# Multiplexed, universal probe-based rare variant detection with USE-PCR

**Authors:** John Alvarado, Lucien Jacky, Dominic Yurk, Aaron Aguiar, Paul Belitz, Jerrod J. Schwartz

PMC · DOI: 10.1038/s41598-025-08814-5 · Scientific Reports · 2025-07-04

## TL;DR

USE-PCR is a new PCR method that improves detection of rare genetic variants with high accuracy and scalability.

## Contribution

USE-PCR introduces universal probes and advanced encoding for accurate, multiplexed rare variant detection.

## Key findings

- USE-PCR achieves 92.6% accuracy at high template copy and 97.6% at low template copy.
- It enables simultaneous detection of 32 single nucleotide variants with up to 86.5% accuracy in cancer cell lines.
- The method shows linear correlation across platforms and a dynamic range of four orders of magnitude.

## Abstract

Polymerase chain reaction (PCR) is an essential tool in research and diagnostics but is limited by the number of resolvable targets, reliance on target-specific probes, and assay-specific data interpretation. To overcome these challenges, we introduce Universal Signal Encoding PCR (USE-PCR), a novel approach combining universal hydrolysis probes, amplitude modulation, multispectral encoding, and standardized analysis for robust, scalable target detection. Using 32 synthetic templates, USE-PCR demonstrates a mean target identification accuracy of 92.6% ± 10.7% at high template copy and 97.6% ± 4.4% at low template copy, with linear correlation coefficients of 0.99 across four dPCR platforms and a dynamic range spanning four orders of magnitude. Integrating USE-PCR with RNase H-based detection chemistry enables 32 single nucleotide variants to be called simultaneously with up to 86.5% accuracy in cancer cell lines. Together, these results position USE-PCR as a transformative platform for high throughput, multiplexed analyte detection with applications in research and clinical settings.

The online version contains supplementary material available at 10.1038/s41598-025-08814-5.

## Linked entities

- **Diseases:** cancer (MONDO:0004992)

## Full-text entities

- **Diseases:** cancer (MESH:D009369)

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12227673/full.md

## Figures

5 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12227673/full.md

## References

2 references — full list in the complete paper: https://tomesphere.com/paper/PMC12227673/full.md

---
Source: https://tomesphere.com/paper/PMC12227673