B RIEFINGS IN BIOINF ORMATICS . VOL 14. NO 3. 375^390
Advance Access published on 3 July 2012
doi:10.1093/bib/bbs030
Bioinformatics tools and challenges
in structural analysis of lipidomics
MS/MS data
Ju«rgen Hartler, Ravi Tharakan, Harald C. Ko«feler, David R. Graham and Gerhard G. Thallinger
Submitted: 29th February 2012; Received (in revised form) : 16th May 2012
Abstract
Lipidomics, the systematic study of the lipid composition of a cell or tissue, is an invaluable complement to knowledge gained by genomics and proteomics research. Mass spectrometry provides a means to detect hundreds of
lipids in parallel, and this includes low abundance species of lipids. Nevertheless, frequently occurring isobaric and
isomeric lipid species complicate lipidomics analyses from an analytical and bioinformatics perspective. Various
MS/MS strategies have evolved to resolve ambiguous identifications of lipid species, and these strategies have been
supported by corresponding bioinformatics analysis tools. This review intends to familiarize readers with available
bioinformatics MS/MS analysis tools and databases, the structural information obtainable from these, and their
applicability to different MS/MS strategies. Finally, future challenges in detecting double bond positions are investigated from a bioinformatics perspective.
Keywords: lipidomics; MS/MS data analysis; high-throughput; MS/MS detection algorithms; analysis tools
INTRODUCTION
The high-throughput analysis of diverse biomolecules has provided fundamental insights into complex mechanisms in life and disease. These ‘omics’
research fields are characterized by large-scale analyses of various biological materials in a highthroughput manner. Lipidomics, a relatively young
sub-discipline of metabolomics, explores the whole
lipid content of cells, tissues and organisms [1, 2].
This scientific field has become of major interest recently, because its findings provide knowledge complementary to established disciplines such as
transcriptomics and proteomics. Novel insights can
be gained because lipids are products of highly regulated metabolic reaction pathways [3]. Recent research indicates the important role of lipids in
major diseases, such as diabetes mellitus [4],
Alzheimer’s disease [5, 6], cancer [7, 8], multiple
sclerosis [9] and schizophrenia [10]. The effects of
lipids in nutrition and atherosclerosis are well
known [11–13]. Furthermore, lipids are key factors in
cell signalling [14] and control membrane traffic [15].
Hence, the detection of many novel mechanisms by
lipidomics studies can be anticipated [16].
Corresponding author. Gerhard G. Thallinger, Institute for Genomics and Bioinformatics, Graz University of Technology, 8010 Graz,
Austria. Tel.: þ43-316-873-5343; Fax: þ43-316-873-105343; E-mail: [email protected]
Ju«rgen Hartler is a postdoctoral researcher at the Austrian Centre of Industrial Biotechnology and the Institute for Genomics and
Bioinformatics, Graz University of Technology. His research focuses on the development of software applications to foster highthroughput analysis of biomolecules, particularly analyses by mass spectrometry.
Ravi Tharakan is a PhD student at the Johns Hopkins University School of Medicine, Cellular and Molecular Medicine Graduate
Training Program. His research interests are neuroimmunology and neuropathology, as well as development of bioinformatics tools to
mine ‘omics’ datasets.
Harald C. Ko«feler is director of the core facility for mass spectrometry and lipidomics at the Medical University Graz. His research
interest is focused on development of high resolution lipidomic LC-MS/MS methods.
David R. Graham is an assistant professor at the Johns Hopkins School of Medicine in Baltimore MD, USA. His research interests
include using multiomics approaches in the understanding of the role of lipid rafts in HIV pathogenesis and other physiological
processes including cardiovascular disease.
Gerhard G.Thallinger is Head of Bioinformatics at the Institute for Genomics and Bioinformatics, Graz University of Technology
and director of the Core Facility Bioinformatics of the Austrian Centre of Industrial Biotechnology. His research focuses on the analysis
of data from high-throughput technologies with emphasis on mass spectrometry and next generation sequencing.
ß The Author 2012. Published by Oxford University Press. For Permissions, please email: [email protected]
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Hartler et al.
The detailed investigation of the lipidome in a
high-throughput manner has become generally possible by advances in mass spectrometric technologies
[17–21], which outperform other technologies particularly in their sensitivity and specificity [22].
Particularly soft ionization techniques like electrospray ionization (ESI) or matrix-assisted laser desorption (MALDI) increased the sensitivity for low
abundance species. These techniques ionize the original lipid molecule by attaching adduct ions without
cleaving the parent molecule, permitting the transfer
of intact molecules to the mass spectrometer.
Consequently, single-stage MS alone allows the
identification and quantitation of intact lipid species
only. This information is not sufficient for comprehending all underlying biological mechanisms, however, because the chemical structure of lipids often
leads to lipid species of the same mass but different
structures. The functions and effects of these isobaric
species are typically substantially different in metabolism, signaling and the development of diseases. For
example, Kritchevsky et al. [23] showed that the position of fatty acids (FAs) at the glycerol backbone (sn
position) has an impact on the development of atherosclerosis. Additionally, it was found that the FA
composition of triacylglycerols (TG) and especially
the FA at the sn-2 position plays a key role in nutrition [24]. This hypothesis is sustained by the fact
that ‘healthy’ native vegetable oils contain predominantly unsaturated acids at sn-2 [25]. Another
example for the importance of FA position is
signaling, where it is commonly assumed that for
diacylglycerol signaling, the mono-unsaturated or
saturated FAs are at the sn-1 position of the glycerol
while the poly-unsaturated ones are at sn-2 [26]. Also
important is the detection of the double bond position in the FA chain, because, e.g. different linoleic
acids have different effects on adipocytes [27].
Therefore, it is important to discriminate isobaric
lipid species based on their underlying structure.
MS/MS technology, also termed tandem mass spectrometry, is the major strategy for this. Basically one
single m/z value is isolated, fragmented by collisioninduced dissociation (CID) and the resulting
fragment spectrum is recorded. Identification of
compounds is achieved by assignment of structurespecific fragments. Unfortunately, the analysis of
fragmentation spectra is not as straightforward as it
is in proteomics, where the usual low energy CID
cleavage sites of peptides are located at the amide
bond between the amino acids, resulting in b and y
fragments [28, 29]. These fragmentation patterns are
highly conserved throughout peptides and proteins
and reveal the amino acid sequence of the protein. In
contrast to proteins, lipids show a much higher diversity of fragmentation patterns than amino acids.
While the peptide bond is the favored cleavage site
in proteins, lipids do not have any single-favored
cleavage site or ionization polarity due to the huge
heterogeneity of their chemical structures and functional groups. The major determinants for the appearance of lipid MS/MS spectra are (i) type of mass
spectrometer, (ii) collision energy [30], (iii) charge
state of ions, (iv) ionization mode (positive or negative), (v) adduct ions [31] and (vi) the lipid class
under investigation. Thus, quite often a specific experimental setup is required to reveal essential structural information. A plethora of studies has been
performed for investigating and improving fragmentation processes, and subsequently for the generation
of MS/MS interpretation rules [31–62]. Because of
the large number of diverse fragmentation patterns,
the development of bioinformatics tools for the interpretation of lipidomics MS/MS spectra is a challenging task.
In this article, we systematically review available
tools for the analysis of MS/MS data in lipidomics,
including software originating from metabolomics
which are applicable for lipids. We first give a brief
overview of mass spectrometry technologies, to categorize the tools. Details about current experimental
approaches using MS in lipidomics can be found in
several reviews [63–66]. This review focuses on MS/
MS interpretation tools; computational lipidomics
MS analysis tools and other available bioinformatics
resources are examined in detail in [67, 68] and [69],
respectively.
STRUCTURAL ANALYSIS OF LIPIDS
BY MASS SPECTROMETRY
The most abundant lipids in mammalian systems are
glycerolipids. Glycerolipids contain glycerol, a C3
carbohydrate, as the central chemical structure.
One hydroxy group is attached to each of the
three backbone carbons and serves as a link for further chemical moieties. The three central glycerol
carbons have a stereo numbering and are referred
to as sn-1, sn-2 and sn-3 position. The chemical
moieties attached to the three hydroxy groups by
ester bonds are usually either FAs or a phosphate
group (Figure 1). The phosphate group is exclusively
Bioinformatics tools for lipidomics MS/MS data
377
Figure 1: Molecular building blocks of glycerophospholipids are FAs esterified at sn-1 and/or sn-2 positions of the
glycerol backbone. The sn-3 position is esterified with a polar head group. The high isobaric diversity of lipids is illustrated by some of the possible underlying structural isomers of PI 38:4 (C47H83P1O13). (A) sn-1 FA 18:0, sn-2 FA
20:4, sn-3 InsP; (B) sn-1 FA 20:4, sn-2 FA 18:0, sn-3 InsP; (C) sn-1 FA 18:1(11Z), sn-2 FA 20:3, sn-3 InsP; (D) sn-1 FA
18:1(9Z), sn-2 FA 20:3, sn-3 InsP; (E) sn-1 FA 18:1(9E), sn-2 FA 20:3, sn-3 InsP.
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Hartler et al.
attached on sn-3 and often further linked with a
polar metabolite (e.g. choline, ethanolamine,
serine, inositol). This whole entity is specific for glycerophospholipids and is called polar head group.
Depending on the lipid class, FAs can be attached
on any of the sn positions. FAs are the core components of lipids because they serve as molecular energy
stores and they are essential for generation of hydrophobic cellular confinement structures. FAs show a
high degree of variability according to their carbon
and double bond number. In mammalians, the most
common fatty acyl chain lengths range from 12
to 26 carbons with up to 6 double bonds.
Glycerophospholipid structures with a polar head
group and either one or two FAs at sn-1/sn-2 position are predominantly responsible for maintenance
of cellular membrane structures, whereas glycerolipids without polar head group and one, two or three
esterified FAs are usually to be found in the realm of
energy storage. Additionally, fatty alkyls can be attached to the glycerol backbone via an ether bond,
plasmalogens being a typical example. If a lipid species is identified by single stage MS only (no fragmentation), the result typically contains the lipid
class, the number of carbon atoms in the FA
chains, and the number of double bonds [70]; e.g.
PI 38:4 corresponds to phosphatidyl inositol containing the PI head group at sn-3, 38 carbon atoms in the
two chains at sn-1 and sn-2, and 4 double bonds in
these 2 chains. The number of carbon atoms in the
individual chains remains unknown by single stage
MS, as does, not surprisingly, the position of double
bonds. Conventional MS/MS can determine the respective lipid class, the individual FA chains, and in
many cases their position on the glycerol backbone
(Figure 2), if there is sufficient knowledge about the
fragmentation process for the experimental setup.
There are essentially two MS approaches in lipidomics (Figure 3), which are preferably used in combination with the soft ionization technique ESI:
(i) the shotgun approach that analyzes samples directly from crude extracts and (ii) the
Figure 2: MS/MS spectrum of PI 18:0/20:4 (m/z 885.5) acquired with an LTQ ion trap mass spectrometer in negative ESI. Ions 1, 2 and 3 correspond to inositol phosphate ions, which are characteristic for the polar head group.
Ion 4 represents inositol phosphate with the glycerol backbone attached and is thus also characteristic for PI. The
ions labeled with 5 and 6 are the carboxylates of FA 18:0 and FA 20:4, respectively. The ion cluster at 7^10 represents neutral losses of FA 20:4 and FA 18:0, either as FAs (7, 9) or as ketenes (8, 10). Finally, peaks 11 and 12 derive
from neutral loss of either FA 20:4 or FA 18:0 followed by an additional loss of inositol from the head group.
According to Hsu and Turk [31], two ion pairs are specific for the location of the FAs. (i) Charge driven processes
favor the neutral loss of FA þ inositol at sn-2 for PI. 11 >12 indicates FA 20:4 to be on sn-2. (ii) Secondary fragmentation processes favor the formation of carboxylates of FA at sn-1. 5 > 6 indicates FA 18:0 to be on sn-1. According
to this spectrum, the observed lipid is PI 18:0/20:4, and not PI 20:4/18:0.
Bioinformatics tools for lipidomics MS/MS data
379
Figure 3: Lipidomics workflow. While identification by shotgun lipidomics relies on (exact) molecular mass and
fragment-specific structural information (A), LC^MS approaches offer the possibility to take retention time as an
additional level of identification certainty into account (B). On the downside, LC^MS methods are usually more
time consuming and more complex to standardize for quantitation. Generation of MS/MS data is more important
in shotgun approaches due to the lack of chromatographic separation. In shotgun approaches, almost no time constraints exist for acquisition of MS/MS data, whereas in LC^MS approaches the chromatographic peak width of
each compound is the natural time constraint.
chromatography approach that pre-separates the analytes before online infusion in the mass spectrometer.
For the latter, liquid chromatography (LC) is the
preferred technology for lipidomics due to its high
sensitivity and its applicability to a variety of lipid
classes [71]. Accordingly, only tools for LC–MS analysis will be discussed.
Shotgun mass spectrometry in lipidomics has advantages in its relatively easy experimental setup and
rapid quantitative analysis of major lipid species [64].
The easy quantitation is a result of the infusion of the
whole extract at once, thus supplying stable signal
intensities. However, the simultaneous injection of
all analytes causes suppression of low abundance species during the ionization process. Nevertheless, low
abundance species can be detected by MS/MS technologies [72] using precursor ion scans (PIS), neutral
loss scanning (NLS) and single reaction monitoring
(SRM; or multiple reaction monitoring - MRM)
(Figure 4). PIS and NLS are full scan methods
mainly offered by triple quadrupole or quadrupole
time-of-flight (Q-TOF) MS devices. These methods
only detect compounds that create defined fragments
in MS/MS scans [65], which can be based on polar
head groups, fatty acyl moieties or even specific
backbone features. In the case of PIS, all precursor
masses of a certain fragment ion are detected,
whereas in NLS Mass Analyzer 1 and 3 are simultaneously operated at a certain mass offset equivalent
to the uncharged molecular weight of the neutral
loss under investigation. In contrast, SRM operates
at fixed m/z values for Analyzer 1 and 3, resulting in
highly specific precursor/fragment pairs, which is in
many cases indicative for just one compound. The
drawback of SRM is its targeted nature, which does
not allow detection of unanticipated lipid species.
Examples for combination of different PIS/NLS
experiments are the multiple precursor ion scanning
(MPIS) approach developed in the Shevchenko lab
[73, 74] and the multi-dimensional mass spectrometry-based shotgun lipidomics method developed by
Han et al. [65]. Product ion spectra on each lipid
compound of interest are usually the best way to
fully characterize a precursor ion [58, 75].
LC–MS in lipidomics is characterized by at least
one additional layer of separation preceding m/z analysis. A chromatographic separation step substantially
increases the number of detectable lipids due to
reduced suppression effects in the ion source
[76–78]. In this manner, e.g. for reverse phase
LC–MS, the identification of very low abundance
analytes is possible without any manual intervention
in the analysis process [79], which is especially useful
for high-throughput platforms. An impressive example for the sensitivity of the LC–MS approach is
the detection of a minor class of ether-linked TG by
normal-phase chromatography followed reversed
phase LC–MS [80]. In addition to m/z values,
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Hartler et al.
Figure 4: Frequently used MS/MS applications
(A) Product ion scan: precursor ions are selected in
Analyzer 1, fragmented by CID in Analyzer 2 and the
resulting fragment (product) ions are detected in a full
scan by Analyzer 3. (B) PIS: Analyzer 1 is in scanning
mode while Analyzer 2 fragments every m/z value by
CID and Analyzer 3 is fixed at the m/z of an expected
diagnostic fragment ion. In this mode, precursor ions
are only recorded when they show a certain diagnostic
fragment ion. (C) NLS: Analyzer 1 scans a defined m/z
range, Analyzer 2 fragments the precursor ion by CID
and Analyzer 3 scans the defined m/z range at a certain
mass offset, equivalent to the expected diagnostic neutral loss. Thus, precursor ions are only recorded when
they show a defined neutral loss. (D) SRM: Analyzer 1
selects one defined precursor mass, Analyzer 2 fragments it by CID and Analyzer 3 selects one defined
fragment ion. In this case, signals are only recorded
when specified consistent precursor/fragment mass
pairs show up.
LC–MS also offers retention time values for identification purposes. Targeted SRM can be used with
LC–MS as well, as shown by the LIPID Metabolites
And Pathways Strategy (LIPID MAPS) consortium
[81, 82]. A successful example for high resolution full
scan LC–MS is shown by Koulman et al. [83].
Although their instrumentation lacked MS/MS capabilities, sub-ppm mass accuracies and retention time
were sufficient for identification of certain glycerophospholipid classes in fast survey scans. Higher confidence in identifications can be achieved when
MS/MS spectra are additionally acquired and used
during data analysis. On LTQ-Orbitrap and
LTQ-FT instruments, simultaneous acquisition of
high resolution precursor spectra and low
resolution MS/MS product spectra is possible, resulting in three layers of information for any given
compound: exact mass, retention time and structural
features [84–86].
Irrespective of the MS approach (shotgun or LC),
structural information on the lipid species can only
be revealed by information on the fragmentation
process of the lipid classes of interest. However, the
appearance of an MS/MS spectrum can vary tremendously depending on the mass spectrometer used, the
collision energy [30], charge state of analyte, ion
mode (positive or negative) and the adduct ions
used [31]. Hsu, Turk and co-workers [31–36,
38–40, 43, 44, 46, 48–51, 53–62] have published
many fragmentation rules readily applicable for automated analysis tools. However, these rules are not
sufficient for detecting the positions of the double
bonds at the FA chains. Two novel strategies tackle
this challenge. The first one, Ozone-Induced–
Dissociation (OzID), creates characteristic fragmentations revealing the double bond-positions [87–90].
It is based on the fact that ozone cleaves the double
bonds in ionized lipids, whereupon the fragments are
detectable in the MS scans. The second strategy relies
on the principle that fragments in MS/MS reveal
information about the double bond position via
charge remote fragmentations [57, 91–95], whereupon either high energy CID or spectra MS3 are
required.
MS/MS ANALYSIS TOOLS FOR
LIPIDOMICS
The way available bioinformatics tools tackle complex MS/MS analysis for lipidomics is manifold.
Existing solutions are mostly tailored to the specific
requirements of a specific laboratory; therefore, generally applicable software is scarce. Thus, to establish
a largely automated high-throughput platform to
obtain structural information about lipids, the experimental strategy strongly depends on the mass
spectrometer used, the lipid classes of interest, the
MS/MS analysis strategy and also the analysis tool.
Despite the variety of available applications, the
underlying principles can be used for categorization
into four groups: (i) tools using reference libraries
and spectrum similarity searches; (ii) tools for the
analysis of shotgun data; (iii) tools for the analysis
of LC–MS data and (iv) tools providing simple
MS/MS searches, where the focus is to give hints
toward possible structures and annotations of candidate hits. In this section, these categories and their
available tools are examined in detail; an overview is
given in Table 1.
Bioinformatics tools for lipidomics MS/MS data
381
Table 1: Overview of available MS/MS analysis tools.
Name
Type
Ref. spec.
Customizable
Availability
MS
URL
AMDMS-SL
Shotgun
No
Yes
Free
High, low
HMDB
LipidInspector
LipidQA
Similarity
Shotgun
Shotgun
Yes
No
Yes
No
No
No
Web-service
Upon request
Free
High, low
Low
Low
Lipid Search
Lipid View
LC
Shotgun
Adjust
No
No
No
Web-service
Commercial
High
Low
LipidXplorer
Shotgun
No
Yes
Open-source
High, low
MassBank
Profiler-Merger-Viewer
XCMS2
Similarity
LC
Similarity
Yes
No
Yes
Yes
Yes
No
Open-source
Upon request
Free
Low
High
High, low
http://shotgunlipidomics.com/programs/
programs.htm
http://www.hmdb.ca
None
http://msr.dom.wustl.edu/Personnel/
Staff_Scientist_Song_Haowei.htm
http://metabo.umin.jp
http://www.absciex.com/products/
software/lipidview-software
http://sourceforge.net/projects/
lipidxplorer
http://www.massbank.jp/
None
http://metlin.scripps.edu/xcms/
Cyberlipid Center
GOLM
LIPIDAG
LIPIDAT
Lipid Bank
Lipid Library
LMSD
LIPID MAPS
Database
Database
Database
Database
Database
Database
Database
Database
No search
No search
No search
No search
No search
No search
No search
No
No
No
No
No
No
No
No
No
Web-service
Web-service
Web-service
Web-service
Web-service
Web-service
Web-service
Web-service
N/A
N/A
N/A
N/A
N/A
N/A
N/A
High, low
http://www.cyberlipid.org
http://gmd.mpimp-golm.mpg.de/
http://www.lipidag.tcd.ie/
http://www.lipidat.tcd.ie/
http://lipidbank.jp/
http://lipidlibrary.aocs.org/
http://www. lmsd.tcd.ie
http://www.lipidmaps.org/
The column‘Ref. spec.’ indicates whether the MS/MS search is based on experimental reference spectra, whereupon‘adjust’means that experimental
reference spectra were used for adjusting theoretical ones.‘Customizable’ stands for customization options for self-defined lipid species and potential extensibility. MS reflects the resolution type of the mass spectrometers to which the software is dedicated. High encompasses MS instruments
such as FTand Orbitrap, and low encompasses low-to-medium resolution that is provided by instruments such as ion trap, triple quadrupoles and
Q-TOF.
Spectral similarity search tools
Common to all the spectral similarity search tools are
the following steps: (i) compilation of a database
containing reference spectra; (ii) filtering of these
spectra by precursor m/z; (iii) similarity search between experimental spectrum and remaining reference spectra and (iv) scoring and output of potential
identification candidates. This strategy is quite similar
to proteomics search engines [96–100] except that
the spectral reference database in proteomics is
in silico calculated from protein sequence databases,
rather than determined empirically. Even though
the tools presented here have been primarily developed for metabolomics, they contain a certain
amount of lipid spectra. Thus, searches of uncharacterized spectra versus these databases can give useful
hints regarding potential candidate lipid species.
The first tool, XCMS2 [101], features the
METLIN database [102], which contains a library
of Q-TOF MS/MS spectra. The XCMS2 similarity
search relies on a modified version of the ‘shared
peak count’ method that takes precursor mass filtering into account. The algorithm generates a
similarity matrix and a distance matrix between
the observed and the reference spectrum and
calculates corresponding scores. Of these two
scores, a percentage match score is calculated that
defines whether the reference component is a
potential match. The apparent restriction to
Q-TOF data limits the applicability of XCMS. An
array of metabolomics experiments is publically
available through XCMS2 online at (https://
xcmsonline.scripps.edu/).
The second tool is the Human Metabolome
Database (HMDB) [103, 104]. HMDB offers more
MS/MS spectra of lipid species than XCMS2 at this
time. Furthermore, HMDB includes experimental
MS/MS spectra for Fourier Transform–Ion
Cyclotron Resonance–MS (FT–ICR–MS), Ion
Trap, Q-TOF and Triple Quadrupole instruments
at various collision energies. Interestingly, HMDB
supports searches for NMR spectra as well.
According to Wishart et al. [103], the scoring strategy
is similar to a previously published proteomics scoring scheme [105], whereas details about the metabolomics search are not published. In that article, the
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Hartler et al.
similarity was calculated by a linear equation from
weighted values of SEQUEST [96] result parameters. The weighting coefficients were determined
by multivariate discriminant analysis of a training
data set of more than 3000 MS/MS spectra.
General applicability of HMDB is reduced by its restriction to human metabolites only.
The third tool is the MassBank [106]. It contains
both MS and MS/MS spectra of various metabolites
including phospholipids. The similarity search relies
on a modified version of the cosine correlation algorithm (described by Stein et al. [107]). However, the
best reported positive predictive value was 50%
[106], which limits MassBank’s applicability in
high-throughput data analyses. Nevertheless,
MassBank can be a useful instrument to detect candidate species or candidate fragments. In contrast to
other databases, MassBank allows the contribution of
self-created reference spectra.
The MS/MS tools presented so far are able
to identify the lipid FA chains by similarity search
against a database of precompiled spectra. Generally,
the use of a spectral database has the drawback that
the acquired spectra are often limited to specific types
of mass spectrometers and experimental setup parameters such as collision energy and adduct ions used, as
the appearance of fragmentation spectra can vary tremendously depending on these parameters. For example Hsu and Turk [31] demonstrated that an
ITMS instrument returns different ions containing
structural information than a TSQ instrument, and
that adduct ions have a major influence.
Furthermore, the use of reference libraries is especially questionable in shotgun experiments, because
mixed spectra containing more than one isobaric
precursor occur frequently [108]. Moreover, such
approaches can detect only lipids that are already
part of the mass spectral database. The structural
elucidation of newly identified components is
not always possible, because the databases can
hardly be extended by end-users. In this respect,
XCMS2 and MassBank have the advantage of
being extensible by self-created reference spectra
while HMDB provides just the download of the
spectral database and not the tool itself. However,
in any case, the quality of the detection relies on
the existence of reference spectra, and it is hard to
foresee which components are required for unknown biological samples. Thus, these solutions are
of limited use in experiments where the lipid species
are unknown a priori.
Shotgun MS/MS tools
In shotgun analyses, the whole sample extract enters
the mass spectrometer without prior separation of
compounds. The specificity and sensitivity of this
technique derives from the application of MS/MS,
where PIS, NLS and SRM are preferably used. A
usual strategy is to scan for class-specific head
groups and probable FA chains by PIS or NLS.
Correspondingly, in this group, tools dealing with
structural elucidation were reported relatively early.
Moreover, for many lipid classes, these tools are even
able to determine the position of the FAs at the
glycerol backbone, i.e. the regio-specific position,
by comparison of fragment intensities. Generally,
for this category many more MS/MS high-throughput analysis tools are available than for the others.
The reason for this is presumably the appealing analytical simplicity of the method and the fact that it
derives its analytical power from MS/MS.
The first representative in this category is the
LipidProfiler software package [73]. This tool
exploits the MPIS approach, where several PIS of
multiple precursors are generated in parallel. By
scanning for head groups and FAs, the detection of
many co-existing isobaric substances is possible.
Regiospecificity is defined for many analytes as
well. The package was developed in collaboration
with AB Sciex, which limits its applicability to
vendor-specific MS instruments. Additionally, it is
not extensible with newly identified lipid species,
although the software offers 44 lipid classes containing 25 000 lipid species [109]. Currently, the software is marketed under the name LipidView.
A drawback of the MPIS technology is that simultaneous parallel acquisition of NLS is not possible,
which is necessary for detection of the head groups
of phosphatidyl ethanolamine and phosphatidyl serine. To circumvent this problem, an MS strategy was
developed where a survey scan of 2 s is employed,
followed by two consecutive MS/MS scans in datadependent acquisition (DDA-fragment spectra are
automatically acquired by the instrument from the
most abundant molecular ions determined in a preceding single stage MS spectrum) mode to cover PIS
and NLS [74]. This kind of data can be analyzed with
LipidInspector [74], which was developed by
Scionics Computer Innovations and is available
upon request from the authors.
The LipidQA software platform [110] is dedicated
to the analysis of MS/MS spectra of product ion
scans in DDA mode, which differs from the
Bioinformatics tools for lipidomics MS/MS data
traditional shotgun approach of the previous two
tools. The package was tested on low-to-medium
resolution instruments (linear ion trap, triple quadrupole, Q-TOF). The strategy is similar to the spectral similarity search tools previously described,
because it also relies on a library of reference spectra.
The similarity search is based on the so-called ID
score which is a measure of the percentage of
fragment ion m/z values of the 40 most intense
peaks matching to the theoretical spectrum.
Furthermore, the software detects the regio-specific
sn-positions. The development team of LipidQA
is itself engaged in investigations of novel lipid fragmentation pathways [31–36, 38–40, 43, 44, 46,
48–51, 53–62, 93], which is certainly beneficial for
the quality of reference spectra. However, the software is currently available for phospholipids and
sphingomyelins only.
The fourth tool pursues the strategy of multidimensional mass spectrometry (MDMS), where the
analysis is based on a series of MS, PIS and NLS
scans that are acquired under varying experimental
conditions [111] (a review about this technique has
been published recently [65]). The AMDMS-SL
package [112] exploits the information in a twostep procedure, which quantitates unambiguously
detectable high abundance components first, and
then uses these results to quantitate low abundance
or overlapping species. The identification is based on
an extensible ‘building block’ library containing information about backbone, head group, adduct ions,
ionization mode and a formula for the FA chains.
This formula incorporates the elements for the possible connection to the backbone and variables for
the carbon atoms and the number of the double
bonds in the chain (ranges for variables are customizable). Out of this information, theoretical lipid species are calculated and used for the search.
Furthermore, class-specific fragments and losses can
be defined for PIS and NLS, respectively. Mass spectrometrically, a lipid class specific ionization is
required. The acquired MS, PIS and NLS scans are
searched against the theoretical lipids, which results
in a candidate peak list for each lipid class. Then, to
identify the FAs, theoretical fragments are searched
against PIS and NLS spectra, where a comparison of
the intensity of the fragments is used to determine
the regio-specific position. The proposed concept is
attractive due to its extensibility and can be seen as
the first generic attempt for the structural elucidation
of lipid species.
383
The LipidXplorer package takes a further step into
this direction by introducing the concept of the molecular fragmentation query language (MFQL) [108].
The control for lipid species identification is transferred completely to the user. The software organizes
MS and MS/MS spectra of all acquired experiments
in database-like structure in a flat-file format. MFQL
queries are used to generate m/z values of signal ions
to search this database. The queries are defined for
each lipid class and can contain similar ‘building
blocks’ such as in AMDMS-SL (head group, FA
chain constraints, etc.), but they are not exclusively
restricted to these. For example, AMDMS-SL looks
for certain precursor masses in MS for high-resolution data and checks for expected chain fragments
in PIS and NLS to assign the FA chains. In
LipidXplorer, the user can define in the query
which check is to be performed at which MS
level. Furthermore, these rules can be combined by
Boolean operators. In this manner, the algorithm is
no longer restricted to certain signal ions for determination of the FA chains. This concept ensures independence of MS/MS scan types (such as PIS, NLS,
SRM and DDA), MS instrumentation and experimental setup parameters such as collision energy and
adduct ions [113].
In summary, in shotgun-based lipidomics, excellent tools are available for the analysis of MS/MS
spectra. Shotgun tools can identify the FA composition even for isomeric structures, where in many
cases the regio-specific position can be detected
too. Furthermore, some approaches are more generally applicable already, showing a trend leading away
from applications tailored to specific devices and experimental setups. The major disadvantage lies in the
shotgun approach itself, because mixed spectra of
isomeric and isobaric substances occur frequently
without chromatographic separation. However, this
problem is tackled by smart application of PIS, NLS
and SRM, in many cases.
LC^MS/MS tools
In contrast to shotgun lipidomics, in LC–MS the
sample is released gradually into the mass spectrometer. The advantage is an additional layer of separation that even permits the segregation of positional
isomers [114]. Additionally, the analytical distribution of the analytes allows the ion source to supply
more ions, and thereby reduces the suppression of
low abundance species. Furthermore, the MS/MS
spectra in LC–MS are usually product ion scans,
384
Hartler et al.
which contain all the fragment ions yielded by the
collision process. Because the analytes enter the
mass spectrometer gradually, the time for detecting
MS/MS spectra is limited. Consequently, only the
most intense peaks are selected for MS/MS analysis,
whereas structural information of low abundance
species is absent. Moreover, software tools for LC–
MS/MS analysis of lipids are scarce.
The Profiler–Merger–Viewer tool provides a
semi-automated analysis that requires a specialized
experimental setup, and is applicable to high-resolution mass spectrometers only [115]. The mass spectrometer performs a full scan followed by two single
ion-monitoring (SIM) scans of the two most abundant peaks. From these peaks MS2 and MS3 scans are
conducted. The software searches the SIM scans for
potential hits. Then, the predictable existence of possible neutral losses in MS2 is used as an additional
exclusion criterion for false positives. The MS2 and
MS3 scans are used to determine the potential FA
composition, whereupon the regio-specific position
is not reported. The output is a list of chromatograms
of potential candidates that must be manually selected for quantitation. The software is easily extensible, but manual peak selection can be cumbersome
for datasets from high-throughput studies.
Furthermore, the restriction to high-resolution data
and the specialized experimental setup limits its field
of application considerably.
The Lipid Search tool [85] contains a database of
more than 200 000 theoretical m/z values for lipids
and their corresponding theoretical fragments.
Typically, the m/z values for the fragments are created in silico, and they are additionally adjusted by
experimentally obtained data. The MS/MS detection includes parameters such as scan type, adduct
ions and m/z tolerance; however, details about the
search algorithm are not reported. This approach
proved its value in the detection of various lipid
classes of phospholipids from tissue samples [85].
Remarkably, even positional isomers and low abundance alkylacyl and alkenylacyl isobaric species were
detected. However, Lipid Search was primarily used
as identification tool in this study, while quantities
were obtained by manual calculation. The tool is
provided as a web application and currently covers
phospholipids only. No means for extension to other
lipid classes are available.
LC–MS/MS bears the potential for detecting very
low abundance lipid species due to the chromatographic separation and concentration of the lipids.
However, available tools are not broadly applicable
because of their limitation to specific lipid classes and
MS devices, and their semi-automated nature.
Generic solutions as available for the shotgun approach are desirable for this technique. MS/MS extensions to tools that already achieve good sensitivity
and specificity by MS analysis [116, 117] give rise to
novel analytical perspectives, particularly because
LC–MS/MS is more suited to top–down research
than the shotgun technique.
Annotation databases for lipids
After identification of lipid compounds, the species
must be annotated with respect to structure and
function. Several tools are available for the annotation of lipids identified by LC– or GC–MS. The
LIPID MAPS database provides the broadest range
of tools [118]. The service is intended to map the
lipidome in a systems biology based manner, comprising a comprehensive ontology and classification
system for lipids [119, 120]. There, lipids are stratified into eight classes: fatty acyls, glycerolipids, glycerphospholipids, sphingolipids, sterol lipids, prenol
lipids, polyketides and the neologism saccharolipids,
which includes all FAs linked to a sugar backbone.
This new term was introduced because the term
‘glycolipids’ is ambiguous, as all lipid classes include
glycan derivatives. Additionally, a standardized set of
guidelines for drawing structures of lipids is available.
For the purpose of further characterization of
identified lipid species, LIPID MAPS introduces a
comprehensive suite of annotation tools. First,
a basic search function is provided, which allows a
user to search a compound of interest against several
different databases: a lipid database search (LMSD)
[121] for direct lipid annotation, a molecular structure-based search and a lipid standards search. Useful
for laboratories with bioinformatic personnel, all the
database searches can be accessed programmatically.
Finally, LIPID MAPS also contains tools for searching mass spectrometry results, an outline of the lipid
classification scheme, a database of published lipidomics datasets according to biology, tools for creating
pathway diagrams from lipid datasets and structure
drawing tools.
A second database that may be useful for lipid
annotation is the GOLM metabolome database
[122, 123]. This database is designed for GC–MS
studies of metabolite compounds, and uses mass
spectral tags, containing spectra and retention time,
to store compound identifications, as well as a data
Bioinformatics tools for lipidomics MS/MS data
model for chemical substances. The database is primarily a metabolite database, and so may be less appropriate for lipid searches. Good annotations are
available for lipids, including stereoisomer and isotopomer information, molecular and monoisotopic
masses, along with link-outs to KEGG [124] and
ChemSpider [125], both of containing a large
amount of information on biochemical pathway involvement and chemical structure, respectively. An
interesting feature of this database is the ability to use
decision trees to predict the functional groups of
compounds. However, this latter service does not
seem to be fully operational yet.
Another database useful for functional annotation
of lipid species is LipidBank [126], the database of the
Japanese Conference on the Biochemistry of Lipids.
The database provides a search engine, similar to
LIPID MAPS, which takes as input a LipidBank
ID, a lipid name, or formulae and a molecular
weight range. Furthermore, one interesting feature
is that the database is also searchable by chemical
synthesis, source or biological activity. This gives
an easy way to mine pathways directly through the
identifications database. The database also provides
archived spectral data, such as UV spectra, IR spectra, NMR spectra and MS spectra, along with chromatogram data and some physical properties.
Finally, there are several other lipid knowledge
bases, which may be useful for manual annotation
of lipid identifications. These include The Lipid
Library [127], Cyberlipid Center [128] and the
Caffrey Labs collection of databases [129], which include LIPIDAT [130], a database of thermodynamic
information on many lipid species, LIPIDAG [131], a
database of lipid miscibility, LMSD [132], a database
of lipid structures and CMCD, a database of critical
micellar concentrations. Each of these databases may
provide useful information not only for lipid annotation but also for basic lipid analysis techniques and
chemical biology. However, several of these databases are no longer being updated, and much of
the relevant information from them seems to have
already been integrated into more current databases,
such as LIPID MAPS.
CONCLUSIONS
Lipidomics is a young discipline among the ‘omics’
sciences, but it has been recognized as an essential
complement to well-established disciplines such as
transcriptomics and proteomics. Although mass
385
spectrometry has been a crucial factor for highthroughput lipidomics, the elucidation of lipid structure is far from trivial. Detailed structural assignment
requires
sophisticated
MS/MS
approaches.
However, available analysis software is often tailor
made and consequently only useful for a specific experimental setup. Exceptions exist in shotgun analysis
[108, 112, 113] where special emphasis was placed
on general applicability and easy extensibility for
end-users. Similar applications are not yet available
for LC–MS/MS, where solutions are limited to specific lipid classes or experimental setups, and fully
automated analysis is not supported. Compounds
not covered by the tools must be interpreted manually or searched by similarity search tools or annotation databases, which primarily allow the analysis of a
single spectrum only.
Lipidomics analysis software can determine the
FAs composition and regio-specific positions.
Standard MS/MS methods do not allow the identification of the double bond positions, but two novel
approaches make this possible. The OzID technology [87–90] cleaves C–C double bonds by ozonolysis in the ESI ion source. This leads to characteristic
fragments revealing the double bond position in MS
scans. However, this method has some drawbacks
that complicate bioinformatics analysis: (i) the additional fragments can originate from different intact
lipid precursors; (ii) the additional fragments can
result in additional overlapping isomers or isobars
that have to be classified by MS/MS; (iii) lipids
cannot be quantified easily, because the cleaved fragments reduce the intensity of the intact lipid species;
the intensity of the fragments, which can contain
isomers, has to be considered. To tackle these challenges, MS algorithms will require a much higher
level of sophistication that must work in concert
with MS/MS fragment detections. The second
approach relies on the sophisticated application of
MS/MS strategies to detect characteristic fragments for double bonds in MS/MS [57, 91–95].
Computationally, detecting double bond fragments
is similar to detecting FA fragments. Thus, presented
generic approaches [108, 112, 113] could possibly be
of use.
The analytical progress in lipid structural elucidation opens up novel challenges for bioinformatics.
Additionally, lipidomics MS and MS/MS trend towards high-throughput analysis [133]. Novel tools or
extensions to existing software are required to keep
pace with these advancements. For the development
386
Hartler et al.
of such tools, extensibility and general applicability
shall in particular be considered in the design, to
open them to a wider community. Generic
approaches can be more easily adapted to analytical
requirements and subsequently, the advantages of the
software more quickly conveyed to end-users.
Consequently, high-throughput platforms are more
rapidly implemented, effectively promoting lipidomics research.
Key Points
MS is the analytical method of choice in lipidomics due to its high
sensitivity, specificity and large dynamic range, but due to isobaric and isomeric lipid species, MS/MS has to be applied for
structural elucidation.
MS/MS fragmentation processes of lipids depend on various experimental parameters; consequently, generic bioinformatics solutions are required, to keep pace with analytical advancements.
Available software tools can be classified into four categories: (i)
tools using reference libraries and spectrum similarity searches;
(ii) tools for the analysis of shotgun data; (iii) tools for the analysis of LC^MS data; (iv) tools providing simple MS/MS searches,
where the focus is structure annotation.
Some generic bioinformatics solutions for shotgun lipidomics
exist, whereas the LC^MS approach is poorly supported.
Extensibility and general applicability must be a key consideration in the conception of novel MS/MS tools to keep pace with
analytical advances.
4.
5.
6.
7.
8.
9.
10.
11.
12.
13.
14.
Acknowledgements
The authors thank Gerald N. Rechberger and Fritz Spener from
the Lipidomics Research Center Graz for helpful discussions and
encouragement.
15.
16.
17.
FUNDING
This work was supported, in part, by the Austrian
Ministry of Science and Research GEN-AU project
BIN (FFG Grant 820962) and by the EU-FP7 project LipidomicNet (EC Grant 202272). The Austrian
Centre of Industrial Biotechnology (ACIB) contribution was supported by FFG, bmvit, mvwfi, ZIT,
Zukunftsstiftung Tirol and Land Steiermark within
the Austrian COMET program (FFG Grant
824186).
18.
19.
20.
21.
22.
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