Automated Quenching and Extraction for High-Precision Metabolomics Uncovers Distinct Metabolome Trajectories across Escherichia coli Lineages

Abstract Quantitative microbial metabolomics requires rapid quenching and highly standardized extraction, yet these pre-analytical steps remain labor-intensive and difficult to reproduce, limiting time-resolved intracellular measurements under controlled bioreactor cultivation. Here, we present an integrated robotic platform that couples parallel fermenter operation with circulation-based sampling, immediate cold-alcohol quenching, automated refrigerated centrifugation, interface-aware supernatant removal, and solvent extraction with controlled heating and mixing, followed by plate-based storage for liquid chromatography–tandem mass spectrometry (LC–MS/MS). To benchmark the precision of the automated preparation module, we compared repeated LC–MS/MS measurements of a single extract with measurements of independently processed samples subjected to post-quench automated harvesting, extraction, and transfer. The analysis was restricted to 76 analytes consistently detected in all replicates of both datasets. The mean coefficients of variation increased from 6.92% for repeated LC–MS/MS measurements to 8.82% for automated preparation, corresponding to a modest increase of 1.90 percentage points. These results indicate that post-quench automated preparation introduces a small but measurable additional source of variability while maintaining mean analytical variation below 10%. We then performed time-course endo- and exometabolomics across four widely used Escherichia coli lineages (BW25113, BL21(DE3), W, and C). Principal component analysis resolved strain-specific trajectories throughout batch growth and revealed strain-specific metabolic phenotypes, including coordinated accumulation of sugar phosphates and 2-C-methyl-d-erythritol 4-phosphate (MEP) pathway intermediates in strain C, and comparatively high intracellular pools of branched-chain amino acids in strain W. This platform enables standardized and traceable automated preparation for dense, time-resolved metabolomics and provides directly measurable phenotypes for host selection and metabolic engineering.