Improving Baseline Subtraction for Increased Sensitivity of Quantitative PCR Measurements

TL;DR

This paper introduces an optimization-based algorithm that improves baseline subtraction in qPCR measurements, significantly increasing sensitivity and accuracy for viral DNA detection, which is crucial for COVID-19 diagnostics.

Contribution

The paper presents a novel optimization method utilizing control experiments to enhance baseline subtraction in qPCR, leading to tenfold sensitivity improvements over standard methods.

Findings

Achieved up to a tenfold increase in sensitivity.

Enhanced detection of late-cycle amplification signals.

Reduced false-negative rates in viral DNA screening.

Abstract

Motivated by the current COVID-19 health-crisis, we examine the task of baseline subtraction for quantitative polymerase chain-reaction (qPCR) measurements. In particular, we present an algorithm that leverages information obtained from non-template and/or DNA extraction-control experiments to remove systematic bias from amplification curves. We recast this problem in terms of mathematical optimization, i.e. by finding the amount of control signal that, when subtracted from an amplification curve, minimizes background noise. We demonstrate that this approach can yield a decade improvement in sensitivity relative to standard approaches, especially for data exhibiting late-cycle amplification. Critically, this increased sensitivity and accuracy promises more effective screening of viral DNA and a reduction in the rate of false-negatives in diagnostic settings.