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Table 1 "-omics" in Environmental Health (except genomics).

From: The impact of new research technologies on our understanding of environmental causes of disease: the concept of clinical vulnerability

  Transcriptomics Epigenomics Proteomics Metabolomics
Definition & sources of samples Focuses on gene expression profiling, which is the assessment of the mixture of mRNAs that is present in a specific biological sample. mRNA from all types of tissues can be used. Focuses on epigenetic changes in DNA and chromatin and on their impact on regulation of gene expression. These changes are independent of DNA sequence, and are involved in 'gene silencing'. Genomic DNA from all types of tissues can be used. The study of proteins carried out to understand the amount and functioning of proteins in biological systems. All types of tissues and biofluids can be used for proteomic analysis. Plasma, a readily accessible fluid is most commonly used. The measurement of all the metabolites in a specified biological sample. Samples of a biological origin are analyzed using techniques that produce simultaneous detection, thereby providing metabolite profiles. It is mainly conducted on bio-fluids such as urine or plasma, and sometimes tissue extracts, in vitro cultures, and supernatants.
A quantitative technique is used to associate differences in mRNA mixtures originating from different groups of individuals to phenotype differences between the groups. It is strongly constrained by the intrinsic heterogeneity and instability of mRNA. Both the presence of specific forms of mRNA and the levels in which these forms occur are parameters that provide information concerning gene expression. Microarray is the most commonly used research tool. Currently limited to the analysis of gene methylation profiles. The standard method consists in sequencing of DNA segments containing potentially methylated fragments after modification of DNA with sodium bisulfite, which selectively modifies non-methylated cystosine, thus creating a base change which does not take place when cytosine is methylated. This change can be identified either by sequencing or by genotyping using custom-made 'genome-wide' oligonucleotide arrays. Reasonable consensus on the use of mass spectrometry for final identification of proteins/peptides but technologies for sample fractionation are variable. These technologies rely on 3 main approaches: (1) 2 dimensional electrophoresis to select protein spots that are eluted from gels and analysed by MS; (2) combined chromatographic approaches to trap abundant proteins and separate the less abundant ones before MS; (3) use of matrixes of immobilized chemicals to adsorb proteins based on different criteria (charge, hydrophobicity, affinity, binding to specific ions), followed by desorption and MS (SELDI/TOF). A variety of analytical metabolic profiling tools used include H NMR spectroscopy and MS with a prior online separation step like high-performance liquid chromatography, ultra-performance liquid chromatography, or gas chromatography.
Analysis Hierarchical clustering and principal component analysis are commonly used statistical approaches for the identification of gene sets. For the interpretation of the relevance of differently expressed gene sets, data analysis approaches that are able to integrate microarray data with prior knowledge on the involvement of genes in biological processes are needed. Conventional statistical methods are used to detect disease - gene methylation associations. Analyses of genome-wide methylation data involve hierarchical clustering and discriminant analysis. Current approaches to analyze the protein composition of biofluids or tissue homogenates generate large amounts of data. A variety of statistical methods are currently available to epidemiologists including discriminant analysis. Both random (measurement) and systematic (bias) errors should be considered as a necessary component of proteomic analyses. Data generated by these analytical techniques are often combined with multivariate data analysis, e.g. (orthogonal) partial least square, clustering, discriminant analyses and other similar approaches for generating and interpreting the metabolic profiles of the investigated samples.