UPLC-ESI-TOFMS-Based Metabolomics and Gene Expression Dynamics Inspector Self-Organizing Metabolomic Maps as Tools for Understanding the Cellular Response to Ionizing Radiation

Abstract

Global transcriptomic and proteomic profiling platforms have yielded important insights into the complex response to ionizing radiation (IR). Nonetheless, little is known about the ways in which small cellular metabolite concentrations change in response to IR. Here, a metabolomics approach using ultraperformance liquid chromatography coupled with electrospray time-of-flight mass spectrometry was used to profile, over time, the hydrophilic metabolome of TK6 cells exposed to IR doses ranging from 0.5 to 8.0 Gy. Multivariate data analysis of the positive ions revealed doseand time-dependent clustering of the irradiated cells and identified certain constituents of the water-soluble metabolome as being significantly depleted as early as 1 h after IR. Tandem mass spectrometry was used to confirm metabolite identity. Many of the depleted metabolites are associated with oxidative stress and DNA repair pathways. Included are reduced glutathione, adenosine monophosphate, nicotinamide adenine dinucleotide, and spermine. Similar measurements were performed with a transformed fibroblast cell line, BJ, and it was found that a subset of the identified TK6 metabolites were effective in IR dose discrimination. The GEDI (Gene Expression Dynamics Inspector) algorithm, which is based on self-organizing maps, was used to visualize dynamic global changes in the TK6 metabolome that resulted from IR. It revealed dose-dependent clustering of ions sharing the same trends in concentration change across radiation doses. "Radiation metabolomics," the application of metabolomic analysis to the field of radiobiology, promises to increase our understanding of cellular responses to stressors such as radiation.High-throughput studies of the molecular and cellular effects of ionizing radiation (IR) have depended on "omic"1 profiling technologies, particularly genomic, transcriptomic, and proteomic platforms.2-6 Such efforts have discovered IR-induced perturbations of DNA, RNA, and protein molecules and have been successful in developing markers that provide information about IR-induced phenomena such as the threshold dose.4,7,8 Furthermore, integrating data from combinations of such platforms, in the spirit of emerging systems biology, has given investigators the ability to reconstruct and analyze IR-responsive For assessment of the potential application of metabolomics to radiobiology, a UPLC-ESI-TOFMS metabolomic assay was established to evaluate the changes in small-molecule concentration that occur after γ-irradiation of cells in culture. The high-resolution of ultraperformance liquid chromatography (UPLC) and the accurate mass measurement of electrospray ionization time-of-flight mass spectrometry (ESI-TOFMS) were combined to generate protonated molecular ion matrices consisting of peak areas identified by specific m/ z values and retention times. The hydrophilic metabolomes of the lymphoid TK6 cell line and the hTERT-transformed fibroblast BJ cell line were analyzed following different doses of IR over a period of 16 h. The overall...

Dealing with the unknown: Metabolomics and Metabolite Atlases

“…In addition to parametric and nonparametric hypothesis tests, these include principal component analysis (PCA) [31], partial-least-squares discriminant analysis (PLS-DA) [3], and self organizing networks (GEDI) [32]. These tools can reduce the complexity of data by allowing researchers to focus efforts on groups of features that exhibit similar abundance patterns.…”

Spectroscopic and chemometric approaches to radiobiological analyses

“…Methodologies that examine the total concentration of metabolic species within the cell after exposure to ionising radiation have recently been described [16][17][18]. Vibrational spectroscopy is a global term that is applied to the complementary techniques of infrared and Raman spectroscopy, modalities that allow the measurement of the concentration and nature of the organic species within a sample.…”

Analysis of LC−MS Data for Characterizing the Metabolic Changes in Response to Radiation

“…An aliquot of each sample supernatant was deposited in an autosampler vial, and 5 µL was separated on a 50 mm ×2.1 mm Acquity 1.7 µm C18 column using an Acquity UPLC system online with Q-TOF Premier (Waters Corp, Milford, MA). The method for data acquisition is described by Patterson et al 29 The gradient mobile phase consisted of 0.1% formic acid (A) and acetonitrile containing 0.1% formic acid (B). A typical 10 min sample run consisted of 0.5 min of 100% solvent A followed by an incremental increase of solvent B up to 100% for the remaining 9.5 min.…”

“…For example, Gene Expression Dynamics Inspector, 28 which was originally developed for microarray data analysis, was used to identify the subsets of dose-dependent metabolites in a study of cellular response to radiation. 29 Other methods, such as Significant Analysis of Microarray (SAM) or Empirical Bayesian Analysis of Microarray (EBAM), have been incorporated into the metabolomics data analysis software MetaboAnalyst. 27 Other tools such as MeltDB 30 provide various methods including t test, principle component analysis (PCA), independent component analysis (ICA), clustering, etc.…”