A dual-sensing thermo-chemical ISFET array for DNA-based diagnostics.

This paper presents a 32x32 ISFET array with in-pixel dual-sensing and programmability targeted for on-chip DNA amplification detection. The pixel architecture provides thermal and chemical sensing by encoding temperature and ion activity in a single output PWM, modulating its frequency and its duty cycle respectively. Each pixel is composed of an ISFET-based differential linear OTA and a 2-stage sawtooth oscillator. The operating point and characteristic response of the pixel can be programmed, enabling trapped charge compensation and enhancing the versatility and adaptability of the architecture. Fabricated in 0.18 μm standard CMOS process, the system demonstrates a quadratic thermal response and a highly linear pH sensitivity, with a trapped charge compensation scheme able to calibrate 99.5% of the pixels in the target range, achieving a homogeneous response across the array. Furthermore, the sensing scheme is robust against process variations and can operate under various supply conditions. Finally, the architecture suitability for on-chip DNA amplification detection is proven by performing Loop-mediated Isothermal Amplification (LAMP) of phage lambda DNA, obtaining a time-to-positive of 7.71 minutes with results comparable to commercial qPCR instruments. This architecture represents the first in-pixel dual thermo-chemical sensing in ISFET arrays for Lab-on-a-Chip diagnostics

Amplification curve analysis: Data-driven multiplexing using real-time digital PCR

Information about the kinetics of PCR reactions are 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 realtime 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 co-amplification 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 towards 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

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