Method for lipidomic analysis: p53 expression modulation of sulfatide, ganglioside, and phospholipid composition of U87 MG glioblastoma cells

Abstract

Lipidomics can complement genomics and proteomics by providing new insight into dynamic changes in biomembranes; however, few reports in the literature have explored, on an organism-wide scale, the functional link between nonenzymatic proteins and cellular lipids. Here, we report changes induced by adenovirus-delivered wild-type p53 gene and chemotherapy of U87 MG glioblastoma cells, a treatment known to trigger apoptosis and cell cycle arrest. We compare polar lipid changes in treated cells and control cells by use of a novel, sensitive method that employs lipid extraction, one-step liquid chromatography separation, high-resolution mass analysis, and Kendrick mass defect analysis. Nano-LC FT-ICR MS and quadrupole linear ion trap MS/MS analysis of polar lipids yields hundreds of unique assignments of glyco- and phospholipids at sub-ppm mass accuracy and high resolving power (m/Deltam50% = 200 000 at m/z 400) at 1 s/scan. MS/MS data confirm molecular structures in many instances. Sulfatides are most highly modulated by wild-type p53 treatment. The treatment also leads to an increase in phospholipids such as phosphatidyl inositols, phosphatidyl serines, phosphatidyl glycerols, and phosphatidyl ethanolamines. An increase in hydroxylated phospholipids is especially noteworthy. Also, a decrease in the longer chain gangliosides, GD1 and GM1b, is observed in wild-type p53 (treated) cells.

Fig. 1

Overview of lipidomic analysis of GBM workflow. (1 A) Number of brain and flank tumors of GBM10 and GBM43. (1 B) In vivo tumor harvest information and mouse xenograft model validation via bioluminescence imaging. (1 C) Biomolecule extraction procedure. (1 D–E) Separation and ionization of lipid method for mass spectrometry. (2 A–B) Data processing of mass spectrometry data using MzMine2. (2 C) Statistical analysis (2 D) Lipid identification using the database, LIPID MAPS. (2 E) Validation of the data by performing hierarchical clustering.

Fig. 2

GBM biological sample preparation for omics study. (A) GBM10 and GBM43 cells were expanded as flank tumors and harvested. Human GBM10 and GBM43 tumor cells were cultured and expanded in vitro prior to implantation. (B) GBM cells were then implanted into the flank (3×10⁵ cells/flank) and the right cerebrum (3×10⁶ cells/brain) of each mouse and tumors were allowed to develop for 21 to 24 days. (C) Tumor samples from intracranial and flank sites were harvested by excision. The tumors were flash frozen immediately and stored in −80°C. (D) Proteins, metabolites, and lipids of GBM were extracted. The 3 fractions of the biomolecules (polar metabolite, protein, and lipid) are indicated.

Fig. 3

m/z and fold effect comparison of each tissue type. The m/z and fold effect range of differentially expressed lipids between brain and flank tumors are indicated. In general, lipidomic profiles of the flank tumors of GBM expressed a greater fold change (between −35and −20) when compared to the brain tumor profiles. m/z value of the identified lipids were mostly concentrated in the range of 750 and 900.

Fig. 4

Decreased trend of thirty significantly expressed lipids and lipid classes. Thirty most significantly identified lipids from each tissue type (all flank, GBM10 brain, and GBM43 brain tumors) were plotted and grouped in four lipid species. These lipids include glycerophosphoserines, glycerophosphocholines, glycerophosphoethanolamines, and triradylglycerols. * symbol on the lipid structure represents the lipids that are identified as more than one lipid classes.

Fig. 5

Hierarchical clustering output of xenograft tissues. Two different hierarchical clustering methods (DIANA and AGNES) were utilized to validate the data analysis method. Clusters were created by m/z values and peak intensity from the data generated by MzMine2. Top two graphs were generated from the positive ion mode data and bottom two graphs were generated from the negative ion mode data.

Fig. 6

Tumor size comparison between different GBM tumors and control brain tissue. The box plot represents the tumor size of different tissue types. The mass of GBM10 brain tumors (n=5), GBM43 brain tumors (n=5), and GBM10 (n=5) tumors were similar. However, the size of GBM43 flank tumors (n=5) from the xenograft model was significantly smaller compared to other tumor tissues. One of GBM43 flank tumor did not grow, which counted as 0mg. Four tumors were harvested for the lipid extraction. However, three among these four tumors were too small for the extraction.

Fig. 7

Normal cells primarily use glucose to generate energy and fulfill the requirements for cell growth. Cancer cells alter glucose metabolism and bypass the TCA cycle through the “Warburg effect,” in order to sustain rapid cell growth. Decreased amounts of lipids, which may occur through lipolysis in cellular organelles such as ER and Golgi, may suggest that glioblastoma cells use fatty acids as an alternative fuel source.