Droplet Isothermal Amplification For Nucleic Acid Quantification

Abstract

Nucleic acid quantification (NAQ) is extensively employed for gene expression analysis, monitoring viral loads, detecting rare or dysfunctional cells, and assessing treatment regimes. The gold standard, quantitative polymerase chain reaction (qPCR), and the recent alternative, droplet digital PCR (ddPCR), provide accurate quantification of nucleic acids (NA). Albeit the requirement of thermal cycling and separate platforms for droplet generation, NA amplification, and signal detection, in the case of ddPCR increases the assay complexity and time, limiting its broad applicability. In this work, we have developed an integrated droplet isothermal amplification-based NAQ (idNAQ) platform that enables facile and fast NAQ with a large dynamic range. First, we adapted the isothermal amplification method, Recombinase Polymerase Amplification (RPA), for NAQ. We demonstrate a fast (• 40 minutes) semi-quantitative RPA (qRPA) assay with the endpoint intensity ratio (EIR) for DNA quantification with a 6-log order range. Since the EIR model estimates the amplicon levels at the end of the reaction, real-time monitoring of the amplification reaction (unlike in the case of qPCR) is no longer required. With qRPA, we demonstrate viral load detection from the serum of dengue-infected patients with comparable performance to qPCR. The later section discusses the translation of the qRPA NAQ to a microfluidic droplet format. Droplet RPA (dRPA) displays similar kinetics to the bulk reaction suggesting successful optimization of droplet conditions for RPA. dRPA in the low concentration regime follows Poisson distribution that enables digital quantification as in the case of ddPCR. On the other hand, at a higher starting concentration of DNA (non-digital regime, >10 DNA per droplet), the RPA amplification in droplets exhibits heterogeneous intensity puncta due to rapid amplification and incomplete mixing leading to the formation of DNA ‘amplification globules’. We use a supervised machine, learning-based regression model with these intensity features as inputs to accurately predict the target concentration of up to 10^5 molecules per droplet. Combining these two modalities of dRPA yields a dynamic range of >7 log orders of concentration that are comparable to qPCR. Finally, we demonstrate the successful integration of all unit operations onto a single microfluidic device for droplet RPA and quantification. Different microfluidic designs were optimized for monodisperse droplet generation and image acquisition from a large incubation area that allowed the successful implementation of quantitative dRPA in a single device.