Perspective Metz, Baker, Schymanski et al.


i nherently quantitative and offers the ability to eluci- date molecular structures, but suffers from low mea- surement sensitivity and throughput [60,61]. The utility of NMR in exposome studies is limited to measuring relatively high-abundance xenobiotics and to assessing biological response to exposure. On the other hand, MS analyses when coupled with chromatography are highly sensitive, but not absolutely quantitative unless stable-isotope-labeled


internal standards, matrix-


matched external standards or the standard addition method are used. However, the high dynamic range and low of MS approaches tend to outweigh the typical limitation to semiquantitative analyses (if desired, full quantitation is possible using any of the approaches mentioned above) and offer the potential to measure both xenobiotic chemicals and the biological responses to exposure. Chromatography (e.g., LC; GC) coupled with MS is thus the most widely used analytical plat- form for exposome studies [46–52]. A typical workflow for untargeted analysis using chromatography coupled with MS involves SPE or liquid–liquid extraction of the sample, drying of the extracts and reconstitution in a suitable buffer or solvent, followed by analysis. If GC-MS is used, then chemical derivatization of extracted molecules may be required to increase their volatility and enhance their separation. For GC-MS, electron ionization (EI) is typically used to impart the required charge to the analyte for detection by the mass spectrometer, although chemical ionization can also be used. Molecular fragmentation occurs simul- taneously with EI, and commercial and open access reference libraries of spectra can be used to identify detected molecules [18,22,62], often in conjunction with other information such as retention time and/or reten- tion indices [22]. For LC-MS, ESI is most commonly used to introduce analyte ions to the instrument, and data are typically collected by repeatedly scanning over a wide mass range (e.g., 100–1000 m/z) to detect as many molecules as possible. In most LC-MS experi- ments, intact molecular ions are fragmented using collision induced dissociation to produce MS/MS spectra, which can be used to identify the structures of detected molecules in conjunction with libraries of reference spectra [20,28,63] or tentatively identify can- didates using in silico fragmentation approaches com- bined with candidate look-up [26,64–66]. The benefit of untargeted analysis is that it is not biased by a priori assumptions and so offers the best opportunity to discover novel markers of exposure or to characterize the response to exposure by measuring a broad swath of chemical space. Caveats, however include possible artifacts in the data due to the lack of optimization of sample preparation procedures, difficulty detecting very low-abundance analytes in the presence of high-


31 Bioanalysis (2017) Bioanalysis (2017) 9(1), 81–98(1)


abundance analytes (particularly when using LC-MS due to ESI suppression) and an incomplete representa- tion of chemical space in spectral reference libraries, leading to, in some cases, limited confidence in the identification of detected species where reference stan- dards are not available.


Molecular identification confidence As described above, the identification confidence of molecules measured in targeted analyses is generally very high, since methods are optimized for the chemis- try of interest and the use of authentic standards ensures that the correct molecule is identified each time. There- fore, the discussion in this section will focus on the con- fidence in identification of metabolites and chemicals detected in untargeted, MS-based analyses. The use of untargeted acquisition techniques brings


a whole set of identification challenges with it as, unlike targeted acquisition, no preliminary hypothesis as to the number and identity of analytes of interest has been generated. It is possible (and increasingly common) to perform target analysis on data acquired with untargeted methods, and this is the best starting point for any untargeted investigation. This requires the availability of in-house reference standards (mea- sured with the same technique) and, for quantitative results, internal standards and appropriate calibration curves [54,67]. Confident identification requires the match of two orthogonal pieces of experimental data between standard and sample, such as matching exact mass of the molecular ion and fragments as well as retention time. Typically, targets are ‘screened’ in data from untargeted analysis by searching for the exact mass in peak lists or via extracted ion chromatograms, hence the term ‘target screening’ [54]. In cases where investigations wish to pursue a sub-


stance of interest for which the reference standard is not available in house, a so-called ‘suspect screen- ing’ can be performed. The exact masses of the ions relating to the substance(s) of interest can be used to search the data from untargeted analyses either via peak lists or extracted ion chromatograms, as is per- formed for target screening. Unlike target screening, however, additional steps need to be undertaken to prove (or disprove) the identity of the suspect, since a standard is not available. This is discussed further below. The lists used for suspect screening can vary widely and depend on the study question. One could consider screening the Human Metabolome Database (HMDB) [68] or other metabolite-specific databases as suspect screening for metabolomics. In environmental studies, one could use a list of registered pharmaceuti- cals [69] or pesticides [70], surfactants [53] or even all sub- stances registered under the Registration, E valuation,


future science group


Page 1  |  Page 2  |  Page 3  |  Page 4  |  Page 5  |  Page 6  |  Page 7  |  Page 8  |  Page 9  |  Page 10  |  Page 11  |  Page 12  |  Page 13  |  Page 14  |  Page 15  |  Page 16  |  Page 17  |  Page 18  |  Page 19  |  Page 20  |  Page 21  |  Page 22  |  Page 23  |  Page 24  |  Page 25  |  Page 26  |  Page 27  |  Page 28  |  Page 29  |  Page 30  |  Page 31  |  Page 32  |  Page 33  |  Page 34  |  Page 35  |  Page 36  |  Page 37  |  Page 38  |  Page 39  |  Page 40  |  Page 41  |  Page 42  |  Page 43  |  Page 44  |  Page 45  |  Page 46  |  Page 47  |  Page 48  |  Page 49  |  Page 50  |  Page 51  |  Page 52  |  Page 53  |  Page 54  |  Page 55  |  Page 56  |  Page 57  |  Page 58