Abstract
The integration of untargeted metabolomics and transcriptomics from the same population of cells or tissue enhances the confidence in the identified metabolic pathways and understanding of the enzyme–metabolite relationship. Here, we optimised a simultaneous extraction method of metabolites/lipids and RNA from ependymoma cells (BXD-1425). Relative to established RNA (mirVana kit) or metabolite (sequential solvent addition and shaking) single extraction methods, four dual-extraction techniques were evaluated and compared (methanol:water:chloroform ratios): cryomill/mirVana (1:1:2); cryomill-wash/Econospin (5:1:2); rotation/phenol-chloroform (9:10:1); Sequential/mirVana (1:1:3). All methods extracted the same metabolites, yet rotation/phenol-chloroform did not extract lipids. Cryomill/mirVana and sequential/mirVana recovered the highest amounts of RNA, at 70 and 68% of that recovered with mirVana kit alone. sequential/mirVana, involving RNA extraction from the interphase of our established sequential solvent addition and shaking metabolomics-lipidomics extraction method, was the most efficient approach overall. Sequential/mirVana was applied to study a) the biological effect caused by acute serum starvation in BXD-1425 cells and b) primary ependymoma tumour tissue. We found (a) 64 differentially abundant metabolites and 28 differentially expressed metabolic genes, discovering four gene-metabolite interactions, and (b) all metabolites and 62% lipids were above the limit of detection, and RNA yield was sufficient for transcriptomics, in just 10 mg of tissue.
Highlights
Published methods typically extract RNA and metabolites from different regions of a given tissue specimen, which limits integration of datasets as neither the cell genotype nor phenotype is necessarily homogenous throughout tissue, in malignant cancerous tissue [9,12,13]
The dual extraction methods of metabolites/RNA (n = 2–4; methanol:water:chloroform ratios are shown in brackets): cryomill/mirVana method (1:1:2) [17], cryomill and wash/Econospin columns method (5:1:2; referred to as cryomill-wash/Econospin) [15], cell extract rotation/phenol-chloroform method (9:10:1; referred to as rotation/phenolchloroform) [16] and sequential solvent addition and shaking/mirVana method (1:1:3; referred to as sequential/mirVana), as detailed in the methods/supplementary methods, were compared to a standard routine method for metabolite extraction [28], which was used as a positive control, sequential solvent addition and shaking (1:1:3)
The total ion counts (TICs) of the quality controls (QCs) had a relative standard deviation (RSD) of 7% and 6% in metabolomics and lipidomics analyses, respectively, which was deemed an acceptable level of variation by Hutschenreuther et al and
Summary
Integrated omics techniques can improve the understanding of mechanisms through which a cellular phenotype is generated, where each technique allows insight into cell regulation and function at a different but inter-related level of the genome [1,2]. The transcriptome reveals which genes are expressed/repressed and contribute to the phenotype, allowing inference from the genome landscape (which could be aberrant due to DNA mutations or chromosomal rearrangements) [4]. If the expressed genes encode metabolic enzymes, such expression levels could directly help to understand an increase in activity of a metabolic pathway and aid in the discovery of drug targets for diseased cells, amenable to pharmacological inhibition [5,6]. Aberrantly expressed genes which encode non-enzymatic proteins may be reflective of aberrant metabolism within a diseased cell or sub-cellular niche, albeit indirectly [7]
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