LAP-MALDI MS coupled with machine learning: an ambient mass spectrometry approach for high-throughput diagnostics

Area of Science:

• Biotechnology
• Analytical Chemistry
• Veterinary Diagnostics

Background:

• Early disease detection is crucial for effective diagnostics and public health.
• Mass spectrometry (MS), particularly MALDI MS profiling, has potential but limited success in early disease detection.
• Existing methods often lack speed, cost-effectiveness, and adaptability for large-scale screening.

Purpose of the Study:

• To introduce liquid atmospheric pressure MALDI MS (LAP-MALDI MS) profiling as a novel platform for rapid, large-scale, and cost-effective disease analysis.
• To demonstrate the platform's utility in early disease diagnosis and monitoring therapeutic response.
• To showcase its application in detecting bovine mastitis and antimicrobial resistance (AMR).

Main Methods:

• Development and application of a liquid atmospheric pressure MALDI MS (LAP-MALDI MS) profiling technique.
• Analysis of milk samples from dairy cows for pre-clinical disease detection.
• Utilizing the same MS setup and sample preparation for both disease diagnosis and AMR detection.

Main Results:

• LAP-MALDI MS profiling detected bovine mastitis two days before clinical signs, with up to 70% sensitivity and 100% specificity.
• The study involved weekly analysis of milk samples from approximately 500 cows over six months.
• The platform demonstrated potential for antimicrobial resistance (AMR) detection using a similar methodology.

Conclusions:

• LAP-MALDI MS profiling is a viable, rapid, and cost-effective platform for large-scale disease screening.
• The technology enables pre-symptomatic detection of diseases like bovine mastitis.
• This platform shows promise for future applications in diagnostics, including AMR detection.