Significant Expansion of Real-Time PCR Multiplexing with Traditional Chemistries using Amplitude Modulation

From Genotype to Phenotype: Raman Spectroscopy and Machine Learning for Label-Free Single-Cell Analysis

Raman spectroscopy has made significant progress in biosensing and clinical research. Here, we describe how surface-enhanced Raman spectroscopy (SERS) assisted with machine learning (ML) can expand its capabilities to enable interpretable insights into the transcriptome, proteome, and metabolome at the single-cell level. We first review how advances in nanophotonics-including plasmonics, metamaterials, and metasurfaces-enhance Raman scattering for rapid, strong label-free spectroscopy. We then discuss ML approaches for precise and interpretable spectral analysis, including neural networks, perturbation and gradient algorithms, and transfer learning. We provide illustrative examples of single-cell Raman phenotyping using nanophotonics and ML, including bacterial antibiotic susceptibility predictions, stem cell expression profiles, cancer diagnostics, and immunotherapy efficacy and toxicity predictions. Lastly, we discuss exciting prospects for the future of single-cell Raman spectroscopy, including Raman instrumentation, self-driving laboratories, Raman data banks, and machine learning for uncovering biological insights.

Complete Prevention of Bubbles in a PDMS-Based Digital PCR Chip with a Multifunction Cavity

In a chamber-based digital PCR (dPCR) chip fabricated with polydimethylsiloxane (PDMS), bubble generation in the chambers at high temperatures is a critical issue. Here, we found that the main reason for bubble formation in PDMS chips is the too-high saturated vapor pressure of water at an elevated temperature. The bubbles should be completely prevented by reducing the initial pressure of the system to under 13.6 kPa to eliminate the effects of increased-pressure water vapor. Then, a cavity was designed and fabricated above the PCR reaction layer, and Parylene C was used as a shell covering the chip. The cavity was used for the negative generator in sample loading, PDMS degassing, PCR solution degassing in the digitization process and water storage in the thermal reaction process. The analysis was confirmed and finally achieved a desirable bubble-free, fast-digitization, valve-free and no-tubing connection dPCR.

Next generation multiplexing for digital PCR using a novel melt-based hairpin probe design

Digital PCR (dPCR) is a powerful tool for research and diagnostic applications that require absolute quantification of target molecules or detection of rare events, but the number of nucleic acid targets that can be distinguished within an assay has limited its usefulness. For most dPCR systems, one target is detected per optical channel and the total number of targets is limited by the number of optical channels on the platform. Higher-order multiplexing has the potential to dramatically increase the usefulness of dPCR, especially in scenarios with limited sample. Other potential benefits of multiplexing include lower cost, additional information generated by more probes, and higher throughput. To address this unmet need, we developed a novel melt-based hairpin probe design to provide a robust option for multiplexing digital PCR. A prototype multiplex digital PCR (mdPCR) assay using three melt-based hairpin probes per optical channel in a 16-well microfluidic digital PCR platform accurately distinguished and quantified 12 nucleic acid targets per well. For samples with 10,000 human genome equivalents, the probe-specific ranges for limit of blank were 0.00%-0.13%, and those for analytical limit of detection were 0.00%-0.20%. Inter-laboratory reproducibility was excellent (r 2 = 0.997). Importantly, this novel melt-based hairpin probe design has potential to achieve multiplexing beyond the 12 targets/well of this prototype assay. This easy-to-use mdPCR technology with excellent performance characteristics has the potential to revolutionize the use of digital PCR in research and diagnostic settings.

Analytical Performance and Concordance with Next-Generation Sequencing of a Rapid, Multiplexed dPCR Panel for the Detection of DNA and RNA Biomarkers in Non-Small-Cell Lung Cancer

FDA approval of targeted therapies for lung cancer has significantly improved patient survival rates. Despite these improvements, barriers to timely access to biomarker information, such as nucleic acid input, still exist. Here, we report the analytical performance and concordance with next-generation sequencing (NGS) of a highly multiplexed research-use-only (RUO) panel using digital PCR (dPCR). The panel's analytical sensitivity and reactivity were determined using contrived DNA and RNA mixes. The limit of blank was established by testing FFPE curls classified as negative by pathology. Concordance was established on 77 FFPE samples previously characterized using the Oncomine Precision Assay®, and any discordant results were resolved with Archer Fusionplex® and Variantplex® panels. The analytical sensitivity, reported as the estimated mutant allele fraction (MAF), for DNA targets ranged from 0.1 to 0.9%. For RNA targets (ALK, RET, ROS, NTRK 1/2/3 Fusions, and MET Exon 14 skipping alteration), the analytical sensitivity ranged from 23 to 101 detected counts with 5 ng of total RNA input. The population prevalence-based coverage ranged from 89.2% to 100.0% across targets and exceeded 99.0% in aggregate. The assay demonstrated >97% concordance with respect to the comparator method.

New realm of precision multiplexing enabled by massively-parallel single molecule UltraPCR

PCR has been a reliable and inexpensive method for nucleic acid detection in the past several decades. In particular, multiplex PCR is a powerful tool to analyze many biomarkers in the same reaction, thus maximizing detection sensitivity and reducing sample usage. However, balancing the amplification kinetics between amplicons and distinguishing them can be challenging, diminishing the broad adoption of high order multiplex PCR panels. Here, we present a new paradigm in PCR amplification and multiplexed detection using UltraPCR. UltraPCR utilizes a simple centrifugation workflow to split a PCR reaction into ∼34 million partitions, forming an optically clear pellet of spatially separated reaction compartments in a PCR tube. After in situ thermocycling, light sheet scanning is used to produce a 3D reconstruction of the fluorescent positive compartments within the pellet. At typical sample DNA concentrations, the magnitude of partitions offered by UltraPCR dictate that the vast majority of target molecules occupy a compartment uniquely. This single molecule realm allows for isolated amplification events, thereby eliminating competition between different targets and generating unambiguous optical signals for detection. Using a 4-color optical setup, we demonstrate that we can incorporate 10 different fluorescent dyes in the same UltraPCR reaction. We further push multiplexing to an unprecedented level by combinatorial labeling with fluorescent dyes - referred to as "comboplex" technology. Using the same 4-color optical setup, we developed a 22-target comboplex panel that can detect all targets simultaneously at high precision. Collectively, UltraPCR has the potential to push PCR applications beyond what is currently available, enabling a new class of precision genomics assays.

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.

Current and emerging trends in techniques for plant pathogen detection

Plant pathogenic microorganisms cause substantial yield losses in several economically important crops, resulting in economic and social adversity. The spread of such plant pathogens and the emergence of new diseases is facilitated by human practices such as monoculture farming and global trade. Therefore, the early detection and identification of pathogens is of utmost importance to reduce the associated agricultural losses. In this review, techniques that are currently available to detect plant pathogens are discussed, including culture-based, PCR-based, sequencing-based, and immunology-based techniques. Their working principles are explained, followed by an overview of the main advantages and disadvantages, and examples of their use in plant pathogen detection. In addition to the more conventional and commonly used techniques, we also point to some recent evolutions in the field of plant pathogen detection. The potential use of point-of-care devices, including biosensors, have gained in popularity. These devices can provide fast analysis, are easy to use, and most importantly can be used for on-site diagnosis, allowing the farmers to take rapid disease management decisions.

Next-generation molecular diagnostics: Leveraging digital technologies to enhance multiplexing in real-time PCR

Real-time polymerase chain reaction (qPCR) enables accurate detection and quantification of nucleic acids and has become a fundamental tool in biological sciences, bioengineering and medicine. By combining multiple primer sets in one reaction, it is possible to detect several DNA or RNA targets simultaneously, a process called multiplex PCR (mPCR) which is key to attaining optimal throughput, cost-effectiveness and efficiency in molecular diagnostics, particularly in infectious diseases. Multiple solutions have been devised to increase multiplexing in qPCR, including single-well techniques, using target-specific fluorescent oligonucleotide probes, and spatial multiplexing, where segregation of the sample enables parallel amplification of multiple targets. However, these solutions are mostly limited to three or four targets, or highly sophisticated and expensive instrumentation. There is a need for innovations that will push forward the multiplexing field in qPCR, enabling for a next generation of diagnostic tools which could accommodate high throughput in an affordable manner. To this end, the use of machine learning (ML) algorithms (data-driven solutions) has recently emerged to leverage information contained in amplification and melting curves (AC and MC, respectively) - two of the most standard bio-signals emitted during qPCR - for accurate classification of multiple nucleic acid targets in a single reaction. Therefore, this review aims to demonstrate and illustrate that data-driven solutions can be successfully coupled with state-of-the-art and common qPCR platforms using a variety of amplification chemistries to enhance multiplexing in qPCR. Further, because both ACs and MCs can be predicted from sequence data using thermodynamic databases, it has also become possible to use computer simulation to rationalize and optimize the design of mPCR assays where target detection is supported by data-driven technologies. Thus, this review also discusses recent work converging towards the development of an end-to-end framework where knowledge-based and data-driven software solutions are integrated to streamline assay design, and increase the accuracy of target detection and quantification in the multiplex setting. We envision that concerted efforts by academic and industry scientists will help advance these technologies, to a point where they become mature and robust enough to bring about major improvements in the detection of nucleic acids across many fields.

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.

Mosquito tagging using DNA-barcoded nanoporous protein microcrystals

Conventional mosquito marking technology for mark-release-recapture (MRR) is quite limited in terms of information capacity and efficacy. To overcome both challenges, we have engineered, lab-tested, and field-evaluated a new class of marker particles, in which synthetic, short DNA oligonucleotides (DNA barcodes) are adsorbed and protected within tough, crosslinked porous protein microcrystals. Mosquitoes self-mark through ingestion of microcrystals in their larval habitat. Barcoded microcrystals persist trans-stadially through mosquito development if ingested by larvae, do not significantly affect adult mosquito survivorship, and individual barcoded mosquitoes are detectable in pools of up to at least 20 mosquitoes. We have also demonstrated crystal persistence following adult mosquito ingestion. Barcode sequences can be recovered by qPCR and next-generation sequencing (NGS) without detectable amplification of native mosquito DNA. These DNA-laden protein microcrystals have the potential to radically increase the amount of information obtained from future MRR studies compared to previous studies employing conventional mosquito marking materials.

Evaluation of a High-Definition PCR Assay for the Detection of SARS-CoV-2 in Extracted and Nonextracted Respiratory Specimens Collected in Various Transport Media

Objectives

We conducted an analytic and clinical comparison of a novel high-definition polymerase chain reaction PCR (HDPCR) assay to traditional real-time PCR (RT-PCR) for the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in upper respiratory specimens.

Methods

Analytic performance of RT-PCR, HDPCR, and extraction-free HDPCR was established through replicate testing of a serially diluted clinical specimen containing SARS-CoV-2. A clinical comparison of all 3 assays was conducted using 351 prospectively collected upper respiratory swab specimens obtained from symptomatic and asymptomatic individuals collected in various transport media.

Results

RT-PCR and HDPCR assays using extracted nucleic acid demonstrated similar analytic limits of detection (LoD) and clinical performance, with 100% positive and negative agreement. Extraction-free HDPCR demonstrated a 1.5 to 2.0 log₁₀ increase in LoD based on cycle threshold values. However, clinical performance of extraction-free HDPCR remained high, demonstrating 97.8% positive and 99.6% negative agreement with RT-PCR. An overall increase in “invalid” and “presumptive” results was observed when using the extraction-free method, but this was highly variable based on transport medium used.

Conclusions

HDPCR performs similar to RT-PCR for the detection of SARS-CoV-2. The use of an extraction-free HDPCR protocol maintained high clinical performance despite reduced analytic LoD, with the benefit of reduced hands-on time and cost of reagents associated with nucleic acid extraction.

Microdroplet-based one-step RT-PCR for ultrahigh throughput single-cell multiplex gene expression analysis and rare cell detection

Gene expression analysis of individual cells enables characterization of heterogeneous and rare cell populations, yet widespread implementation of existing single-cell gene analysis techniques has been hindered due to limitations in scale, ease, and cost. Here, we present a novel microdroplet-based, one-step reverse-transcriptase polymerase chain reaction (RT-PCR) platform and demonstrate the detection of three targets simultaneously in over 100,000 single cells in a single experiment with a rapid read-out. Our customized reagent cocktail incorporates the bacteriophage T7 gene 2.5 protein to overcome cell lysate-mediated inhibition and allows for one-step RT-PCR of single cells encapsulated in nanoliter droplets. Fluorescent signals indicative of gene expressions are analyzed using a probabilistic deconvolution method to account for ambient RNA and cell doublets and produce single-cell gene signature profiles, as well as predict cell frequencies within heterogeneous samples. We also developed a simulation model to guide experimental design and optimize the accuracy and precision of the assay. Using mixtures of in vitro transcripts and murine cell lines, we demonstrated the detection of single RNA molecules and rare cell populations at a frequency of 0.1%. This low cost, sensitive, and adaptable technique will provide an accessible platform for high throughput single-cell analysis and enable a wide range of research and clinical applications.

Robust Multichannel Encoding for Highly Multiplexed Quantitative PCR

The gold standard of molecular pathogen detection is the quantitative polymerase chain reaction (qPCR). Modern qPCR instruments are capable of detecting 4-6 analytes in a single sample: one per optical detection channel. However, many clinical applications require multiplexing beyond this traditional single-well capacity, including the task of simultaneously testing for SARS-CoV-2 and other respiratory pathogens. This can be addressed by dividing a sample across multiple wells, or using technologies such as genomic sequencing and spatial arrays, but at the expense of significantly higher cost and lower throughput compared with single-well qPCR. These trade-offs represent unacceptable compromises in high-throughput screening scenarios such as SARS-CoV-2 testing. We demonstrate a novel method of detecting up to 20 targets per well with standard qPCR instrumentation: high-definition PCR (HDPCR). HDPCR combines TaqMan chemistry and familiar workflows with robust encoding to enable far higher levels of multiplexing on a traditional qPCR system without an increase in cost or reduction in throughput. We utilize HDPCR with a custom 20-Plex assay, an 8-Plex assay using unmodified predesigned single-plex assays from Integrated DNA Technologies and a 9-Plex pathogen panel inclusive of SARS-CoV-2 and other common respiratory viruses. All three assays were successful when tested on a variety of samples, with overall sample accuracies of 98.8, 98.3, and 100%, respectively. The HDPCR technology enables the large install base of qPCR instrumentation to perform mid-density multiplex diagnostics without modification to instrumentation or workflow, meeting the urgent need for increased diagnostic yield at an affordable price without sacrificing assay performance.

Ligation-Enabled Fluorescence-Coding PCR for High-Dimensional Fluorescence-Based Nucleic Acid Detection

Polymerase chain reaction (PCR) is by far the most commonly used method of nucleic acid amplification and has likewise been employed for a plethora of diagnostic purposes. Nonetheless, multiplexed PCR-based detection schemes have hitherto been largely limited by technical challenges associated with nonspecific interactions and other limitations inherent to traditional fluorescence-based assays. Here, we describe a novel strategy for multiplexed PCR-based analysis called Ligation-eNabled fluorescence-Coding PCR (LiNC PCR) that exponentially enhances the multiplexing capability of standard fluorescence-based PCR assays. The technique relies upon a simple, preliminary ligation reaction in which target DNA sequences are converted to PCR template molecules with distinct endpoint fluorescence signatures. Universal TaqMan probes are used to create target-specific multicolor fluorescence signals that can be readily decoded to identify amplified targets of interest. We demonstrate the LiNC PCR technique by implementing a two-color-based assay for detection of 10 ovarian cancer epigenetic biomarkers at analytical sensitivities as low as 60 template molecules with no detectable target cross-talk. Overall, LiNC PCR provides a simple and inexpensive method for achieving high-dimensional multiplexing that can be implemented in manifold molecular diagnostic applications.

Diagnostic Sequences That Distinguish M. avium Subspecies Strains

Over a decade ago Mycobacterium avium subspecies paratuberculosis (Map) specific genes were initially identified in a whole genome context by comparing draft genome sequences of Map strain K-10 with Mycobacterium avium subspecies hominissuis (Mah) strain 104. This resulted in identification of 32 Map specific genes, not including repetitive elements, based on the two-genome comparison. The goal of this study was to define a more complete catalog of M. avium subspecies-specific genes. This is important for obtaining additional diagnostic targets for Johne's disease detection and for understanding the unique biology, evolution and niche adaptation of these organisms. There are now over 28 complete genome sequences representing three M. avium subspecies, including avium (Maa), Mah, and Map. We have conducted a comprehensive comparison of these genomes using two independent pan genomic comparison tools, PanOCT and Roary. This has led to the identification of more than 250 subspecies defining genes common to both analyses. The majority of these genes are arranged in clusters called genomic islands. We further reduced the number of diagnostic targets by excluding sequences having high BLAST similarity to other mycobacterial species recently added to the National Center for Biotechnology Information database. Genes identified as diagnostic following these bioinformatic approaches were further tested by DNA amplification PCR on an additional 20 M. avium subspecies strains. This combined approach confirmed 86 genes as Map-specific, seven as Maa-specific and three as Mah-specific. A single-tube PCR reaction was conducted as a proof of concept method to quickly distinguish M. avium subspecies strains. With these novel data, researchers can classify isolates in their freezers, quickly characterize clinical samples, and functionally analyze these unique genes.

Amplification Curve Analysis: Data-Driven Multiplexing Using Real-Time Digital PCR

Information about the kinetics of PCR reactions is encoded in the amplification curve. However, in digital PCR (dPCR), this information is typically neglected by collapsing each amplification curve into a binary output (positive/negative). Here, we demonstrate that the large volume of raw data obtained from real-time dPCR instruments can be exploited to perform data-driven multiplexing in a single fluorescent channel using machine learning methods, by virtue of the information in the amplification curve. This new approach, referred to as amplification curve analysis (ACA), was shown using an intercalating dye (EvaGreen), reducing the cost and complexity of the assay and enabling the use of melting curve analysis for validation. As a case study, we multiplexed 3 carbapenem-resistant genes to show the impact of this approach on global challenges such as antimicrobial resistance. In the presence of single targets, we report a classification accuracy of 99.1% (N = 16188), which represents a 19.7% increase compared to multiplexing based on the final fluorescent intensity. Considering all combinations of amplification events (including coamplifications), the accuracy was shown to be 92.9% (N = 10383). To support the analysis, we derived a formula to estimate the occurrence of coamplification in dPCR based on multivariate Poisson statistics and suggest reducing the digital occupancy in the case of multiple targets in the same digital panel. The ACA approach takes a step toward maximizing the capabilities of existing real-time dPCR instruments and chemistries, by extracting more information from data to enable data-driven multiplexing with high accuracy. Furthermore, we expect that combining this method with existing probe-based assays will increase multiplexing capabilities significantly. We envision that once emerging point-of-care technologies can reliably capture real-time data from isothermal chemistries, the ACA method will facilitate the implementation of dPCR outside of the lab.

Evaluation of a Novel Multiplex High-Definition PCR Assay for Detection of Tick-Borne Pathogens in Whole-Blood Specimens

The prevalence of tick-borne infections has been steadily increasing in both number and geographic distribution in the United States and abroad. This increase, in conjunction with the continued recognition of novel pathogens transmitted by ticks, has made accurate diagnosis of these infections challenging. Mainstay serologic tests are insensitive during the acute phase of infection and are often cross-reactive with similar pathogenic and nonpathogenic organisms. Further, they are unable to reliably differentiate active versus past infection which can lead to misdiagnosis and incorrect understanding of the epidemiology and incidence of specific tick-borne pathogens. We evaluated a novel multiplexed high-definition PCR (HDPCR) Tickborne Panel (TBP) assay (ChromaCode, Carlsbad, CA) for the detection of nine tick-borne pathogens or groups associated with human illness. The HDPCR technology enables multiplex identification of multiple targets in a single fluorometric channel based on fluorescent signal modulation using a limiting probe design. A collection of 530 whole-blood specimens collected from patients being evaluated for tick-borne infections, in addition to a panel of 93 simulated specimens, were used to challenge the HDPCR TBP. The results were compared to a clinically validated traditional multiplexed PCR test with additional sequence analysis and clinical history collected to aid in resolving discrepancies. Among clinical specimens the TBP demonstrated 100% sensitivity for the identification of Anaplasma phagocytophilum, Borrelia miyamotoi, Borrelia mayonii, and Rickettsia rickettsii The sensitivity for identification of B. burgdorferi was 44.4% compared to a composite gold standard. Among simulated specimens containing single or multiple targets present at 10³ to 10⁵ copies/PCR, the sensitivity of TBP was 100% for all targets, with a combined specificity of 99.5%. Of note, an increased rate of false-positive results was observed among simulated specimens that contained multiple targets. Based on these data, we find the HDPCR TBP to be a useful adjunct for the diagnosis of tick-borne infections in patients with suspected tick-borne illness.