Development and Application of a High-Throughput Microbial Clone Picking Workstation Based on Machine Vision

doi:10.12211/2096-8280.2025-038

Abstract

Abstract:

Microbial clone picking, a pivotal step in genetic engineering and biological experiments, involves accurately and rapidly isolating single colonies with desired qualities from petri dishes teeming with numerous clones and inoculating them into culture media for further propagation or analysis. In high-throughput settings, this task is cumbersome due to its sheer volume, intricate record-keeping requirements, and the risk of cross-contamination, making manual operations impractical to achieve timely and precise results. To address this challenge, we present the design and manufacture of an automated clone picking workstation that carries out efficient clone selection with 96-channel pneumatic pick-up pins without 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, providing a valuable tool and a practical approach for the automation development of high-throughput laboratories.

Key words: laboratory automation, machine vision, colony picker, synthetic biology

摘要：

微生物克隆挑选是基因工程生物实验中的关键环节，需要从生长有大量克隆菌落的培养皿中将符合质量要求的单菌落准确、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，本文提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，证明了所提出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。

关键词: 合成生物学, 自动化平台, 深度学习, 克隆挑选

CLC Number:

TP273

ZHANG Jiankang, WANG Wenjun, GUO Hong-ju, 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, DOI: 10.12211/2096-8280.2025-038.

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, DOI: 10.12211/2096-8280.2025-038.

Figures/Tables 20

Fig. 1 Overall form of clone selection workstation

Fig. 2 System composition of clone selection workstation

Fig. 3 Electrical schematic diagram

Fig. 4 Work area arrangement of clone selection workstation

Fig. 5 Problems existing in colony images that affect recognition

Fig. 6 Flowchart of colony identification and location

Fig. 7 Petri dish

Fig. 8 Dish trainning curve

Fig. 9 Identification and labeling of Petri dishes

Table 1 Distribution of the dataset

数据集

Data set

训练集

Training set

测试集

Test set

完整菌落图像

Complete colony images

挑选出小图像块

Small image blocks Selected

5160

1466

Fig. 10 Colony-training curve

Fig. 11 Grayscale image generated by reasoning

Fig. 12 Identification of qualified colonies

Fig. 13 UI design

Fig. 14 Control interface framework of the workstation

Fig.15 Clone selection process

Fig. 16 Image comparison before and after picking

Fig. 17 Deep well plate containing colony culture medium

Fig. 18 Verification of sterilization effect

Table 2 A comparison of technical parameters of this device with other three cloning selection workstations

设备型号	通道数量	挑取方式	CCD 分辨率(Pixel/mm)	菌落识别尺寸 (mm)	菌落识别准确度 (%)	挑取准确率 (%)	有效像素 (万)
QPix 420	96	挑针	22	≥ 0.1	0.5-0.7mm菌落，>97%	＞ 98%	500
Hedylax T200智能微生物菌落挑选工作站	单通道或8通道	吸头	/	/	/	≥ 98%	2500
全自动菌落挑选工作站G3000	2或4	挑针	/	≥ 0.5	/	1 mm以上98%	600-2000
本设备	96	挑针	30	≥0.2	≥ 98%	≥ 98%	1000

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