five

Molecular identification of Biomphalaria snails.

收藏
Figshare2026-03-24 更新2026-04-28 收录
下载链接:
https://figshare.com/articles/dataset/_p_Molecular_identification_of_i_Biomphalaria_i_snails_p_/31846215
下载链接
链接失效反馈
官方服务:
资源简介:
This study explores the use of Matrix-Assisted Laser Desorption/Ionization Time-of-Flight mass spectrometry (MALDI-TOF MS) to identify and differentiate Biomphalaria snails infected with the parasite S. mansoni, which causes schistosomiasis. The study was conducted on two snail species, Biomphalaria pfeifferi (collected in the field in Senegal) and Biomphalaria glabrata (a laboratory strain). The snails were infected in the laboratory with S. mansoni miracidia, and their infection was confirmed by cercariae emission tests and quantitative PCR (qPCR). MALDI-TOF MS was then used to analyse proteins from infected and uninfected snails to identify spectral differences. Based on protein profiles, the results of MALDI-TOF mass spectrometry made it possible to accurately differentiate between S. mansoni-infected snails and uninfected snails. An increase in the number of peaks detected and their intensity was observed for the spectra of S. mansoni-infected snails compared to uninfected snails. The application of principal component analysis (PCA) to these mass spectrometry profiles confirmed the discrimination between the two groups according to their infection status. In addition, specific discriminating peaks were identified for each snail species, allowing for the distinction of infected from uninfected snails. The present study revealed, for the first time, that MALDI-TOF MS appears to be a rapid, reliable, and specific tool for the diagnosis of schistosomiasis in snails, offering promising prospects for the surveillance and control of this disease in endemic areas. However, further work is needed to establish a MALDI-TOF MS reference spectra database specific to Schistosoma parasites and to standardise sample collection, storage, and preparation in order to apply this technique in the field.
创建时间:
2026-03-24
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作