on the feature of the cycle threshold values in amplification curves and conducted continuous detection and correction on the whole. The cycle threshold value distribution was closer to the ideal normal distribution to avoid the influence of interference. Thus, the model achieves a more accurate classification between positive and negative results. The corrective process was applied to plasmid samples and resulted in an accuracy improvement from 92 to 99%. The coefficient of variation was below 5% when considering the quantitation of a range between 100 and 10,000 copies. At the same time, by utilizing this model, the distribution of cycle threshold values at the endpoint can be predicted with fewer thermal cycles, which can reduce the cycling time by around 25% while maintaining a consistency of more than 98%. Therefore, using the NCSM can effectively enhance the quantitative accuracy and increase the detection efficiency based on the real-time dPCR platform.

Endonuclease IV and T4 ligase enhanced detection of mutations in low abundance

We propose a mutant detection approach based on endonuclease IV and DNA ligase in combination with qPCR. The enzymes functioned cooperatively to facilitate PCR for low abundance DNA detection. We demonstrate that our approach can distinguish mutations as low as 0.01%, indicating the potential application of this strategy in early cancer diagnosis.

Precise determination of reaction conditions for accurate quantification in digital PCR by real-time fluorescence monitoring within microwells

Real-time digital polymerase chain reaction (qdPCR) provides enhanced precision in the field of molecular diagnostics by integrating absolute quantification with process information. However, the optimal reaction conditions are traditionally determined through multiple iterative of experiments. Therefore, we proposed a novel approach to precisely determine the optimal reaction conditions for qdPCR using a standard process, employing real-time fluorescence monitoring within microwells. The temperature-sensitive fluorophore intensity presented the real temperature of each microwell. This enabled us to determine the optimal denaturation and annealing time for qdPCR based on the corresponding critical temperatures derived from the melting curves and amplification efficiency, respectively. To confirm this method, we developed an ultrathin laminated chip (UTL chip) and chose a target that need to be absolutely quantitative. The UTL chip was designed using a fluid‒solid‒thermal coupling simulation model and exhibited a faster thermal response than a commercial dPCR chip. By leveraging our precise determination of reaction conditions and utilizing the UTL chip, 40 cycles of amplification were achieved within 18 min. This was accomplished by precisely controlling the denaturation temperature at 2 s and the annealing temperature at 10 s. Furthermore, the absolutely quantitative of DNA showed good correlation (R2 > 0.999) with the concentration gradient detection using the optimal reaction conditions with the UTL chip for qdPCR. Our proposed method can significantly improve the accuracy and efficiency of determining qdPCR conditions, which holds great promise for application in molecular diagnostics.

Digital PCR Partition Classification

BACKGROUND

Partition classification is a critical step in the digital PCR data analysis pipeline. A range of partition classification methods have been developed, many motivated by specific experimental setups. An overview of these partition classification methods is lacking and their comparative properties are often unclear, likely impacting the proper application of these methods.

CONTENT

This review provides a summary of all available digital PCR partition classification approaches and the challenges they aim to overcome, serving as a guide for the digital PCR practitioner wishing to apply them. We additionally discuss strengths and weaknesses of these methods, which can further guide practitioners in vigilant application of these existing methods. This review provides method developers with ideas for improving methods or designing new ones. The latter is further stimulated by our identification and discussion of application gaps in the literature, for which there are currently no or few methods available.

SUMMARY

This review provides an overview of digital PCR partition classification methods, their properties, and potential applications. Ideas for further advances are presented and may bolster method development.

Research Progress of Nucleic Acid Detection Technology for Genetically Modified Maize

Genetically modified (GM) maize is one of the earliest GM crops to have achieved large-scale commercial cultivation globally, and it is of great significance to excel in the development and implementation of safety policy regarding GM, and in its technical oversight. This article describes the general situation regarding genetically modified maize, including its varieties, applications, relevant laws and regulations, and so on. From a technical point of view, we summarize and critically analyze the existing methods for detecting nucleic acid levels in genetically modified maize. The nucleic acid extraction technology used for maize is explained, and the introduction of traditional detection techniques, which cover variable-temperature and isothermal amplification detection technology and gene chip technology, applications in maize are described. Moreover, new technologies are proposed, with special attention paid to nucleic acid detection methods using sensors. Finally, we review the current limitations and challenges of GM maize nucleic acid testing and share our vision for the future direction of this field.

Comparison of Real-Time PCR and Digital PCR for Detection of Plasma Epstein-Barr Virus DNA in Nasopharyngeal Carcinoma

Plasma Epstein-Barr virus (EBV) DNA is an established biomarker for endemic nasopharyngeal carcinoma. However, existing real-time quantitative PCR (qPCR) assays are limited by poor interlaboratory reproducibility. This is a barrier to biomarker integration into staging systems and management. It was hypothesized that EBV digital PCR (dPCR) would have similar sensitivity but improved precision relative to qPCR. Using the World Health Organization EBV standard and patient specimens, the NRG-HN001 BamHI-W qPCR, two commercial EBNA-1 qPCR assays, and two laboratory-developed dPCR assays amplifying the BamHI-W, EBNA-1, and EBER targets were compared. Testing was conducted in the North American reference laboratory for the NRG-HN001 randomized trial. The EBV dPCR assays achieved similar performance compared with qPCR. Although dPCR does not require quantitation standards, different dPCR thresholding algorithms yielded significant qualitative and quantitative variation. This was most evident with low levels of EBV DNA. No-template control-informed thresholding (ddpcRquant) mitigated false-positive/false-negative findings. The NRG-HN001 BamHI-W qPCR and laboratory-developed BamHI-W droplet dPCR offered higher sensitivity, lower limit of blank, higher precision at low plasma EBV DNA levels (≤1500 IU/mL), and higher overall agreement with clinical specimens versus single-copy qPCR/dPCR targets (EBNA-1/EBER). These data confirm the rationale for using the BamHI-W target to define prognostic thresholds and indicate that both qPCR and dPCR methods harmonized to the World Health Organization standard can provide the necessary analytical performance.

Adaptive Filtering Framework to Remove Nonspecific and Low-Efficiency Reactions in Multiplex Digital PCR Based on Sigmoidal Trends

Real-time digital polymerase chain reaction (qdPCR) coupled with machine learning (ML) methods has shown the potential to unlock scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One promising application of this emerging field explores single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves, also known as data-driven multiplexing. However, accurate target classification is compromised by the presence of undesired amplification events and not ideal reaction conditions. Therefore, here, we proposed a novel framework to identify and filter out nonspecific and low-efficient reactions from qdPCR data using outlier detection algorithms purely based on sigmoidal trends of amplification curves. As a proof-of-concept, this framework is implemented to improve the classification performance of the recently reported data-driven multiplexing method called amplification curve analysis (ACA), using available published data where the ACA is demonstrated to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named adaptive mapping filter (AMF), to adjust the percentage of outliers removed according to the number of positive counts in qdPCR. From an overall total of 152,000 amplification events, 116,222 positive amplification reactions were evaluated before and after filtering by comparing against melting peak distribution, proving that abnormal amplification curves (outliers) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to assess classification performance before and after AMF, showing an improved sensitivity of 1.2% when using inliers compared to a decrement of 19.6% when using outliers (p-value < 0.0001), removing 53.5% of all wrong melting curves based only on the amplification shape. This work explores the correlation between the kinetics of amplification curves and the thermodynamics of melting curves, and it demonstrates that filtering out nonspecific or low-efficient reactions can significantly improve the classification accuracy for cutting-edge multiplexing methodologies.