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. 2021 Apr 14;11(4):240.
doi: 10.3390/metabo11040240.

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

Alison Woodward  1 , Alina Pandele  1 , Salah Abdelrazig  2 , Catherine A Ortori  2 , Iqbal Khan  3 , Marcos Castellanos Uribe  3 , Sean May  3 , David A Barrett  2 , Richard G Grundy  1 , Dong-Hyun Kim  2 , Ruman Rahman  1
Affiliations

Integrated Metabolomics and Transcriptomics Using an Optimised Dual Extraction Process to Study Human Brain Cancer Cells and Tissues

Alison Woodward et al. Metabolites. .

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.

Keywords: RNA; cancer; dual-extraction; integrated omics; metabolite.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

Figures

Figure 1
Figure 1
Statistical discrimination of treated (serum-starved) and control (serum-replete) BXD-1425 cells analysed by LC-MS; (a) metabolomics, (b) lipidomics. OPLS-DA scores plot. Four data points representing four replicates of each of treated (serum-starved, ▲) and control (serum-replete, ●) can be observed grouped together. (a) R2X 0.814, R2Y 0.995, Q2 0.979; (b) R2X 0.880, R2Y 0.998, Q2 = 0.994.
Figure 1
Figure 1
Statistical discrimination of treated (serum-starved) and control (serum-replete) BXD-1425 cells analysed by LC-MS; (a) metabolomics, (b) lipidomics. OPLS-DA scores plot. Four data points representing four replicates of each of treated (serum-starved, ▲) and control (serum-replete, ●) can be observed grouped together. (a) R2X 0.814, R2Y 0.995, Q2 0.979; (b) R2X 0.880, R2Y 0.998, Q2 = 0.994.
Figure 2
Figure 2
Differential accumulation of metabolites in treated (serum-starved) and control (serum-replete) cells. Differences in peak height were statistically significant for all metabolites displayed. Statistical significance was defined as a combination of p < 0.05 (in a univariate t-test with an FDR cut-off of 0.05) and VIP ≥ 1 (variable important for projection in a multivariate OPLS-DA test). The level of confidence of identification is given as superscripted numbers. * Sphingolipid is [SP hydroxy,hydroxy,methyl(10:2/2:0)] 6R-(8-hydroxydecyl)-2R-(hydroxymethyl)-piperidin-3R-ol.
Figure 3
Figure 3
Statistical discrimination of treated (serum-starved) and control (serum-replete) BXD-1425 cells analysed by Affymetrix array. (a) PCA plot shows distinct clustering of treated (red) and control (blue) groups; (b) hierarchical clustering of the 128 differentially expressed genes. Intensity of colour is directly proportional to the difference in mean expression and ranges from blue (downregulated) to red (upregulated). C—control; T—treatment.
Figure 4
Figure 4
Integrated analysis of genes and metabolites extracted from treated (serum-starved) relative to control (serum-replete) BXD-1425 cells. (a) Compound reaction networks of the metabolites and genes were visualised using MetScape: metabolites (red) and genes (blue) are presented as nodes. (b) The metabolite–gene associated networks were mainly related to purine metabolism (yellow).
Figure 5
Figure 5
Integrated gene–metabolite interactions facilitated by dual-extraction from the same population of cells. The depicted networks reveal aberrant metabolites (red) associated with dysregulated genes (blue) between treated and control BXD-1425 cells (>1.0 fold). The integrative network was generated using a MetScape plugin for Cytoscape. Significantly altered genes or metabolites (green border) in serum-starved relative to serum-replete cells are represented as upregulated/downregulated or high/low abundance, respectively, by an increase or decrease in node size in comparison to other genes or metabolites. (a) Direct statistically significant edge between N-acetylputrescine and SAT1, (b) edge between L-proline and P4HA1, (c) edge between L-glutamine and FMO3, (d) edge between L-citrulline and DDAH1.
Figure 6
Figure 6
Schematic of metabolite/RNA dual extraction procedures and the single extraction reference methods.
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