Intelliwaste: NMR of 13C-labeled Spent Media Enables Non-Invasive Metabolic Fingerprinting of Pluripotent Stem Cells and LIS1-Associated Neuropathology

Abstract

Metabolic dysregulation is increasingly recognized as a key contributor to neurodevelopmental disorders. Here, we present Intelliwaste, a non-invasive, cost-effective method for profiling carbon metabolism in pluripotent stem cells and brain organoids using 13C-labeled metabolites and 1H and 13C NMR spectroscopy. This approach enables longitudinal analysis of extracellular fluxes without disrupting cell viability. We apply Intelliwaste to human embryonic stem cells (hESCs) cultured in a defined media enriched with >95% 13C1-Glucose. Under these conditions, 13C3-lactate emerged as the most abundant labeled product, with 20-50-fold lower fluxes to 13C3-alanine, 13C2-acetate, 13C3-serine, and 13C3-pyruvate, and 100-300-fold lower fluxes to 13C1-formate and multiple 13C-labeled glutamate species. These profiles allow for precise quantification of fractional metabolic isotopic labeling and glucose-derived carbon flow. To demonstrate biological utility, we first examine the effect of L-glutamine omission, which selectively reduces 13C3-alanine/13C3-lactate and 13C4-glutamate/13C3-lactate flux ratios, while the 13C3-Glutamate/13C3-Lactate and 13C2-Glutamate/13C3-Lactate flux ratios remained unchanged. These findings suggest a specific role for extracellular glutamine in modulating the activity of alanine aminotransferase and pyruvate carboxylase. We then characterized LIS1 mutant hESCs--a model of lissencephaly--and observed significantly increased flux ratios involving 13C4-, 13C3-, and 13C2-glutamate relative to 13C3-lactate, indicating enhanced glutamate production via the TCA cycle. These findings establish Intelliwaste as a powerful tool for metabolic profiling in the study of human neurodevelopment and disease. Its non-destructive nature makes it particularly well-suited for tracking metabolic changes during differentiation and in patient-derived organoid models of neurological disorders.