Literature DB >> 30869628

An Automated Microfluidic System for Morphological Measurement and Size-Based Sorting of C. Elegans.

Xianke Dong, Pengfei Song, Xinyu Liu.   

Abstract

This paper reports a vision-based automated microfluidic system for morphological measurement and size-based sorting of the nematode worm C. elegans. Exceeding the capabilities of conventional worm sorting microfluidic devices purely relying on passive sorting mechanisms, our system is capable of accurate measurement of the worm length/width and active sorting of worms with the desired sizes from a mixture of worms with different body sizes. This function is realized based on the combination of real-time, vision-based worm detection and sizing algorithms and automated on-chip worm manipulation. A double-layer microfluidic device with computer-controlled pneumatic valves is developed for sequential loading, trapping, vision-based sizing, and sorting of single worms. To keep the system operation robust, vision-based algorithms on detecting multi-worm loading and worm sizing failure have also been developed. We conducted sorting experiments on 319 worms and achieved an average sorting speed of 10.4 worms per minute (5.8 s/worm) with an operation success rate of 90.3%. This system will facilitate the worm biology studies where body size measurement and size-based sorting of many worms are needed.

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Year:  2019        PMID: 30869628     DOI: 10.1109/TNB.2019.2904009

Source DB:  PubMed          Journal:  IEEE Trans Nanobioscience        ISSN: 1536-1241            Impact factor:   2.935


  4 in total

1.  Red blood cell recognition and posture estimation in microfluidic chip based on lensless imaging.

Authors:  Jianwei Li; Li Dai; Ningmei Yu; Yinfeng Wu
Journal:  Biomicrofluidics       Date:  2021-05-28       Impact factor: 3.258

Review 2.  Microfluidic Technologies for High Throughput Screening Through Sorting and On-Chip Culture of C. elegans.

Authors:  Daniel Midkiff; Adriana San-Miguel
Journal:  Molecules       Date:  2019-11-25       Impact factor: 4.411

3.  Automated recognition and analysis of head thrashes behavior in C. elegans.

Authors:  Hui Zhang; Shan Gao; Weiyang Chen
Journal:  BMC Bioinformatics       Date:  2022-03-07       Impact factor: 3.169

4.  Paper-Supported High-Throughput 3D Culturing, Trapping, and Monitoring of Caenorhabditis Elegans.

Authors:  Mehdi Tahernia; Maedeh Mohammadifar; Seokheun Choi
Journal:  Micromachines (Basel)       Date:  2020-01-17       Impact factor: 2.891

  4 in total

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