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EntoSieve:昆虫大量样本的自动大小分选,以助力准确的宏条形码和元条形码分析。

EntoSieve: Automated Size-Sorting of Insect Bulk Samples to Aid Accurate Megabarcoding and Metabarcoding.

作者信息

Ascenzi Aleida, Wührl Lorenz, Feng Vivian, Klug Nathalie, Pylatiuk Christian, Cerretti Pierfilippo, Meier Rudolf

机构信息

Department of Biology and Biotechnologies "Charles Darwin", Sapienza University of Rome, Rome, Italy.

Museum of Zoology (MZUR), Sapienza University of Rome, Rome, Italy.

出版信息

Mol Ecol Resour. 2025 Aug;25(6):e14097. doi: 10.1111/1755-0998.14097. Epub 2025 Mar 11.

Abstract

Widespread insect decline necessitates the development and use of standardized protocols for regular monitoring. These methods have to be rapid, efficient and cost-effective to allow for large-scale implementation. Many insect sampling and molecular methods have been developed. These include Malaise trapping, high-throughput DNA barcoding ('megabarcoding') and metabarcoding. The latter allows for assessing the species diversity in whole samples using few steps, but sample heterogeneity in terms of body size remains a challenge since large insects contribute disproportionately more mtDNA than small ones. This can potentially overwhelm the template DNA from small species that then go undetected. Size-sorting can mitigate this problem, but no satisfying automated, rapid and non-destructive solutions are available. We introduce the EntoSieve, a low-cost and DIY motorized instrument that disentangles and sorts abundant insect bulk samples into several body size fractions while minimizing damage to specimens, thus reducing the risk of DNA contamination across size fractions (e.g. legs of large specimens in small body size fraction). EntoSieve utilizes readily available components, 3D-printed parts and customizable meshes, thus enabling parallelization at low cost. We here show the efficiency of the EntoSieve for three samples with more than 10,000 specimens using three sieving protocols and assess the impact on specimen integrity. Efficiency ranged from 92% to 99%, achieved within 18-60 min, and specimen damage was not significant for subsamples. By facilitating rapid pre-processing, the device contributes to producing morphologically valuable vouchers for megabarcoding and is likely to improve compositional diversity accuracy across size classes when using metabarcoding.

摘要

昆虫数量的普遍减少使得制定和使用标准化的定期监测方案成为必要。这些方法必须快速、高效且具有成本效益,以便能够大规模实施。已经开发了许多昆虫采样和分子方法。这些方法包括马氏网诱捕、高通量DNA条形码技术(“宏条形码技术”)和元条形码技术。后者只需几步就能评估整个样本中的物种多样性,但由于大型昆虫贡献的线粒体DNA比小型昆虫多得多,样本在体型方面的异质性仍然是一个挑战。这可能会使小型物种的模板DNA被大量掩盖,从而导致这些物种未被检测到。大小分选可以缓解这个问题,但目前还没有令人满意的自动化、快速且无损的解决方案。我们推出了EntoSieve,这是一种低成本的自制电动仪器,它可以将大量丰富的昆虫样本解开并分成几个体型部分,同时将对标本的损害降至最低,从而降低不同体型部分之间DNA污染的风险(例如,小体型部分中大型标本的腿)。EntoSieve使用现成的组件、3D打印部件和可定制的筛网,从而能够低成本地实现并行化。我们在此展示了EntoSieve使用三种筛分方案对三个包含超过10000个标本的样本的处理效率,并评估了对标本完整性的影响。效率在92%至99%之间,在18 - 60分钟内完成,且子样本的标本损害不显著。通过促进快速预处理,该设备有助于为宏条形码技术生成形态学上有价值的凭证,并且在使用元条形码技术时可能会提高不同体型类别的组成多样性准确性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bd45/12225705/2c04e624a66d/MEN-25-e14097-g001.jpg

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