Rapid identification of medically important mosquitoes by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry

Abstract Background Accurate and rapid identification of dipteran vectors is integral for entomological surveys and is a vital component of control programs for mosquito-borne diseases. Conventionally, morphological features are used for mosquito identification, which suffer from biological and geographical variations and lack of standardization. We used matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) for protein profiling of mosquito species from North India with the aim of creating a MALDI-TOF MS database and evaluating it. Methods Mosquito larvae were collected from different rural and urban areas and reared to adult stages. The adult mosquitoes of four medically important genera, Anopheles, Aedes, Culex and Armigerus, were morphologically identified to the species level and confirmed by ITS2-specific PCR sequencing. The cephalothoraces of the adult specimens were subjected to MALDI-TOF analysis and the signature peak spectra were selected for creation of database, which was then evaluated to identify 60 blinded mosquito specimens. Results Reproducible MALDI-TOF MS spectra spanning over 2–14 kDa m/z range were produced for nine mosquito species: Anopheles (An. stephensi, An. culicifacies and An. annularis); Aedes (Ae. aegypti and Ae. albopictus); Culex (Cx. quinquefasciatus, Cx. vishnui and Cx. tritaenorhynchus); and Armigerus (Ar. subalbatus). Genus- and species-specific peaks were identified to create the database and a score of > 1.8 was used to denote reliable identification. The average numbers of peaks obtained were 55–60 for Anopheles, 80–100 for Aedes, 30–60 for Culex and 45–50 peaks for Armigeres species. Of the 60 coded samples, 58 (96.67%) were correctly identified by MALDI-TOF MS with a score > 1.8, while there were two unreliable identifications (both Cx. quinquefasciatus with scores < 1.8). Conclusions MALDI-TOF MS appears to be a pragmatic technique for accurate and rapid identification of mosquito species. The database needs to be expanded to include species from different geographical regions and also different life-cycle stages to fully harness the technique for entomological surveillance programs.