Development and application of a high-throughput microbial clone picking workstation based on machine vision

ZHANG Jiankang; WANG Wenjun; GUO Hongju; BAI Beichen; ZHANG Yafei; YUAN Zheng; LI Yanhui; LI Hang

doi:10.12211/2096-8280.2025-038

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Development and application of a high-throughput microbial clone picking workstation based on machine vision

Research Article | 更新时间：2025-09-05

- Development and application of a high-throughput microbial clone picking workstation based on machine vision
- Synthetic Biology Journal Vol. 6, Issue 4, Pages: 956-971(2025)
- 作者机构：
  
  1.生物芯片北京国家工程研究中心，北京 102206
  2.博奥生物集团有限公司，北京 102206
- 作者简介：
- 基金信息：
- DOI：10.12211/2096-8280.2025-038
  CLC： TP273
- Received：29 April 2025，
  
  Revised：2025-06-25，
  
  Published：31 August 2025
- 稿件说明：
移动端阅览
张建康，王文君，郭洪菊，白北辰，张亚飞，袁征，李彦辉，李航. 基于机器视觉的高通量微生物克隆挑选工作站研制及应用［J］. 合成生物学， 2025， 6（4）： 956-971

ZHANG Jiankang， WANG Wenjun， GUO Hongju， BAI Beichen， ZHANG Yafei， YUAN Zheng， LI Yanhui， LI Hang. Development and application of a high-throughput microbial clone picking workstation based on machine vision［J］. Synthetic Biology Journal， 2025， 6（4）： 956-971
张建康，王文君，郭洪菊，白北辰，张亚飞，袁征，李彦辉，李航. 基于机器视觉的高通量微生物克隆挑选工作站研制及应用［J］. 合成生物学， 2025， 6（4）： 956-971 DOI： 10.12211/2096-8280.2025-038.

ZHANG Jiankang， WANG Wenjun， GUO Hongju， BAI Beichen， ZHANG Yafei， YUAN Zheng， LI Yanhui， LI Hang. Development and application of a high-throughput microbial clone picking workstation based on machine vision［J］. Synthetic Biology Journal， 2025， 6（4）： 956-971 DOI： 10.12211/2096-8280.2025-038.

摘要

微生物克隆挑选是基因工程生物实验中的关键环节，需要从生长有大量菌落的培养皿中将符合质量要求的单克隆菌落准确、快速挑取出来并接种到培养基中，以便进一步扩大培养或检测。在高通量实验中，克隆挑选环节任务量大、记录繁复、容易交叉污染，依靠人工操作难以在短时间准确完成。针对这一难题，本项目成功研制出一种自动化克隆挑选工作站，通过菌落图像的深度学习实现克隆定位和筛选，并利用机器人技术完成挑取-接种-清洗-高温灭菌过程。在所研制的可视化工作界面中，工作站系统能够个性化编辑适用于多种微生物克隆的多项实验操作流程。通过样机验证实验结果，证明了所研制系统和方法的可行性和有效性，为高通量实验室自动化发展提供了有效工具和有益实践。

Abstract

Microbial clone picking

a crucial step in genetic engineering and biological experiments

involves the accurate and rapid isolation of single colonies with desired characteristics from petri dishes teeming with numerous clones

followed by their inoculation into culture media for further propagation or analysis. In high-throughput settings

this task becomes burdensome due to its vast volume

complex record-keeping requirements

and the risk of cross-contamination

rendering manual operations impractical for achieving timely and precise results. To address this challenge

we present the design and manufacture of an automated clone picking workstation that performs efficient clone picking using 96-channel pneumatic pick-up pins

eliminating the need for consumables. The pins can be reused after ultrasonic cleaning and sterilization at high temperature following the previous picking cycle

making it more economical and environmentally friendly

compared with other methods that use disposable pipettes or picking needles. The pins can be replaced to adapt to different types of bacterial strains to meet various experimental requirements. The grab integrated on the picking head can rotate 360° and transfer the plates to different work positions.In the aspect of colony detection

the photos are automatically taken by an optical system

and the positioning and screening of colonies are achieved through the deep learning of numerous colony images by the software

which was designed independently. The precision image recognition technology is coupled with robotic and automated control technologies to enable seamless processes for picking

inoculating

cleaning

and drying. The High-Efficiency Particulate Air Filter and ultraviolet

sterilization prevent cross-contamination

ensuring the experimental environment meets the required standards. This workstation is equipped with an independent operation computer and has developed a set of user-friendly software that enables personalized editing of multiple experimental protocols tailored to diverse microbial clone types. It can also communicate with external devices

via

TCP/IP protocols

facilitating the integration for conducting experiments such as fully automated synthetic biology. The validation experiment of bacterial colony picking was conducted by a prototype machine to test the selection efficiency. The success of the experiment suggests that the proposed system and method are feasible and effective

offering a valuable tool and a practical approach for the automation development of high-throughput laboratories.

关键词

Keywords

references

MACARRÓN R , HERTZBERG R P . Design and implementation of high throughput screening assays [J ] . Molecular Biotechnology , 2011 , 47 ( 3 ): 270 - 285 .

HUGHES S R , BUTT T R , BARTOLETT S , et al . Design and construction of a first-generation high-throughput integrated robotic molecular biology platform for bioenergy applications [J ] . SLAS Technology , 2011 , 16 ( 4 ): 292 - 307 .

孙梦楚 , 陆亮宇 , 申晓林 , 等 . 基于荧光检测的高通量筛选技术和装备助力细胞工厂构建 [J ] . 合成生物学 , 2023 , 4 ( 5 ): 947 - 965 .

SUN M C , LU L Y , SHEN X L , et al . Fluorescence detection-based high-throughput screening systems and devices facilitate cell factories construction [J ] . Synthetic Biology Journal , 2023 , 4 ( 5 ): 947 - 965 .

卢挥 , 张芳丽 , 黄磊 . 合成生物学自动化装置iBioFoundry的构建与应用 [J ] . 合成生物学 , 2023 , 4 ( 5 ): 877 - 891 .

LU H , ZHANG F L , HUANG L . Establishment of iBioFoundry for synthetic biology applications [J ] . Synthetic Biology Journal , 2023 , 4 ( 5 ): 877 - 891 .

STEPHENSON A , LASTRA L , NGUYEN B , et al . Physical laboratory automation in synthetic biology [J ] . ACS Synthetic Biology , 2023 , 12 ( 11 ): 3156 - 3169 .

PAVAN M . Setting up an automated biomanufacturing laboratory [J ] . Methods in Molecular Biology , 2021 , 2229 : 137 - 155 .

MARTIN V J J , PITERA D J , WITHERS S T , et al . Engineering a mevalonate pathway in Escherichia coli for production of terpenoids [J ] . Nature Biotechnology , 2003 , 21 ( 7 ): 796 - 802 .

WIN M N , SMOLKE C D . Higher-order cellular information processing with synthetic RNA devices [J ] . Science , 2008 , 322 ( 5900 ): 456 - 460 .

PADDON C J , WESTFALL P J , PITERA D J , et al . High-level semi-synthetic production of the potent antimalarial artemisinin [J ] . Nature , 2013 , 496 ( 7446 ): 528 - 532 .

CHAO R , MISHRA S , SI T , et al . Engineering biological systems using automated biofoundries [J ] . Metabolic Engineering , 2017 , 42 : 98 - 108 .

唐婷 , 付立豪 , 郭二鹏 , 等 . 自动化合成生物技术与工程化设施平台 [J ] . 科学通报 , 2021 , 66 ( 3 ): 300 - 309 .

TANG T , FU L H , GUO E P , et al . Automation in synthetic biology using biological foundries [J ] . Chinese Science Bulletin , 2021 , 66 ( 3 ): 300 - 309 .

ZIMMERMANN M , ZIMMERMANN-KOGADEEVA M , WEGMANN R , et al . Mapping human microbiome drug metabolism by gut bacteria and their genes [J ] . Nature , 2019 , 570 ( 7762 ): 462 - 467 .

CERBINI T , LUO Y Q , RAO M S , et al . Transfection, selection, and colony-picking of human induced pluripotent stem cells TALEN-targeted with a GFP gene into the AAVS1 safe harbor [J ] . Journal of Visualized Experiments , 2015 ( 96 ): 52504 .

DJATNA T , HADI M Z . Implementation of an ant colony approach to solve multi-objective order picking problem in beverage warehousing with drive-in rack system [C ] // 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS) , 28 -29 October 2017, Bali , Indonesia , 2017: 137 - 142 .

HUANG C , HE K , LIU C L , et al . A colony picking robot with multi-pin synchronous manipulator [C ] // 2018 IEEE International Conference on Information and Automation(ICIA) , 11 -13 August 2018, Wuyi Mountain , Fujian , China , 2018: 7 - 12 .

张依朗 . 基于机器视觉的高通量自动菌落挑取系统研究 [D ] . 北京 : 北京化工大学 , 2021 .

ZHANG Y L . Research on high throughput automatic colony picking sysetem based on machine vision [D ] . Beijing : Beijing University of Chemical Technology , 2021 .

CHEN W B , ZHANG C C . Automated bacterial colony counting for clonogenic assay [M ] // Dental Computing and Applications . New York : IGI Global , 2009 : 134 - 145 .

GOROKHOV O , FAZYLOV R , KAZACHUK M , et al . Bacterial colony detection method for microbiological photographic images [C ] // 2022 International Joint Conference on Neural Networks (IJCNN) . July 18-23, 2022 , Padua, Italy. IEEE , 2022 : 1 - 8 .

ZHANG L . Phase contrast microscopy cell population segmentation: a survey [EB/OL ] . arXiv , 2019 : 1911 . 11111 . ( 2019-11-29 )[ 2025-03-01 ] . https://arxiv.org/pdf/1911.11111 https://arxiv.org/pdf/1911.11111 .

LAWLESS C , WILKINSON D J , YOUNG A , et al . Colonyzer: automated quantification of micro-organism growth characteristics on solid agar [J ] . BMC Bioinformatics , 2010 , 11 : 287 .

REHMAN A , SALEEM Z , AMJAD J , et al . A comparison of bacterial colonies count from petri dishes utilizing hough transform and traditional manual counting [EB/OL ] . arXiv , 2025 : 2505 . 20365 . ( 2025-05-26 )[ 2025-06-01 ] . https://arxiv.org/abs/2505.20365 https://arxiv.org/abs/2505.20365 .

RUSS J C , NEAL F B . The image processing handbook [M/OL ] . 7th ed . Boca Raton : CRC Press , 2016 . ( 2018-09-03 )[ 2025-06-25 ] . https://doi.org/10.1201/b18983 https://doi.org/10.1201/b18983 .

MOEN E , BANNON D , KUDO T , et al . Deep learning for cellular image analysis [J ] . Nature Methods , 2019 , 16 ( 12 ): 1233 - 1246 .

WAKABAYASHI R , AOYANAGI A , TOMINAGA T . Rapid counting of coliforms and Escherichia coli by deep learning-based classifier [J ] . Journal of Food Safety , 2024 , 44 ( 4 ): e13158 .

SHEN B Y , CHEN X , CAI D L , et al . Real-Space Imaging of the Ordered Small Molecule Orientations in Porous Frameworks by Electron Microscopy [EB/OL ] . arXiv , 2020 : 2001 . 09588 .( 2020-01-27 )[ 2025-06-01 ] . https://arxiv.org/abs/2001.09588 https://arxiv.org/abs/2001.09588 .

ALI AKBAR S , GHAZALI K H , HASAN H , et al . Rapid bacterial colony classification using deep learning [J ] . Indonesian Journal of Electrical Engineering and Computer Science , 2022 , 26 ( 1 ): 352 .

ANDREINI P , BONECHI S , BIANCHINI M , et al . a deep learning approach to bacterial colony segmentation [C/OL ] //KŮRKOVÁ V, MANOLOPOULOS Y, HAMMER B, et al. Artificial neural networks and machine learning-ICANN 2018 . 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018 , Proceedings, Part III. Cham: Springer , 2018 . ( 2018-09-18 ) [2025-06-01] . https://doi.org/10.1007/978-3-030-01424-7_51 https://doi.org/10.1007/978-3-030-01424-7_51 .

RONNEBERGER O , FISCHER P , BROXET T , et al . U-Net: convolutional networks for biomedical image segmentation [EB/OL ] . arXiv , 2015 : 1505 . 04597 . ( 2015-05-18 )[ 2025-06-01 ] . https://arxiv.org/pdf/1505.04597.pdf https://arxiv.org/pdf/1505.04597.pdf .

OKTAY O , SCHLEMPER J , LE FOLGOC L , et al . Attention U-Net: learning where to look for the pancreas [EB/OL ] . arXiv , 2018 : 1804 . 03999 . ( 2018-05-20 )[ 2025-06-01 ] . https://arxiv.org/abs/1804.03999 https://arxiv.org/abs/1804.03999 .

HE K M , GKIOXARI G , DOLLÁRET P , et al . Mask R-CNN for object detection and instance segmentation [EB/OL ] . arXiv , 2017 : 1703 . 06870 . ( 2018-01-24 )[ 2025-06-01 ] . https://arxiv.org/abs/1703.06870 https://arxiv.org/abs/1703.06870 .

JHA D , SMEDSRUD P H , JOHANSEN D , et al . A comprehensive study on colorectal polyp segmentation with ResUNet++, conditional random field and test-time augmentation [J ] . IEEE Journal of Biomedical and Health Informatics , 2021 , 25 ( 6 ): 2029 - 2040 .

曹淑艳 , 陈雪景 , 张洵洋 . 中国塑料行业绿色低碳发展研究报告 [R/OL ] . [ 2025-06-01 ] . https://www.ccetp.cn/newsinfo/4697881.html https://www.ccetp.cn/newsinfo/4697881.html .

CAO S Y , CHEN X J , ZHANG X Y . Green and low carbon development of the plastics industry in China [R/OL ] . [ 2025-06-01 ] . https://www.ccetp.cn/newsinfo/4697881.html https://www.ccetp.cn/newsinfo/4697881.html .

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