Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y Servicios de Alta Tecnología (INDICASAT AIP), Panama, Republic of Panama.
Centro de Neurociencias, INDICASAT AIP, Panama, Republic of Panama.
PLoS Negl Trop Dis. 2020 Oct 27;14(10):e0008849. doi: 10.1371/journal.pntd.0008849. eCollection 2020 Oct.
Matrix-assisted laser desorption/ionization (MALDI) time-of-flight mass spectrometry is an analytical method that detects macromolecules that can be used for proteomic fingerprinting and taxonomic identification in arthropods. The conventional MALDI approach uses fresh laboratory-reared arthropod specimens to build a reference mass spectra library with high-quality standards required to achieve reliable identification. However, this may not be possible to accomplish in some arthropod groups that are difficult to rear under laboratory conditions, or for which only alcohol preserved samples are available. Here, we generated MALDI mass spectra of highly abundant proteins from the legs of 18 Neotropical species of adult field-collected hard ticks, several of which had not been analyzed by mass spectrometry before. We then used their mass spectra as fingerprints to identify each tick species by applying machine learning and pattern recognition algorithms that combined unsupervised and supervised clustering approaches. Both Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) classification algorithms were able to identify spectra from different tick species, with LDA achieving the best performance when applied to field-collected specimens that did have an existing entry in a reference library of arthropod protein spectra. These findings contribute to the growing literature that ascertains mass spectrometry as a rapid and effective method to complement other well-established techniques for taxonomic identification of disease vectors, which is the first step to predict and manage arthropod-borne pathogens.
基质辅助激光解吸电离飞行时间质谱(MALDI-TOF MS)是一种分析方法,可用于检测可用于节肢动物蛋白质组指纹图谱和分类鉴定的大分子。传统的 MALDI 方法使用新鲜的实验室饲养的节肢动物标本来构建具有高质量标准的参考质谱文库,以实现可靠的鉴定。然而,在某些难以在实验室条件下饲养的节肢动物群中,或者仅可获得酒精保存样本的情况下,这可能无法实现。在这里,我们从野外采集的成年硬蜱的 18 种新热带物种的腿中生成了高度丰富的蛋白质的 MALDI 质谱,其中一些以前没有通过质谱分析过。然后,我们使用它们的质谱作为指纹,通过应用机器学习和模式识别算法来识别每个蜱种,这些算法结合了无监督和监督聚类方法。主成分分析(PCA)和线性判别分析(LDA)分类算法都能够识别来自不同蜱种的光谱,当应用于已经存在于节肢动物蛋白质光谱参考文库中的野外采集标本时,LDA 实现了最佳性能。这些发现有助于不断增加的文献,确定质谱是一种快速有效的方法,可补充其他经过充分验证的疾病载体分类鉴定技术,这是预测和管理节肢动物传播病原体的第一步。