Research Article: Ion mobility spectrometry combined with ultra performance liquid chromatography/mass spectrometry for metabolic phenotyping of urine: Effects of column length, gradient duration and ion mobility spectrometry on metabolite detection

Date Published: August 22, 2017

Publisher: Elsevier

Author(s): Paul D. Rainville, Ian D. Wilson, Jeremy K. Nicholson, Giorgis Issacs, Lauren Mullin, James I. Langridge, Robert S. Plumb.


The need for rapid and efficient high throughput metabolic phenotyping (metabotyping) in metabolomic/metabonomic studies often requires compromises to be made between analytical speed and metabolome coverage. Here the effect of column length (150, 75 and 30 mm) and gradient duration (15, 7.5 and 3 min respectively) on the number of features detected when untargeted metabolic profiling of human urine using reversed-phase gradient ultra performance chromatography with, and without, ion mobility spectrometry, has been examined. As would be expected, reducing column length from 150 to 30 mm, and gradient duration, from 15 to 3 min, resulted in a reduction in peak capacity from 311 to 63 and a similar reduction in the number of features detected from over ca. 16,000 to ca. 6500. Under the same chromatographic conditions employing UPLC/IMS/MS to provide an additional orthogonal separation resulted in an increase in the number of MS features detected to nearly 20,000 and ca. 7500 for the 150 mm and the 30 mm columns respectively. Based on this limited study the potential of LC/IMS/MS as a tool for improving throughput and increasing metabolome coverage clearly merits further in depth study.

Partial Text

The use of metabolic phenotyping (metabonomics/metabolomics) to discover biomarkers of organismal response to environmental and physiological change is now widespread. In biomedical applications metabolic phenotyping, or metabotyping [1], [2], is being deployed as a method for finding novel, mechanistic, biomarkers of disease with obvious potential for improving diagnosis, patient stratification and both predicting and monitoring patient response to therapy. Because of the need for the robust identification of analytes current analytical platforms for metabolic phenotyping are based around techniques such as nuclear magnetic resonance (NMR) spectroscopy or mass spectrometry (MS), which have the potential for structural characterization as well as detection and quantification. In the case of MS analysis can be performed by direct infusion (DIMS) [3] or, following hyphenation, in combination with a separation technique. Separation techniques such as GC-MS [4], LC-MS [5], [6], SFC-MS [7] and CE-MS [8] have all been employed, to greater or lesser extents, for the metabotyping of a very wide range of clinical samples, from biofluids such as urine, blood-derived products, bile etc., to cells, tissue and faecal extracts etc. LC/MS-based methods, particularly those centred on its more efficient ultra performance, or ultra high performance variants (UPLC/UHPLC), based on separations made using high flow rates and sub 2 μm packing materials, have delivered improved methods for metabolic phenotyping compared to conventional HPLC-MS approaches. However, there remains the difficulty of balancing the desire for rapid, high throughput, analysis versus the need to maximise metabolome coverage for biomarker discovery. In particular, as the separation time is reduced to increase sample throughput ion suppression (due to peak co-elution) increases, reducing the number of features detected (e.g. see Refs. [9], [10]). Another reason for the loss of some of these features can be that the signals arising from low intensity analytes, which might well be detected with longer separations, are effectively lost due to both “system noise” and co-elution with ions of much higher intensity. Indeed, even when separations are not compressed to maximise throughput, the loss of signals for some low intensity analytes co-eluting with higher intensity ones is likely. An obvious way of reducing the problems of co-elution is to use strategies such as 2-dimensional separations but this clearly does not solve the problem of maximising throughput. Another potential means of maximising metabolite detection without increasing analysis time is to employ ion mobility spectrometry (IMS) prior to MS detection in a hyphenated LC-IMS-MS system. The use of IMS also offers the potential to gain further structural information via accessing the collision cross section information for molecules of interest. The rapid time scale of ion mobility separations, typically in the 10s of milliseconds range, makes such a configuration ideal for coupling between UPLC-based separations, with peaks eluting over a few seconds and TOF mass spectrometry which operates on a microsecond time scale. The use of the “drift time” within the ion optics can allow analytes of interest to be separated and detected even in the presence of a co-eluting isobaric species. This orthogonal separation therefore provides an increase in peak capacity, in an analogous manner to two-dimensional LC, but without an increase in analysis time. However, it is important to recognize that, as this separation is performed post ionization in the vacuum region of the mass spectrometer, whilst potentially increasing the number of analytes detected using IMS will not compensate for the loss of analytes as a result of ion suppression as this is an ionization phenomenon.

As shown here, the incorporation of IMS as a separation modality between LC and MS significantly increased the number of features detected in a metabolic phenotyping experiment. The increase in the number of features detected varied from 41% for the 7.5-min analysis to 17% for the 3-min analysis and 23% for the longer 15-min separation. The reason(s) for the observed increase in feature detection clearly needs further investigation but is most likely due to a combination of separation of co-eluting compounds, noise reduction, resolution of isobaric components and separation of fragment ions. Reducing the column length from 150 mm to 75 or 3 mm resulted in an almost linear reduction in feature detection. The addition of IMS to the 7.5 min analysis increased the number of features to 81% of the 15 min analysis and the 3 min analysis to almost 50% of the 15 min analysis.




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