An automated digital colony picker monitors growth and metabolite production, eliminating the need for culturing cells

https://www.nature.com/articles/s41467-025-63929-7

http://english.cas.cn/newsroom/research_news/life/202510/t20251014_1089412.shtml

The group around Jian XU from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology (QIBEBT) has developed a fully automated “Digital Colony Picker” (DCP). This device identifies and retrieves high-performance microbial clones by simultaneously monitoring their growth and metabolite production—eliminating the need for culture plates, sampling needles, or manual picking.

Designed for the “design-build-test-learn” framework widely adopted in synthetic biology, the DCP streamlines the traditionally slow, labor-intensive “test” phase into a fast, parallel workflow with little hands-on time. It has a microfluidic chip containing 16,000 addressable microchambers that isolate single cells and track their expansion into micro-colonies. An integrated AI engine conducts time-lapse analysis of both brightfield and biosensor signals to quantify growth kinetics and metabolite production in real time. Once target colonies are identified, a laser-induced bubble technique exports them as droplets directly into standard culture plates. This contact-free transfer minimizes cross-contamination and preserves cell viability.

The equipment which was tested for identifying high-yield or lactate-tolerant Zymomonas mobilis mutants is  broadly applicable to adaptive evolution studies, functional gene discovery, and phenotype screening across diverse microbial species.

The iMAPS program which includes use of this device has been described in ore details under https://imaps.info/ 

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https://en.people.cn/n3/2026/0324/c90000-20439477.html

At the Qingdao Humanoid Robot Data Training Center in Laoshan District of Qingdao, humanoid robots are trained for jobs such as intelligent industrial manufacturing, smart home, and commercial services. Data collectors here control robots to complete specific tasks like logistics sorting, supermarket restocking, kitchen operations, and component assembly. Through thousands of repetitions and trials, massive amounts of motion data are generated, endowing robots with a smarter “intelligent brain,” and helping humanoid robots enter all walks of life to serve thousands of households.

https://www.cas.cn/syky/202602/t20260226_5102870.shtml

https://doi.org/10.1186/s40168-026-02339-3

A research team at the CAS Qingdao Institute of Bioenergy and Bioprocess Technology has developed RamEx, an integrated analysis framework for Ramanome big data. This platform, tailored to the characteristics of Raman spectroscopy data, establishes a one-stop workflow from data reading and standardized preprocessing to downstream data mining, centered on automated quality control algorithms and efficient parallel computing processes. It also demonstrates a systematic analysis of microbial metabolomical heterogeneity and metabolic pattern differentiation at the single-cell level.

Raman genomics deep analysis can track the dynamic changes in the composition of macromolecules such as lipids, proteins, and nucleic acids in different cells, thus revealing the differentiation and succession patterns of microbial metabolic states at the population scale with single-cell precision. This provides new research ideas and technical pathways for understanding the functional organization and environmental adaptation mechanisms of complex communities.

http://english.cas.cn/newsroom/research-news/202602/t20260224_1151116.shtml

https://link.springer.com/article/10.1186/s40168-026-02339-3

Scientists from the CAS Qingdao Institute of Bioenergy and Bioprocess Technology have developed a novel computational tool, RamEx, designed to resolve the computational bottleneck in high-throughput microbial Ramanomics.

RamEx streamlines the full Ramanomic analysis pipeline, from data preprocessing and automated quality control to advanced data mining. An Iterative Convolutional Outlier Detection (ICOD) algorithm tackles spectral noise in an unsupervised manner to dynamically identify and eliminate spectral artifacts, ensuring high-quality input for downstream analysis.

The platform’s performance was validated using diverse datasets, including pathogenic bacteria, probiotics, and yeast fermentation systems. Notably, RamEx successfully captured phenotypic heterogeneity in genetically identical yeast cells by detecting subtle metabolic fluctuations and tracking the dynamic accumulation of intracellular macromolecules, including lipids, proteins, and nucleic acids.

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