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

A CRISPR/Cas13a system based on a dumbbell-shaped hairpin combined with DNA-PAINT to establish the DCP-platform for highly sensitive detection of Hantaan virus RNA

Rapid and sensitive detection of specific RNA sequences is crucial for clinical diagnosis, surveillance, and biotechnology applications. Currently, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) is the gold standard for RNA detection; however, it is associated with long processing time, complex procedures, and a high false-positive rate. To address these challenges, we developed a novel sensing platform based on CRISPR/Cas13a that incorporates a dumbbell-shaped hairpin and DNA-PAINT for rapid, highly specific, and sensitive RNA analysis. By leveraging the CRISPR/Cas13a system, this platform enables the cleavage of dumbbell-shaped hairpins, which subsequently allows the cleaved primers to initiate cyclic amplification of fluorescent signals. These signals are further enhanced by the binding and dissociation phenomena inherent to DNA-PAINT technology, ultimately achieving remarkable triple signal amplification. Additionally, the system effectively discriminates Hantaan virus RNA from Seoul virus in real samples. Importantly, the platform can be easily adapted for the detection of other RNAs by simply reconfiguring the hybridization region of crRNA. In conclusion, this platform represents a "five-in-one" RNA detection approach that integrates reliability, versatility, robustness, high specificity, and superior quantitative capabilities. It provides novel insights for direct RNA detection based on CRISPR/Cas13a and demonstrates significant potential for advancement in viral diagnostics.

Spatial-Temporal Cube Denoising for Real-Time Digital PCR Melting Analysis to Improve the Accuracy of Multiplex Detection

Real-time digital melting curves combine highly sensitive real-time digital polymerase chain reaction (PCR) with high-resolution melting curve analysis to achieve multiplex detection, which optimizes PCR efficiency and improves identification capability. However, due to the noise interference during the experiment, it is challenging to accurately obtain the melting temperature by extracting the microwell signal only from a single image at each temperature, further affecting the accuracy and resolution of multiplex detection. In this work, a spatial-temporal cube denoising model (STCDM) was established, which explicitly integrates the spatial and temporal dimensions to address the noise inherent in melting images. By constructing a three-dimensional spatial-temporal cube, the STCDM performs block denoising to effectively mitigate noise across both dimensions, leading to more accurate and reliable multiplex detection. The correction results demonstrated an improvement in melting temperature accuracy from 92% to 98%, with a resolution within 0.6 °C and good repeatability. Therefore, on the basis of the real-time dPCR platform, using the STCDM can significantly enhance the accuracy, driving the advancement of multiplex detection technology.

A capillary-driven microfluidic device for performing spatial multiplex PCR

Multiplex polymerase chain reaction (PCR) tests multiple biomarkers or pathogens that cause overlapping symptoms, making it an essential tool in syndromic testing. To achieve a multiplex PCR on chip, a design based on capillary-driven fluidic actuation is proposed. Our silicon chip features 22 reaction chambers and allows primers and probes to be pre-spotted in the reaction chambers prior to use. The design facilitates rapid sample loading through a common inlet channel, delivering reagents to all reaction chambers in less than 10 s. A custom clamping mechanism combined with a double depth cavity design ensures proper sealing during temperature cycling without the need for extra reagents like oil. Temperature cycling and fluorescence imaging were performed using custom-made hardware. As a proof of concept, two single nucleotide polymorphisms (SNPs), CyP2C19*2 and PCSK9 were detected. These results demonstrate the feasibility of on-chip multiplex PCR, compatible with different assays in parallel and requiring only a single pipetting step for reagent loading, without active fluidic actuation like pumping.

A case report of confirmed difficult pulmonary tuberculosis based on the hybrid capture-based tNGS method

BACKGROUND

Early diagnosis of pulmonary tuberculosis can greatly reduce the harm caused by the disease. However, traditional diagnostic methods have various shortcomings in diagnosing pulmonary tuberculosis. Currently, with the increasing popularity, iteration, and decreasing costs of Next-generation sequencing (NGS) testing technology, NGS is being more widely applied in the diagnosis of pulmonary tuberculosis.

CASE PRESENTATION

A 29-year-old male presented with "fever accompanied by cough for more than 20 days." Multiple chest CT scans revealed progressive enlargement of the right hilar lymph nodes and thickening of the interlobular septa in the right upper lobe. Routine testing of bronchoalveolar lavage fluid, search for tuberculosis bacilli, bacterial and fungal cultures, X-pert MTB/RIF, and multiplex PCR-based targeted Next-generation sequencing (mp-tNGS) results were all inconclusive. Finally, bronchoalveolar lavage fluid was sent for hybrid capture-based targeted Next-generation sequencing (hc-tNGS) testing, and special staining of the enlarged lymph nodes confirmed the diagnosis of pulmonary tuberculosis.

CONCLUSION

The hc-tNGS has significant value in diagnosing pulmonary tuberculosis, especially in cases that are difficult to detect with other methods. In the future, this could gradually become a routine diagnostic method for pulmonary tuberculosis, enhancing the accuracy of early diagnosis.

Biosensing for rapid detection of MDR, XDR and PDR bacteria

The emergence of multidrug-resistant (MDR), extensively drug-resistant (XDR), and pandrug-resistant (PDR) bacteria poses a significant threat to global public health, complicating the management of infectious diseases and increasing morbidity and mortality rates. Rapid and sensitive detection of these resistant pathogens is crucial for effective treatment and infection control. This manuscript provides a comprehensive overview of various biosensor technologies developed for the rapid identification and quantification of MDR and XDR bacteria. We discuss the principles of operation, sensitivity, specificity, and practical applications of different biosensing platforms, including electrochemical, optical, and piezoelectric sensors. Additionally, we explore recent advancements in nanomaterials and microfluidics that enhance biosensor performance and enable point-of-care testing. The manuscript also addresses the challenges faced in the implementation of these technologies in clinical settings, such as regulatory hurdles and the need for standardization. A systematic literature review was conducted to identify relevant studies. Databases utilized include PubMed and Scopus, covering the time frame from 2015 to 2024. The literature screening criteria focused on the inclusion of only clinically validated studies to ensure the reliability and applicability of the findings. By highlighting the potential of biosensors to revolutionize the detection of drug-resistant bacteria, this work aims to inform researchers, clinicians, and policymakers about the critical role of innovative diagnostic tools in combating antibiotic resistance and improving patient outcomes.

Portable microfluidic devices for monitoring antibiotic resistance genes in wastewater

Antibiotic resistance genes (ARGs) pose serious threats to environmental and public health, and monitoring ARGs in wastewater is a growing need because wastewater is an important source. Microfluidic devices can integrate basic functional units involved in sample assays on a small chip, through the precise control and manipulation of micro/nanofluids in micro/nanoscale spaces, demonstrating the great potential of ARGs detection in wastewater. Here, we (1) summarize the state of the art in microfluidics for recognizing ARGs, (2) determine the strengths and weaknesses of portable microfluidic chips, and (3) assess the potential of portable microfluidic chips to detect ARGs in wastewater. Isothermal nucleic acid amplification and CRISPR/Cas are two commonly used identification elements for the microfluidic detection of ARGs. The former has better sensitivity due to amplification, but false positives due to inappropriate primer design and contamination; the latter has better specificity. The combination of the two can achieve complementarity to a certain extent. Compared with traditional microfluidic chips, low-cost and biocompatible paper-based microfluidics is a very attractive test for ARGs, whose fluid flow in paper does not require external force, but it is weaker in terms of repeatability and high-throughput detection. Due to that only a handful of portable microfluidics detect ARGs in wastewater, fabricating high-throughput microfluidic chips, developing and optimizing recognition techniques for the highly selective and sensitive identification and quantification of a wide range of ARGs in complex wastewater matrices are needed.

Multiplex digital profiling of DNA methylation heterogeneity for sensitive and cost-effective cancer detection in low-volume liquid biopsies

Molecular alterations in cancerous tissues exhibit intercellular genetic and epigenetic heterogeneity, complicating the performance of diagnostic assays, particularly for early cancer detection. Conventional liquid biopsy methods have limited sensitivity and/or ability to assess epigenetic heterogeneity of rare epiallelic variants cost-effectively. We report an approach, named REM-DREAMing (Ratiometric-Encoded Multiplex Discrimination of Rare EpiAlleles by Melt), which leverages a digital microfluidic platform that incorporates a ratiometric fluorescence multiplex detection scheme and precise digital high-resolution melt analysis to enable low-cost, parallelized analysis of heterogeneous methylation patterns on a molecule-by-molecule basis for the detection of cancer in liquid biopsies. We applied the platform to simultaneously assess intermolecular epigenetic heterogeneity in five methylation biomarkers for improved, blood-based screening for early-stage non-small cell lung cancer. In a cohort of 48 low-volume liquid biopsy specimens from patients with indeterminant pulmonary nodules, we show that assessment of intermolecular methylation density distributions can notably improve the performance of multigene methylation biomarker panels for the early detection of cancer.

Spotted fever diagnosis using molecular methods

Rickettsiosis is a disease caused by bacteria belonging to the genus Rickettsia, and is a potentially fatal zoonotic disease of great medical and veterinary importance. Given the urgent need to develop new diagnostic methods for detecting this disease, the present review aimed to evaluate the number of publications dedicated to the identification of Rickettsia spp. in human samples using molecular methods, such as polymerase chain reaction and its variations. To this end, a bibliographical survey covering articles published in the past ten years was conducted using the PudMed platform with the keywords "spotted fever" and "Rickettsia," both combined with "diagnosis." A growing number of publications in this area reflects an increasing interest in research, especially since 2015. From 2015 to February 2024, several promising results were tested and many studies were able to detect the genetic sequences of interest. Therefore, the absence of a standard diagnosis method highlights the critical need for developing an effective technique capable of accurately detecting the etiological agent and ensuring accurate diagnosis of the disease.

Multiplex molecular assays for the laboratory-based and point-of-care diagnosis of infections caused by seasonal influenza, COVID-19, and RSV

INTRODUCTION

SARS-CoV-2, seasonal influenza, and respiratory syncytial virus (RSV) are major causes of acute respiratory infections in all age groups and responsible for an enormous socio-economic burden. The recently coined term 'tripledemic' describes co-circulation of these three viruses, a novel epidemiological paradigm that poses profound public health implications.

AREAS COVERED

Real-time reverse transcription polymerase chain reaction (RT-PCR) is now considered the reference method for the diagnosis of SARS-CoV-2, influenza, and RSV infections. Syndromic-based multiplex RT-PCR panels that simultaneously detect several respiratory viruses have become increasingly common. This review explores available molecular diagnostics (MDx) platforms for the diagnosis of SARS-CoV-2, influenza, and RSV in the same biological sample. Within some limitations of the published validation and diagnostic accuracy studies, both laboratory-based and point-of-care multiplex panels proved highly performant in identifying SARS-CoV-2, influenza A, influenza B, and RSV. Improved operational efficiency and faster turnaround times make these assays potentially cost-effective or even cost-saving.

EXPERT OPINION

The adoption of multiplex MDx assays for the contemporary detection of SARS-CoV-2, influenza, RSV, and other respiratory pathogens will likely increase in the next few years. To maximize the clinical usefulness and cost-effectiveness of these assays, locally issued guidelines and protocols on their implementation should be adopted.

Exploring the feasibility of utilizing universal primers in detecting mucormycosis pathogens: An in-silico analysis

This study aimed to design and evaluate a universal primer for Polymerase Chain Reaction (PCR)- based detection of mucormycosis-causing fungi by targeting their Internal Transcribed Spacer (ITS) sequences. In-silico analysis was conducted to assess primer suitability. Using Clustal Omega and Primer-BLAST, ITS sequences of 32 fungi species causing mucormycosis were aligned and subjected to primer design. Generated primers were sorted and in silico PCR simulations were performed to identify primers capable of amplifying all fungal species. Instead of identifying one pair of universal primer, in silico PCR analysis identified a panel of 14 primer pairs designed from full-length ITS sequences, and a panel of 12 primer pairs designed from conserved regions, that could detect 31 species. The study recommends a panel of 12 primers for multiplex-PCR to detect mucormycosis-causing fungi instead of a long list of PCR analyses for each fungus in diagnosing mucormycosis.

Healthcare as a driver, reservoir and amplifier of antimicrobial resistance: opportunities for interventions

Antimicrobial resistance (AMR) is a global health challenge that threatens humans, animals and the environment. Evidence is emerging for a role of healthcare infrastructure, environments and patient pathways in promoting and maintaining AMR via direct and indirect mechanisms. Advances in vaccination and monoclonal antibody therapies together with integrated surveillance, rapid diagnostics, targeted antimicrobial therapy and infection control measures offer opportunities to address healthcare-associated AMR risks more effectively. Additionally, innovations in artificial intelligence, data linkage and intelligent systems can be used to better predict and reduce AMR and improve healthcare resilience. In this Review, we examine the mechanisms by which healthcare functions as a driver, reservoir and amplifier of AMR, contextualized within a One Health framework. We also explore the opportunities and innovative solutions that can be used to combat AMR throughout the patient journey. We provide a perspective on the current evidence for the effectiveness of interventions designed to mitigate healthcare-associated AMR and promote healthcare resilience within high-income and resource-limited settings, as well as the challenges associated with their implementation.

Advancements in rapid diagnostics and genotyping of Piscirickettsia salmonis using Loop-mediated Isothermal Amplification

Introduction

Piscirickettsia salmonis, the causative agent of Piscirickettsiosis, poses a significant threat to the Chilean aquaculture industry, resulting in substantial economic losses annually. The pathogen, first identified as specie in 1992, this pathogen was divided into two genogroups: LF-89 and EM-90, associated with different phenotypic mortality and pathogenicity. Traditional genotyping methods, such as multiplex PCR, are effective but limited by their cost, equipment requirements, and the need for specialized expertise.

Methods

This study validates Loop-mediated Isothermal Amplification (LAMP) as a rapid and specific alternative for diagnosing P. salmonis infections. We developed the first qPCR and LAMP assay targeting the species-conserved tonB receptor gene (tonB-r, WP_016210144.1) for the specific species-level identification of P. salmonis. Additionally, we designed two genotyping LAMP assays to differentiate between the LF-89 and EM-90 genogroups, utilizing the unique coding sequences Nitronate monooxygenase (WP_144420689.1) for LF-89 and Acid phosphatase (WP_016210154.1) for EM-90.

Results

The LAMP assays demonstrated sensitivity and specificity comparable to real-time PCR, with additional benefits including rapid results, lower costs, and simplified operation, making them particularly suitable for field use. Specificity was confirmed by testing against other salmonid pathogens, such as Renibacterium salmoninarum, Vibrio ordalii, Flavobacterium psychrophilum, Tenacibaculum maritimum, and Aeromonas salmonicida, with no cross-reactivity observed.

Discussion

The visual detection method and precise differentiation between genogroups underscore LAMP's potential as a robust diagnostic tool for aquaculture. This advancement in the specie detection (qPCR and LAMP) and genotyping of P. salmonis represents a significant step forward in disease management within the aquaculture industry. The implementation of LAMP promises enhanced disease surveillance, early detection, and improved management strategies, ultimately benefiting the salmonid aquaculture sector.

Rapid, Portable, and Electricity-free Sample Extraction Method for Enhanced Molecular Diagnostics in Resource-Limited Settings

The COVID-19 pandemic has highlighted the need for rapid and reliable diagnostics that are accessible in resource-limited settings. To address this pressing issue, we have developed a rapid, portable, and electricity-free method for extracting nucleic acids from respiratory swabs (i.e. nasal, nasopharyngeal and buccal swabs), successfully demonstrating its effectiveness for the detection of SARS-CoV-2 in residual clinical specimens. Unlike traditional approaches, our solution eliminates the need for micropipettes or electrical equipment, making it user-friendly and requiring little to no training. Our method builds upon the principles of magnetic bead extraction and revolves around a low-cost plastic magnetic lid, called SmartLid, in combination with a simple disposable kit containing all required reagents conveniently prealiquoted. Here, we clinically validated the SmartLid sample preparation method in comparison to the gold standard QIAamp Viral RNA Mini Kit from QIAGEN, using 406 clinical isolates, including 161 SARS-CoV-2 positives, using the SARS-CoV-2 RT-qPCR assays developed by the US Centers for Disease Control and Prevention (CDC). The SmartLid method showed an overall sensitivity of 95.03% (95% CI: 90.44-97.83%) and a specificity of 99.59% (95% CI: 97.76-99.99%), with a positive agreement of 97.79% (95% CI: 95.84-98.98%) when compared to QIAGEN's column-based extraction method. There are clear benefits to using the SmartLid sample preparation kit: it enables swift extraction of viral nucleic acids, taking less than 5 min, without sacrificing significant accuracy when compared to more expensive and time-consuming alternatives currently available on the market. Moreover, its simplicity makes it particularly well-suited for the point-of-care where rapid results and portability are crucial. By providing an efficient and accessible means of nucleic acid extraction, our approach aims to introduce a step-change in diagnostic capabilities for resource-limited settings.

A computational framework to improve cross-platform implementation of transcriptomics signatures

The emergence of next-generation sequencing technologies and computational advances have expanded our understanding of gene expression regulation (i.e., the transcriptome). This has also led to an increased interest in using transcriptomic biomarkers to improve disease diagnosis and stratification, to assess prognosis and predict the response to treatment. Significant progress in identifying transcriptomic signatures for various clinical needs has been made, with large discovery studies accounting for challenges such as patient variability, unwanted batch effects, and data complexities; however, obstacles related to the technical aspects of cross-platform implementation still hinder the successful integration of transcriptomic technologies into standard diagnostic workflows. In this article, we discuss the challenges associated with integrating transcriptomic signatures derived using high-throughput technologies (such as RNA-sequencing) into clinical diagnostic tools using nucleic acid amplification (NAA) techniques. The novelty of the proposed approach lies in our aim to embed constraints related to cross-platform implementation in the process of signature discovery. These constraints could include technical limitations of amplification platform and chemistry, the maximal number of targets imposed by the chosen multiplexing strategy, and the genomic context of identified RNA biomarkers. Finally, we propose to build a computational framework that would integrate these constraints in combination with existing statistical and machine learning models used for signature identification. We envision that this could accelerate the integration of RNA signatures discovered by high-throughput technologies into NAA-based approaches suitable for clinical applications.

Multiplex detection of bacterial pathogens by PCR/SERS assay

Bacterial infections are a leading cause of death globally. The detection of DNA sequences correlated to the causative pathogen has become a vital tool in medical diagnostics. In practice, PCR-based assays for the simultaneous detection of multiple pathogens currently rely on probe-based quantitative strategies that require expensive equipment but have limited sensitivity or multiplexing capabilities. Hence, novel approaches to address the limitations of the current gold standard methods are still in high demand. In this study, we propose a simple multiplex PCR/SERS assay for the simultaneous detection of four bacterial pathogens, namely P. aeruginosa, S. aureus, S. epidermidis, and M. smegmatis. Wherein, specific primers for amplifying each target gDNA were applied, followed by applying SERS nanotags functionalized with complementary DNA probes and Raman reporters for specific identification of the target bacterial pathogens. The PCR/SERS assay showed high specificity and sensitivity for genotyping bacterial pathogen gDNA, whereby as few as 100 copies of the target gDNA could be detected. With high sensitivity and the convenience of standard PCR amplification, the proposed assay shows great potential for the sensitive detection of multiple pathogen infections to aid clinical decision-making.

Deep learning enhanced multiplex detection of viable foodborne pathogens in digital microfluidic chip

Culture plating is worldwide accepted as the gold standard for quantifying viable foodborne pathogens. However, it is time-consuming (1-2 days) and requires specialized laboratory and personnel. This study reported a deep learning enhanced digital microfluidic platform for multiplex detection of viable foodborne pathogens. The new method used a Time-Lapse images driven EfficientNet-Transformer Network (TLENTNet) to type and quantify the bacteria through spatiotemporal features of bacterial growth and digital enumeration of bacterial culture. First, the bacterial sample was prepared with LB medium and injected into a pre-vacuumed microfluidic chip with an array of 800 microwells to encapsulate at most one bacterium in each well. Then, a programmed sliding microscopic platform was used to scan all microwells every 15 min, capturing time-lapse images of bacterial growth within each microwell. Finally, the TLENTNet was used to facilitate bacterial typing and quantification. Under optimal conditions, this platform was able to detect four bacterial species (S.typhimurium, E. coli O157:H7, S. aureus and B. cereus) with an average accuracy of 97.72% and a detection limit of 63 CFU/mL in 7 h.

Smart-Plexer: a breakthrough workflow for hybrid development of multiplex PCR assays

Developing multiplex PCR assays requires extensive experimental testing, the number of which exponentially increases by the number of multiplexed targets. Dedicated efforts must be devoted to the design of optimal multiplex assays ensuring specific and sensitive identification of multiple analytes in a single well reaction. Inspired by data-driven approaches, we reinvent the process of developing and designing multiplex assays using a hybrid, simple workflow, named Smart-Plexer, which couples empirical testing of singleplex assays and computer simulation to develop optimised multiplex combinations. The Smart-Plexer analyses kinetic inter-target distances between amplification curves to generate optimal multiplex PCR primer sets for accurate multi-pathogen identification. In this study, the Smart-Plexer method is applied and evaluated for seven respiratory infection target detection using an optimised multiplexed PCR assay. Single-channel multiplex assays, together with the recently published data-driven methodology, Amplification Curve Analysis (ACA), were demonstrated to be capable of classifying the presence of desired targets in a single test for seven common respiratory infection pathogens.

Analytical and diagnostic performance characteristics of reverse-transcriptase loop-mediated isothermal amplification assays for dengue virus serotypes 1-4: A scoping review to inform potential use in portable molecular diagnostic devices

Dengue is a mosquito-borne disease caused by dengue virus (DENV) serotypes 1-4 which affects 100-400 million adults and children each year. Reverse-transcriptase (RT) quantitative polymerase chain reaction (qPCR) assays are the current gold-standard in diagnosis and serotyping of infections, but their use in low-middle income countries (LMICs) has been limited by laboratory infrastructure requirements. Loop-mediated isothermal amplification (LAMP) assays do not require thermocycling equipment and therefore could potentially be deployed outside laboratories and/or miniaturised. This scoping literature review aimed to describe the analytical and diagnostic performance characteristics of previously developed serotype-specific dengue RT-LAMP assays and evaluate potential for use in portable molecular diagnostic devices. A literature search in Medline was conducted. Studies were included if they were listed before 4th May 2022 (no prior time limit set) and described the development of any serotype-specific DENV RT-LAMP assay ('original assays') or described the further evaluation, adaption or implementation of these assays. Technical features, analytical and diagnostic performance characteristics were collected for each assay. Eight original assays were identified. These were heterogenous in design and reporting. Assays' lower limit of detection (LLOD) and linear range of quantification were comparable to RT-qPCR (with lowest reported values 2.2x101 and 1.98x102 copies/ml, respectively, for studies which quantified target RNA copies) and analytical specificity was high. When evaluated, diagnostic performance was also high, though reference diagnostic criteria varied widely, prohibiting comparison between assays. Fourteen studies using previously described assays were identified, including those where reagents were lyophilised or 'printed' into microfluidic channels and where several novel detection methods were used. Serotype-specific DENV RT-LAMP assays are high-performing and have potential to be used in portable molecular diagnostic devices if they can be integrated with sample extraction and detection methods. Standardised reporting of assay validation and diagnostic accuracy studies would be beneficial.

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.