Developed by researchers at the Chinese Academy of Sciences, the AI-RACS system advances microbial research with high-throughput workflows by automating the isolation of aluminum-resistant microorganisms.

Researchers from the Single Cell Center of the Qingdao Institute of Bioenergy and Bioprocess Technology, part of the Chinese Academy of Sciences (CAS), in collaboration with partners, have developed a Raman-enabled artificial intelligence cell sorting system (AI-RACS). ) system. This advanced system automates the isolation and functional analysis of aluminum-resistant microorganisms (ATM) from acidic soil, representing a significant leap from manual, labor-intensive methods to highly efficient automated workflows.

This study was published on: Analytical Chemistry.

Microbiomes (dynamic communities of microorganisms) offer untapped potential for the development of biotechnology and environmental sustainability. However, their complexity creates problems for the isolation and detailed study of specific functional microbes.

To solve this problem, the AI-RACS system combines optical tweezers, single-cell Raman spectroscopy (SCRS), and artificial intelligence. This integration transforms single-cell microbial research from low-throughput manual processes into high-throughput automated workflows, enabling precise identification, classification, and collection of single cells.

Groundbreaking development in the isolation of ATMs

The researchers used the RACS-Seq/Culture tool to identify and sequence ATMs in acidic soil samples. Using SCRS to assess the metabolic activity of cells exposed to aluminum, researchers successfully identified and isolated 13 aluminum-resistant strains. Burkholderia spp., Rhodanobacter spp. And Staphylococcus aureus. These strains showed higher metabolic activity compared to strains identified by traditional breeding methods. Using SCRS as a quantitative biomarker allowed researchers to detect and classify metabolically active microbes with unprecedented accuracy.

“AI-RACS allows us to explore how ATMs evolve in toxic red soils, providing new insights into microbial survival and improving soil health,” said corresponding author of the study, Professor Yuting Liang, from the CAS Institute of Soil Science.

First author of the study from the Single Cell Center, Dr. “Our goal is to develop a system that automates single cell analysis while increasing the accuracy and throughput needed to study complex microbial communities,” said Zhidian Diao. “This system allows researchers to study microbiomes virtually in situ with high efficiency.”

The AI-RACS system creates new opportunities in areas such as resource recovery, environmental management and industrial biotechnology.