Exploring Dynamic Metabolome of the HepG2 Cell Line: Rise and Fall.
Olga I KiselevaIlya Yu KurbatovViktoriia A ArzumanianEkaterina V IlgisonisIgor V VakhrushevAlexey Yu LupatovElena A PonomarenkoEkaterina V PoverennayaPublished in: Cells (2022)
Both biological and technical variations can discredit the reliability of obtained data in omics studies. In this technical note, we investigated the effect of prolonged cultivation of the HepG2 hepatoma cell line on its metabolomic profile. Using the GC × GC-MS approach, we determined the degree of metabolic variability across HepG2 cells cultured in uniform conditions for 0, 5, 10, 15, and 20 days. Post-processing of obtained data revealed substantial changes in relative abundances of 110 metabolites among HepG2 samples under investigation. Our findings have implications for interpreting metabolomic results obtained from immortal cells, especially in longitudinal studies. There are still plenty of unanswered questions regarding metabolomics variability and many potential areas for future targeted and panoramic research. However, we suggest that the metabolome of cell lines is unstable and may undergo significant transformation over time, even if the culture conditions remain the same. Considering metabolomics variability on a relatively long-term basis, careful experimentation with particular attention to control samples is required to ensure reproducibility and relevance of the research results when testing both fundamentally and practically significant hypotheses.
Keyphrases
- electronic health record
- mass spectrometry
- induced apoptosis
- single cell
- big data
- case control
- ms ms
- endothelial cells
- working memory
- cancer therapy
- signaling pathway
- current status
- oxidative stress
- cross sectional
- data analysis
- machine learning
- endoplasmic reticulum stress
- artificial intelligence
- human health
- high resolution
- deep learning
- cell death
- atomic force microscopy
- high speed