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

Abstract

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.

Coupling machine learning and high throughput multiplex digital PCR enables accurate detection of carbapenem-resistant genes in clinical isolates

“…Generally, digital PCR offers a number of clear advantages compared to qPCR, representing an attractive alternative tool for molecular diagnostics (24). Due to its high throughput and the single molecule environment, dPCR is extremely suitable for machine learning based algorithm and high-level multiplex assays, which further increases the diagnostic capabilities and enables multiple infection detection (14). Main disadvantages of dPCR are related to the complexity in the experimental setup and the slower turn-around time.…”

“…The proposed method, referred to as AMCA, trains a supervised machine learning model to combine the predictions of ACA and MCA as reported in previous studies from Moniri et al (1,14). In this study, the MCA method consists of applying a logistic regression model to melting T m values extracted from each melting curve.…”

“…Recently, our group has demonstrated that the large volume of data obtained from real-time digital PCR (dPCR) instruments can be exploited to perform data-driven multiplexing in a single fluorescent channel, reporting a 99.28% accuracy of target identification when using synthetic DNA in a 9-plex format (1). The ML method used, called AMCA, leverage kinetic and thermodynamic information encoded in the amplification and melting curves, to perform target identification in multiplexed environments (14,15,16). Here we evaluate, for the first time, the performance of the AMCA methodology for detection of the most common types of serine-beta-lactamase ( bla KPC and bla OXA-48 ) and metallo-beta-lactamase ( bla IMP , bla VIM and bla NDM ) in clinical isolates (17,18).…”

“…This result represented an increase of 10% over using melting curve analysis, indicative of the potential benefits of this methodology for diagnostic and screening applications. The ML method used (AMCA) leverages kinetic and thermodynamic information encoded in the amplification and melting curves to perform target identification in multiplexed environments (Moniri et al, 2019, 2020b; Rodriguez-Manzano et al, 2019). Here we evaluate, for the first time, the analytical performance of AMCA method compared to Xpert Carba-R Cepheid and Resist-3 O.K.N assays when tested on clinical isolates for detection of the most common types of serine-beta-lactamases ( bla KPC and bla OXA-48 ) and metallo-beta-lactamases ( bla IMP , bla VIM and bla NDM ) (Maurer et al, 2015; Lim et al, 2018).…”

Determination of Advantages and Limitations of qPCR Duplexing in a Single Fluorescent Channel

“…Recently, a novel single fluorescent channel-based multiplexing method was proposed using intercalating dyes, and the result was processed based on multidimensional standard curves. 30 − 32 Moreover, the data set was achieved by commercial qPCR instruments, and the absolute quantification was determined, thus extending the use of these devices.…”