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

Abstract

Background Schistosomiasis is a major neglected tropical disease that afects 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-fight (MALDI-TOF) mass spectrometry (MS) has become the gold standard diagnostic method for the identifcation of bacteria and fungi in many microbiological laboratories. Preliminary studies have recently shown promising results for parasite identifcation using this method. The aims of this study were to develop and validate a species-specifc database for adult Schistosoma identifcation, and to evaluate the efects of diferent storage solutions (ethanol and RNAlater) on spectra profles. Methods Adult worms (males and females) of S. mansoni and S. japonicum were obtained from experimentally infected mice. Species identifcation 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 identifcation and diferentiation of the Schistosoma species. Results All the spectra were correctly identifed by internal validation. For external validation, 58 new Schistosoma samples were analyzed, of which 100% (58/58) were correctly identifed to genus level (log score values≥1.7) and 81% (47/58) were reliably identifed to species level (log score values≥2). The spectra profles showed some diferences depending on the storage solution used. All the machine learning algorithms classifed the samples correctly. Conclusions MALDI-TOF MS can reliably distinguish adult S. mansoni from S. japonicum.