Metabolomic profiling of cultured cancer cells Academic Article uri icon

Overview

MeSH Major

  • Metabolomics
  • Neoplasms, Experimental

abstract

  • Quantitative proteomics approaches have been developed-and now begin to be implemented on a high-throughput basis-to fill-in the large gap between the genomic/transcriptomic setup of (cancer) cells and their phenotypic/behavioral traits, reflecting a significant degree of posttranscriptional regulation in gene expression as well as a robust posttranslational regulation of protein function. However, proteomic profiling assays not only fail to detect labile posttranslational modifications as well as unstable protein-to-protein interactions but also are intrinsically incapable of assessing the enzymatic activity, as opposed to the mere abundance, of a given protein. Thus, determining the abundance of theoretically all the metabolites contained in a cell/tissue/organ/organism may significantly improve the informational value of proteomic approaches. Several techniques have been developed to this aim, including high-performance liquid chromatography (HPLC) coupled to quadrupole time-of-flight (Q-TOF) high-resolution mass spectrometry (HRMS). This approach is particularly advantageous for metabolomic profiling as it offers elevated accuracy and improved sensitivity. Here, we describe a simple procedure to determine the complete complement of intracellular metabolites in cultured malignant cells by HPLC coupled to Q-TOF HRMS. According to this method, (1) cells are collected and processed to minimize contaminations as well as fluctuations in their metabolic profile; (2) samples are separated by HPLC and analyzed on a Q-TOF spectrometer; and (3) data are extracted, normalized, and deconvoluted according to refined mathematical methods. This protocol constitutes a simple approach to determine the intracellular metabolomic profile of cultured cancer cells. With minimal variations (mostly related to sample collection and processing), this method is expected to provide reliable metabolomic data on a variety of cellular samples.

publication date

  • January 2014

Research

keywords

  • Academic Article

Identity

Language

  • eng

Digital Object Identifier (DOI)

  • 10.1016/B978-0-12-801329-8.00008-8

PubMed ID

  • 24924132

Additional Document Info

start page

  • 165

end page

  • 78

volume

  • 543