Kreitmann, L. | Search bioRxiv

bioRxiv2026

Stoichiometric Encoding of Amplification Kinetics Enables High-Level Multiplexing in Single-Channel TaqMan Real-Time PCR

Multiplexing in TaqMan real-time PCR (qPCR) is limited to four-six targets per reaction by fluorophore spectral overlap. Amplification curve analysis (ACA) can discriminate several targets within one fluorescence channel from amplification curves (AC) kinetic features, but has been confined mainly to digital qPCR (dqPCR) and is sensitive to domain shifts between instruments and specimen types. Here, we co-design biochemical encoding of target-specific information into ACs and their computational decoding to achieve single-channel 8-plex TaqMan qPCR on a standard thermocycler. Using asymmetric PCR, we show that forward primer, reverse primer and probe concentrations act as a programmable encoding mechanism, generating a 27-condition design space of reproducible non-canonical ACs, conserved from digital to bulk qPCR. Applying this stoichiometric encoding to an 8-plex central nervous system pathogens panel increased inter-target separability, raising the mean silhouette score from 0.329 to 0.711. To decode ACs without labelled target-domain bulk qPCR data, we pretrained a transformer encoder by self-supervised learning on more than 170,000 unlabelled dqPCR curves and aligned source and target domains using conditional domain adversarial training. On contrived clinical specimens, the framework classified a canonical 7-plex respiratory virus panel with 83.6% accuracy (versus 67.7% for a supervised baseline) and the encoded 8-plex panel with 89.8% accuracy. Furthermore, stoichiometric encoding enabled resolution of double- and triple-target co-amplification events with 98.7% accuracy in controlled dqPCR experiments. This approach expands the information capacity of existing qPCR instrumentation without new hardware, fluorophores or oligonucleotides, offering a low-cost route to syndromic molecular diagnostics.

molecular biology↗

bioRxiv2022

An adaptive filtering framework for non-specific and inefficient reactions in multiplex digital PCR based on sigmoidal trends

Real-time digital PCR (qdPCR) coupled with artificial intelligence has shown the potential of unlocking scientific breakthroughs, particularly in the field of molecular diagnostics for infectious diseases. One of the most promising applications is the use of machine learning (ML) methods to enable single fluorescent channel PCR multiplex by extracting target-specific kinetic and thermodynamic information contained in amplification curves. However, the robustness of such methods can be affected by the presence of undesired amplification events and nonideal reaction conditions. Therefore, here we proposed a novel framework to filter non-specific 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 ML-based Amplification Curve Analysis (ACA), using available data from a previous publication where the ACA method was used to screen carbapenemase-producing organisms in clinical isolates. Furthermore, we developed a novel strategy, named Adaptive Mapping Filter (AMF), to consider the variability of positive counts in digital PCR. Over 152,000 amplification events were analyzed. For the positive reactions, filtered and unfiltered amplification curves were evaluated by comparing against melting peak distribution, proving that abnormalities (filtered out data) are linked to shifted melting distribution or decreased PCR efficiency. The ACA was applied to compare classification accuracies before and after AMF, showing an improved sensitivity of 1.18% for inliers and 20% for outliers (p-value < 0.0001). This work explores the correlation between kinetics of amplification curves and thermodynamics of melting curves and it demonstrates that filtering out non-specific or low efficient reactions can significantly improve the classification accuracy for cutting edge multiplexing methodologies.

molecular biology↗