Significance of the Science

Lyme disease is the most common tick-borne illness in North America and Europe, and cases are rising as ticks spread to new regions. Early diagnosis is critical, but the current laboratory method is a slow two-step process that often misses early infections. There is an urgent need for a rapid, accurate test that can be used at the point of care or even in resource-limited settings. This work introduces a single-tier, paper-based diagnostic test that can detect Lyme disease antibodies quickly and reliably, with performance comparable to the current gold-standard multi-step testing.

Highlights

• Introduces a peptide-based, single-tier vertical flow assay (xVFA) for Lyme disease

• Provides results in under 20 minutes using only a drop of blood serum

• Uses machine learning to combine up to 25 multiplexed immunoreaction signals for high accuracy

• Demonstrates 95.5% sensitivity and 100% specificity in blinded validation studies

• Enables low-cost, point-of-care or at-home testing with smartphone readout

Summary

The diagnosis of Lyme disease has traditionally relied on a two-step testing process that is time-consuming, expensive, and limited in its ability to detect early cases. To overcome these limitations, researchers developed a synthetic peptide-based vertical flow assay (xVFA) that detects IgM and IgG antibodies against Borrelia burgdorferi, the bacteria responsible for Lyme disease. This paper-based test integrates a multiplexed panel of 9 antigen peptides with a smartphone-based reader and machine learning interpretation, delivering results in less than 20 minutes. Blinded validation using samples from the Lyme Disease Biobank and the CDC demonstrated a sensitivity of 95.5% and a specificity of 100%, accurately distinguishing Lyme disease from other similar conditions. The xVFA represents a low-cost, portable diagnostic tool that could replace the current two-tier standard, enabling earlier treatment, improved surveillance, and broader access to reliable testing.

Read more (open access): https://www.nature.com/articles/s41467-024-51067-5