Pazmiño-Betancourth Mauro, Casas Gómez-Uribarri Ivan, Mondragon-Shem Karina, Babayan Simon A, Baldini Francesco, Rafuse Haines Lee
School of Biodiversity, One Health and Veterinary Medicine, University of Glasgow, G12 8QQ, Glasgow, United Kingdom.
Department of Vector Biology, Liverpool School of Tropical Medicine, L3 5QA, Liverpool, United Kingdom.
Biol Methods Protoc. 2024 Aug 17;9(1):bpae058. doi: 10.1093/biomethods/bpae058. eCollection 2024.
Tsetse are the insects responsible for transmitting African trypanosomes, which cause sleeping sickness in humans and animal trypanosomiasis in wildlife and livestock. Knowing the age of these flies is important when assessing the effectiveness of vector control programs and modelling disease risk. Current methods to assess fly age are, however, labour-intensive, slow, and often inaccurate as skilled personnel are in short supply. Mid-infrared spectroscopy (MIRS), a fast and cost-effective tool to accurately estimate several biological traits of insects, offers a promising alternative. This is achieved by characterising the biochemical composition of the insect cuticle using infrared light coupled with machine-learning (ML) algorithms to estimate the traits of interest. We tested the performance of MIRS in estimating tsetse sex and age for the first-time using spectra obtained from their cuticle. We used 541 insectary-reared of two different age groups for males (5 and 7 weeks) and three age groups for females (3 days, 5 weeks, and 7 weeks). Spectra were collected from the head, thorax, and abdomen of each sample. ML models differentiated between male and female flies with a 96% accuracy and predicted the age group with 94% and 87% accuracy for males and females, respectively. The key infrared regions important for discriminating sex and age classification were characteristic of lipid and protein content. Our results support the use of MIRS as a rapid and accurate way to identify tsetse sex and age with minimal pre-processing. Further validation using wild-caught tsetse could pave the way for this technique to be implemented as a routine surveillance tool in vector control programmes.
采采蝇是传播非洲锥虫的昆虫，非洲锥虫可导致人类昏睡病以及野生动物和家畜的动物锥虫病。在评估病媒控制项目的有效性和对疾病风险进行建模时，了解这些苍蝇的年龄很重要。然而，目前评估苍蝇年龄的方法劳动强度大、速度慢，而且由于缺乏技术人员，往往不准确。中红外光谱（MIRS）是一种快速且经济高效的工具，可准确估计昆虫的几种生物学特征，提供了一种很有前景的替代方法。这是通过使用红外光结合机器学习（ML）算法来表征昆虫角质层的生化成分，以估计感兴趣的特征来实现的。我们首次使用从采采蝇角质层获得的光谱测试了MIRS在估计采采蝇性别和年龄方面的性能。我们使用了541只在昆虫饲养室饲养的采采蝇，雄性分为两个不同年龄组（5周和7周），雌性分为三个年龄组（3天、5周和7周）。从每个样本的头部、胸部和腹部收集光谱。ML模型区分雄性和雌性采采蝇的准确率为96%，预测雄性和雌性年龄组的准确率分别为94%和87%。区分性别和年龄分类的关键红外区域是脂质和蛋白质含量的特征。我们的结果支持使用MIRS作为一种快速准确识别采采蝇性别和年龄的方法，且预处理最少。使用野外捕获的采采蝇进行进一步验证可为该技术作为病媒控制项目中的常规监测工具的实施铺平道路。
