Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for differential identification of adult Schistosoma worms

Ebersbach, J. C. and Sato, M. O. and de Araújo, M. P. and Sato, M. and Becker, S. L. and Sy, I.. (2023) Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry for differential identification of adult Schistosoma worms. Parasit Vectors, 16. p. 20.

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BACKGROUND: Schistosomiasis is a major neglected tropical disease that affects up to 250 million individuals worldwide. The diagnosis of human schistosomiasis is mainly based on the microscopic detection of the parasite's eggs in the feces (i.e., for Schistosoma mansoni or Schistosoma japonicum) or urine (i.e., for Schistosoma haematobium) samples. However, these techniques have limited sensitivity, and microscopic expertise is waning outside endemic areas. Matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) mass spectrometry (MS) has become the gold standard diagnostic method for the identification of bacteria and fungi in many microbiological laboratories. Preliminary studies have recently shown promising results for parasite identification using this method. The aims of this study were to develop and validate a species-specific database for adult Schistosoma identification, and to evaluate the effects of different storage solutions (ethanol and RNAlater) on spectra profiles. METHODS: Adult worms (males and females) of S. mansoni and S. japonicum were obtained from experimentally infected mice. Species identification was carried out morphologically and by cytochrome oxidase 1 gene sequencing. Reference protein spectra for the creation of an in-house MALDI-TOF MS database were generated, and the database evaluated using new samples. We employed unsupervised (principal component analysis) and supervised (support vector machine, k-nearest neighbor, Random Forest, and partial least squares discriminant analysis) machine learning algorithms for the identification and differentiation of the Schistosoma species. RESULTS: All the spectra were correctly identified by internal validation. For external validation, 58 new Schistosoma samples were analyzed, of which 100% (58/58) were correctly identified to genus level (log score values >/= 1.7) and 81% (47/58) were reliably identified to species level (log score values >/= 2). The spectra profiles showed some differences depending on the storage solution used. All the machine learning algorithms classified the samples correctly. CONCLUSIONS: MALDI-TOF MS can reliably distinguish adult S. mansoni from S. japonicum.
Faculties and Departments:09 Associated Institutions > Swiss Tropical and Public Health Institute (Swiss TPH)
UniBasel Contributors:Becker, Sören Leif
Item Type:Article, refereed
Article Subtype:Research Article
ISSN:1756-3305 (Electronic)1756-3305 (Linking)
Note:Publication type according to Uni Basel Research Database: Journal article
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Last Modified:13 Feb 2023 07:55
Deposited On:13 Feb 2023 07:55

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