Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry

• M. sibonii and M. morganii play an underexplored role in clinical microbiology. • Random Forest enhances MALDI-TOF to distinguish M. morganii and M. sibonii . • Machine learning approach can be integrated into routine laboratory workflows. • Tool for species identification: https://bigbiomed.shinyapps.io/MorgaMSclassifR . The genus Morganella, including clinically isolated species M. sibonii and M. morganii , has a still underexplored role in clinical microbiology. Despite the clinical relevance of Morganella spp., current MALDI-TOF commercial systems fail to differentiate these species. Whole genome sequencing (WGS) remains the most effective method to distinguish species. However, this method is not adapted for routine lab workflow. Enhancing MALDI-TOF’s accuracy could make it a rapid and effective approach for distinguishing Morganella species in routine laboratory diagnostics. This study aims to improve the performance of MALDI-TOF for identifying Morganella spp. using WGS as the gold-standard reference method. We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. Whole genome sequencing was used to characterize these strains and perform phylogenetic analysis, categorizing 209 strains as M. morganii and 26 as M. sibonii . The ML-based classifiers showed improved identification accuracy (44 of the 160 designed with accuracy at 1). Also, MS analysis identified 11 peaks able to discriminate between M. morganii and M. sibonii . Through development of a publicly-available online ML-based classifier, this study has improved the capacity of MALDI-TOF for distinguishing Morganella spp . , providing a reliable, user-friendly solution suited to routine clinical diagnostics and supporting a better understanding of the roles of M. morganii and M. sibonii in human pathology.