Bridging the Utility Gap Between MALDI-TOF and WGS for Affordable Outbreak Cluster Detection

Abstract

Rapid and accurate detection of emerging outbreak clusters can help contain the spread of diseases with epidemic potential. Among the available pathogen matching methods that can be used to support the task, whole genome sequencing (WGS) offers the highest discriminatory power but is expensive and time-consuming. On the other hand, Matrix-Assisted Laser Desorption Ionization–Time of Flight (MALDI-TOF) mass spectrometry is gaining attention for being a rapid and cost-effective, albeit less precise, alternative. In order to combine the strengths of both MALDI-TOF and WGS, we present MSMAP, the first machine learning framework that establishes a mapping between MALDI-TOF mass spectra and the single nucleotide polymorphism (SNP) distances obtained from WGS analysis. We demonstrate the effectiveness of MSMAP in retrieving WGS-defined outbreak clusters on synthetic mass spectrum data and on proprietary data with paired MALDI-TOF and SNP information. The results show that MSMAP augments MALDI-TOF with the discriminatory power of WGS, thus bridging their utility gap and paving the way toward fast, accurate and cost-effective outbreak cluster detection.