Improving bacteria identification from digital melt assay via oligonucleotide-based temperature calibration

Area of Science:

• Molecular Diagnostics
• Microbiology
• Bioengineering

Background:

• Polymicrobial bacterial infections pose a significant global health challenge.
• Accurate identification of all causative species requires advanced diagnostic tools.
• Digital melt analysis, a microfluidic chip-based digital PCR with high-resolution melt (HRM), shows promise but faces challenges with temperature uniformity.

Purpose of the Study:

• To develop a novel temperature calibration method for digital melt analysis.
• To address temperature nonuniformities across digital chips that hinder accurate species identification.
• To enhance the diagnostic capabilities of digital melt for polymicrobial infections.

Main Methods:

• Introduced a synthetic DNA fragment with a known melting temperature as a calibrator.
• Incorporated the calibrator into a digital melt assay targeting the 16S V1-V6 region.
• Utilized machine learning for mixture composition prediction to assess calibration's impact.

Main Results:

• The calibrator generated visible melt curves across the chip, enabling alignment and correction of temperature-induced nonuniformities.
• Improved uniformity of digital melt curves for three bacterial species.
• Significantly enhanced mixture composition prediction accuracy for polymicrobial samples.

Conclusions:

• This work presents the first DNA calibrator-supplemented assay for nanoarray digital melt.
• The calibration method effectively reduces melt curve variability and improves identification accuracy.
• This approach holds potential for advancing diagnostics of polymicrobial infections and other diseases.