MYCOLab Ai Logo - gold and army green

Speciation analysis of fungi by liquid atmospheric pressure MALDI mass spectrometry

Summary

Scientists developed a fast new method using a technique called LAP-MALDI mass spectrometry to identify dangerous fungal infections within minutes instead of days. The method analyzes the unique fatty acids and proteins in fungal cells to distinguish between different species. This could help doctors quickly identify which fungal infection a patient has and choose the right treatment, potentially saving lives.

Background

Fungal pathogens pose a growing threat to global health, with invasive fungal infections contributing to approximately 3.8 million deaths annually. Traditional fungal identification methods based on microscopy, culture morphology, and biochemical assays are slow, requiring 1-3 days, and limited by phenotypic overlap between species. Molecular approaches like PCR are time-consuming and require specialized equipment.

Objective

To demonstrate the application of liquid atmospheric pressure MALDI (LAP-MALDI) mass spectrometry for rapid fungal identification through simultaneous analysis of lipid and protein profiles. The study applies LAP-MALDI to Candida albicans and Saccharomyces cerevisiae to establish species-specific biomarkers and proteoform characterization.

Results

Species-specific lipid profiles were observed in the m/z 600-1100 range, dominated by phospholipids including phosphatidylcholines (PCs), phosphatidylethanolamines (PEs), and phosphatidylinositols (PIs). C. albicans was identified by the mature WHS11 protein (7 kDa) with 100% sequence coverage showing N-terminal methionine cleavage and acetylation. S. cerevisiae was identified by glyceraldehyde-3-phosphate dehydrogenase 3 and HSP12 protein (11.5 kDa), both species-specific.

Conclusion

LAP-MALDI MS demonstrates significant potential for rapid fungal classification through combined lipid and protein profiling, offering substantial improvements over conventional MALDI biotyping. The ability to perform top-down MS/MS proteoform sequencing without requiring machine learning-based predictive models provides detailed pathogen characterization. Future work will expand the fungal species panel and optimize workflows for clinical implementation.
  • Published in:Analytical and Bioanalytical Chemistry,
  • Study Type:Proof-of-concept experimental study,
  • Source: 10.1007/s00216-025-06094-6, PMID: 40911099
Scroll to Top