Automated multi-dataset INST $^{13}$C metabolic flux analysis at microliter scale reveals robust fluxes but variable metabolite pools in $Corynebacterium~glutamicum$

Abstract

Isotopically non-stationary metabolic flux analysis (INST $^{13}$C-MFA) provides unique insights into cellular physiology but is typically limited by low throughput and high experimental costs. Here, we present a miniaturized and automated workflow that integrates transient isotope labeling experiments with advanced computational modeling to enable parallel INST $^{13}$C-MFA at microliter scale. The approach is demonstrated for an evolved $Corynebacterium~glutamicum$ strain capable of efficient growth on ethanol, a substrate for which isotopically stationary $^{13}$C-MFA is inherently limited due to low labeling diversity. Using robotic liquid handling, rapid hot isopropanol quenching, and LC-QToF-MS-based analytics, highly informative datasets were generated from parallel 48-well experiments with different ethanol tracers. Multi-dataset INST $^{13}$C-MFA unlocked joint estimation of intracellular fluxes and metabolite pool sizes and significantly improved flux precision compared to single-dataset analyses. While net fluxes were robust across datasets, pool size estimates exhibited variability and did not converge under joint inference, highlighting a fundamental methodological difference to single-dataset INST $^{13}$C-MFA. The resulting multi-dataset flux map reveals a central role of the glyoxylate shunt during growth on ethanol, consistent with metabolic adaption to C2-based substrate utilization. Overall, this work demonstrates that automated multi-dataset INST $^{13}$C-MFA is technically feasible and provides high-quality flux analysis at a fraction of the cost of conventional lab-scale bioreactor-based approaches. The presented workflow establishes a scalable framework for high-throughput quantitative fluxomics in microbial biotechnology and supports integration into iterative strain engineering and biofoundry pipelines.