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

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

Ngôn ngữ: eng

Ký hiệu phân loại:

Thông tin xuất bản: England : Molecular ecology resources , 2025

Mô tả vật lý:

Bộ sưu tập: NCBI

ID: 687696

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.
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