Background: Altered metabolism is a hallmark of cancer and metabolic reprogramming can regulate several malignant properties to drive tumourigenesis. Metabolic processes contribute to carcinogenesis by modulating proliferation, survival and differentiation. Here, we studied how metabolic pathways are deregulated in cancer stem-like cells, a specific pool of cells that is thought to be responsible for cancer growth and recurrence. Cancer stem-like cells are particularly relevant in glioblastoma (GBM), the most lethal form of primary brain tumours.Methods: We have analysed the transcriptome and metabolome of an established GBM cell line (U87) and a patient-derived GBM stem-like cell line (NCH644) exposed to neurosphere or monolayer culture conditions. By integrating transcriptome and metabolome data, we identified key metabolic pathways and gene signatures that are associated with stem-like and differentiated states in GBM cells.Results: By principal component analysis and hierarchical clustering, we demonstrated that neurospheres and monolayer cells differ substantially in their metabolism and gene regulation. Furthermore, by performing a joint pathway analysis of transcriptome and metabolome data, we found that neurosphere culture conditions induce a similar metabolic rewiring in the two cellular systems and significantly regulate the same metabolic pathways. Finally, arginine biosynthesis was identified as the most significantly regulated pathway in neurospheres from both cell lines, although individual nodes of this pathway were distinctly regulated in the two cellular systems.Conclusions: Neurosphere conditions, as opposed to monolayer conditions, cause a transcriptomic and metabolic rewiring that may be crucial for the regulation of stem-like features. Arginine biosynthesis may be a key metabolic pathway in stemness regulation, by supporting the specific needs of the different cell populations. Different GBM cell lines show distinct regulation of these metabolic pathways, which are crucial to direct metabolites towards nucleotide or arginine synthesis, respectively. Finally, as part of open science data, the data set generated is of great value as a resource for the scientific community.