Fast and ergonomic extraction of adherent mammalian cells for NMR-based metabolomics studies

Cellular metabolomics has become key to elucidate mechanistic aspects in various fields such as cancerology or pharmacology, and is rapidly becoming a standard phenotyping tool accessible to the broad biological community. Acquisition of reliable spectroscopic datasets, such as nuclear magnetic resonance (NMR) spectra, to characterize biological systems depends on the elaboration of robust methods for cellular metabolites extraction. Previous studies have addressed many issues raised by these protocols, however with little pondering on ergonomic and practical aspects of the methods that impact their scalability, reproducibility and hence their suitability to high-throughput studies or their use by non-metabolomics experts. Here, we optimize a fast and ergonomic protocol for extraction of metabolites from adherent mammalian cells for NMR metabolomics studies. The proposed extraction protocol, including cell washing, metabolism quenching and actual extraction of intracellular metabolites, was first optimized on HeLa cells. Efficiency of the protocol, in its globality and for the different individual steps, was assessed by NMR quantification of 27 metabolites from cellular extracts. We show that a single PBS wash provides a seemly compromise between contamination from growth medium and leakage of intracellular metabolites. In HeLa cells, extraction using pure methanol, without cell scraping, recovered a higher amount of intracellular metabolites than the reference methanol/water/chloroform method with cell scraping, with yields varying across metabolite classes. Optimized and reference protocols were further tested on eight cell lines of miscellaneous nature, and inter-operator reproducibility was demonstrated. Our results stress the need for tailored extraction protocols and show that fast protocols minimizing time-consuming steps, without compromising extraction yields, are suitable for high-throughput metabolomics studies. Graphical abstract.