Front Microbiol. 2026 Mar 9;17:1782552. doi: 10.3389/fmicb.2026.1782552. eCollection 2026.
ABSTRACT
Current subtyping methods are often restricted by labor intensity and high costs. To address this, this study integrated matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) with whole-genome sequencing (WGS) to characterize Salmonella isolates and investigate the correlation between spectral features and genomic data. Between 2023 and 2024, 96 Salmonella isolates from Yixing, Jiangsu Province, China, underwent serotyping, WGS, and MALDI-TOF MS profiling. Serotyping and Multilocus Sequence Typing (MLST) analysis resolved 25 serovars and 21 sequence types. Machine learning models based on spectral features achieved area under the curve (AUC) values exceeding 0.90 for Salmonella typhimurium, ST11, ST155, ST19, and ST34. Specific discriminatory mass peaks were identified, and their correlations with genomic annotations were investigated through peak-gene co-occurrence analysis. The findings indicate that discriminatory MALDI-TOF MS peaks can serve as statistical indicators for specific genomic features, reflecting underlying genomic differences. This study proposes a machine learning-based classification strategy that enables rapid analysis of MALDI-TOF MS spectra in routine diagnostics, thereby extending the application of mass spectrometry in Salmonella subtyping. This strategy functions as a high-throughput pre-filter to concentrate WGS efforts on high-risk clones for accelerated outbreak investigation.
PMID:41878745 | PMC:PMC13006217 | DOI:10.3389/fmicb.2026.1782552
