PCR Multiplexing Based on a Single Fluorescent Channel Using Dynamic Melting Curve Analysis

DNA melting analysis

Melting is a fundamental property of DNA that can be monitored by absorbance or fluorescence. PCR conveniently produces enough DNA to be directly monitored on real-time instruments with fluorescently labeled probes or dyes. Dyes monitor the entire PCR product, while probes focus on a specific locus within the amplicon. Advances in amplicon melting include high resolution instruments, saturating DNA dyes that better reveal multiple products, prediction programs for domain melting, barcode taxonomic identification, high speed microfluidic melting, and highly parallel digital melting. Most single base variants and small insertions or deletions can be genotyped by high resolution amplicon melting. High resolution melting also enables heterozygote scanning for any variant within a PCR product. A web application (uMelt, http://www.dna-utah.org) predicts amplicon melting curves with multiple domains, a useful tool for verifying intended products. Additional applications include methylation assessment, copy number determination and verification of sequence identity. When amplicon melting does not provide sufficient detail, unlabeled probes or snapback primers can be used instead of covalently labeled probes. DNA melting is a simple, inexpensive, and powerful tool with many research applications that is beginning to make its mark in clinical diagnostics.

Bacteriophages in nature: recent advances in research tools and diverse environmental and biotechnological applications

Bacteriophages infect and replicate within bacteria and play a key role in the environment, particularly in microbial ecosystems and bacterial population dynamics. The increasing recognition of their significance stems from their wide array of environmental and biotechnological uses, which encompass the mounting issue of antimicrobial resistance (AMR). Beyond their therapeutic potential in combating antibiotic-resistant infections, bacteriophages also find vast applications such as water quality monitoring, bioremediation, and nutrient cycling within environmental sciences. Researchers are actively involved in isolating and characterizing bacteriophages from different natural sources to explore their applications. Gaining insights into key aspects such as the life cycle of bacteriophages, their host range, immune interactions, and physical stability is vital to enhance their application potential. The establishment of diverse phage libraries has become indispensable to facilitate their wide-ranging uses. Consequently, numerous protocols, ranging from traditional to cutting-edge techniques, have been developed for the isolation, detection, purification, and characterization of bacteriophages from diverse environmental sources. This review offers an exploration of tools, delves into the methods of isolation, characterization, and the extensive environmental applications of bacteriophages, particularly in areas like water quality assessment, the food sector, therapeutic interventions, and the phage therapy in various infections and diseases.

Demystifying PCR tests, challenges, alternatives, and future: A quick review focusing on COVID and fungal infections

The polymerase chain reaction (PCR) technique is one of the most potent tools in molecular biology. It is extensively used for various applications ranging from medical diagnostics to forensic science and food quality testing. This technique has facilitated to survive COVID-19 pandemic by identifying the virus-infected individuals effortlessly and effectively. This review explores the principles, recent advancements, challenges, and alternatives of PCR technique in the context of COVID-19 and fungal infections. The introduction of PCR technique for anyone new to this field is the primary aim of this review and thereby equips them to understand the science of COVID-19 and related fungal infections in a simplistic manner.

Melt-Encoded-Tags for Expanded Optical Readout in Digital PCR (METEOR-dPCR) Enables Highly Multiplexed Quantitative Gene Panel Profiling

Digital PCR (dPCR) is an important tool for precise nucleic acid quantification in clinical setting, but the limited multiplexing capability restricts its applications for quantitative gene panel profiling. Here, this work describes melt-encoded-tags for expanded optical readout in digital PCR (METEOR-dPCR), a simple two-step assay that enables simultaneous quantification of a large panel of arbitrary genes in a dPCR platform. Target genes are quantitatively converted into DNA tags with unique melting temperatures through a ligation approach. These tags are then counted and distinguished by their melt-curve profiles on a dPCR platform. A multiplexing capacity of M^N, where M is the number of resolvable melting temperature and N is the number of fluorescence channel, can be achieved. This work validates METEOR-dPCR with simultaneous DNA copy number profiling of 60 targets using dPCR in cancer cells, and demonstrates its sensitivity for estimating tumor fraction in mixed tumor and normal DNA samples. The rapid, quantitative, and highly multiplexed METEOR-dPCR assay will have wide appeal for many clinical applications.

Deep Domain Adaptation Enhances Amplification Curve Analysis for Single-Channel Multiplexing in Real-Time PCR

Data-driven approaches for molecular diagnostics are emerging as an alternative to perform an accurate and inexpensive multi-pathogen detection. A novel technique called Amplification Curve Analysis (ACA) has been recently developed by coupling machine learning and real-time Polymerase Chain Reaction (qPCR) to enable the simultaneous detection of multiple targets in a single reaction well. However, target classification purely relying on the amplification curve shapes faces several challenges, such as distribution discrepancies between different data sources (i.e., training vs testing). Optimisation of computational models is required to achieve higher performance of ACA classification in multiplex qPCR through the reduction of those discrepancies. Here, we proposed a novel transformer-based conditional domain adversarial network (T-CDAN) to eliminate data distribution differences between the source domain (synthetic DNA data) and the target domain (clinical isolate data). The labelled training data from the source domain and unlabelled testing data from the target domain are fed into the T-CDAN, which learns both domains' information simultaneously. After mapping the inputs into a domain-irrelevant space, T-CDAN removes the feature distribution differences and provides a clearer decision boundary for the classifier, resulting in a more accurate pathogen identification. Evaluation of 198 clinical isolates containing three types of carbapenem-resistant genes (blaNDM, blaIMP and blaOXA-48) illustrates a curve-level accuracy of 93.1% and a sample-level accuracy of 97.0% using T-CDAN, showing an accuracy improvement of 20.9% and 4.9% respectively. This research emphasises the importance of deep domain adaptation to enable high-level multiplexing in a single qPCR reaction, providing a solid approach to extend qPCR instruments' capabilities in real-world clinical applications.

Similar color analysis based on deep learning (SCAD) for multiplex digital PCR via a single fluorescent channel

Digital PCR (dPCR) has recently attracted great interest due to its high sensitivity and accuracy. However, the existing dPCR depends on multicolor fluorescent dyes and multiple fluorescent channels to achieve multiplex detection, resulting in increased detection cost and limited detection throughput. Here, we developed a deep learning-based similar color analysis method, namely SCAD, to achieve multiplex dPCR in a single fluorescent channel. As a demonstration, we designed a microwell chip-based diplex dPCR system for detecting two genes (bla_NDM and bla_VIM) with two kinds of green fluorescent probes, whose emission colors are difficult to discriminate by traditional fluorescence intensity-based methods. To verify the possibility of deep learning algorithms to distinguish the similar colors, we first applied t-distributed stochastic neighbor embedding (tSNE) to make a clustering map for the microwells with similar fluorescence. Then, we trained a Vision Transformer (ViT) model on 10 000 microwells with two similar colors and tested it with 262 202 microwells. Lastly, the trained model was proven to have highly accurate classification ability (>98% for both the training set and the test set) and precise quantification ability on both bla_NDM and bla_VIM (ratio difference <0.10). We envision that the developed SCAD method would significantly expand the detection throughput of dPCR without the need for other auxiliary equipment.

RT-PCR and Matrix-Assisted Laser Desorption-Ionization Time-of-Flight Mass Spectrometry Diagnostic Target Performance Reflects Circulating Severe Acute Respiratory Syndrome Coronavirus 2 Variant Diversity in New York City

As severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) continues to circulate, multiple variants of concern have emerged. New variants pose challenges for diagnostic platforms because sequence diversity can alter primer/probe-binding sites (PBSs), causing false-negative results. The Agena MassARRAY SARS-CoV-2 Panel (Agena Bioscience) uses RT-PCR and mass spectrometry to detect five multiplex targets across N and ORF1ab genes. Herein, we use a data set of 256 SARS-CoV-2-positive specimens collected between April 11, 2021, and August 28, 2021, to evaluate target performance with paired sequencing data. During this time frame, two targets in the N gene (N2 and N3) were subject to the greatest sequence diversity. In specimens with N3 dropout, 69% harbored the Alpha-specific A28095U polymorphism that introduces a 3'-mismatch to the N3 forward PBS and increases risk of target dropout relative to specimens with 28095A (relative risk, 20.02; 95% CI, 11.36 to 35.72; P < 0.0001). Furthermore, among specimens with N2 dropout, 90% harbored the Delta-specific G28916U polymorphism that creates a 3'-mismatch to the N2 probe PBS and increases target dropout risk (relative risk, 11.92; 95% CI, 8.17 to 14.06; P < 0.0001). These findings highlight the robust capability of Agena MassARRAY SARS-CoV-2 Panel target results to reveal circulating virus diversity, and they underscore the power of multitarget design to capture variants of concern.

An image-to-answer algorithm for fully automated digital PCR image processing

The digital polymerase chain reaction (dPCR) is an irreplaceable variant of PCR techniques due to its capacity for absolute quantification and detection of rare deoxyribonucleic acid (DNA) sequences in clinical samples. Image processing methods, including micro-chamber positioning and fluorescence analysis, determine the reliability of the dPCR results. However, typical methods demand high requirements for the chip structure, chip filling, and light intensity uniformity. This research developed an image-to-answer algorithm with single fluorescence image capture and known image-related error removal. We applied the Hough transform to identify partitions in the images of dPCR chips, the 2D Fourier transform to rotate the image, and the 3D projection transformation to locate and correct the positions of all partitions. We then calculated each partition's average fluorescence amplitudes and generated a 3D fluorescence intensity distribution map of the image. We subsequently corrected the fluorescence non-uniformity between partitions based on the map and achieved statistical results of partition fluorescence intensities. We validated the proposed algorithms using different contents of the target DNA. The proposed algorithm is independent of the dPCR chip structure damage and light intensity non-uniformity. It also provides a reliable alternative to analyze the results of chip-based dPCR systems.

Temperature non-uniformity detection on dPCR chips and temperature sensor calibration

A microfluidic-based digital polymerase chain reaction (dPCR) chip requires precise temperature control as well as uniform temperature distribution to ensure PCR efficiency. However, measuring local temperature and its distribution over thousands of μL/nL-volume samples with minimum disturbance is challenging. Here, we present a method of non-contact localized temperature measurement for determination of the non-uniformity of temperature distribution over a dPCR chip. We filled the dPCR chip with a PCR solution containing amplified DNA fragments with a known melting temperature (T_M). We then captured fluorescent images of the chip when it was heated from 70 to 99 °C, plotted the fluorescence intensity of each partition as a function of temperature, and calculated measured T_M values from each partition. Finally, we created a 3-D map of the dPCR chip with the measured T_M as the parameter. Even when the actual T_M of the PCR solution was constant, the measured T_M value varied between locations due to temperature non-uniformity in the dPCR chip. The method described here thereby characterized the distribution of temperature non-uniformity using a PCR solution with known T_M as a temperature sensor. Among the non-contact temperature measurement methods, the proposed T_M-based method can determine the temperature distribution within the chip, instead of only at the chip surface. The method also does not suffer from the undesirable photobleaching effect of fluorescein-based temperature measurement method. Temperature determination over the dPCR chip based on T_M allowed us to calibrate the temperature sensor and improve the dPCR configuration and precision. This method is also suitable for determining the temperature uniformity of other microarray systems where there is no physical access to the system and thus direct temperature measurement is not possible.

We present a method of non-contact localized temperature measurement for determination of the non-uniformity of temperature distribution over a dPCR chip mounted on two different thermal cycling configurations.

Detection of Salmonella typhimurium ATCC 14028 in Powder Prepared Traditional Medicines Using Real-Time PCR

The detection of Salmonella typhimurium ATCC 14028 using real-time PCR on powdered traditional medicinal products was carried out in the microbiology and molecular biology testing laboratory of the Food and Drug Administration in Gorontalo. This research aims to provide a reference for alternative testing methods in testing the products of traditional powder preparations on the market. The sample consisted of 10 traditional powder preparations spiked with positive control of S. typhimurium ATCC 14028 phase 2. The method used in the study was real-time PCR analysis using the SYBR® Green method, while DNA isolation using the direct PCR method. Data analysis was performed by analyzing the sample's melting temperature (Tm) curve and comparing it with positive control. The results showed that S. typhimurium ATCC 14028 was detected in samples at an average Tm value of 84.18°C, with ranges of 84.0-84.5°C. For positive control, the Tm value was at 85.2°C, while for the negative control, the Tm value was not detected. Based on these data, it can be concluded that S. typhimurium ATCC 14028 in traditional medicine products powder preparations can be detected using real-time PCR.

Multiplexed digital polymerase chain reaction as a powerful diagnostic tool

The digital polymerase chain reaction (dPCR) multiplexing method can simultaneously detect and quantify closely related deoxyribonucleic acid sequences in complex mixtures. The dPCR concept is continuously improved by the development of microfluidics and micro- and nanofabrication, and different complex techniques are introduced. In this review, we introduce dPCR techniques based on sample compartmentalization, droplet- and chip-based systems, and their combinations. We then discuss dPCR multiplexing methods in both laboratory research settings and advanced or routine clinical applications. We focus on their strengths and weaknesses with regard to the character of biological samples and to the required precision of such analysis, as well as showing recently published work based on those methods. Finally, we envisage possible future achievements in this field.