Increasing Compound Identification Rates in Untargeted Lipidomics Research with Liquid Chromatography Drift Time–Ion Mobility Mass Spectrometry

Abstract

Unknown metabolites represent a bottleneck in untargeted metabolomics research. Ion mobility-mass spectrometry (IM-MS) facilitates lipid identification because it yields collision cross section (CCS) information that is independent from mass or lipophilicity. To date, only a few CCS values are publicly available for complex lipids such as phosphatidylcholines, sphingomyelins, or triacylglycerides. This scarcity of data limits the use of CCS values as an identification parameter that is orthogonal to mass, MS/MS, or retention time. A combination of lipid descriptors was used to train five different machine learning algorithms for automatic lipid annotations, combining accurate mass ( m/ z), retention time (RT), CCS values, carbon number, and unsaturation level. Using a training data set of 429 true positive lipid annotations from four lipid classes, 92.7% correct annotations overall were achieved using internal cross-validation. The trained prediction model was applied to an unknown milk lipidomics data set and allowed for class 3 level annotations of most features detected in this application set according to Metabolomics Standards Initiative (MSI) reporting guidelines.

An untargeted lipidomic approach for qualitative determination of latent fingermark glycerides using UPLC-IMS-QToF-MSE

“…41,65,66 Complete separation can be extremely difficult to achieve for neutral lipid extracts. 52,56,67,68 Under optimised separation conditions, resolution of TGs within an ECN group (known as 'critical pairs') can be achieved, but this comes at the cost of substantially longer run times than the method presented here. 42,[69][70][71] Complete separation of some positional isomers can require as long as several hours.…”

“…40,78,80 The use of IMS confers an advantage in this regard, as coeluting TG species may be resolved on the basis of drift time, which is a reflection of differences in molecular structure and m/z. 68,71,86 Due to the close structural similarities between critical pairs, and therefore close drift times, it was necessary to reduce the fractions of the drift peak width for isotope clustering and high-to low energy association from the default value of 0.5. This process enabled the removal of critical pairs and their assigned fragments from the mass spectral data, facilitating the identification of fatty acyl losses for all but 6 of the 85 identified TG families.…”

Trapped ion mobility spectrometry and PASEF enable in-depth lipidomics from minimal sample amounts

“…Nested in-between LC and MS, IMS provides an additional dimension of separation based on the ions' shape and size (collisional cross section, CCS). This is particularly interesting for lipidomics, as it provides an opportunity to separate otherwise unresolved isomers [19][20][21][22] . Furthermore, the chemical structure of lipids is closely linked to the CCS, which allows predictions by machine learning and could facilitate lipid identification [23][24][25][26] .…”

Lipidomics from sample preparation to data analysis: a primer

“…In recent years there have been major efforts to establish lipid CCS databases for DTIMS and traveling wave IMS [65][66][67][68][69]. Blazenovic et al, for example, proposed a system of lipid annotation derived from accurate mass, retention time, CCS, and degree of unsaturation values as well as the number of carbons, which should promote the identification certainty for lipids from untargeted workflows [65]. In a similar fashion, Leaptrot et al introduced an experimentally derived database of CCS values of glycerophospholipids and sphingolipids [67].…”